mutations discovered was O6-methylguanine-DNA methyltransferase (MGMT) promoter silencing, which reduces tumor cells' ability to repair DNA damage from alkylating agents such as temozolomide (TMZ). Hegi et al. [26] subsequently observed that MGMT promoter methylation silencing was observed in 45% of GBM patients, who demonstrated a survival benefit when treated with a combination of TMZ and radiotherapy versus radiotherapy alone (21.7 months versus 15.3 months). It is critical that future GBM monitoring integrates imaging and genetic data in order to provide accurate prognostic information and guide personalized therapies.

### 3.3.2 Challenges of Personalized Therapy

Discoveries in genetic profiling have spurred the development of new targeted therapies [27] with over 140 clinical trials presently evaluating personalized or targeted therapies for GBMs alone. These therapies are tailored to exploit genetically driven therapeutic targets. However, an apparent roadblock to these individualized approaches is the growing evidence of GBM intra-tumoral heterogeneity. Patel et al. demonstrated that GBMs consist of a mixture of cells with variable gene expression profiles using single-cell RNA sequencing [28]. Likewise, Sottoriva et al. observed genome-wide variability using surgical multisampling approach from 11 GBM patients [29]. Thus, each brain tumor may reflect multiple unique tumor habitats with corresponding differences in response and resistance to therapy, challenging the identification, development, and implementation of individualized care.

### 3.3.3 MRI Biomarkers of Tumor Biology and Genetic Heterogeneity

Both spatial and temporal variations in genetic expression result in alterations in tumor biology, including changes in apoptosis, cellular proliferation, cellular invasion, and angiogenesis [30]. In turn, these biologic changes manifest in the heterogeneous imaging features of brain tumors, resulting in varying degrees of enhancement and edema. For example, imaging changes on contrast-enhanced MRI result from the breakdown of the blood-brain barrier and can demonstrate areas of necrosis as a marker for apoptosis. Additionally, MRI sequences based on physiology such as apparent diffusion coefficient (ADC) and perfusion imaging have been shown to relate to tumoral cellularity and angiogenesis, respectively. Furthermore, promising efforts have shown that tumors with lower cerebral blood volume (CBV) on perfusion are more likely to be IDH mutants and have longer overall survival (OS) [31, 32]. Other reports have used enhancement patterns and ADC to predict IDH status with some success [33, 34]. Currently, efforts to provide molecular classification for brain tumors based on these MRI features have had mixed results. For example, classification of IDH and MGMT mutant status has had some success; however, methods for 1p19q and EGFR have demonstrated less reproducibility [35–37]. Different mutations may have similar MRI
features, and a “single” tumor can have multiple different muta-
tions internally. Several approaches have emerged to provide stan-
dardized visual interpretation of gliomas for tissue classification.
For example, the Visually AcceSAble Rembrandt Images (VASARI)
feature set is a rule-based lexicon to improve the reproducibility of
interpretation [38]. However, these methods rely on human visual
interpretation, which is inherently subjective and prone to inter-
rater variability. Ultimately, steps are needed to provide reliable and
reproducible methods to accurately classify molecular subtypes a
priori.

4 Potential Applications for Machine Learning

4.1 Segmentation

Radiographic assessment serves an important role for clinical
follow-up and research trials in oncology. Currently, the RANO
criteria rely on 2D measurements of the enhancing disease as well as
subjective assessment of the FLAIR non-enhancing tumor, which is
then used to guide treatment strategies. However, the postsurgical
cavity tends to be highly irregular in shape, which may increase the
difficulty in obtaining accurate and reproducible measurements.
Additionally, linear measurements obtained for cystic and necrotic
tumors are often overestimated [39]. Intuitively, 3D segmentation
provides a more accurate method for assessing tumor size com-
pared to linear 2D approaches and techniques [40–42]. For exam-
ple, Dempsey et al. [43] observed that 3D segmentation allows for
better survival prediction compared with traditional diameter-
based analysis.

Deep learning, an emerging branch of artificial intelligence, has
been shown to rapidly outperform other machine learning
approaches' imaging benchmarks for various computer vision
tasks [44, 45], including imaging 3D segmentation tasks. For
example, Zhang et al. [46] observed that a CNN approach per-
formed significantly better than other techniques, including ran-
dom forest, support vector machine (SVM, a traditional linear
machine learning technique), coupled level sets, and majority vot-
ing for brain segmentation.

Since 2012, the Multimodal Brain Tumor Image Segmentation
(BraTS) challenge has demonstrated the efficacy of deep learning
approaches for tumor segmentation [47]. This unique dataset pro-
vides developers access to GBM images, which now includes over
2000 patients from 37 institutions. As result, multiple groups have
developed fully automated brain tumor segmentation tools which
rely on various AI techniques to identify lesion margins and provide
a more accurate estimate for disease burden (Fig. 3) [48–51]. In
2020, Isensee et al. [52] took first place with Sørensen-Dice coeffi-
cient scores of 88.95, 85.06, and 82.03 for whole tumor, tumor
core, and enhancing tumor, respectively. Most recently in 2021,
<figure><img src="image_294.png" /><figcaption>**Fig. 3** Example of automated glioma segmentation using deep learning showing FLAIR edema segmentation (left) as well as segmentation of enhancing tissue (right). (Courtesy Peter Chang, MD)</figcaption></figure>

BraTS has partnered with the Radiological Society of North America (RSNA) and the American Society of Neuroradiology (ASNR) [53].

**4.2 Surveillance** As described previously, psPD cases are not reliably distinguished from true progression using RANO criteria with a recent meta-analysis suggesting that upward of 36% are underdiagnosed [54]. In fact, the only accepted methods to distinguish true PD from treatment-related psPD are invasive tissue sampling and short interval clinical follow-up with imaging, which may delay and compromise disease management in an aggressive tumor [16, 17].

Traditional machine learning models have been previously utilized for psPD characterization from radiologic imaging. Hu et al.'s [55] SVM approach examining multi-parametric MRI data yielded an optimized classifier for psPD with a sensitivity of 89.9% and specificity of 93.7%. Though deep learning methods have been leveraged less frequently, they are showing promise for
characterizing psPD versus true PD [56-58]. Jang et al. [56] assessed a deep learning, a long short-term memory network combined with a CNN (CNN-LSTM), to determine psPD versus tumor PD in GBM. Their dataset consisted of clinical and MRI data from 2 institutions, with 59 patients in the training cohort and 19 patients in the testing cohort. Their CNN-LSTM structure, utilizing both clinical and MRI data, outperformed the two comparison models of CNN-LSTM with MRI data alone and a random forest structure with clinical data alone, yielding an AUC (area under the curve) of 0.83, an AUPRC (area under the precision-recall curve) of 0.87, and an F-1 score of 0.74 [56]. More recently, Lee et al. [58] also utilized a CNN-STM to distinguish PD from psPD with an accuracy range of 0.62-0.75. These examples indicate that utilization of a deep learning approach can outperform a more traditional machine learning approach in analyzing images.

## 4.3 Molecular Classification

Radiogenomics focuses on bridging the associations between medical imaging and gene expression data in order to aid in the understanding of underlying disease mechanisms and improve diagnostics [59]. Certain molecular and genetic alterations in tissue can be observed computationally in terms of radiological appearance, including shape and texture of tissue. Radiogenomics, which leverages the interplay between radiological and genetic features in oncology, is important to improve patient treatment decisions, and artificial intelligence has become a key player that has led to significant advancements in these areas. AI-based radiogenomics has the potential to better characterize diagnosis, prognosis, and survival prediction by detecting key features in images that identify molecular characteristics of disease.

In gliomas, one of the earliest groups that used neural networks to predict tumoral genetic subtypes from imaging features was Levner et al. [60]. In this study, features were extracted from space-frequency texture analysis on the S-transform of brain MRIs to predict MGMT promoter methylation status in newly diagnosed GBM patients. Levner's group achieved an accuracy of 87.7% across 59 patients, among which 31 patients had biopsy-confirmed MGMT promoter methylated tumors. Residual CNN methods have also been used to predict MGMT promoter methylation status [61], as well as IDH mutation status. For example, Chang et al. developed a CNN to simultaneously classify IDH1, 1p19q codeletion, and MGMT promoter methylation status with high accuracy from imaging data derived from 259 patients in the Cancer Imaging Archives dataset [35]. Chang et al. also developed a principal component analysis approach to disentangle the final feature layer and determine the most influential features for each classification (Fig. 4). These features largely overlap with what has been described in the literature by subjective visual assessment. Ryu et al. [62] evaluated glioma heterogeneity via textural analysis and
<figure><img src="image_295.png" /><figcaption>**Fig. 4** MRI separating gliomas by MGMT methylation status. Features include thick enhancement with central necrosis (a) with infiltrative edema patterns (b). In contrast, features predictive of MGMT promoter methylated status include nodular and heterogeneous enhancement (c) with masslike FLAIR edema (d). (Copyright American Journal of Neuroradiology, adapted, with permission, from reference [35])</figcaption></figure>

distinguished low- and high-grade gliomas with 80% accuracy. Additionally, Drabycz et al. [63] were able to classify MGMT promoter methylation status in glioblastoma patients with 71% accuracy using a textural analysis approach.

# 5 Summary

In summary, present challenges in brain tumor imaging in part stem from the heterogeneity of the disease, which results in challenges related to disease characterization. However, the application of novel AI, ML, and DL approaches for brain tumor imaging aims to improve many of these areas due to its ability to accurately and reliably detect imaging patterns beyond human perception. Numerous public competitions (e.g., BraTS) have also spurred the field and have recently begun collaborations with multiple
imaging societies, including the RSNA and ASNR. Ultimately, there is optimism that these tools will continue to yield new opportunities to enhance discovery and care in the future.

## Acknowledgments

The authors wish to acknowledge Jack Grinband, PhD (Columbia University, New York NY); Brent Weinberg, MD PhD (Emory, Atlanta GA); and Peter Chang, MD (University of California, Irvine, Irvine CA), for their expertise and support. The authors also acknowledge the supportive administrative team from the Center for Artificial Intelligence in Diagnostic Medicine at the University of California, Irvine.

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# Machine Learning for Neurodevelopmental Disorders

Clara Moreau, Christine Deruelle, and Guillaume Auzias

## Abstract

Neurodevelopmental disorders (NDDs) constitute a major health issue with >10% of the general worldwide population affected by at least one of these conditions—such as autism spectrum disorders (ASD) and attention deficit hyperactivity disorders (ADHD). Each NDD is particularly complex to dissect for several reasons, including a high prevalence of comorbidities and a substantial heterogeneity of the clinical presentation. At the genetic level, several thousands of genes have been identified (polygenicity), while a part of them was already involved in other psychiatric conditions (pleiotropy). Given these multiple sources of variance, gathering sufficient data for the proper application and evaluation of machine learning (ML) techniques is essential but challenging. In this chapter, we offer an overview of the ML methods most widely used to tackle NDDs' complexity—from stratification techniques to diagnosis prediction. We point out challenges specific to NDDs, such as early diagnosis, that can benefit from the recent advances in the ML field. These techniques also have the potential to delineate homogeneous subgroups of patients that would enable a refined understanding of underlying physiopathology. We finally survey a selection of recent papers that we consider as particularly representative of the opportunities offered by contemporary ML techniques applied to large open datasets or that illustrate the challenges faced by current approaches to be addressed in the near future.

**Key words** Neurodevelopmental disorders, Autism spectrum disorders, Attention deficit hyperactivity disorders, Machine learning, Pattern recognition, Classification, Clustering, Stratification

## 1 A Brief Introduction to Neurodevelopmental Disorders

Neurodevelopmental disorders (NDDs) cover a large range of pathologies. This term can be used to refer to known genetic syndromes such as fragile X syndrome or, in a much broader sense, include conditions with multifactorial etiology such as autism spectrum disorders (ASD), attention deficit hyperactivity disorders (ADHD), or developmental dyslexia. Even more broader are the definitions from the DSM-5 or the ICD-10 which also encompasses intellectual disabilities (ID), communication disorders, specific learning disorders, and motor disorders [1]. NDDs embrace defects that disturb the developmental function of the brain, which could lead to neuropsychiatric complications, learning
difficulties, language or non-verbal communication problems, or
motor function disabilities. However, although there is a tight
intrication between NDDs and psychiatric disorders—for whom
manifestations come later in life—phenomenological categories
used in the adult population do not apply consistently in NDDs.
The latter are conditions for which the cause or the onset is located
during gestation or birth and should be distinguished from late-
onset disorders. We refer to [2–4] for a historical view of the
standardized tools allowing for reliable and valid categorical dis-
tinctions, available to the community since the 2000s.

NDDs constitute a critical health problem in our society. More
than 10% of the general worldwide population is affected by neu-
rodevelopmental disorders [5]. The consequences of NDDs impact
a person's lifetime, so patient management represents a major cost
for society. Important healthcare advances have improved the life
course of several NDDs (e.g., very low birth weight preterm
infants, congenital hydrocephalus) and extended the expected life-
span of others (e.g., cystic fibrosis). The assessment and study of
individuals with NDDs become thus an increasingly crucial issue.
Researchers and clinicians have strongly emphasized the impor-
tance of early identification and intervention to improve the level
of functioning. However, because of the high complexity intrinsic
to these pathologies, we face a lot of misdiagnoses or even missed
diagnoses which prevent early and effective therapeutic interven-
tions. As an illustration, 1/5 of children diagnosed with ADHD or
ASD in the population are currently misdiagnosed, which leads to a
failure to get the adequate treatment or the administration of an
unnecessary one.

NDDs are particularly complex to approach and to diagnose for
several reasons. First, comorbidities are common in NDDs.
Comorbid clinical features have been shown to be the rule rather
than the exception in NDDs, adding to the complexity of proper
diagnostic boundaries' delineation. Over a third of individuals with
ASD meet criteria for ADHD, obsessive-compulsive disorder
(OCD), disruptive behavior disorders, anxiety and mood disorders,
intellectual disability, or epilepsy, inducing various diagnostic com-
binations [2, 6, 7]. This overlap across conditions probably origi-
nates from a shared neurological etiology. As a consequence,
studies that exclude other psychiatric disorders have limited trans-
lational application because of the pathophysiological overlap
between many comorbid disorders (see Fig. 1 for an illustration of
this issue).

In relation to this first issue, neurodevelopmental disorders
overlap a lot in terms of etiology because of important epidemio-
logical comorbidity and community of symptoms [8]. NDDs show
indeed considerable overlap both neuropsychologically, physiolog-
ically, and genetically. For instance, the presence of certain behav-
ioral characteristics, such as attention problems, does not
<figure><img src="image_296.png" /><figcaption>**Fig. 1** *Left:* As introduced in Subheading 1, the complexity of NDDs comes from the combination of multiple sources of heterogeneity acting at different levels and that overlap across conditions as illustrated here with ASD, ADHD, and intellectual disability (ID). *Right:* As described in Subheading 2, ML approaches are instrumental to characterize and overcome the heterogeneity at each level with dedicated techniques</figcaption></figure>

systematically indicate a specific diagnostic entity (e.g., ADHD), but instead, attention problems occur across a large variety of disorders (such as in ASD or in anxiety disorders). When biological bases are considered, the level of heterogeneity remains elevated. A wide range of neurological substrates have been associated with individual disorders. For example, ADHD has been associated with differences in gray matter within the anterior cingulate cortex, caudate nucleus, pallidum, striatum, cerebellum, prefrontal cortex, premotor cortex, and most parts of the parietal lobe [9].

Similarly, at the genetic level, both common and rare, and structural as well as sequence, variations have been identified as contributing to NDDs. There are multiple examples in which the identical variant has been found to contribute to a wide range of formerly distinct diagnoses, including autism, schizophrenia, epilepsy, intellectual disability, and language disorders. These include variations in chromosomal structure at 16p11.2, rare de novo point mutations at the gene *SCN2A*, and common single nucleotide polymorphism (SNP) mapping near loci encoding the genes *ITIH3*, *AS3MT*, *CACNA1C*, and *CACNB2*. In the case of autism, high genetic heritability (70–80%) with more than 1000 genes contributing to ASD has been yielded [10]. These selected examples point that heterogeneity in these pathologies is clearly multidimensional [3]. As a result, conferral of a diagnosis based on DSM-5
or ICD-10 criterion ascribes an underlying cause to the various behavioral difficulties without a method available to verify that the disorder arises from underlying biological dysfunction.

The specificity of NDDs relative to psychiatric disorders (covered in Chapter 32) is that the challenges induced by the intricacy of a spectrum of conditions are potentialized by the developmental dimension. Indeed, the developmental transformation is a major contributor to the multidimensional heterogeneity across individuals affected by NDDs. Brain developmental trajectory exhibits marked variations across individuals [11, 12], but also across brain regions [13, 14]. The development course concerns cognitive, neuronal, and epigenetic maturation processes that follow distinct, yet inter-dependent, nonlinear trajectories [15, 16]. During development, reorganization and competition for function are highly active. Compensatory mechanisms can thus interfere with potential alterations of the nervous system in individuals with NDDS. The timing of these alterations is of high relevance as different neural systems are selectively vulnerable to injury at different phases of prenatal and postnatal development [17]. This plasticity partially explains the heterogeneity in behavioral and cognitive dysfunction associated with early alteration, ranging from subtle to diffuse and profound. In addition, the functional impairments can be observed immediately in some individuals, while in others, the full range of deficits may not manifest until later in life [18].

As a consequence, early diagnosis is key since early medical intervention would benefit from the remarkable plasticity of the immature brain, allowing the patient to adapt and/or develop compensatory mechanisms. On the basic research side, investigating earlier allows to reduce the influence of compensatory mechanisms and secondary perturbations. Studies focused on young children are more likely to reach the causes, whereas in adult populations, consequential or adaptation abnormalities likely contaminate the observations.

There are thus crucial needs in NDDs for a better detection of early, subtle signs of neurodevelopmental pathology and more accurate prediction of the evolution of the impairments. Gaining insight on the pathophysiological processes and the identification of more homogeneous subtypes is also required for the identification of new targets for drug development.

To address these needs, collective efforts have been made to constitute large public datasets giving access to sufficient amounts of multidimensional data covering the dimensions mentioned above (see, e.g., [19]). Recently, we have witnessed the constitution of large databases trying to address these issues and which we will refer to in the following chapters. We can mention, for instance, ABCD [20], ABIDE [21], EU-AIMS [22], and ADHD200 [23] (see Chapter 24, for general considerations regarding the rise of openly accessible large datasets). It induced a crucial need for
statistical approaches tailored for the data-rich setting and thus called for closer collaboration with the field of machine learning.

Unsurprisingly, the NDDs having the largest prevalence, and, thus, the greater societal impact and the easier recruitment, are largely overrepresented in these databases. As a consequence, they are also overrepresented in the literature of ML techniques applied to NDDs. In the remainder of this chapter, we focus on ASD and ADHD. With regard to the characteristics mentioned above, we argue that ASD and ADHD are highly representative of the NDDs in general. As detailed in Boxes 1 and 2, they are the two most common neurodevelopmental disorders observed in childhood, and they present considerable variability, both within and across conditions. These two syndromes share most of their comorbidities, while 40–83% of children with ASD also have ADHD [24], and 28–87% of children with ASD show symptoms of ADHD [25]. See [26] for a comparison of the outcomes from recent neuroimaging studies in these two disorders. As a consequence of this heterogeneous clinical presentation, we clearly face a lack of objective criteria for diagnosis for these two disorders as well as for the other NDDs.

**Box 1 Autism Spectrum Disorder (ASD)**

ASD is a complex neurodevelopmental condition with life-long impacts. Current prevalence is estimated to be at least 1.5% in developed countries. The male-to-female ratio is estimated to be 4:1 in this pathology. This sex ratio varies, however, according to intellectual disability (ID): reported median sex ratios of 6:1 among normal-functioning subjects and 1.7:1 among cases with moderate to severe ID [27]. Individuals with ASD suffer from a specific combination of deficits in social communication and repetitive behaviors, severely restricted interests, and sensory behaviors from early in life. Despite the vast resources devoted to the study of ASD, its pathogenesis remains largely unknown. Recent genetic studies have identified a number of rare de novo mutations and provided insight into polygenic risk, epigenetics, and gene-by-environment interaction related to autism or autistic traits [28]. In addition, epidemiologic investigations focusing on nongenetic factors have identified advanced parental age and preterm birth as risk factors for ASD and have suggested that prenatal exposure to air pollution and short inter-pregnancy interval are also potential risk factors. See, e.g., [29] for more detailed information.
**Box 2 Attention Deficit Hyperactivity Disorder (ADHD)**

ADHD is one of the most common neurodevelopmental disorders, characterized by inappropriate and developmentally harmful levels of inattention, hyperactivity, and impulsivity. It affects boys more often than girls. Its prevalence in the general population is between 3% and 4%. ADHD is diagnosed according to strictly defined criteria, but there is still no reliable biomarker of the pathology. The causes of ADHD are complex and multifactorial, with genetics, early environment, and gene-environment interplay being involved. Although ADHD is highly heritable, and multiple types of genetic variants are associated with the disease, none of them can be used as diagnostic. Diagnostic thresholds are given by both the ICD-10 and the DSM-5, but the clinical features of ADHD behave as continuously distributed dimensions and vary considerably between individuals. Clinical features are heterogeneous. ADHD profiles include not only its definite symptoms (hyperactivity-impulsiveness, inattention) and features of other neurodevelopmental disorders but also additional cognitive deficits such as impaired working memory and planning. Early comorbidity with developmental, learning, and psychiatric problems, such as ASD, is very frequent. ADHD is lifelong, but its course and outcome are highly variable. Core symptoms such as the hyperactivity observed at preschool age may turn into inattention and executive dysfunction in older children, for instance. See, e.g., [30] for further information.

**2 What Are the Main Challenges in These Conditions That Can Be Addressed Using Machine Learning?**

Given these multiple sources of variance, gathering sufficient amounts of data for the proper application and evaluation of machine learning (ML) techniques is essential, but also very challenging. As underlined earlier and illustrated on Fig. 1, NDDs, and more specifically the two we focus on, present a number of specific challenges that can be formulated in terms of heterogeneity, trajectory of development, and comorbidities.

In this section, we give an overview of the methods most widely used in the NDDs’ literature and point to specific challenges that can benefit from the recent advances from the ML field. We refer readers interested in an exhaustive view of the available approaches and their performances in the context of NDDs to the following
recent review papers [31–36]. We organize this overview by follow-
ing the historical evolution of the methods used in the field. The
first applications of ML techniques were focused on classification
tasks. Indeed, classification techniques can be designed for the
prediction of later evolution and are thus in principle well suited
to address the challenge of early diagnosis. We then observe a
progressive shift toward regression, latent space decomposition,
and stratification purposes. These approaches have the potential
to uncover more homogeneous subpopulations of patients that
would enable the refined understanding of underlying physiopa-
thology. More recently, specific approaches have been proposed for
characterizing the atypical brain maturation trajectory in NDDs.
Finally, we discuss the potential of deep learning techniques for
learning representations that might represent a major step toward
prediction at the individual level, which is crucial for translation
into clinical applications.

## 2.1 The Classical Analysis Approach Failed to Reach Consensus

Historically, the classical analysis approach consisted in designing a
study starting from the definition of an “atypical” population of
interest, based on particular clinical scores selected among the
behavioral assessments used for diagnosis. This population of inter-
est is compared to a group of control subjects, following a feature
defined a priori such as “the volume of a specific cortical region
estimated from anatomical MRI.” As extensively described in, e.g.,
[37–39], this corresponds to statistically testing the hypothesis:
Does the atypical population differ, on average, from controls in
the selected feature? Statistically speaking, this amounts to a case-
control study using univariate hypothesis testing for one or a few
features. The large literature of early studies following this approach
allowed to refine the characterization of the different sources of
heterogeneity presented above and shed light on the lack of
biological validity of categorical representations of NDDs that
manifest in the evolution of the nosology, for instance, moving
from “autism” to “autism spectrum disorders” [3]. However, as
we progressed in our understanding of the interactions between
genetics, biological brain, and behavior, the limits of the group
statistics and univariate approaches became obvious.

### 2.1.1 Limitations of Classical Univariate Analysis Techniques

The univariate approach is prevalent in the literature for historical
reasons. It relies on the implicit assumption that different brain
regions and/or different features are independent, while more and
more evidence supports the opposite view: effects are spread across
several brain regions, possibly located far from each other. Knowing
the various sources of variance in NDDs' data described earlier, it is
unlikely that a single feature may capture a large portion of that
variation and thus be interpreted in terms of underlying biological
processes. It is thus not surprising that the effect sizes reported in
meta-analyses remain small. In addition to potentially reduced
statistical power, the problem of inflated false discovery rate in univariate analysis framework has been raised and extensively discussed [40]. Multivariate approaches are much more relevant in this context. Indeed, combining in a multivariate approach a group of features having small effect size when considered independently might lead to a large effect [38].

## 2.1.2 Limitations of Group Statistics

As extensively discussed in [41], group statistics all focus on first-order statistics (group means), thereby seeking a pattern of atypicality that is consistent across the population (i.e., the “average patient”). Indeed, mean group differences may reflect a systematic shift in the distribution of the clinical group and thus provide useful information on altered processes in that population. However, those differences do not delineate variability within groups [38]. In addition, the evolution of the DSM by regrouping conditions that were considered in previous versions as distinct (e.g., Asperger and pervasive developmental disorders not otherwise specified) induced an increase in the heterogeneity of the populations included in studies on ASD [37]. Group comparisons based on diagnosis thus present the major caveat of ignoring psychiatric comorbidities, which are common in NDDs. It thus becomes obvious that group statistics applied to populations defined based on diagnostic categories are inadequate. Indeed, categorical diagnoses from the DSM are increasingly found to be incongruent with emerging neuroscientific evidence that points toward shared neurobiological dysfunction underlying NDDs [42]. See, e.g., [39] for extensive discussions on the limitations of the diagnostic-first approach in comparison to the alternative strategy that begins at the level of molecular factors enabling the study of mechanisms related to biological risk, irrespective of diagnoses or clinical manifestations.

The combination of univariate statistics and mean group difference analysis applied to heterogeneous populations with small sample sizes resulted in highly inconsistent findings. Indeed, most of the published findings are not consistent and were not replicated. The recent challenge [43] further illustrates the intrinsic limitation of the group statistics framework, but also that state-of-the-art ML techniques do not systematically outperform classical approaches in such a binary classification task. In this context, deep learning techniques were prone to overfitting with poor generalization to unseen dataset, while simpler approaches had a stable prediction performance when applied to new data. It is important to stress that several limitations from this early literature do fully apply to more advanced ML techniques and/or multivariate data analysis strategies. While the problem of inflated false discovery rate in univariate analysis framework has been extensively discussed [40], the problems related to the improper evaluation and validation of ML
techniques (e.g., overfitting and biases induced by inadapted cross-validation strategy or absence of a truly independent test set) emerge in the recent literature [31, 44–46]. While discussing the limitations of cross-validation for estimating the potential overfitting of statistical models is beyond the topic of this chapter, we stress the crucial importance of raising awareness of these aspects. We refer interested readers to essential guidelines and recommendations that have been provided in [43, 47–51]. Indeed, uncovering potential biases in the models’ validation strategy is a tedious but essential step. Abraham et al. [52] is a nice illustration of the major gains in interpretation resulting from an extensive analysis of the most influential factors.

## 2.2 Promises of ML in NDDs

The rise of big data and the sustained advances in ML enable in principle the integration of various and heterogeneous characteristics such as behavioral profiles, imaging phenotypes, and genomics. The extraction and manual construction of features from each data type, also termed as *feature engineering*, did undergo continuous progress in tight relation with innovations in the acquisition processes. As an illustration, the imaging phenotype today covers a wide range of features extracted mainly from MRI data. For instance, a variety of measures can be extracted from diffusion-weighted imaging [53], from basic estimation in each voxel such as the *fractional anisotropy* to higher-level connectivity measures in each anatomically defined *fiber tract*, or even connections between distant anatomical regions (structural connectivity). On the genetics side, polygenic risk scores (PRS) are additive models developed to estimate the aggregate effects of thousands of common variants with very small individual effects. They can be computed for any individual to estimate the risk/probability for a particular trait conferred by common variants [54]. Feature engineering is a crucial step in the analysis since the biological relevance of the features directly impacts the interpretation, and the strategy used to manage potential interaction across different features might determine the performance of the analysis procedure more than the ML algorithm itself. In parallel, the increase in the size of the available data enables the training of more complex algorithms, making it possible to investigate central questions related to the dynamics of normal and abnormal development by means of advanced ML techniques.

## 2.3 Classification and Prediction: Supervised Learning for NDDs

Classification techniques consist in learning a model allowing to separate different groups of subjects based on a set of training data that have been labeled and are thus subtypes of *supervised* machine learning techniques. In this context, classification techniques integrate biological and/or behavioral measures in order to extract a *predictive pattern* corresponding to the diagnosis. Classification techniques used in the literature of NDDs are the same as those used in the field of psychiatry and span the whole range of methods
detailed in Chapters 1, 2, 3, 4, 5, and 6, from simple linear models to most recent deep networks. References [31, 32, 34, 51, 55] provide a detailed overview of the recent applications of classification techniques in the context of ASD and ADHD. The general trends indicate that linear discriminant and logistic regression classifiers were prominent until around 2014, most studies focusing on a single modality (usually structural or functional MRI). Support vector machines (SVM) then became the most commonly used approach due to their performance in the small-sample high-dimension regime but also their ability to perform nonlinear classification. Approaches based on ensembles of classifiers were more recently developed to combine data from several modalities or acquired in different settings (e.g., different scanners). Even more recently, deep learning techniques’ neural networks were applied to populations of a few hundred subjects. We will discuss the potential of these advanced approaches later, in a dedicated section. In terms of input data types, structural and functional MRI modalities are overrepresented in comparison to diffusion MRI, EEG, and behavioral data. Classification techniques based on genetics are getting more and more attention (e.g., using polygenic risk scores). Due to the complex and specific data preprocessing required for each modality (see, e.g., [43]), combining features extracted from several modalities into a multimodal classification technique represents important additional challenges. Only a few studies did explore the potential of combining several modalities so far (e.g., 4 studies among 57 reviewed in [31]), but the initiatives for sharing preprocessed data such as those in [23, 56] will facilitate this type of analyses in the future. Multimodal classification techniques did not demonstrate major performance gain so far, but further improvements can be expected by better exploiting the complementarity of the information across different modalities [32]. In terms of classification performances, the high accuracy (>80%) reported in early studies tended to decrease, while sample size increased [31, 32], suggesting that the impressive results obtained on small cohorts were affected by overfitting, sampling biases, and artificially reduced heterogeneity within and across the populations involved. Note that the decreasing effect sizes of group comparison studies might also be related to the evolution in the definition of autism toward a more inclusive and heterogeneous population [57].

In parallel with this decrease with time in the performance, the research field on psychopathology did initiate a shift, moving away from diagnostic categories based on symptoms to the concept of dimensions related to more objective measures and having better cognitive and biological validity. In particular, the US National Institute of Mental Health initiated in 2009 the Research Domain Criteria (RDoC) project to develop a classification system for mental disorders based upon fundamental dimensions of neurobiology
and observable behavior that cut across current heterogeneous disorder categories [58, 59]. Of note, this research classification system diverges from one intended for routine clinical use in multiple respects [60]. Following this progressive conceptual shift, the major methodological challenge to be addressed moved away from classification and diagnostic prediction to latent space decomposition and stratification.

## 2.4 Latent Space Decomposition and Clustering: Unsupervised Learning for NDDs

Following the progressive confirmation of the inadequacy of mutually exclusive diagnostic categories, behavioral assessments for quantifying ASD traits in any given individual were introduced, such as the autism spectrum quotient questionnaire [61] and the Social Responsiveness Scale (SRS) [62]. A number of studies used these scores to demonstrate that ASD traits are also present in the typically developing population as well as in other NDDs such as ADHD [63]. These studies supported the view of a continuum across NDDs and emphasized the need for novel approaches to identify general psychopathology dimensions that cut through diagnostic boundaries. Such data-driven dimensions would ultimately enable the identification of new targets for treatment development and to stratify the NDDs in subgroups more appropriate for treatment selection [58, 59, 64]. Uncovering the hidden intrinsic structure in the data is a well-known ML problem that has been formulated as *unsupervised* learning in opposition to *supervised* learning tasks such as classification where the algorithm learns to predict a label based on a training set for which the true label is known (see Chapters 1 and 2 or, e.g., [65]). Unsupervised ML techniques consist in fitting a statistical model to the data by implementing specific assumptions regarding the relationships between the input features and on the supposed hidden structure. A general assumption to all unsupervised techniques is that there exists a non-negligeable degree of correlation across some of the features in the actual data, which justifies the search for a more compact optimal representation. Depending on the assumptions regarding the hidden structure to discover, unsupervised techniques can be divided into two classes: latent space decomposition and clustering. Latent space decomposition techniques aim at projecting the data onto a new feature space of lower dimension in which a large portion of the variance can be explained by a few factors. The underlying assumption is that the projected features vary continuously along the axes of this compact subspace. In contrast, clustering techniques seek to partition the data into distinct groups (often termed as population stratification) so that the observations within each group are similar to each other, while observations in different groups differ from each other. The underlying assumption is thus that a categorical representation is more appropriate than in the case of the latent space decomposition approach. In contrast with the classification task, the algorithm is
designed in this case to identify homogeneous subpopulations
within and across diagnostic categories. Several recent approaches
propose a unified framework combining the advantages of both the
dimensional and categorical models [3, 66, 67].

All unsupervised approaches face two main challenges in the
context of NDDs. First, since we are dealing with a limited amount
of data, the number of dimensions or clusters that can be identified
needs to remain limited in order to avoid the curse of dimensional-
ity, i.e., when an infinite number of solutions can fit data equally
well [64, 65, 68]. As a consequence, in the majority of studies, the
set of input features (and thus the dimension of the input space) is
selected based on data availability or prior knowledge, which raises
the problem of establishing an optimal set of variables of particular
relevance for NDDs [69]. Automated feature selection procedures
can be used to reduce the dimensions to be explored (see [69] for a
recap of the approaches explored so far in ASD), but the funda-
mental problem of limited amount of data relative to the very large
dimension to explore remains [64]. The second major challenge is
the validation, since with unsupervised approaches no ground truth
data is available by definition, unlike in the case of supervised
ML. The relevance of the resulting dimensions or clusters should
be assessed in terms of interpretability relative to external measures
that would ideally have some clinical relevance. Replication on a
fully independent dataset allows to assess the generalizability and
reduces the risk of overfitting. This is however very hard to achieve
since the number of datasets available with identical measures is
limited. As a consequence, it is crucial to keep in mind that unsu-
pervised learning is only meaningful in relation to some context
[70]. As extensively discussed in [64], “due to the vast dimension-
ality of the human population (based on environment, behavior,
biology/physiology, etc.) there are multiple ways that the popula-
tion might be subcategorized that are valid and ‘real’; however, any
given subgrouping might not be important for the question we
care about.”

Contrary to the classification task where the literature is very
rich, latent space decomposition and stratification studies in NDDs
are emerging approaches, and only few findings have been pub-
lished so far. Two recent publications review unsupervised
approaches applied to neuroimaging in the context of ASD: [31]
covered 19 studies published since 2018, and [69] identified
12 studies among which 2 were already included in [31]. For an
extensive review covering the literature back to 2001, see [71]. The
methods used range from the most common such as principal
component analysis for latent space decomposition and K-means
for clustering to more advanced techniques such as nonnegative
matrix factorization, spectral clustering, Gaussian mixture models,
and Bayesian latent factor analysis such as Indian buffet processes.
Most advanced approaches such as Bayesian latent factor analysis
techniques enable to infer the number of latent factors and number of putative subpopulations from the data and can be interpreted in terms of both categorical and dimensional aspects of the heterogeneity in NDDs [69, 72]. On the genomics side, multivariate approaches such as canonical correlation analysis and partial least square regression are the tools of choice for investigating the relationship between genomic variants, neuroimaging features, psychiatric conditions, and behavioral traits [39]. The development of specific methods allowing to better model the multivariate genetic covariance structure in genome-wide association studies is a very active field. For instance, [73] introduced a new approach called genomic structural equation modeling, which allows to investigate shared genetic effects across phenotypes, while concurrently testing for causes of divergence. Importantly, this evolution in the methods reflects the progressive integration of latent space decomposition and clustering techniques into unified approaches. A promising avenue of research that benefited from access to larger datasets in the past years consists in combining neuroimaging and genomics. Indeed, the effects of latent factors derived from genomics on neuroimaging endophenotypes demonstrate higher reproducibility and larger effect size than in the previous literature [39, 74].

In terms of evaluation and performances, the studies are highly dependent on the data and the assumptions that are made, either implicitly or explicitly. An illustration of this dependency on the application is the variation in the number of subtypes reported, ranging from two to six across the neuroimaging studies on ASD included in the two reviews [31, 69]. In [71], the authors cover a much broader literature (159 articles) by relaxing inclusion criteria compared to the two others. This exhaustive review identifies seven validation strategies, defined as follows: “cross-method replication,” “subtype separation,” “independent replication,” “temporal stability,” “external validation,” “parallel validation,” and “predictive validation.” They provide the distribution of the number of identified subtypes across the reviewed studies, with a range of values varying between 1 and 16, but 82% of all studies report between two and four subtypes. Of note, this chapter underlies as major challenges the access to large and multidimensional datasets and the design of an unbiased validation framework. We refer interested readers to [71], in particular for the didactic description of the various validation strategies that apply to the literature of ASD and more generally to psychiatry or other clinical groups.

Normative modeling gained great interest in the context of psychiatry recently, and the first applications to NDDs confirm the particular relevance of this approach in this context. Marquand et al. [75] introduced normative modeling as an alternative to clustering for parsing heterogeneity across the full range of population variation, i.e., spanning both clinical and healthy cohorts. In the approach
proposed by [75], the normative models were estimated using Gaussian process regression [76]. The flexibility of this Bayesian method enables to define a mapping between any quantitative biological measures and clinically relevant variables and offers desirable properties such as robustness to overfitting and principled ways for tuning hyper-parameters. Gaussian process regression is flexible but does not scale with an increase in sample size. More importantly, this technique can lead to inaccurate uncertainty estimates when the data are non-Gaussian [77]. Less demanding alternative approaches have been proposed. In [78], the authors used a non-parametric local weighted regression to fit a smooth curve through data points. Based on the assumption that the estimated regression is likely to be smooth, [79] proposed to estimate non-linear effects using a smoothing spline model. This approach is a special case of Gaussian process regression. It is thus less adaptive, but presents a lower computational cost than Gaussian process regression. Fraza et al. [80] presented a novel framework based on spline interpolation combined with likelihood warping and Bayesian estimation that allows to scale normative modeling to big data cohorts. Another approach based on generalized additive models was proposed in [81, 82]. The very last version of normative models was presented recently by [83] with the generalized additive models for location, scale, and shape (GAMLSS), a flexible modeling framework that can model heteroskedasticity, nonlinear effects of variables, and hierarchical structure of the data. As demonstrated in [84] with features extracted from more than 120,000 MRI, these models can be estimated on very large datasets. They are however not suitable for small datasets since the higher flexibility of such a model would be detrimental and might lead to overfitting.

Normative models are highly relevant for analyzing neuroimaging data since they can be fit at each brain location to estimate regional specificity. In the context of NDDs, two advantages are particularly critical. First, normative modeling is efficient to disentangle the effects related to brain maturation dynamics and neurodevelopmental diseases in a data-driven way. Indeed, the Bayesian framework enables estimating distinct variance components. The effect of age within the reference cohort is estimated by nonlinear interpolation, which is appropriate in this period of highly active neurodevelopment [14, 85].

Second, normative modeling provides uncertainty measures to quantify the variation across the estimated mean within the reference cohort and the deviation of each patient from the group mean. This enables the detection and mapping of subject-specific patterns of abnormality in each individual. The statistical inference at the level of the individual participant is the key to explicitly characterize the heterogeneity underlying clinical conditions. It represents a concrete alternative to the limitations of the case-control analysis
seeking a pattern of atypicality that is consistent across the popula-
tion as discussed in Subheading 2.1. In the normative modeling
framework, a deviation map is computed for each individual based
on extreme values statistics, which does not require that atypical-
ities overlap across participants. These individual deviation maps
can then be analyzed (e.g., using unsupervised ML approaches
described in Subheading 2.4) to identify distinct patterns of abnor-
mality, i.e., to characterize putative subpopulations.

See [41, 83, 86, 87] for further description of the normative modeling framework and recommendations to guide future applications. The release of two python packages contributed to the widespread use of this approach: https://github.com/ppsp-team/PyNM and https://github.com/amarquand/PCNtoolkit. A didactic tutorial with a step-by-step comparison of the different normative modeling approaches on synthetic data illustrating their advantages and limitations is available online here: https://github.com/ppsp-team/PyNM/tree/master/tutorials.

2.6 Potential and
Challenges of Deep
Learning

Deep learning is a class of ML algorithms characterized by their
specific internal architecture as multi-layered neural networks.
These multiple layers enable the striking capacity to progressively
extract higher-level features without extensive prior injection. Their
advantages compared to previous approaches are of crucial impor-
tance in a large range of applications and explain the considerable
attention gained by DL in the wider scientific community. See, e.g.,
[88] for a detailed description of the DL methods used in the
literature to investigate the neuroimaging correlates of psychiatric
and neurological disorders. Conceptually, DL techniques are par-
ticularly relevant for the investigation of NDDs for the following
reasons:

* *Integrated learning of hierarchy of features.* As mentioned in Subheading 2.2, classical ML algorithms leverage sets of structured features extracted from the input data. This feature engineering step relies on a priori regarding the data and has a strong influence on the performances. DL algorithms process directly the raw data without requiring prior feature extraction. During the learning, the algorithm can determine the optimal hierarchy of most relevant features for representing the data, resulting in a more objective process.

* *Learning relevant spatial relationships from neuroimaging data.* In the context of neuroimaging, a striking advantage of DL is its capacity to learn relevant spatial relationships among the image domain, such as an atrophy distributed across a network of several brain regions supporting a specific function [89]. In classical ML techniques, the feature engineering step and the learning phase are dissociated, such that relevant spatial
relationships may be lost. On the contrary, this spatial relation-
ship might be preserved by DL techniques and integrated into
the optimal hierarchy of features.

* *Learning nonlinear relationships and biologically relevant compact representations.* As already discussed in Subheading 2.5, nonlinear relationships across data or dimensions relevant to NDDs are expected. Conceptually, the combination of the multiple layers available in DL architectures enables to encode this nonlinearity into a cascade of nonlinear transformations while reducing the input space into a lower-dimensional “latent space,” providing a compact representation of the data. The recent works from [89–91] demonstrated that DL can exploit the presence of nonlinearity in neuroimaging data to learn generalizable representations highly relevant for characterizing the human brain. They combined supervised and unsupervised tasks in a DL framework which consisted in learning the representation from classification tasks (predicting age and sex) and then applying decomposition and clustering techniques to the latent space. These studies strongly support that DL approaches can provide more accurate mappings of the effects of age and sex on brain MRI than simpler models. The resulting representations obtained in these works are instrumental for refining the link between cognition and underlying brain systems. Another promising avenue of research denoted as scientific machine learning (https://sciml.ai) consists in injecting traditional scientific mechanistic models into modern deep learning architectures in order to combine the benefits of efficient data-driven automatic learning with better interpretability and integration of biophysical constraints. *See* [92] for a review discussing the potential of these approaches in computational neuroscience and [93] for an example application to neuroimaging data. DL techniques can thus learn representations of data that have the potential to help explain the biological underpinnings of mental disorders, providing that enough data is available.

3 A Non-exhaustive Survey of Existing Papers on Machine Learning for NDDs and Their Limitations

We refer to the recent reviews [31, 32, 34, 51, 55, 69, 71], for a
complete overview of the literature of the field. Here, we survey a
selection of very recent works that we consider particularly relevant
with respect to the opportunities offered by recent ML techniques
applied to large open datasets, or that illustrate the challenges faced
by current approaches, to be addressed in the near future.
## 3.1 Using ML
Techniques on
Neuroimaging Data to
Predict the Diagnosis

An international challenge (146 challengers) has been organized to predict ASD diagnosis based on several neuroimaging modalities [43]. This challenge was conducted on the largest sample available to date (>2000 individuals from the ABIDE dataset and a second, private dataset not open to challengers). An additional dataset from the EU-AIMS project [22] was used to evaluate the reproducibility of the prediction on an independent dataset (out-of-sample prediction). The ten best submissions used either logistic regression as a first-layer predictor, linear vector classification, or a combination of different methods. Best algorithms managed to predict ASD diagnosis with an in-sample AUC of 0.80. Resting-state fMRI data was a better diagnostic predictor than anatomical MRI, and simple logistic regression performed better than complex graph convolutional deep learning models (likely due to overfitting). Finally, the performances of the best algorithms decreased to an out-of-sample AUC of 0.72 (on the external sample). Authors projected that 10,000 individuals might be necessary to reach the optimal prediction.

Another study of interest was led by the consortium “Infant Brain Imaging Study” (IBIS) [94]. The authors investigated whether infants at high familial risk for autism present early postnatal atypical brain volume. A deep learning algorithm used surface area at 6 and 12 months to successfully predict an early diagnosis of autism in infants at high risk of autism at 24 months (in-sample predictive value of 81%, no out-of-sample prediction accuracy provided). These results should be tempered by several major pitfalls. First, the diagnosis of ASD is very challenging at that early age. Second, the sample size was very small (15 high-risk infants diagnosed with autism at 24 months) and thus does not comply with the recommended practices for predictive modeling [46]. Third, the specificity of the results with respect to other NDDs was not assessed. A confirmation of the reproducibility of these results in a larger, external cohort would thus be much welcome.

Overall, these results showed that applying prediction algorithms on large enough imaging data could be instrumental for the early detection of ASD and therefore early intervention. In line with the conclusions of previous reviews [31, 69], these studies also demonstrated the relevance of using imaging data as an intermediate phenotype between the biological cause (e.g., deletion of the gene content at the 16p11.2 chromosomal segment) and the associated phenotype (e.g., ASD, ADHD, intellectual disability).

## 3.2 Latent Space
Decomposition and
Subtyping Approaches
Applied to NDDs

Complementary works are aiming to face clinical and biological heterogeneity in NDDs using a subtyping approach based on imaging data. Using hierarchical clustering methods on neuroanatomical data, Hong and colleagues [95] identified three distinct morphometric subtypes in ASD: ASD-I characterized by cortical thickening, increased surface area, and tissue blurring; ASD-II with
cortical thinning and decreased geodesic distance; and ASD-III with increased geodesic distance. These groups were associated with gradual symptom severities and might help tackle the well-known clinical heterogeneity issue introduced in Subheading 1. The genetic contribution to the observed clinical heterogeneity was investigated across eight psychiatric conditions including ASD and ADHD [96] with common variants. Exploratory factor analysis (EFA) on GWAS cross-disorders' summary results led to the identification of three genetically inter-related groups of disorders, explaining together 51% of the genetic variation across NDDs and psychiatric conditions. The first factor linked anorexia nervosa, OCD, and Tourette syndrome. The second one was associated with major depression, bipolar disorder, and schizophrenia. The last one encompassed early-onset NDDs (ASD, ADHD, Tourette syndrome) and major depression. Similar to EFA results, hierarchical genetic clustering identified the same three subgroups among the eight disorders. These methods therefore have a great potential to uncover new biologically relevant diagnostic categories.

Such overlaps across clinical diagnoses have also been characterized at the imaging level. Patel et al. [19] determined a common pattern of group differences in cortical thickness across six disorders—including ASD, OCD, ADHD, schizophrenia, bipolar, and major depression disorders—and their link with gene expression profiles. Analyses of correlation and clustering revealed a shared profile of differences across disorders with 48% of variance explained, associated with pyramidal-cell gene expression. Analyses of gene co-expression highlighted two pre- and postnatal clusters associated with this common brain profile of group differences, enriched with genes associated with these disorders. Kebets and colleagues [97] applied partial least square regression (PLSR) to resting-state fMRI and cognitive metrics in participants with either ASD, ADHD, schizophrenia, or bipolar disorders. They identified three latent components (general psychopathology, cognitive dysfunction, and impulsivity) with unique fMRI signatures. Connectivity patterns of the somatosensory-motor network were main drivers across the three components. Similar findings on the somatosensory-motor network have been observed by [98] and extended to rare genetic mutations that confer high risk for neuropsychiatric conditions. Kernbach et al. [42] designed a hierarchical Bayesian modeling framework to derive hidden disease dimensions from RS-fMRI data across a population of ADHD, ASD, and controls. Using these methods, the number of components is inferred from the data. They obtained 45 hidden components that were then reduced to 3 main factors for better interpretation. For each of these three identified factors, the authors characterized the associated fMRI coupling patterns and symptom measures from the clinical questionnaires. These brain-
derived factors predicted the classification of subjects as ADHD, ASD, or control with an accuracy of 67%, computed using a variant of cross-validation called pre-validation described in [99]. This variant is expected to enable a fairer evaluation of the group labels than cross-validation, but still leaves room for errors compared to out-of-sample predictions [46].

Latent space decomposition techniques have been also used to identify general principles of the hierarchical brain organization—denoted as functional gradients—that locate sensory-motor networks at one end and the transmodal default-mode network at the other end [100, 101]. Hong and colleagues [102] hypothesized that NDDs' conditions may preferentially affect the sensory-motor dimension. They used surface-based analytical models to compare the first functional gradient (explaining 24% of the connectome variance) in ASD vs. controls and showed that both extremes of the rostrocaudal gradient were decreased in ASD. Interestingly, vertex-wise analyses revealed that such diminution in ASD was driven by transmodal medial PFC and posterior cingulate regions [102].

Combining large-scale multidimensional data is perceived as the golden standard to correctly apply ML algorithms. However, only a few precision medicine studies managed so far to do so. In [103], the authors extracted electronic health records, familial whole-exome sequences, and neurodevelopmental gene expression patterns in a large sample of ASD patients. Their goal was to identify biologically homogeneous ASD subtypes. For this purpose, the authors used spatiotemporal expression data from typically developing human brains to identify clusters of exons that are co-expressed during early human brain development. Based on prior knowledge on sexually different prenatal gene expression in ASD, they focused the analysis on a set of clusters that are differentially expressed between males and females. They then selected inherited, likely gene-disrupting variants among all the ASD-segregating ones by leveraging a large dataset of families who have one child with ASD and one unaffected sibling. They mapped variants back to exon clusters to identify 33 clusters of neurodevelopmentally co-regulated, ASD-segregating deleterious variants. The functional enrichment analysis of the identified exon clusters (detailed in [103]) revealed a new molecular convergence on lipid regulation, with variants expected to collectively alter LDL, cholesterol, and triglyceride levels. They confirmed that children with ASD have blood lipid profiles that are significantly outside the physiological range. Finally, they characterized the diagnostic spectrum of the dyslipidemia-associated ASD subtype and confirmed its specificity by comparing with individuals with ASD and no dyslipidemia. This work demonstrated the potential of combining massive amounts of multimodal data for uncovering new ASD subtypes.
## 3.3 Normative Modeling

In [104], the authors applied normative modeling to a large sample of ASD and controls males covering a wide age range (5–40 years). They investigated the potential of age-related effects on cortical thickness to serve as an individualized metric of atypicality in individuals with ASD. They reported that only a small subgroup of patients showed age-atypical cortical thickness. By comparing with conventional case-control analyses, they observed that most case-control differences were driven by a small subgroup of patients with high atypicality for their age. Highly consistent results were obtained in another application of normative modeling to a different ASD cohort [105], despite important variations across these studies. The population of the second work was composed of both males and females, and sex was included as a factor in the normative model. In addition, the normative models were estimated using different approaches (non-parametric regression in [104], Gaussian process regression in [105]). The overall consistent results despite the methodological differences support the relevance of the normative modeling approach for NDDs. In a follow-up study, [106] applied the spectral clustering technique to the atypicality maps computed at the individual level as deviation in the cortical thickness with respect to the normative model estimated in [105]. They identified five subtypes of individuals with ASD and assessed their separability using a multi-class linear SVM. Each subpopulation was then characterized in terms of demographic and clinical measures as well as association with polygenic scores for seven traits (autism, ADHD, epilepsy, full IQ, neuroticism, schizophrenia, and cross-disorder risk for psychiatric disorders). Importantly, they observed striking differences in the spatial patterns of cortical thickness atypicality maps between subtypes: three clusters showed reduced cortical thickness relative to the normative pattern, whereas two clusters showed an increased cortical thickness. These distinct and opposing atypicalities across different subtypes could explain the inconsistency in the previous case-control analyses. A last study did apply normative modeling to an adult population of ADHD patients [107]. The authors estimated a normative model predicting regional gray and white matter volumes across the brain from age and sex. They observed deviations shared across patients in gray matter in the cerebellum, temporal regions, and the hippocampus. They also provided a measure of the inter-individual variation between ADHD patients with extreme deviations in specific regions in more than 2% of the participants. Overall, these results highlighted the relevance of the normative modeling approach to understanding the heterogeneity in NDDs.

## 3.4 Genetic Features to Predict Cognitive Deficit in NDDs

As extensively discussed in [39], attempts to dissect mechanisms of NDD have mainly used a top-down approach, starting with a diagnosis and moving down to brain intermediate phenotypes and to genes. By contrast, the recruitment of groups based on the
presence of a genetic risk factor for NDDs allows for the investigation of pathways related to a particular biological risk for psychiatric symptoms (bottom-up approach). Clinical routine with genomic microarrays revealed that copy number variants are present in 10-15% of children with neurodevelopmental conditions [108]. Genetic-first approaches can however only be applied to a few recurrent pathogenic mutations frequent enough to establish a case-control study design. Thus, the effect of the vast majority of rare deleterious risk variants remains undocumented. Because a highly diverse landscape of rare variants confers a higher risk to a spectrum of NDDs, studies focusing on individual mutations will not be able to properly disentangle the relationship between mutations, molecular mechanisms, and diagnoses. Huguet and colleagues [109] speculated that large effect size pathogenic deletions may be attributable to the sum of individual effects of genes encompassed in each copy number variation. They introduced a new framework to estimate the effect of any pathogenic deletion on intelligence quotient (IQ). Using several types of functional annotations of rare genetic deletions associated with NDDs, the proposed framework predicted their impact on IQ with 76% accuracy [109]. They showed that haploinsufficiency scores—probability of being loss of function intolerant (pLI)—best explain the cognitive deficits. Follow-up works specifically on ASD confirmed that this score was the best predictor of IQ deficit and autism risk (odds ratio) [110, 111]. Deletion of 1 point of pLI was associated with a decrease of 2.6 points of IQ in autism.

## 3.5 Deep Learning Applied to NDDs

A deep learning-based framework has been recently introduced to predict the regulatory contribution of non-coding mutations to autism [112]. Authors constructed a deep convolutional network to model the functional impact of each individual mutation (single nucleotide polymorphism). They first identified that ASD probands (n = 1700 families) were carriers of a higher rate of transcriptional and post-transcriptional regulation disrupting de novo mutations compared with their siblings. They also revealed a convergent pattern of coding and non-coding mutations.

In [113], the authors analyzed resting-state fMRI (RS-fMRI) data from 260 subjects with ADHD and 343 healthy controls from the ADHD-200 database. They proposed to represent RS-fMRI data from each individual as a graph that integrates both temporal and spatial correlation of regional time-series signals. An original graph convolutional neural network architecture was introduced to characterize the brain functional connectome. The model also included seven non-imaging variables (age, gender, handedness, IQ measurement, and three Wechsler Intelligence Scale evaluation IQ variables) and was trained to distinguish ADHD patients from HC. Several experiments showed a performance gain compared to previous methods including SVM, logistic regression, and conventional graph convolutional networks. The proposed method
outperformed other competing approaches, including SVM and logistic regression, with an AUC of 75 (72.0% accuracy, 71.6% specificity, and 72.2% sensitivity) on a tenfold cross-validation. A leave-study-site-out experiment demonstrated the robustness of the proposed model for unseen data from different study sites, and experiments with simplified versions of the model showed the relevance of each proposed improvement. Most discriminative regions were mainly located in the frontal lobe, occipital lobe, subcortical lobe, temporal lobe, and cerebellum—with hypoconnections mainly between the frontal, parietal, and temporal lobes and widespread hyper-connections.

These studies support that new methodological improvements can be expected from the very active field of deep learning applications to neuroimaging and genetics data. As pointed in [88], the anticipated increase in sample size in NDDs studies will allow fit more complex models, which might reveal larger differences in performances compared to conventional methods. The literature of DL applications to NDDs is however still in its initial stages, and major challenges such as tendency to overfitting [43] have to be carefully addressed in future studies.

## 3.6 Discussion

The review of the selected recent studies presented above demonstrates that the application of ML in NDDs is a very active field of research, with encouraging perspectives. This field indeed benefits directly from initiatives to openly share data [114], which did increase the sample size involved across studies, and favored the engagement of ML scientists. The paradigm shift from diagnostic-first to genetic-first and from one diagnostic at a time to cross-diagnoses approaches is afoot, with a clear rise of large-scale studies based on normative modeling and deep learning approaches. Methodological works continue to introduce new innovative ML approaches specifically designed to address the central tasks in NDDs. Importantly, the adoption of best practices for the validation and replication of the results across independent datasets as stated in [46] is clearly encouraged by the recent reviews [31, 32, 34, 51, 55, 69, 71]. However, the validation is limited by insufficient access to large enough datasets combining multiscale data (genetics, transcriptomic, proteomic, metabolomic, neuroimaging features, phenomics). There is no open dataset so far offering that level of granularity. Indeed, the imaging field is just reaching the sample size allowing for running modern ML techniques for some but not all modalities. For instance, large-scale studies involving diffusion-weighted imaging are clearly lacking in NDDs, probably due to insufficient access to appropriate data. The genomic field is not ready yet, and several domains remain relatively new (e.g., first genome sequenced in 2000, next-generation sequencing techniques in 2010) and expensive (e.g., RNA-Seq data) [115, 116]. Such data will provide—in the near future—massive potential for accurate classification and appropriate validation.
# 4 Open Challenges and Conclusion

Methodological improvements described in Subheading 2 and studies reviewed in Subheading 3 are encouraging for concrete impact on clinical practice in the future. However, such clinical translation is raising major challenges that should be addressed.

## 4.1 Potential Bias in Data and Processing Pipelines

Despite the large amount of new approaches released by recent literature, some potential biases in analysis pipelines should be mentioned. For instance, the analysis of functional networks computed from RS-fMRI relies on a complex succession of processing steps. Several of these processing steps actually correspond to implementing assumptions regarding the data. However, the validity of these assumptions and their influence on the subsequent results are not sufficiently discussed in the literature. *See*, for instance, [117] for a quantitative evaluation of the impact of the brain parcellation procedure on functional connectivity analyses. Another major barrier to reproducibility is the lack of compatibility among programming languages, software versions, and operating systems as illustrated in [118]. This report highlights the challenges and potential solutions to be implemented at both the individual researcher and community levels in order to enable the appropriate reuse of published methods.

On the data side, the limitations related to the absence of recording of potentially influencing factors are not sufficiently investigated and acknowledged. As pointed, e.g., in [119]: “The extent of brain differences in disease may depend critically on a patient’s age, duration of illness, course of treatment, as well as adherence to the treatment, polypharmacy and other unmeasured factors. Differences in ancestral background, as determined based on genotype, are strongly related to systematic differences in brain shape. Any realistic understanding of the brain imaging measures must take all these into account, as well as acknowledge the existence of causal factors perhaps not yet known or even imagined.” As a concrete illustration, [120, 121] recently reported significant alterations in brain morphometry induced by prematurity, a factor that was not considered by any of the studies we reviewed here. Such uncontrolled factors might introduce considerable bias in the learning process. The ML research field has identified this pitfall, and several solutions to prevent unexpected implications in clinical applications are actively debated [122–124].

## 4.2 Interpretability and Biological Substrates

Even in the absence of bias, the interpretation of the outcome of any ML algorithm in the context of clinical application represents a critical challenge. More than the level of raw performance, the level of expertise required from medical doctors in (1) the recording and (2) the analysis of the data compared to “expertise-free” raw data is
a question that requires more attention. We refer to [125] for a
thoughtful discussion on the need for clarification of the role of
ML-based tools in relation to clinicians' decisions and actions in
clinical practice. The authors call for a more systematic demonstra-
tion that models learning from non-clinician-initiated data outper-
form models based on clinician-initiated data. They purposely
argue that models driven by features derived from the actions of
clinicians and not related to the underlying physiology might intro-
duce some deleterious circularity. Indeed, the outcome of such a
model might potentially confuse more than support a clinician in
his decisions.

Then—regarding the interpretation in terms of
pathophysiology—the challenge is to relate the decisions of any
ML techniques to putative underlying biological processes. Meth-
odological innovations will enhance the explainability of ML mod-
els, but explainability and transparency do not imply interpretability
[126, 127]. Another major challenge is to assess the biological
relevance of the features extracted from the data during the learning
procedure. Purely data-driven approaches are limited by the diffi-
culty to relate the parameters of the model to biological knowledge.
A promising perspective consists in inserting biological priors
directly in predictive models. See [92] for an introductory review
to this type of approach in the context of computational neurosci-
ence and (https://sciml.ai) for further information on the
emerging field of scientific machine learning. However, extensive
basic research at conceptual, methodological, and experimental
levels are required to fill the gap between measures accessible
in vivo in patients and the biophysiology acting at cellular and
molecular levels. See, for instance, [128] for an illustration of the
complexity of this challenge, where the authors propose a frame-
work integrating different levels of interactions, from genes to cells,
circuits, and clinical expression, to better understand and treat
cortical malformations. As discussed in [129] for ASD, research
designs aiming at a better conceptual integration between different
levels of brain organization are required to characterize the cascade
of pathogenic processes in NDDs.

**4.3 Conclusion**

In NDDs, as in healthcare in general, ML has a role to play in
addressing the longstanding deficiencies such as serious diagnostic
errors, mistakes in treatment, and waste of resources [130]. Indeed,
ML will undoubtedly help redefine NDDs' categories and other
mental illnesses more objectively, identify them at an early stage,
and contribute to more adapted treatments. The rise of ML is the
occasion to improve the standardization of practice and to enforce
the generalization of open science with preregistration and data
sharing or federated learning. In addition, the field has to demon-
strate high and reproducible performances in the real-world clinical
environment. Finally, major conceptual, ethical, and socio-technical
challenges have to be addressed.
Acknowledgments

We would like to thank the editor and Guillaume Dumas who served as a reviewer for their insightful feedback that has improved our manuscript. This work was supported by the French government under the management of Agence Nationale de la Recherche, reference ANR-19-CE45-0014.

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# Machine Learning and Brain Imaging for Psychiatric Disorders: New Perspectives

Ivan Brossollet, Quentin Gallet, Pauline Favre, and Josselin Houenou

## Abstract

Psychiatric disorders include a broad panel of heterogeneous conditions. Among the most severe psychiatric diseases, in intensity and incidence, depression will affect 15-20% of the population in their lifetime, schizophrenia 0.7-1%, and bipolar disorder 1-2.5%. Today, the diagnosis is solely based on clinical evaluation, causing major issues since it is subjective and as different diseases can present similar symptoms. These limitations in diagnosis lead to limitations in the classification of psychiatric diseases and treatments. There is therefore a great need for new biomarkers, usable at an individual level. Among them, magnetic resonance imaging (MRI) allows to measure potential brain abnormalities in patients with psychiatric disorders. This creates datasets with high dimensionality and very subtle variations between healthy subjects and patients, making machine and statistical learning ideal tools to extract biomarkers from these data. Machine learning brings different tools that could be useful to tackle these issues. On the one hand, supervised learning can support automated classification between different psychiatric conditions. On the other hand, unsupervised learning could allow the identification of new homogeneous subgroups of patients, refining our understanding of the classification of these disorders. In this chapter, we will review current research applying machine learning tools to brain imaging in psychiatry, and we will discuss its interest, limitations, and future applications.

**Key words** Psychiatry, Depression, Schizophrenia, Bipolar disorder, Machine learning, Artificial intelligence, Neuroimaging, MRI, Clustering, Classification

## 1 Introduction

Major psychiatric conditions affecting adults can be classified into several groups: affective disorders (e.g., bipolar disorders, major depressive disorders), psychotic disorders (e.g., schizophrenia), anxiety disorders (e.g., obsessive-compulsive disorders), neurode- velopmental disorders (e.g., autism), and substance use disorders. We will focus this chapter on the two first categories, as they carry a high individual and societal burden and are highly prevalent throughout the world.
**1.1 Major Depressive Disorder**

Major depressive disorder (MDD) is defined by the occurrence of one or more major depressive episodes without any manic or hypo-manic episodes in the lifetime. Its prevalence can vary significantly according to the studies, but exceeds 15% of the population during their lifetime [1], and affects two women for one man. Depression can affect people at any time during their life [2]. Nowadays, the diagnosis is based on structured interviews, and the clinical criteria are given by, among others, two classification manuals: the Interna-tional Classification of Diseases [1] and the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) [3]. According to the DSM-5, to meet the criteria for a major depressive episode, five of the nine following symptoms must be present over a 2-week period: depressed mood or anhedonia (loss of interest or pleasure), change in weight or appetite, sleep distur-bances (insomnia or hypersomnia), psychomotor retardation or restlessness, loss of energy or fatigue, low self-esteem or guilt, difficulty in concentrating or indecisiveness, and thoughts of death or suicidal thoughts. Patients with MDD are at an increased risk of other comorbid disorders. Most commonly, they may pres-ent alcohol abuse or dependence, anxiety disorders such as panic disorder, obsessive-compulsive disorder, and generalized anxiety disorder. Treatment options for MDD include a variable combina-tion of pharmacotherapy (antidepressants such as serotonin selec-tive reuptake inhibitors or tricyclics) and psychotherapy (cognitive behavioral therapy, interpersonal therapy, etc.). Despite consider-able progress in its diagnosis and treatment, MDD remains under-diagnosed and underestimated and remains a challenge for healthcare institutions, especially since one of the main risks of mood disorders (BD or MDD) is suicidal behavior.

**1.2 Bipolar Disorder**

Bipolar disorder (BD) is defined as a chronic mood disorder char-acterized by episodes of depression and episodes of abnormal exci-tation (mania, hypomania), separated by periods of “euthymia” (without any symptoms of major mood episode) [3]. This mood disorder affects around 1% of the world's adult population [4], regardless of continent, socioeconomic status, or ethnicity. The course of BD is lifelong, but is heterogeneous in terms of number of episodes, relapses, polarity (i.e., higher number of manic or depressive episodes), and response to treatment. The impact of the disease on cognitive function and quality of life can be major [4]. Diagnosis, treatment, health, and social care are major goals in the management of BD.

Manic episodes are defined by a period lasting at least 1 week, during which patients exhibit elevated mood and increased motor activity. The intensity of these symptoms defines the manic or hypomanic nature of the episode. During a manic episode, patients may experience psychotic symptoms such as hallucinations, delu-sions, disorganized thinking, and sleep disturbances. The delusions may be consistent with the manic mood, with individuals displaying
grandiosity, megalomania, or messianic ideas. Impaired judgment and risk of endangering the patient often lead to hospitalization. Hypomanic episodes are characterized by lower symptom intensity (abnormally high, expansive, or irritable mood, as well as abnormal increase in activity or energy, most of the day) and must last at least 4 consecutive days. Although there are no pathognomonic features of bipolar or unipolar depression, some clinical features are useful in distinguishing them: bipolar depression usually occurs at an earlier age, and the episodes are also more frequent and shorter, show an abrupt onset and termination, and are more frequently associated with substance abuse. Patients with bipolar depression may also present atypical symptoms, such as hypersomnia and weight instability. Psychosis (delusions and hallucinations) and catatonia are also more frequent in bipolar depression, whereas somatic complaints are more common in unipolar depression. The presence of a family history of mania is also a relevant indicator of bipolar depression. The establishment of the diagnosis of BD is a major challenge and has several consequences: stabilizing the disease, allowing good social reintegration, avoiding relapses and side effects, and, finally, limiting the long-term effects of the disease, particularly on the cognitive level. Treatment strategies usually combine pharmacotherapy (mostly mood stabilizers) and psychosocial care, tailored to each patient. Mood stabilizers aim at decreasing the frequency of major mood episodes. Lithium, some anticonvulsants (such as valproate and carbamazepine), and some antipsychotics (such as aripiprazole, quetiapine, or olanzapine) are the three classes of available mood stabilizers. Psychosocial care includes cognitive rehabilitation strategies, psychoeducation, and interpersonal social and rhythm therapies.

## 1.3 Schizophrenia

The annual incidence of schizophrenia is 0.2–0.4 per 1000, with a lifetime prevalence of about 0.8% [5], which can slightly vary between countries and cultural groups [6]. These differences are reduced when stricter diagnostic criteria are used for schizophrenia, such as the ones of the DSM-5. Research conducted by the WHO has further confirmed this observation by showing that schizophrenic disorder prevalence is similar across a wide range of cultures and countries, including developed and developing countries [6]. Its sex ratio is around 1:1.

Schizophrenia is characterized by three main types of symptoms, namely, positive symptoms, negative symptoms, and cognitive impairment [7]. Positive symptoms involve a loss of contact with reality; the patient has false beliefs (delusions) and perceptual experiences not shared with others (hallucinations) and may exhibit behavioral oddities. People with schizophrenia can experience different kinds of hallucinations: auditory, visual, olfactory, gustatory, or tactile. About delusions, patients with schizophrenia may have persecutory delusions, control delusions (e.g., belief in telepathy), grandiose delusions (e.g., belief in being a god), and somatic
delusions (e.g., belief that one's body is rotting from the inside) [8]. Negative symptoms are characterized by a deficit state during which basic emotional and behavioral processes are diminished or absent. The most common negative symptoms are blunted affect, anhedonia, avolition, apathy, and alogia (i.e., reduction in the amount or content of speech). Negative symptoms are more frequent and less fluctuating over time than positive symptoms [9]. They are also strongly associated with poor psychosocial functioning [10]. Cognitive impairments in schizophrenia include deficits with attention and concentration, psychomotor speed, learning and memory, and executive function. A decline in cognitive abilities from premorbid functioning is present in most of the patients, with cognitive functioning after the onset of the illness being relatively stable over time [10]. Despite this decline, cognitive functioning in some patients could be in the normal range. As for the negative symptoms, cognitive impairment is strongly associated with poor psychosocial functioning, particularly with regard to social and professional lives.

The etiology of schizophrenia is complex and multifactorial. Genetic and environmental factors seem to play a major role. The risk of developing schizophrenia is higher in patients' relatives than in the general population [11, 12]. Adoption and twin studies have shown that this increased risk is genetic, with the risk being increased by the presence of an affected first-degree relative [12]. There are two main approaches to the treatment of schizophrenia: pharmacological and psychosocial treatments [13]. Antipsychotics constitute the main medication, with major effects on reducing positive symptoms and preventing relapses. First-generation antipsychotics include molecules such as chlorpromazine or haloperidol. Second-generation antipsychotics were developed to decrease the neurological and cognitive side effects. They are the most used molecules nowadays (quetiapine, aripiprazole, risperidone, clozapine, etc.). In contrast, their effects on negative symptoms and cognitive impairment are much more moderate [14]. Psychosocial interventions improve the management of schizophrenia, e.g., through symptom management or relapse prevention. Other specific interventions that can improve the outcome of schizophrenia include family psychoeducation, supported employment, social skills training, psychoeducation, cognitive behavioral therapy, and integrated treatment of comorbid substance abuse [8].

The remainder of this chapter is organized as follows: We first describe the challenges in psychiatry that can potentially be addressed with machine learning. We then provide a non-exhaustive state of the art of machine learning with magnetic resonance imaging in psychiatry. We finally highlight the limitations of current approaches and propose perspectives for the field. Studies reviewed in this chapter are summarized in Table 1.
# 2 Challenges for Machine Learning in Psychiatry

Diagnosis and treatment are based on clinical diagnostic criteria developed from the subjective human experience, rather than on objective markers of illness. These criteria have been developed based on experts' opinion and are included in the DSM-5 and ICD-10 manuals. This approach has some limitations. Diagnosis can vary across interview methodologies [50], and clinically identical symptoms can be caused by different underlying conditions. Therefore, the common diagnostic criteria, which are based on symptom manifestation alone, are not always reliable in the clinical context [51]. They are indeed often unstable over time and unspecific [52] and provide little guidance to select the appropriate treatment. These misdiagnoses and misclassifications could lead to a poor therapeutic response and suboptimal management of the illness. Based on these observations, it appears necessary to develop objective markers and a better characterization of these illnesses.

In this section, we will discuss how machine learning could be used to improve diagnosis, to help characterize the different mental illnesses, and to improve treatment response and prognostic approach.

## 2.1 Improving the Diagnosis of Psychiatric Disorders

In the early stages of research on machine learning and psychiatric disorders, researchers wanted to explore whether different diagnoses could be predicted using machine learning algorithms applied to neuroimaging features. They mainly applied machine learning on structural MRI (sMRI) and functional MRI (fMRI) data (during tasks or at rest) [53]. Recent efforts have been made to apply machine learning on diffusion MRI [15], mostly in combination with other modalities [53, 54], and to explore whether adding modalities improves the diagnosis. Classification using machine learning in neuroimaging initially focused on major psychiatric disorders, such as MDD [55], schizophrenia [56], and bipolar disorder [54]. In a second phase, research has broadened the spectrum of psychiatric disorders such as anxiety disorders [23], anorexia [20], substance abuse [57], specific phobia [19], and autism spectrum disorders [58]. Machine learning using EEG has also been investigated for schizophrenia classification [59] as it is an affordable method for functional imaging and since it has a better temporal resolution than fMRI. While lots of machine learning studies in psychiatry focused on neuroimaging data, other fields of research were increasingly interested in using other modalities, such as proteomic, metabolomic [22], and genetic [24] data.

Machine learning also opens perspectives for the identification of relevant features (e.g., the measured variables) for the diagnosis. Using interpretable models such as support vector machines (SVM) or decision trees lets researchers investigate features that are used in
the decision. Deep learning could also be used to find useful
features without a priori preprocessing of the images when it is
used in combination with interpretation techniques [59]. Another
way to identify relevant features for the classification is to compare
the prediction performances of different machine learning models
with different input features. It then allows us to evaluate if the
information present in the different features helps the classification.
For example, this was shown in the study of Lin et al. [16], where
the authors established that the G72 protein alone yielded almost as
much information for the diagnosis of schizophrenia than com-
bined with other G72 single nucleotide polymorphisms. While this
approach could be fruitful to build more resilient and interpretable
algorithms, we should be careful when interpreting their results.
We must keep in mind that statistical algorithms such as the
machine learning ones are designed to predict (classes), while infer-
ence tests (i.e., univariate statistics) usually rely on association
studies, which are more reliable to infer correlation and causal
relations [60]. Moreover, when interpreting SVM weights, for
example, one must keep in mind that some features are only includ-
ing noise but are still important when considered in combination
with other features [61]. For all these reasons, even though finding
important features is necessary to better understand the models,
their interpretation to infer pathophysiology or biomarkers must be
cautious.

## 2.2 Refining the Classification of Psychiatric Disorders

Since there is a significant overlap in the clinical symptoms of different psychiatric disorders, many patients suffer from an important delay in the diagnostic establishment, after a potentially harmful diagnosis wavering. For instance, patients with BD wait on average 10 years before receiving an accurate diagnosis [62] and are often misdiagnosed with unipolar depression for years. As for MDD, it is often underdiagnosed even though fast and accurate diagnosis could avoid long-term cognitive impairment in undertreated patients [63]. For all these reasons, making the right diagnosis as early as possible is a major public health challenge.

Machine learning may be a useful tool to discriminate between different diagnoses. Indeed, the interest in machine learning is not only to distinguish a patient with a psychiatric disorder from a healthy subject – which is not the most difficult task for the clinician – but it could be used to help the clinician when the diagnosis becomes more difficult, e.g., to distinguish bipolar depression from unipolar depression [18] or to identify a patient at risk of psychosis [24].

As studies investigate new biomarkers to differentiate between different conditions, our current classification of psychiatric disorders appears to be limited. There are numerous different classification criteria to describe psychopathology, and theoretical frameworks are evolving rapidly [52], which contributes to our
limited understanding of these disorders. The classification of psy-
chiatric disorders is also a complex issue at the biological level, since
biological boundaries between conditions are not binary and are
blurred by the imprecision of the current genetic and imaging tools
(e.g., between BD and schizophrenia [17]). Moreover, the hetero-
geneity in the clinical presentation of the patients limits the effi-
ciency of a binary classification task. A simple classification
algorithm as SVM will only find the largest and shared biomarkers,
leading to a suboptimal classification.

The question we might ask is whether changing our perspective
and the way we approach psychiatric disorders' heterogeneity will
improve our understanding and management of the patients. To
consider this heterogeneity, unsupervised machine learning seems
to be an appropriate method, as it allows to find new homogeneous
subgroup within the population without preconceptions. Current
research is using unsupervised machine learning to automatically
detect new subgroups (i.e., clusters) of patients based on similar
cognitive [25], genetic [64], and/or cerebral [64] profiles. After
subgrouping, supervised machine learning can be used to automat-
ically classify the patients into one group or another. For instance,
Wu et al. [25] identified two phenotypic groups of patients with BD
using a cognitive task battery. Then, they used classifiers to detect
white matter tracts' microstructural differences between the two
groups. Newly developed algorithms combining supervised
learning and clustering show promising results [65], as they can
disentangle the heterogeneity of some disorders and improve diag-
nostic prediction at the same time. The HYDRA model is one of
those promising algorithms that has already been used to find some
subtypes of Alzheimer disease and to reveal meaningful biomarkers
of this disease at the same time [66]. These semi-supervised clus-
tering algorithms [67] are also starting to be used in psychiatry
[68] as they could help to reveal biomarkers while discriminating
between two different homogeneous classes. Finally, these algo-
rithms are of special interest as they are also handling common
source of variation in the groups to be classified (i.e., the age, the
sex, or other clinical or biological variables) [69].

Other approaches aim to identify differences between the
patients (the cases) and a reference population [70] (the controls).
These so-called normative models drop the hypothesis that the
patients do not belong to a homogeneous group, which is a step
toward a finer analysis. Indeed, recent studies showed important
clinical and biological heterogeneity between the patients, espe-
cially regarding brain structural abnormalities. Therefore, the
hypothesis of an average patient, as it is in classical “case-control”
studies, could limit our understanding of the diseases in the long
term. Normative modeling could overpass this limitation as it
allows to situate a given patient among the “norm” while consider-
ing the strong heterogeneity within the patients’ population. For
instance, Wolfer et al. [70] showed that deviations from the normative model of gray matter volume were frequent in both SZ and BD but highly heterogeneous. However, these models also induce an asymmetry as they hypothesize that the controls are homogeneous, which is debatable in practice. Nevertheless, it appears that subtyping leads to increased predictive accuracy in identifying individuals with mental illnesses compared with healthy controls, even though results are mixed [71]. This approach could gain attention with the development of new tools such as longitudinal normative brain charts that cover the whole lifespan [72].

**2.3 Predicting Evolution and Treatment Response**

Predicting the evolution of psychiatric disorders is an important challenge. As previously mentioned, clinicians' choices are guided by recommendations based on broad symptom classifications, such as depression, anxiety, or psychosis criteria, and become personalized over time through an empirical process of trials and errors. Being able to predict the prognosis of the mental illnesses would allow a better organization of care and more adapted psychoeducation consultations, would let clinicians set up strategies to prevent relapses, and would finally greatly improve the quality of life of the patients. Some studies tried to predict psychotic transition using neuroimaging [29] or using EEG [32] and clinical measures [35]. Schmaal et al. [31] used Gaussian process classifiers based on structural and functional MRI (emotional task) to characterize trajectories of depression (chronic, improvement, and rapid remission). They successfully classified the chronic group vs. the rapid remission group with an accuracy of 73%. Regarding other studies on depression, Kessler et al. [73] used self-reported clinical questionnaires of 1057 patients and machine learning algorithm to predict the course of MDD. They predicted the risk of suicide attempt with an AUC of 0.76 and whether the patient would experience a depressive episode lasting more than 2 weeks with an AUC of 0.71. Tran et al. [34] used electronical record's information such as medication, diagnosis, occurrence of interactions with health services, etc. with the aim of stratifying individuals according to their suicide risk. Interestingly, according to their results, their algorithms predicted the suicide risk better than clinicians, with an AUC of 0.73 vs. 0.57 for the prediction of high suicide risk patient vs. the rest of the population. It could also be possible to predict future substance abuse using neuroimaging data [33] and using combinations of demographic, clinical, cognitive, neuroimaging, and genetic data [30]. For schizophrenia, EEG-based machine learning could also be used to determine at-risk patients [59]. Machine learning could also be useful to predict the outcome of a first episode of psychosis [42] and to adapt the treatment. These studies highlight the possibility to stratify and classify individuals to optimize prognostic assessments, thanks to machine
learning. That would help the clinician to propose personalized
care, such as primary care facilities for patients at high suicidal risk.

Regarding the treatment outcome, the major challenge is to
determine whether machine learning could be used to predict
treatment response. This knowledge would be extremely useful,
as for now therapeutic choices are made through a trial-error
process, which increases the time interval between the apparition
of the symptoms and the administration of the adequate treatment.
This leads to a serious socioeconomic burden and can have debil-
itating consequences. In depression, the interest of the machine
learning approach was tested on pharmacological decision, for
instance, to predict the response to serotonin reuptake inhibitor
medications [27]. The authors were able to predict the treatment
response using EEG-derived features with an accuracy of 87.9%. In
another study, EEG features were also used to predict antipsycho-
tics response in schizophrenia [74]. More recently, studies focused
on anatomical and functional MRI. For instance, Whitfield-Gabrieli
et al. [28] used resting-state fMRI combined with FA maps as well
as initial severity assessment to predict the response to cognitive
behavioral therapy in patients with social anxiety. They were able to
classify good and poor responders with an accuracy of 81% in a
sample of 38 patients. Predicting treatment response is particularly
interesting when the treatment is more invasive, such as for the use
of electroconvulsive therapy (ECT). Indeed, one team showed
(with a sample of 122 depressed patients) that the brain structure
can predict the ECT response with an accuracy of 78% [75]. Finally,
choosing the right treatment is not just about measuring its effec-
tiveness; it is always about balancing the cost and the acceptable
benefit for the patients. In summary, all these features could be
integrated in machine learning algorithms and used by the clini-
cians as tools to improve the accuracy of the therapeutic decisions.

3 MRI and Machine Learning in Psychiatry: State of the Art (Table 1)

To this day, unlike in some medical specialties such as neurology, MRI is rarely used for psychiatric clinical practice. However, it is extensively used in research as it provides a large variety of information about the brain structure and function. Currently, sMRI is the easiest method to implement and the most used in the MRI studies. It is preferentially used to measure the cortex thickness and the cortical surface and to estimate the gray and white matter density and/or volume. Diffusion-weighted imaging (DWI) is less used but provides useful information on the white matter microstructure, thanks to different markers such as fractional anisotropy (the most used), mean diffusivity, and radial diffusivity. fMRI is of particular interest to investigate the neural correlates of cognition and emotion processes and their alteration in patients with
psychiatric conditions. Predictive models are thus useful tools when analyzing MRI data, because they allow to handle high-dimensional inputs and fit more unknown variables than available observations. In neuroimaging, machine learning allows to model sets of effects rather than single effects and thus to build models that describe more than one isolated dimension of cognition.

**3.1 Classification**
**Versus Healthy**
**Controls**

Classification of patients with psychiatric disorder vs. healthy controls is a widely studied area of research. Even though most studies fail to obtain the 80% of accuracy needed for clinical relevance, they yield promising results and give important methodological insights.

Regarding MDD, using sMRI, machine learning studies [55] found accuracies ranging from 67.6% to 90.3%. These results should be taken with great caution since they are usually obtained from small samples. For example, Mwangi et al. [39] obtained an accuracy of 90.3% using relevance vector machines and a sample of 60 subjects. They also showed that the brain regions identified during the features selection process were consistent with those of previous studies that reported gray matter reductions in patients with MDD, which were mostly located in the frontal lobe, the orbitofrontal and cingulate cortex, the middle frontal gyrus, and the inferior and superior gyri [76]. As for fMRI studies, Gao et al. [55] found an accuracy ranging from 56% to 99%; Ramasubbu et al. [36] found a significant accuracy of 66% for very severe depression using resting-state fMRI in 19 control subjects vs. 45 patients with different intensities of depression; and Fu et al. [21] obtained an accuracy of 86% in a sample of 19 patients with MDD and 19 HC who were processing sad faces during fMRI scanning.

Regarding bipolar disorder (BD), a recent literature review counted 25 studies using machine learning with different MRI modalities to classify BD vs. HC [54]. They found a median accuracy of 66% for BD vs. HC classification. Even though most studies used samples of less than 100 subjects, a study stood out by the number of samples. Using 3040 subjects, sMRI, and a linear SVM, Nunes et al. [43] obtained an accuracy of 65.23% using aggregate subject-level analyses and an accuracy of 58.67% when testing on left out sites. Their results, which highlighted the importance of regions such as the hippocampus, the amygdala, and the inferior frontal gyrus for the classification, were in good accordance with previous MRI studies in BD [76-78]. Regarding fMRI, the review of Claude et al. [54] highlighted that machine learning studies performed with an accuracy range between 37.5% and 83.5%. The minimum accuracy was 37.5% for the classification of bipolar depression vs. HC, during angry face processing using a Gaussian process classifier (GPC) [37]. DWI was not investigated much. In the review of Claude et al. [54], only two DWI studies were referenced. Achalia et al. [15] used DWI and machine learning on 60 subjects and obtained an accuracy of 74% for DWI alone. Even
though DWI gave lower classification scores than sMRI (77.8%) and fMRI (80.3%), combining it with other modalities significantly enhanced the accuracy (87.6%). Mwangi et al. [40] also used DWI in combination with sMRI on 30 pediatric patients with BD and obtained a classification accuracy of 78.12%.

Regarding schizophrenia (SZ), Filippis et al. [56] conducted a systematic review focusing on sMRI and fMRI studies that attempt to classify SZ vs. HC. Notably, the study of Salvador et al. [38] focused on a sample of 128 patients with SZ and 127 HC and aimed to compare the classification score of different neuroimaging features such as voxel-based and wavelet-based (a transformation like Fourier transform) morphometry of gray and white matter, vertex-based cortical thickness and volume defined as regions of interest, as well as volumetric measures. They also compared different methods, such as random forest, regressions with different regularization methods and levels, and SVM. The best results were obtained using the voxel-based and wavelet-based morphometry in combination with a SVM, with respective accuracy of 77.2% and 71%. The authors stress on the fact that no algorithm clearly outperforms the others, but that the selection of features has a real influence on the classification accuracy. Another notable study focused on cortical thickness and surface area measurement to differentiate first-episode psychosis from healthy subjects [42]. This study witnessed that regions contributing to the classification accuracy included the default mode network (DMN), the central executive network, the salience network, and the visual network. They observed a classification accuracy of 85.0% for the surface area and 81.8% for the cortical thickness. Pinaya et al. [79] used a deep belief network, which is a deep neural network that extrapolated and interpreted features, on sMRI data from 83 HC and 143 patients with SZ. The deep belief network highlighted an accuracy of 73.6% vs. 68.1% for a classical SVM. It also detected large differences between classes among specific regions, particularly frontal, temporal, parietal, and insular cortices, the corpus callosum, the putamen, and the cerebellum. Finally, as already mentioned in Subheading 2.1, normative models constructed with MRI data could be a useful tool to handle the inter-subject variability in machine learning models [71].

## 3.2 Inter-Illness Classification and Clustering

One major challenge of machine learning studies using MRI is to be able to correctly distinguish or classify patients suffering from different disorders. Several studies focused on the classification between BD and SZ. In their review, Claude et al. [54] found that three studies used sMRI in combination with machine learning algorithms to discriminate between BD and SZ with an accuracy ranging between 58% and 66%. Precisely, Schnack et al. [44] showed good classification performance on an independent dataset, with an average classification accuracy of 66%. Mothi et al. [45]
used K-mean clustering after a non-linear PCA to separate patients with BD, SZ, or schizoaffective disorder. They found out that the separation in three clusters was optimal, comprising a cluster including a major proportion of patients with BD, a second with mostly patients with SZ, and a third with a balanced proportion of the three types of illnesses. To build their clusters, they used clinical and cognitive data and validated the robustness of their results with sMRI data. The cluster including more patients with SZ was the one to have a significantly reduced cortical thickness in the frontal lobe. In addition, the BD and the SZ clusters presented significant cortical thickness reductions in occipital and temporal regions.

Several studies attempted to predict the diagnosis of BD in a population of unipolar, bipolar depression, and healthy controls with a median accuracy of 79% and an accuracy ranging from 50% to 90.69% [54]. Burger et al. [37] focused on the classification of unipolar vs. bipolar depression using different regions of interest. They did not find any significant results using the whole brain but found an accuracy of 63.89% for the classification of BD vs. unipolar depression using a GPC based on a happy face processing paradigm and the amygdala activity. Their best accuracy was of 72.2% for the classification of bipolar vs. unipolar depression, using a fear processing paradigm and GPC on the anterior cingulate gyrus. Overall, the best performance was obtained by Grotegerd et al. [18] In a pilot study, they obtained an accuracy of 90% using fMRI with a happy vs. neutral contrast image and an SVM on 10 BD, 10 HC, and 10 MDD. Using sMRI and DWI with a multiple kernel learning and a sample of 74 MDD and 74 BD, Vai et al. [46] obtained an accuracy of 74.32%, with a positive predictive value of 73.33% (probability that subjects with a positive BD prediction suffer from BD). The accuracy for MDD was 72.97%, indicating the ability to correctly identify people with MDD, with a predictive value of 73.97%. Their models are particularly interesting as they included relevant covariates in their models, such as age, gender, number of previous episodes, and drug load, which can confound and bias the accuracy estimates. Taking into account all these factors helps to increase the performance of the algorithm, as they impact the brain structural measures. It is necessary since these effects were witnessed by the ENIGMA-BD Working Group that used a large cohort of 2447 BD and 4056 HC and found [80] that several commonly prescribed drugs for BD treatment, including lithium, anti-epileptic, and antipsychotic treatments, showed significant associations with cortical thickness and surface area, even after accounting for patients receiving multiple drugs.

## 3.3 Treatment Response and Illness Prediction

Another perspective is the use of MRI and machine learning algorithms to predict treatment response. This was done by the team of Liu et al. [47] who tested the sensitivity to antidepressants in patients with MDD. Precisely, the study included 17 subjects that
<table><caption>**Table 1**
Summary of reviewed studies</caption><thead><tr><th>Study</th><th>Modalities</th><th>Diseases and goal</th><th>Number of subjects</th><th>Methods and validation procedure</th><th>Results</th></tr></thead><tbody><tr><td>Achalia et al. [15]</td><td>sMRI, dMRI, rs-fMRI, cognitive test</td><td>Classification of BD vs. HC</td><td>30 HC, 30 BD</td><td>SVM and feature selection</td><td>Accuracy of 87.60%, sensitivity of 82.3% and specificity of 92.7%</td></tr><tr><td>Lin et al. [16]</td><td>G72 genotypes, proteins levels</td><td>Classification of SZ vs. HC</td><td>89 SZ, 60 HC</td><td>Naive Bayes, logistic regression, decision tree</td><td>Naive Bayes model: sensitivity = 0.7969, specificity = 0.9372, (AUC) = 0.9356</td></tr><tr><td>Kiemann et al. [17]</td><td>Emotion attribution task, localizer task and behavioral task in fMRI</td><td>Classification of Autism spectrum disorder (ASD) vs. HC</td><td>Study 1: ASD: 16, HC: 21<br>Study 2: ASD: 22, HC: 30</td><td>ROI-based MVPA (Multivariate pattern analysis) with a SVM</td><td>Only statistics on the MVPA results</td></tr><tr><td>Grotegerd et al. [18]</td><td>Facial emotion processing, fMRI</td><td>Classification of BD vs. HC vs. Unipolar depression (UD)</td><td>BD: 10, HC: 10, UD: 10</td><td>SVM, GPC</td><td>Best results: happy versus neutral with the SVM for UD vs. BD: 90% accuracy</td></tr><tr><td>Visser et al. [19]</td><td>Generalization task, fMRI</td><td>Identifying area that influence the fear of spiders in spider-phobia stricken patients</td><td>High spider fear patients (HSF): 18, Low spider fear patient (LSF): 20</td><td>SVM-based MVPA</td><td>Several above chance results of correct classification of the stimuli depending on the ROIs</td></tr><tr><td>Lavagnino et al. [20]</td><td>sMRI</td><td>Classification of Anorexia Nervosa (AN) patient versus HC</td><td>AN: 15, HC: 15</td><td>LASSO linear regression</td><td>83.3% accuracy (sensitivity 86.7%, specificity 80.0%)</td></tr><tr><td>Fu et al. [21]</td><td>Sad face processing, fMRI</td><td>Classification of MDD versus HC</td><td>MDD: 19, HC: 19</td><td>SVM</td><td>Accuracy of 86% (84% of sensitivity and 89% of specificity)</td></tr></tbody></table>

(continued)
<table><caption>**Table 1**
(continued)</caption><thead><tr><th>Study</th><th>Modalities</th><th>Diseases and goal</th><th>Number of subjects</th><th>Methods and validation procedure</th><th>Results</th></tr></thead><tbody><tr><td>Setoyama et al. [22]</td><td>Plasma metabolites concentration</td><td>Prediction of suicide ideation (SI) in MDD and BD patients</td><td>Dataset 1: 51 drug-free MDD patients<br>Dataset 2: 23 medicated MDD patients<br>Dataset 3: 27 MDD, 14 BD</td><td>Logistic regression, SVM and random forest. Results reported for each fold of a 10-fold cross validation</td><td>Logistic regression accuracy of SI vs. no SI: 57.14% (mean of the 10-fold)<br>SVM accuracy of SI vs. no SI: 52.08% (mean of the 10-fold)<br>Random forest: overfit</td></tr><tr><td>Lucken et al. [23]</td><td>Fear conditioning task, fMRI</td><td>Panic disorder, MDD</td><td>Panic disorder: 33, Panic disorder with depression: 26</td><td>Adaboost</td><td>Comorbidity status was correctly predicted in 79% of patients (sensitivity: 73%; specificity: 85%) based on brain activation during fear conditioning (corrected for potential confounders: accuracy: 73%; sensitivity: 77%; specificity: 70%)</td></tr><tr><td>Pettersson-Yeo et al. [24]</td><td>Genotype, sMRI, dMRI, cognitive data, fMRI</td><td>Classification of ultra high risk (UHR) patients vs. first episode psychosis patient (FEP) vs. HC</td><td>HC: 23, UHR: 19, FEP: 19</td><td>SVM</td><td>FEP vs. HC (genotype, 67.86%; dMRI, 65.79%; fMRI, 65.79% and 68.42%; cognitive data, 73.69%), UHR versus HC (sMRI, 68.42%; dMRI, 65.79%), and FEP</td></tr></tbody></table>
<table><tr><td>Wu et al. [25]</td><td>Neurocognitive data, dMRI (FA, MD)</td><td>Unsupervised clustering of BD patients confirmed by supervised classification</td><td>BD: 70</td><td>K-means, ElasticNet</td><td>Identification of two phenotypes by K-means</td></tr><tr><td>Drysdale et al. [26]</td><td>rsfMRI</td><td>Unsupervised clustering of depressive disorder</td><td>Dataset 1: 333 DD, 378 HC<br/>Dataset 2: 352 HC, 109 DD, 16 BD II</td><td>K-means, SVM, Logistic regression, LDA with cross validation</td><td>Identification of two phenotypes for depressive disorder</td></tr><tr><td>Khodayari-Rostamabad et al. [27]</td><td>EEG</td><td>Prediction of the response to SSRI antidepressant therapy in treatment-resistant MDD</td><td>21 MDD subjects</td><td>Mixture of factor analysis (MFA)</td><td>Specificity: 80.9%, sensitivity: 94.9%, accuracy: 87.9%</td></tr><tr><td>Whitfield-Gabrieli et al. [28]</td><td>Rs-fMRI, dMRI</td><td>Prediction of cognitive behaviorial therapy results on social anxiety disorder inpatients</td><td>38 social anxiety disorder patients</td><td>Logistic regression and MVPA, leave-one out cross validation</td><td>Better responders vs. worst responders: 81% accuracy, 84% sensitivity and 78% specificity.</td></tr><tr><td>Koutsouleris et al. [29]</td><td>sMRI</td><td>Classification of SZ vs. MDD</td><td>Database 1:104 MDD, 158 SZ, 437 HC<br/>Database 2: 35 BD, 23 FEI, 89 At risk patients</td><td>SVM</td><td>Classification of MD vs. SZ: 76% of balanced accuracy.</td></tr></table>

(continued)
<table><caption>**Table 1**
(continued)</caption><thead><tr><th>Study</th><th>Modalities</th><th>Diseases and goal</th><th>Number of subjects</th><th>Methods and validation procedure</th><th>Results</th></tr></thead><tbody><tr><td>The IMAGEN Consortium et al. [30]</td><td>Stop-signal task, fMRI, personality measure, cognitive test</td><td>Prediction of future alcohol-misuse</td><td>692 teenagers</td><td>ElasticNet logistic regression</td><td>94% of correct classification of binge-drinkers and 34% of non binge-drinkers at the optimum of the Receiver Operating Characteristic curve.</td></tr><tr><td>Schmaal et al. [31]</td><td>Face task and London tower task, fMRI, clinical datas, sMRI</td><td>Prediction of MDD prognosis</td><td>156 MDD, 145 HC</td><td>GPC</td><td>fMRI: 73% of correct classification for chronic patients vs. more favorable trajectories patients. 69% of accuracy for chronic vs. remitted patients with clinical data.</td></tr><tr><td>Ramycad et al. [32]</td><td>EEG</td><td>Prediction of psychosis transition</td><td>53 at risk of psychosis inpatients (18 made a transition to psychosis)</td><td>LASSO regression</td><td>Prediction of psychotic transition: AUC of 0.77</td></tr><tr><td>Bertocci et al. [33]</td><td>reward-task, fMRI</td><td>Prediction of substance abuse in emotionally dysregulated teenagers</td><td>30 emotionally dysregulated female teenagers</td><td>LASSO regression</td><td>Classification of substance abuse vs. no substance abuse: 83.6%</td></tr></tbody></table>
<table><tr><td>Tran et al. [34]</td><td>Electronic medical record (EMR)</td><td>Risk stratification of suicidal behaviour</td><td>7399 subjects with risk assessment</td><td>L1 continuation-ratio model for ordinal outcomes</td><td>Classification of high-risk patients versus the rest using EMR data: AUC of 0.79</td></tr><tr><td>Mechelli et al. [35]</td><td>Clinical data</td><td>Psychotic disorders</td><td>416 subjects at risk for psychosis</td><td>SVM with leave-one-out cross validation</td><td>64.6% of accuracy for the prognostic prediction of transition to psychosis</td></tr><tr><td>Ramasubbu et al. [36]</td><td>Resting-state fMRI, Emotional-faces task fMRI</td><td>Classification of different MDD subgroups vs. HC</td><td>45 MDD, 19 HC</td><td>ICA for feature selection SVM for classification</td><td>Very severe group vs. HC with rs-fMRI: 66% accuracy Rest of the classifications at chance levels</td></tr><tr><td>Bürger et al. [37]</td><td>Emotional faces task fMRI. Features from the anterior cingulate gyrus or the amygdala</td><td>Classification of BD vs. HC vs. UD</td><td>36 HC, 36 BD, 36 UD</td><td>SVM or GPC for the MVPA</td><td>Significant results BD vs. UC with a SVM and fearful faces: 69.44%, with GPC: 72%. UC vs. HC with GPC and happy faces: 66.67%</td></tr><tr><td>Salvador et al. [38]</td><td>sMRI features: vertex-based cortical thickness, grey and white matter voxels/wavelet-based morphometry, regional volumes and interactions.</td><td>Classification of HC vs. SZ vs. BD</td><td>127 HC, 128 BD, 128 SZ</td><td>Logistic regression, Lasso, Ridge and ElasticNet, SVC, RDA, GPC, Random forest</td><td>Best results with VBM. SZ vs. HC: 75%, BD vs. HC: 63% and SZ vs. BD: 62%</td></tr><tr><td>Mwangi et al. [39]</td><td>sMRI features</td><td>Classification of MDD</td><td>30 MDD, 32 HC</td><td>SVM and Relevance Vector Machine, train/test split</td><td>Classification of 90.3% for MDD vs. HC, correlation of Relevance Vector Machine prediction and illness severity</td></tr></table>

(continued)
<table><caption>Table 1
(continued)</caption><thead><tr><th>Study</th><th>Modalities</th><th>Diseases and goal</th><th>Number of subjects</th><th>Methods and validation procedure</th><th>Results</th></tr></thead><tbody><tr><td>Mwangi et al. [40]</td><td>dMRI features</td><td>Prediction of diagnosis in pediatric BD</td><td>16 pediatric BD patients, 16 matched HC</td><td>SVM with leave-one-out cross-validation and grid search</td><td>The SVM algorithm trained with a linear kernel function and FA values performed best with accuracy = 78.12%, sensitivity = 68.75%, specificity = 87.5%</td></tr><tr><td>Costafreda et al. [41]</td><td>sMRI</td><td>Classification of MDD vs. HC and prognosis prediction of CBT and fluotexine treatment</td><td>37 MDD, 37 HC</td><td>SVM and leave-one-out cross-validation</td><td>The whole brain structural neuroanatomy predicted 88.9% of the clinical response. Accuracy of the structural neuroanatomy as a diagnostic marker was 67.6%.</td></tr><tr><td>Xiao et al. [42]</td><td>sMRI features</td><td>Classification of first episode psychosis patients vs. HC</td><td>163 drug naive first episode psychosis patients, 163 HC</td><td>SVM and 10-fold cross-validation</td><td>Surface Area. Accuracy: 85.0% (specificity = 87.0%, sensitivity = 83.0%)<br>Cortical thickness. Accuracy: 81.8% (specificity = 85.0%, sensitivity = 76.9%)</td></tr></tbody></table>
<table><tr><td>Abraham et al. for the ENIGMA Bipolar Disorders Working Group et al. [43]</td><td>sMRI features</td><td>Classification of BD vs. HC 853 BD, 2167 HC</td><td>SVM and 30-fold cross-validation or leave-one-site-out</td><td>Aggregate subject analysis: accuracy = 65.23%<br>Leave-one-site-out cross-validation: accuracy = 58.67%</td></tr><tr><td>Schnack et al. [44]</td><td>sMRI features (GMD)</td><td>Classification of BD vs. SZ vs. HC</td><td>112 SZ, 113 BD and 109 HC</td><td>3 SVM models (HC vs. BD, HC vs. SZ and BD vs. SZ) tested on a validation sample</td></tr><tr><td>Mothi et al. [45]</td><td>Clinical measures, eye movement tracks, cognitive assessment, sMRI</td><td>Unsupervised clustering of BD, SZ and schizoaffective patients</td><td>251 SZ, 164 SZA, 195 BD</td><td>K-means with 10 first components of a PCA ran on clinical measures, eye movement, cognitive assessment</td></tr><tr><td>Vai et al. [46]</td><td>sMRI and dMRI features (FA)</td><td>Classification of BD vs. MDD</td><td>74 BD, 74 MDD</td><td>MVPA with 10-fold cross-validation</td></tr><tr><td>Liu et al. [47]</td><td>sMRI (VBM of GM or WM)</td><td>Classification of treatment resistant depression vs. treatment sensitive depression</td><td>17 TRD, 17 TSD, 17 HC</td><td>MVPA with leave-one-out cross-validation</td></tr><tr><td>Hajek et al. [48]</td><td>sMRI (GM and WM separately)</td><td>Classification of unaffected high-risk offspring (UHRO) versus controls (C)</td><td>45 UHRO, 45 C</td><td>SVM, GPC, leave-2-out cross-validation</td></tr><tr><td colspan="5">(continued)</td></tr></table>
<table><caption>**Table 1**
(continued)</caption><thead><tr><th>Study</th><th>Modalities</th><th>Diseases and goal</th><th>Number of subjects</th><th>Methods and validation procedure</th><th>Results</th></tr></thead><tbody><tr><td>Fleck et al. [49]</td><td>fMRI (continuous performance task with distractions), proton magnetic resonance spectroscopy (PMRS)</td><td>Predicting treatment response</td><td>20 BD that received lithium treatment</td><td>Cascading genetic fuzzy tree</td><td>80% accuracy</td></tr></tbody></table>

Acronyms. AN anorexia nervosa, ASD autism spectrum disorder, BD bipolar disorder, C controls, DD depressive disorder, dMRI diffusion MRI, FA fractional anisotropy, FEP first episode psychosis, fMRI functional MRI, GM gray matter, GMD gray matter density, GPC Gaussian process classifier, HC healthy controls, ICA independent component analysis, MD mean diffusivity, MDD major depressive disorder, MRI magnetic resonance imaging, MVPA multivariate pattern analysis, PCA principal component analysis, PMRS proton magnetic resonance spectroscopy, rs-fMRI resting-state functional MRI, sMRI structural MRI, SI suicide ideation, SVM support vector machine, SZ schizophrenia, SZA schizoaffective, TRD treatment-resistant depression, TSD treatment-sensitive depression, UD unipolar depression, UHRO unaffected high-risk offspring, VBM voxel-based morphometry, WM white matter
were treatment resistant, 17 that were treatment sensitive, and 17 controls. The accuracy of the MVPA models that correctly distinguished resistant and sensitive patients from HC ranged from 85.7% to 91.2% depending on the features used. The authors highlighted differences in structural alterations between responders and non-responders suggesting that structural differences may be related to different responses to antidepressants. Furthermore, they found that the structural abnormalities were larger between responders and HC than between non-responders and HC. These results are somewhat counterintuitive as one would expect resistant patients to show more structural differences from HC than responders. However, this lack of specificity is probably related to a high degree of clinical heterogeneity and the small sample size that does not allow sufficient precision to distinguish more specific abnormalities.

Hajek et al. [48] used machine learning applied to white matter sMRI to distinguish 45 unaffected participants at high genetic risk of BD from 45 low-risk healthy controls with an accuracy of 68.9%. Similarly, Lin et al. [81] successfully classified HR individuals for BD with vs. without (sub)syndromic risk with an accuracy of 83.21% based on the gray matter volume. Finally, a pilot study was conducted using a novel machine learning system based on a “multi-cascade fuzzy genetic tree” with sMRI capable of accurately classifying subjects with BD in a first manic episode into groups that responded or did not respond to lithium treatment [49].

# 4 Limitations and Perspectives

As illustrated in this chapter, numerous studies have been conducted to classify psychiatric disorders and refine the definition of psychiatric subgroups using machine learning. However, methods and results are heterogeneous. In fact, many authors point to a major limitation of most studies, that is, the limited number of samples [53–55]. Claude et al. [54] also pointed out a negative correlation between the accuracy and the number of subjects, leading to think that the results obtained from small samples are artificially high. Another effect of this limited number of samples resides on the fact that models need to be trained on a population that is representative of the population on which we will use them. Indeed, models trained on a young population will be biased when used on an older one, and similar bias could be raised when using a model trained with a population from a specific country on subjects from another country.

As it is difficult to recruit enough patients to obtain a sufficient statistical power, this limitation may persist in the long term, unless
collective efforts for data sharing are undertaken. This issue dec-
pens when looking at more specific subsets of patients. The field
therefore needs more and larger datasets to work on. These datasets
start to be collected, with, e.g., the UK BioBank dataset (~40,000
subjects). Even though they are not focused on psychiatric disor-
ders, they are interesting because they are multimodal datasets,
with genetic, clinical, and MRI data, and some participants will
develop psychiatric syndromes throughout the follow-up. Recent
efforts have been specifically made for psychiatric disorders, e.g., by
the ENIGMA Consortium, a multisite and multimodal project
including several working groups focused on different diseases,
such as bipolar disorder, schizophrenia, autism, ADHD, etc.

Larger datasets are often multisite ones, and they bring their
own challenges. Since the MRI devices that are used for different
studies have different magnetic field strengths, different vendors,
coils, etc., there are large site effects that need to be considered.
These site effects are particularly important for DWI and fMRI, but
they even appear for sMRI [82], the most robust method of imag-
ing. A second source of site effects lies in the preprocessing of the
data, which may vary between different sites and protocols. The
preprocessing steps are of major importance and need to be homo-
genized since different softwares can lead to different results
[83]. The remaining “site effects” can be partially corrected, thanks
to different methods. Statistics-based methods include adjusted
residualizations or ComBat [84, 85], a method originally proposed
to remove batch effects in genomics [86] and then adapted for
DWI and then for sMRI [87]. Other methods are more specific to
MRI, such as RAVEL [88], which aims at capturing the sites’
variability using the signal from the CSF, with mixed results for
now. Since the extent of the efficiency of these corrections is still
under discussion [89], we must consider the site effect in our
models and use validation methods such as leave-one-site-out vali-
dation to evaluate the reproducibility of our approaches.

The site effect highlights a deeper and more fundamental limi-
tation of our studies, the signal-to-noise ratio. That issue, which is
faced by all imaging studies, is particularly present in neuroimaging
for psychiatric diseases as the changes that we are looking for are
subtle and probably not the main causes of variation in our datasets
(e.g., one important cause of variance is the age, which produces
consequent variations in the gray and white matter density [72]).
We therefore need to be vigilant and make specific efforts when
interpreting the results of machine learning algorithms as they can
learn some information that are irrelevant for psychiatric disorders.
Nevertheless, it is possible to improve this signal-to-noise ratio.
One way to do so is to improve the signal; the second is to diminish
the noise. Larger datasets improve the statistical power of the
algorithms but may induce noise (such as the multisite noise). In
addition to the fact that methodological modifications can change
and improve the performance of machine learning, technological
improvements seem to bring better performance as shown by the
team of Iwabuchi et al. [90], who showed that 7 T MRI compared
to 3 T MRI gave higher classification accuracy when distinguishing
patients with schizophrenia vs. controls (77% versus 66%). More-
over, the use of multimodal datasets has shown promising results in
increasing the signal-to-noise ratio in current studies [91]. While
trying to determine to what extent machine learning using MRI
can still improve its results, Schulz et al. [91] highlighted two
interesting perspectives: first, that there is still room for improve-
ment of the classification accuracy by getting larger datasets and
second, that multimodal MRI and more specifically fMRI could
improve the classification.

Other ways to collect data could also be thought about, with,
for example, the use of tools such as smartphones. Data can be
provided through active monitoring (self-reporting), passive mon-
itoring of various activities, mobility, or statistics on phone calls
[92]. Promising results show that voice data from daily phone calls
could be a valid marker of mood states and hold promise for
monitoring BD [93]. Taken together, the development of our
knowledge of machine learning and the growing data resources
could provide new tools for the management of psychiatric disor-
ders soon. However, their development can only be done by con-
sidering the challenges they raise, such as personal data protection,
but also by considering all the ethical issues that these new tools will
raise.

Finally, machine learning in psychiatry is a promising field of
research, with still a lot to do to characterize the different biomar-
kers and psychiatric disorders properly and accurately. The use of
MRI and other clinical and biological features could in a near future
bring new tools for diagnosis, risk assessment, and treatment selec-
tion that could be used by the clinician. However, due to the actual
social stigma around psychiatric disorders and the apparent arbi-
trary character of classification algorithms, their use would need an
important ethical discussion beforehand, notably when people
would like to use them to identify at-risk healthy subjects or when
using them to determine the treatment of already symptomatic
patients.

Acknowledgments

We are grateful to the reviewer, Anton Iftimovici, for his very
helpful comments and suggestions.
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