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# Lifespan Tree of Brain Anatomy: Diagnostic Values for Motor and Cognitive Neurodegenerative Diseases

Pierrick Coupé, Boris Mansencal, José V Manjón, Patrice Péran, Wassilios G

Meissner, Thomas Tourdias, Vincent Planche

► To cite this version:

Pierrick Coupé, Boris Mansencal, José V Manjón, Patrice Péran, Wassilios G Meissner, et al., Lifespan Tree of Brain Anatomy: Diagnostic Values for Motor and Cognitive Neurodegenerative Diseases. Human Brain Mapping, 2025, 46 (13), 10.1002/hbm.70336. hal-05233807

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RESEARCH ARTICLE

OPEN ACCESS

# Lifespan Tree of Brain Anatomy: Diagnostic Values for Motor and Cognitive Neurodegenerative Diseases

Pierrick Coupé<fnref n="1" /> ID | Boris Mansencal<fnref n="1" /> ID | José V. Manjón² ID | Patrice Péran³ ID | Wassilios G. Meissner⁴,⁵,⁶,⁷ ID | Thomas Tourdias⁸,⁹ ID | Vincent Planche⁴ ID

**Correspondence:** Pierrick Coupé (pierrick.coupe@u-bordeaux.fr)

**Received:** 24 March 2025

**Revised:** 30 July 2025

**Accepted:** 21 August 2025

**Funding:** This work was supported by the French National Research Agency (HoliBrain project, ANR-23-CE45-0020-01; IHU3 initiative, ANR-23-IAHU-0001), the Precision and Global Vascular Brain Health Institute funded by the France 2030 investment plan, and by the French government through the University of Bordeaux's France 2030 program/RR1 "IMPACT" and the PEPR StratifyAging and Prodrom-ND. Computational resources were provided by GENCI at IDRIS (allocation 2022-AD011013848R1). Additional support was received from the Ministerio de Ciencia, Innovación y Universidades de España (PID2023-152127OB-I00).

## ABSTRACT

The differential diagnosis of neurodegenerative diseases, characterized by overlapping symptoms, may be challenging. Brain imaging coupled with artificial intelligence has been previously proposed for diagnostic support, but most of these methods have been trained to discriminate only isolated diseases from controls. Here, we develop a novel machine learning framework, named *lifespan tree* of brain anatomy, dedicated to the differential diagnosis between multiple diseases simultaneously. It integrates the modeling of volume changes for 124 brain structures during the lifespan with nonlinear dimensionality reduction and synthetic sampling techniques to create easily interpretable representations of brain anatomy over the course of disease progression. As clinically relevant proof-of-concept applications, we constructed a *cognitive lifespan tree* of brain anatomy for the differential diagnosis of six causes of neurodegenerative dementia and a *motor lifespan tree* of brain anatomy for the differential diagnosis of four causes of parkinsonism using 37,594 MRIs as a training dataset. This original approach significantly enhanced the efficiency of differential diagnosis in the external validation cohort of 1754 cases, outperforming existing state-of-the-art machine learning techniques. *Lifespan tree* holds promise as a valuable tool for differential diagnosis in relevant clinical conditions, especially for diseases still lacking effective biological markers.

## 1 | Introduction

In the field of neurodegenerative diseases, differential diagnosis may be challenging due to overlapping symptoms between different disorders. Accurate diagnosis is, however, essential for prognosis, patient and caregiver information, therapeutic management, and enrolment in clinical trials targeting specific disease mechanisms. Except in the particular case of

Alzheimer's disease (AD) where pathophysiological biomarkers are available (Planche, Bouteloup, et al. 2023) (highlighting tau and amyloid pathology through biofluid analysis and/or positron emission tomography), there are no fully established biomarkers for the diagnosis of frontotemporal dementia (FTD), progressive supranuclear palsy (PSP), dementia with Lewy bodies (DLB), Parkinson's disease (PD) or multiple system atrophy (MSA). Magnetic resonance imaging (MRI) plays

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

© 2025 The Author(s). *Human Brain Mapping* published by Wiley Periodicals LLC.

<footnote n="1">¹CNRS, Université de Bordeaux, Bordeaux INP, LABRI, UMR5800, Talence, France | ²ITACA, Universitat Politècnica de València, Valencia, Spain | ³ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France | ⁴CHU Bordeaux, Service de Neurologie des Maladies Neurodégénératives, IMNC, Bordeaux, France | ⁵Université de Bordeaux, CNRS, IMN, UMR 5293, Bordeaux, France | ⁶Department of Medicine, University of Otago, Christchurch, New Zealand | ⁷New Zealand Brain Research Institute, Christchurch, New Zealand | ⁸Inserm U1215, Neurocentre Magendie, Bordeaux, France | ⁹Service de Neuroimagerie, CHU de Bordeaux, Bordeaux, France</footnote>
a pivotal role in the differential diagnosis of these disorders, providing support for some diagnostic criteria (Rascovsky et al. 2011; Gorno-Tempini et al. 2011; Wenning et al. 2022). MRI provides a detailed representation of the brain, enabling the visualization and measurement of cerebral abnormalities linked to neurodegenerative diseases, such as focal atrophy or suggestive signal abnormalities (Bhattacharya et al. 2002). However, abnormalities can sometimes be subtle or absent, and different disorders can exhibit similar atrophic patterns, complicating definitive diagnosis.

Advancements in diagnostic technology, including the integration of artificial intelligence (AI) with imaging techniques, may improve the accuracy and efficiency of diagnosis (Topol 2019). AI algorithms can analyze complex imaging data, identify subtle patterns, and differentiate between conditions with high precision. The synergy of MRI and AI holds significant promise for the early and accurate detection of neurological disorders. AI-enhanced MRI analysis may not only improve diagnostic accuracy but also may reduce the time required for interpretation, facilitating decision-making and hopefully improving patient outcomes (Mei et al. 2020; Myszczynska et al. 2020).

To date, AI-enhanced MRI analysis has primarily been used for binary classification tasks, distinguishing patients with one pathology from control cases (Myszczynska et al. 2020; Wen et al. 2020). This application is not useful for clinicians, who generally have no doubts about distinguishing a patient with a neurodegenerative disease from a healthy subject. However, neurologists can face difficulties in the differential diagnosis between different neurodegenerative diseases that share common symptoms and common biomarkers (such as dopaminergic denervation on dopamine transporter imaging, which can be found in all causes of parkinsonism). In this context, the application of AI-enhanced MRI analysis for differential diagnosis, or multiclass classification that differentiates between several disorders, is promising but remains underexplored. Challenges include the complexity of differentiating multiple diseases simultaneously, the lack of large datasets for training models (especially for recent deep learning methods), and the interpretability of the provided decisions, which complicates its integration into clinical routine.

To address the challenge of automatic differential diagnosis, the vast majority of previous studies have employed machine learning techniques, such as support vector machines (SVMs), on meaningful features like brain structure volumes (Talai et al. 2021; De Francesco et al. 2023; Ma et al. 2020, 2024; Nguyen et al. 2023, 2024a; Tong et al. 2017; Lampe et al. 2023). Such a framework mitigates the need for large databases (Schulz et al. 2020) and may provide explainable decisions through *post hoc* analysis of the different features' importance. However, SVM may be suboptimal as the number of classes increases (Hsu and Lin 2002).

Recent research has proposed lifespan modeling of brain structures as an effective method for examining the progression of cerebral atrophy in neurodegenerative diseases (Coupé et al. 2019, 2023; Planche et al. 2022, 2024; Planche, Mansencal, et al. 2023). This approach can generate brain charts, or trajectories, depicting the volumes of brain structures across the entire lifespan,

including preclinical stages. Such lifespan modeling of manually selected structures has been used for the binary classification of AD, demonstrating performance comparable to recent deep learning techniques (Coupé et al. 2022). However, such a framework cannot be used when considering multiple diseases simultaneously since structures are differentially vulnerable according to the type of disease.

To address the limitations of current methods, we introduce in this paper a novel framework called *lifespan tree*. This framework integrates lifespan modeling of brain volumes derived from MRI data (Coupé et al. 2019, 2023; Planche et al. 2022, 2024; Planche, Mansencal, et al. 2023) with nonlinear dimensionality reduction (McInnes et al. 2020) and synthetic resampling techniques. The proposed approach was designed to improve multiclass classification of high-dimensional temporal data, especially when the data are irregularly sampled over time and exhibit significant class imbalance. Moreover, this innovative strategy produces a compact and elegant representation of human brain anatomy along the disease progression, allowing for the simultaneous analysis of multiple disorders. Notably, the proposed *lifespan tree* improves the efficiency and interpretability of differential diagnosis.

Herein, we applied our *lifespan tree* on brain anatomy to the challenging problem of differential diagnosis of cognitive and motor impairments that can be caused by multiple neurodegenerative diseases. We first constructed a *cognitive tree* with seven branches to distinguish normal aging from six diseases or syndromes: AD, DLB, PSP, the behavioral variant of FTD (bvFTD), progressive non-fluent aphasia (PNFA), and semantic dementia (SD). We demonstrated the superiority of our method compared to the state-of-the-art SVM-based approach. Second, we addressed the complex clinical scenario of classifying PD and atypical parkinsonism. In this experiment, we showed the capability of a *motor tree* to discriminate between PD, DLB, PSP, and MSA.

# 2 | Methods

## 2.1 | Dataset Description

### 2.1.1 | Training Dataset (N=37,594) for the Construction of Lifespan Tree of Brain Anatomy

Our training dataset was composed of 37,594 baseline T1w MRI from 20 open-access databases: ABIDE (Di Martino et al. 2017), ADHD200 (Bellec et al. 2017), ADNI (Jack et al. 2008), ALLFTD, AOMIC (Snoek et al. 2021), Calgary (Reynolds et al. 2020), CamCAN (Taylor et al. 2017), C-MIND, DLBS, ICBM (Mazziotta et al. 2001), ISYB (Gao et al. 2021), IXI, MIRIAD (Malone et al. 2013), NDAR (Payakachat et al. 2016), OASIS (Marcus et al. 2007), PDBP (Ofori et al. 2016), PIXAR, SALD (Wei et al. 2018), SLIM, and UKBiobank (Bycroft et al. 2018) (see Tables 1 and S1 for details). This dataset was composed of 36,527 cognitively normal subjects (CN), 461 patients with AD, 126 patients with DLB, 155 patients with bvFTD, 41 with PNFA, 39 with semantic dementia, 21 patients with MSA, 133 with PD, and 91 with PSP. For MSA patients, information about the subtype was not available. Therefore, training MSA may comprise
<table><caption>TABLE 1 | Description of the databases used for the construction (training) and for the testing of the cognitive and motor trees.</caption><thead><tr><th></th><th>CN</th><th>AD</th><th>DLB</th><th>bvFTD</th><th>PNFA</th><th>SD</th><th>MSA</th><th>PD</th><th>PSP</th></tr></thead><tbody><tr><td>Training<br/>N=37,594</td><td>N=36,527<br/>F: 18,090/M:<br/>18,437<br/>58 [1–94]</td><td>N=461<br/>F: 229/M: 232<br/>74 [52–96]</td><td>N=126<br/>F: 22/M: 104<br/>69 [50–90]</td><td>N=155<br/>F: 58/M: 97<br/>64 [40–83]</td><td>N=41<br/>F: 22/M: 19<br/>69 [55–82]</td><td>N=39<br/>F: 16/M: 23<br/>64 [50–79]</td><td>N=21<br/>F: 4/M: 17<br/>65 [53–80]</td><td>N=133<br/>F: 48/M: 85<br/>67 [40–81]</td><td>N=91<br/>F: 48/M: 43<br/>68 [52–80]</td></tr><tr><td>Testing<br/>N=1754</td><td>N=528<br/>F: 308/M: 220<br/>70 [30–100]</td><td>N=488<br/>F: 304/M: 184<br/>74 [46–96]</td><td>N=47<br/>F: 8/M: 39<br/>74 [55–89]</td><td>N=90<br/>F: 31/M: 59<br/>62 [45–76]</td><td>N=40<br/>F: 23/M: 17<br/>69 [54–81]</td><td>N=44<br/>F: 20/M: 24<br/>64 [50–85]</td><td>N=117<br/>F: 60/M: 57<br/>66 [42–84]</td><td>N=333<br/>F: 120/M: 213<br/>62 [34–83]</td><td>N=67<br/>F: 38/M: 29<br/>71 [55–86]</td></tr></tbody></table>

Note: The table provides the number of subjects (N) per class, the sex distribution between female (F) and male (M), and the mean age [age range]. Testing and training datasets come from totally independent databases (see Tables S1 and S2 for details). During our experiments, we used brain MRI from cognitively normal subjects (CN), patients with Alzheimer's disease (AD), dementia with Lewy bodies (DLB), progressive supranuclear palsy (PSP), behavioral variant frontotemporal dementia (bvFTD), progressive non-fluent aphasia (PNFA) and semantic dementia (SD), Parkinson's disease (PD), and multiple system atrophy (MSA).

predominant parkinsonism (MSA-P) and predominant cerebellar dysfunction (MSA-C).

### 2.1.2 | Testing Dataset (N=1754)

To validate our model, we built a testing dataset based on six external and independent databases: 4RTNI, AIBL (Ellis et al. 2009), FMSA (Foubert-Samier et al. 2020), NACC (Beekly et al. 2007), NIFD (Wenning et al. 2022), and PPMI (Marek et al. 2011) (see Table 2). Therefore, we validated the generalization capacity of our method and its robustness to domain shift. This dataset was composed of 528 CN, 488 AD, 47 DLB, 90 BvFTD, 40 PNFA, 44 SD, 117 MSA, 333 PD, and 67 PSP. For MSA, we selected only the subtype with MSA-P in the FMSA cohort, that is, we excluded subjects with MSA-C who are not difficult to distinguish from PD and other parkinsonian disorders. For PD, we used only the sporadic form in the PPMI database, so we excluded all genetic forms.

## 2.2 | MRI Processing

All the images considered in our study underwent preprocessing using the AssemblyNet software (https://github.com/volBrain/AssemblyNet) (Coupé et al. 2020). AssemblyNet leverages collective AI to achieve fine-grained segmentation (132 structures) of the entire brain in just 15 min.

The AssemblyNet preprocessing pipeline consisted of several key steps:

* Image denoising (Manjón et al. 2010) to reduce noise and enhance image quality.

* Inhomogeneity correction (Tustison et al. 2010) to address intensity variations across the image.

* Affine registration (Avants et al. 2011) to the Montreal Neurological Institute (MNI) space to ensure spatial alignment across subjects.

* A second inhomogeneity correction in the MNI space (Ashburner and Friston 2005) to further refine intensity uniformity.

* A final intensity standardization step (Manjón and Coupé 2016) to ensure consistency in image intensity across subjects.

By systematically applying these preprocessing steps, we aimed to enhance the quality and consistency of the imaging data, which is crucial to model the pseudo-longitudinal course of atrophy by merging different patients and for comparisons of diseases from different databases.

After preprocessing, the brain underwent segmentation into several structures using an assembly of 250 deep learning models, as detailed in (Coupé et al. 2020). The segmentation process was based on the Neuromorphometrics protocol, which encompasses 132 structures (Klein and Tourville 2012). This protocol follows the “general segmentation protocol” for subcortical structures, as defined by the “MGH Center for Morphometric Analysis”
<table><caption>**TABLE 2** | Comparison between the seven-branch cognitive tree and the state-of-the-art SVM method for the differential diagnosis of cognitive disorders (using external validation datasets).</caption><thead><tr><th>Classification on external datasets (%)</th><th>BACC</th><th>SEN. AD</th><th>SEN. CN</th><th>SEN. DLB</th><th>SEN. bvFTD</th><th>SEN. PNFA</th><th>SEN. SD</th><th>SEN. PSP</th></tr><tr><th></th><th>Top-1</th><th>Top-1</th><th>Top-1</th><th>Top-1</th><th>Top-1</th><th>Top-1</th><th>Top-1</th><th>Top-1</th></tr></thead><tbody><tr><td>Lifespan tree</td><td>55*<br>[52-59]</td><td>44<br>[39-48]</td><td>74<br>[70-78]</td><td>32*<br>[19-46]</td><td>43<br>[33-54]</td><td>33*<br>[18-48]</td><td>91*<br>[81-98]</td><td>71*<br>[61-82]</td></tr><tr><td>SVM on volumes</td><td>46<br>[42-49]</td><td>43<br>[38-47]</td><td>97*<br>[96-98]</td><td>0<br>[0-0]</td><td>51<br>[41-61]</td><td>17<br>[6-30]</td><td>57<br>[41-72]</td><td>58<br>[46-70]</td></tr><tr><th>Classification on external datasets (%)</th><th>BACC</th><th>SEN. AD</th><th>SEN. CN</th><th>SEN. DLB</th><th>SEN. bvFTD</th><th>SEN. PNFA</th><th>SEN. SD</th><th>SEN. PSP</th></tr><tr><th></th><th>Top-2</th><th>Top-2</th><th>Top-2</th><th>Top-2</th><th>Top-2</th><th>Top-2</th><th>Top-2</th><th>Top-2</th></tr><tr><td>Lifespan tree</td><td>71*<br>[68-75]</td><td>60<br>[56-65]</td><td>80<br>[77-83]</td><td>72*<br>[59-85]</td><td>59<br>[48-69]</td><td>50<br>[34-66]</td><td>95*<br>[88-100]</td><td>82<br>[72-91]</td></tr><tr><td>SVM on volumes</td><td>65<br>[61-69]</td><td>77*<br>[73-81]</td><td>100*<br>[99-100]</td><td>13<br>[4-23]</td><td>70*<br>[60-79]</td><td>43<br>[27-58]</td><td>75<br>[61-87]</td><td>79<br>[69-88]</td></tr><tr><th>Classification on external datasets (%)</th><th>BACC</th><th>SEN. AD</th><th>SEN. CN</th><th>SEN. DLB</th><th>SEN. bvFTD</th><th>SEN. PNFA</th><th>SEN. SD</th><th>SEN. PSP</th></tr><tr><th></th><th>Top-3</th><th>Top-3</th><th>Top-3</th><th>Top-3</th><th>Top-3</th><th>Top-3</th><th>Top-3</th><th>Top-3</th></tr><tr><td>Lifespan tree</td><td>79<br>[76-83]</td><td>72<br>[68-76]</td><td>94<br>[92-96]</td><td>89*<br>[80-98]</td><td>59<br>[49-69]</td><td>57<br>[41-73]</td><td>95*<br>[88-100]</td><td>85<br>[76-93]</td></tr><tr><td>SVM on volumes</td><td>78<br>[75-82]</td><td>89*<br>[87-92]</td><td>100*<br>[100-100]</td><td>47<br>[32-61]</td><td>81*<br>[73-89]</td><td>55<br>[39-70]</td><td>86<br>[75-96]</td><td>91<br>[84-97]</td></tr></tbody></table>

Note: For each metric, the confidence interval is provided in brackets. Top-1 means that the correct class is the prediction with the highest probability (usually measurements). Top-2 means that the correct class is within the predictions with the two highest probabilities. Top-3 means that the correct class is within the predictions with the three highest probabilities. The best results are in red.

Abbreviations: AD, Alzheimer's disease; BACC, balanced accuracy; bvFTD, behavioral variant frontotemporal dementia; CN, cognitively normal; DLB, dementia with Lewy bodies; PNFA, progressive non-fluent aphasia; PSP, progressive supranuclear palsy; SD, semantic dementia; SEN, sensitivity (recall); SPE, specificity.

*p* value < 0.05 (significantly better results).

(http://neuromorphometrics.com/Seg/). Additionally, the segmentation of cortical structures adheres to the “BrainCOLOR protocol” (http://neuromorphometrics.com/ParcellationProtocol_2010-04-05.PDF). In our study, we focused on 124 structures, including all gray matter structures and the lateral ventricles. We removed the white matter structures that are less sensitive to the neurodegenerative process.

Subsequently, we conducted a quality control (QC) procedure to meticulously select subjects for inclusion in our training dataset. For all training subjects flagged as failures by an AI-based automatic QC tool (Denis de Senneville et al. 2020), a visual assessment was carried out. This involved individually inspecting the input images and segmentations generated by AssemblyNet using a 3D viewer. If the failure was confirmed by our expert, the subject was excluded from the training dataset (overall, < 2% of the training subjects were rejected). This rigorous QC process ensured the reliability and accuracy of the training data used in our study.

## 2.3 | Volume Normalization

To mitigate inter-subject variability, we employed normalization of all structure volumes using the intracranial cavity volume (ICV) (Manjón et al. 2014). Furthermore, we conducted z-score normalization of all normalized volumes, expressed as a

percentage of ICV. To achieve this, we first calculated the mean and standard deviation for each structure using all CN subjects across the entire lifespan. Subsequently, for a given structure, we applied the same z-score normalization to all subjects, including those with pathologies.

Additionally, we addressed sex differences in volumes by employing a linear model when a significant sex effect was detected (*p* < 0.05), following the approach outlined in Coupé et al. (2023). This ensured that volumes were appropriately compensated for sex differences. Consequently, by expressing volumes as z-scores of normalized and sex-corrected volumes, we effectively compensated for inter-subject, sex, and inter-structure variabilities. This normalization approach enhanced the comparability and robustness of our volumetric measurements across subjects and structures.

## 2.4 | Lifespan Brain Volumetric Trajectories Estimation

To construct our lifespan models of brain structure volumes, we estimated normal and pathological trajectories of structure volumes across the entire lifespan, as illustrated in the left panel of Figure 1. For each brain structure, separate models were estimated for each group to generate trajectories. All our trajectories are based on cross-sectional data, as is commonly done
<figure><img src="image_4.png" /><figcaption>**FIGURE 1** | Schematic overview of the proposed method based on our cognitive tree (AD=Alzheimer's disease. bvFTD=behavioral variant frontotemporal dementia. CN=cognitively normal. DLB=dementia with Lewy bodies. PNFA=progressive non fluent aphasia. PSP=progressive supranuclear palsy. SD=semantic dementia). First, the training step involves converting the lifespan volumetric models of the 124 considered brain structures into a 3D lifespan tree (from N=37594). Synthetic sampling strategy is then applied to balance samples between populations and make uniform sample distributions along age. The nonlinear dimension reduction into a 2D space (x, y) is performed using UMAP manifold learning. The x and y axes are the first two components of the UMAP dimension reduction. The 3D tree is then reconstructed using age as the z-coordinate. Second, during the testing step, a new external test subject is projected into the 3D space of the lifespan tree (purple diamond). To achieve this, the volumes of the 124 brain structures are projected into 2D with the estimated UMAP transform to obtain x and y coordinates. The subject's age is then used as the z-coordinate in the lifespan tree. Finally, the distances between the subject's point and the tree branches are used to estimate the scores for each class. The closest branch (i.e., the highest score) determines the final class of the subject under study. It is important to note that the results provided to the clinician for each patient pertain to both the score of the primary diagnosis and the scores of various differential diagnoses. In this example, an AD patient (in purple) who is 80 years old is projected onto our cognitive lifespan tree. The closest branch is the AD branch, followed by the DLB branch. A cut tree at 80 years old can also be displayed to facilitate the detection of the closest branch (i.e., the class of the subject under study). Moreover, the distance to the normal aging lifespan model overlaid on an MRI atlas can be displayed to assist clinicians in their decision-making.</figcaption></figure>

in lifespan studies covering a large portion of human life, given that comprehensive longitudinal datasets across the entire lifespan do not yet exist (Coupé et al. 2017; Bethlehem et al. 2022; Fjell et al. 2013). Therefore, the trajectories presented should be considered pseudo-lifespan trajectories. However, previous studies have demonstrated a high correlation between trajectories derived from longitudinal data (over restricted age ranges) and those derived from large-scale cross-sectional data (Planche et al. 2022; Fjell et al. 2013).

For the trajectories of CN, we used a large dataset comprising N=36,527 subjects ranging from 1 year to 94 years of age, extracted from the training dataset.

In contrast, for the pathological trajectories, we followed the framework proposed in previous studies (Coupé et al. 2019, 2023, 2022; Planche et al. 2022, 2024; Planche, Mansencal, et al. 2023), which involves a mixture of patients with young CN individuals. This framework has been designed to address the absence of data over the entire lifespan when building

pathological trajectories of sporadic brain disorders (e.g., the youngest AD subject in our training set is 52 years old, while the trajectory spans the entire lifespan). The proposed approach operates under the assumption that neurodegeneration is a slow and progressive process—meaning that patients initially follow normal volumetric patterns before diverging due to disease onset. Thus, the origin of all trajectories is anchored in the same young CN population, while the later segments of each trajectory are estimated from older individuals with the respective disease. The parametric fitting ensures a smooth and plausible transition between young CN data and older patient samples, producing continuous trajectories over the entire lifespan. This modeling approach has recently been validated in genetic FTD, where asymptomatic carriers scanned before symptom onset follow trajectories consistent with the used assumptions (Planche et al. 2025).

Specifically, we incorporated all CN individuals younger than a certain age threshold. As outlined and justified in (Coupé et al. 2023), we determined this threshold as the first centile
value of the distribution of patients' ages in the training patho-
logical populations, corresponding to 44 years for the dataset
of patients with cognitive neurodegenerative diseases and
40 years for the dataset of patients with PD and parkinsonism.
Consequently, we used 5346 CN individuals younger than
44 years to build the *cognitive tree* and utilized 5213 CN indi-
viduals younger than 40 years for the *motor tree*. This adaptive
approach ensures that the threshold value is automatically ad-
justed based on the age distributions of the respective popula-
tions under consideration.

To estimate the volume trajectories, we evaluated several low-
order polynomial models, including linear, quadratic, and cubic
models, following the approach outlined in previous stud-
ies (Coupé et al. 2019, 2023, 2022; Planche et al. 2022, 2024;
Planche, Mansencal, et al. 2023). Our model selection process
follows two main steps:

1. Candidate model generation: We first generated a set of candidate models by fitting polynomials of varying orders. A polynomial model was retained as a candidate only if it satisfied both of the following criteria:

* The *F*-statistic from an ANOVA test, comparing the polynomial model to a constant (null) model, was statistically significant (*p* < 0.05), indicating that the model explains more variance than a flat line.

* All individual polynomial coefficients were statistically significant according to the *T*-statistic (*p* < 0.05), ensuring that each term contributed meaningfully to the model.

2. Model selection: Subsequently, to select the most relevant model among these potential candidates, we employed the Bayesian information criterion (BIC) (Schwarz 1978). The BIC facilitates model selection by balancing model fit and complexity, thereby aiding in the identification of the most parsimonious and informative model for describing the volume trajectories across different age groups and populations.

2.5 | Lifespan Tree Construction

Herein, we introduce a novel lifespan method capable of au-
tomatically extracting pertinent information that effectively
discriminates between groups of patients. This characteristic
empowers our method to be applicable to any classification
problem without requiring manual tuning. More significantly,
our method is well-adapted to handling multiclass problems,
making it particularly valuable for differential diagnosis tasks.
Finally, our aim was to provide an interpretable tool for clini-
cians; therefore, we decided to create a 3D representation of the
brain progression over lifespan (higher dimension could also be
used to better capture brain atrophy, but making visualization
harder).

To construct our 3D *lifespan tree* of the brain anatomy, the
first step involves reducing the dimensionality of our fea-
ture space, which encompasses 125 features comprising
124 structure volumes and age, into a 3D space comprising
2D spatial coordinates along with age. For this purpose, we

opted for a manifold learning approach leveraging UMAP
(Uniform Manifold Approximation and Projection) (McInnes
et al. 2020). UMAP is a nonlinear dimensionality reduction
technique known for its ability to effectively separate patterns
within high-dimensional datasets.

Addressing the challenge of incorporating the temporal nature
of the data, we employed a 2D UMAP-based dimensionality re-
duction technique in conjunction with a posterior 3D reconstruc-
tion of the *lifespan tree* of brain anatomy. Additionally, to ensure
effective separation of all considered populations (i.e., branches
in the tree), including smaller ones, we utilized a Monte Carlo
simulation to generate synthetic samples. This approach enables
us to enhance the discriminative capabilities of our *lifespan tree*
of brain anatomy, ensuring clear separation between groups, as
illustrated in Figure 1.

Recently, an approach called aligned-UMAP based on a sliding
window framework has been introduced to handle time series,
demonstrating its utility in analyzing longitudinal data (Dadu
et al. 2023). While this method offers advantages for studying
time series, its use can complicate the projection of new points
into the reduced space due to the creation of different embed-
dings for each time point.

Given our objective of performing classification on external
samples, we devised an alternative strategy centered on creating
a unique unified 2D embedding and subsequently reconstruct-
ing the time axis (age axis in our case). Initially, we employed
UMAP to reduce the dimensionality of all selected samples
simultaneously from 124 dimensions (representing 124 struc-
tures) to a 2D space. Subsequently, we utilized the age informa-
tion of the samples to project them into a 3D space, where the
third axis corresponds to age (as depicted in Figure 1). Following
this process, the 3D branches, representing the projection of the
lifespan trajectories, can be plotted within our 3D space to con-
struct the *lifespan tree*.

2.6 | Synthetic Sampling

To ensure the effectiveness of our global 2D embedding, care-
ful selection of samples is crucial to achieve a robust sepa-
ration of populations and maintain balanced representation
across all populations and along ages. Instead of directly uti-
lizing volumes (which may lack balance between populations
and across ages), we propose employing synthetic samples
generated through simulation based on lifespan trajectories.
For each year, we generated 100 synthetic samples randomly
drawn from a Gaussian distribution, with the lifespan trajec-
tory values at that age serving as the mean and a standard
deviation equal to 1 (given that trajectories were based on z-
scores). All synthetic samples were projected to illustrate their
distribution (branch size) within the final space, as depicted
in Figure 1.

2.7 | UMAP Training

During UMAP training, we used the Matlab implementa-
tion of UMAP (Meehan et al. 2022) with default parameters
(min_dist = 0.3, metric = Euclidean) except for the number of neighbors, which was set to 10 times the number of considered populations (i.e., 70 for cognitive tree and 40 for motor tree). ¹ In this way, the number of neighbors is automatically adapted to the number of classes. Moreover, to enhance population separation, as done for trajectory estimation, we only used synthetic samples older than the first centile of patients' age in the training population (i.e., 44 years for the cognitive tree and 40 years for the motor tree). This ensures training the UMAP embedding over the most discriminative period of life.

## 2.8 | Classification Using Lifespan Tree

Once the 3D tree was built with the training dataset, we used it to classify subjects from the testing dataset (see Figure 1). To classify each subject, we projected them into the 3D tree space and identified the closest branch in terms of Euclidean distance within the 2D cut corresponding to the subject's age, assigning the subject to the corresponding class. Additionally, we calculated the class membership score by using the squared distance within a Gaussian distribution with a mean of 0 and a standard deviation of 1. This score was then normalized by the sum of scores across all classes to provide a likelihood measure.

## 2.9 | Classification Using SVM

For differential diagnosis based on MRI biomarkers, SVM classification of volumes has demonstrated competitive results in recent literature (Wen et al. 2020; Talai et al. 2021; De Francesco et al. 2023; Nguyen et al. 2023). Therefore, we used a multiclass SVM on volumes (124 features per subject) as our state-of-the-art reference to challenge the relevance of *the lifespan tree*. We also tested a variant incorporating volumes and age, but the results were slightly worse. The SVM-based method used the Matlab version of the multiclass error-correcting output codes classifier based on SVM learners with default parameters (coding = one vs. one, BoxConstraint = 1, Kernel scale = 1, and standardize = false).²

# 3 | Results

## 3.1 | Lifespan Tree of Brain Anatomy

Figure 1 provides a conceptual overview of the proposed *lifespan tree* used to model the progression of brain anatomy in neurodegenerative diseases. The lifespan tree combines lifespan modeling (Coupé et al. 2019) and nonlinear dimensionality reduction (McInnes et al. 2020), yielding a compact representation of brain anatomy evolution across different pathologies over the entire lifespan. First, for each cerebral structure (n=124) and each considered population, a volume trajectory with respect to age is estimated across the entire lifespan to obtain normal and pathological lifespan models. Second, the multiple lifespan trajectories are reduced using a manifold learning method (i.e., UMAP). Finally, the 3D lifespan tree of brain anatomy is reconstructed by using age as the vertical axis. This provides a tree-like structure with a common trunk at early ages, followed by progressive

divergences related to differential patterns of atrophy, each summarized as a specific location within the reduced space. This novel tool offers a visually interpretable, rapid, and flexible framework for the differential diagnosis of various neurological disorders. The position of an external test subject within the 3D tree space directly suggests a potential diagnosis, and a final score for each diagnosis is numerically provided to the clinician.

## 3.2 | Cognitive Tree for the Differential Diagnosis of Cognitive Neurodegenerative Diseases

To demonstrate the differential diagnosis capability of the *lifespan tree* of brain anatomy, we first tested a challenging classification task distinguishing between normal aging and six cognitive disorders. For this purpose, we used brain MRI from 461 patients with AD, 155 with bvFTD, 39 with semantic dementia, 41 with PNFA, 91 with PSP, and 126 with DLB for the construction of the branches of a *cognitive tree*, as well as 5346 healthy young subjects to build the trunk of the tree and 31,181 cognitively normal subjects older than 44 years to build the cognitively normal (CN) branch (see Tables 1 and S1 for details).

For testing, we used an independent dataset consisting of 528 CN, 488 AD, 90 bvFTD, 44 semantic dementia, 40 PNFA, 67 PSP, and 47 DLB subjects from entirely independent external databases (see Tables 1 and S2 for details).

Table 2 presents the classification results obtained on the external testing dataset in terms of Balanced accuracy (BACC) and sensitivity (SEN, or Recall: the proportion of correct positive predictions out of all positive predictions) per class. Instead of Area Under the Curve, BACC is preferred since it is a more appropriate measure for multiclass classification when observations are not balanced between each class. In a multiclass classification problem, the BACC is typically computed as the mean of the recall (i.e., sensitivity) across all classes, thereby giving equal weight to each class regardless of class imbalance. For each metric, we estimated the Top-1, Top-2, and Top-3 values, and their corresponding confidence intervals. The Top-1 metric is the usual metric (i.e., the top prediction is the correct class). The Top-2 metric measures if the correct class is in the top 2 predictions, and Top-3 if the correct class is in the top 3 predictions. Confidence intervals were estimated using bootstrap with 95% confidence (N=10,000) and *p* value between BACC with a paired hypothesis testing (MacKinnon 2009). In this experiment, the *lifespan tree* of brain anatomy obtained significantly better Top-1 BACC (*p* value <0.0001) with 55% (randomness being 14%) while the SVM-based method achieved 46% Top-1 BACC, indicating a lack of sensitivity for some classes, such as DLB, and an over-detection of the CN class (see Table 2 and confusion matrices in Figure S1). As expected, all the Top-2 metrics increased, and our lifespan tree reached a significantly better top-2 BACC (*p* value = 0.0025) of 71% (randomness being 29%), while the SVM-based method achieved a Top-2 BACC of 65%. Finally, for the Top-3 metric (randomness being 43%), both methods obtained a similar BACC around 79% (*p* value = 0.45). This demonstrates that SVM is similar to our method to rank the correct answer within its top 3 predictions out of seven, but our method is significantly better in accurately assigning the correct class in the first or second position.
In addition to differential diagnosis metrics, we evaluated the ability of the methods to detect abnormality (i.e., patients vs. controls) by combining the pathological populations (AD, DLB, PSP, bvFTD, PNFA, and SD) into a single class after tree construction (the tree remains unchanged) but before computing the metrics. In this experiment, the lifespan tree obtained significantly better BACC (*p* value < 0.03) with 78% (randomness being 50%) while the SVM-based method achieved 76% BACC (see Table 3). Moreover, the lifespan tree method achieved a sensitivity of 83% for detecting cognitive disorders, whereas the SVM-based approach reached only 54%, thereby missing a substantial number of cases. On the

other hand, the SVM-based method demonstrated higher specificity (97%) compared to the lifespan tree.

Figure 2 presents our cognitive tree encompassing normal aging and six cognitive disorders (resulting in seven branches). The tree is cut at age 60 years to better visualize the distances between branches at this age, as an illustrative example. We can observe that the branch closest to CN is DLB. Additionally, the DLB branch is situated between the CN and AD branches. As a result, some AD patients (44% Top-1 sensitivity) were misclassified as CN, some CN subjects were identified as DLB patients, and many DLB patients (with only 32% Top-1 sensitivity) were incorrectly assigned to the AD and CN classes (see Table 2 and confusion matrices in Figure S1).

The proximity of the PSP and PNFA branches indicates that these disorders share anatomical similarities. PSP was well-detected with a Top-1 sensitivity of 71%, due to its specific atrophy pattern focused on the thalamus, brainstem, and ventral diencephalon (see Figure 3). Conversely, PNFA yielded a low Top-1 sensitivity of 33%, as each part of its atrophic pattern could be found in at least one other population (see Figure 3).

Similarly, distinct branches were observed for semantic dementia and bvFTD. Semantic dementia subjects were easily distinguishable, with 91% Top-1 sensitivity, thanks to a highly pronounced atrophy pattern (see Figure 3). In contrast, bvFTD patients were more challenging to classify, with a top-1 sensitivity of 43%. While on average this population is distinct from others (i.e., in terms of distance between branches), the

<table><caption>**TABLE 3** | Comparison of the seven-branch cognitive tree with the state-of-the-art SVM method in detecting the presence of pathology (i.e., 528 controls vs. 776 patients). For this analysis, the six pathological populations (AD, DLB, PSP, bvFTD, PNFA, SD) were merged into a single class after classification but prior to metric estimation.</caption><thead><tr><th>Classification on external datasets (%)</th><th>BACC</th><th>SEN.</th><th>SPE.</th></tr></thead><tbody><tr><td>Lifespan tree</td><td>78*<br>[76-81]</td><td>83*<br>[80-85]</td><td>74<br>[70-78]</td></tr><tr><td>SVM on volumes</td><td>76<br>[74-78]</td><td>54<br>[51-56]</td><td>97*<br>[96-99]</td></tr></tbody></table>

Note: The best-performing results are highlighted in red.

Abbreviations: BACC, balanced accuracy; SEN, sensitivity; SPE, specificity.

*p* value < 0.05 (significantly higher results).

<figure><img src="image_5.png" /><figcaption>**FIGURE 2** | Left: The 3D cognitive tree is composed of seven branches corresponding to Cognitively Normal (CN), Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), semantic dementia (SD), progressive non-fluent aphasia (PNFA), progressive supranuclear palsy (PSP), and dementia with Lewy bodies (DLB). Each branch is formed by the mean trajectory of simulated samples (solid colors), with individual simulated samples shown as surrounding semi-transparent dot clouds. Test subjects are represented by solid-colored dots. Right: A 2D cross-section of the tree at age 60 (including test subjects aged between 58 and 62). Branches are shown as large solid-colored circles, while test subjects appear as small solid-colored dots.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**FIGURE 3** | Atlas of the progression of the distance to normal aging (i.e., atrophy severity) for each neurodegenerative diseases reported in the cognitive tree. The four columns represent the anatomical progression of each disease at 60y, 70y, 80y and 90y. AD = Alzheimer's Disease, bvFTD = behavioral variant frontotemporal dementia, DLB = dementia with Lewy bodies, SD = semantic dementia, PNFA = progressive non fluent aphasia, PSP = progressive supranuclear palsy. The distances are estimated between CN trajectories and pathological trajectories over the training dataset.</figcaption></figure>
<table><caption>**TABLE 4** | Comparison between the four-branch motor tree and the state-of-the-art SVM method for the differential diagnosis of Parkinson's disease and parkinsonism (PSP, DLB, and MSA) (using external validation datasets).</caption><thead><tr><th rowspan="2">Classification on external datasets (%)</th><th>BACC</th><th>SEN. DLB</th><th>SEN. MSA</th><th>SEN. PD</th><th>SEN. PSP</th></tr><tr><th>Top-1</th><th>Top-1</th><th>Top-1</th><th>Top-1</th><th>Top-1</th></tr></thead><tbody><tr><td>Lifespan tree</td><td>62*<br>[58-67]</td><td>66*<br>[52-79]</td><td>42*<br>[33-51]</td><td>58<br>[52-62]</td><td>81<br>[71-90]</td></tr><tr><td>SVM on volumes</td><td>56<br>[52-60]</td><td>45<br>[30-60]</td><td>27<br>[19-35]</td><td>68*<br>[62-73]</td><td>87<br>[78-94]</td></tr></tbody></table><table><thead><tr><th rowspan="2">Classification on external datasets (%)</th><th>BACC</th><th>SEN. DLB</th><th>SEN. MSA</th><th>SEN. PD</th><th>SEN. PSP</th></tr><tr><th>Top-2</th><th>Top-2</th><th>Top-2</th><th>Top-2</th><th>Top-2</th></tr></thead><tbody><tr><td>Lifespan tree</td><td>80<br>[76-84]</td><td>86*<br>[75-95]</td><td>67<br>[58-75]</td><td>81<br>[77-86]</td><td>85<br>[79-94]</td></tr><tr><td>SVM on volumes</td><td>81<br>[77-85]</td><td>74<br>[61-86]</td><td>66<br>[57-74]</td><td>87*<br>[83-90]</td><td>97*<br>[92-100]</td></tr></tbody></table>

Note: For each metric, the confidence interval is provided in brackets. Top-1 means that the correct class is the prediction with the highest probability. Top-2 means that the correct class is within the predictions with the two highest probabilities. The best results are in red.
Abbreviations: BACC, balanced accuracy; DLB, dementia with Lewy bodies; MSA, multiple system atrophy; PD, Parkinson's disease; PSP, progressive supranuclear palsy; SEN, sensitivity (recall).
*p value < 0.05 (significantly better results).

classification of test subjects exhibits high variability (see confusion matrices in Figure S1). This might be due to the anatomical heterogeneity of the bvFTD population.

## 3.3 | Motor Tree for Differential Diagnosis of PD and Atypical Parkinsonism

To further demonstrate the clinical relevance of our method, we performed another classification task for distinguishing between disorders that share parkinsonian symptoms, where the differential diagnosis is particularly challenging. For motor tree construction, we used brain MRI from 5213 healthy subjects younger than 40 years for the trunk of the tree and 91 PSP, 126 DLB, 133 PD, and 21 MSA subjects for the branches. For testing, we used 67 PSP, 47 DLB, 333 PD, and 117 MSA subjects from independent datasets (see Table 1).

Table 4 presents the classification results on the external datasets. Similar to the previous experiment, the *lifespan tree* of brain anatomy provided a significantly better (*p* value = 0.03) Top-1 BACC at 62% (with randomness being 25%) compared to the SVM-based method (56% Top-1 BACC). Furthermore, for the Top-2 metrics (with randomness being 50%), both methods obtained a similar BACC of around 80% (*p* value = 0.13). This shows again that our method is better than SVM in detecting the correct class, but both methods similarly rank the correct answer within their Top-2 predictions out of four.

As previously, we also evaluated the ability of the methods to detect atypical parkinsonism vs. PD by combining the atypical populations (DLB, PSP, and MSA) into a single class before computing the metrics. In this experiment, both methods obtained similar BACC (*p*-value = 0.11), with 84% (randomness being 50%) for lifespan tree and with 86% for the SVM-based method, respectively (see Table 5). Moreover, both methods reached similar

<table><caption>**TABLE 5** | Comparison of the four-branch motor tree with the state-of-the-art SVM method in detecting the presence of atypical PD (i.e., 333 PD vs. 231 atypical PD). For this analysis, the three atypical populations (DLB, MSA, and PSP) were merged into a single class after classification but prior to metric estimation.</caption><thead><tr><th>Classification on external datasets (%)</th><th>BACC</th><th>SEN.</th><th>SPE.</th></tr></thead><tbody><tr><td>Lifespan tree</td><td>84<br>[79-89]</td><td>87<br>[84-90]</td><td>81<br>[71-90]</td></tr><tr><td>SVM on volumes</td><td>86<br>[82-90]</td><td>86<br>[83-89]</td><td>86*<br>[82-94]</td></tr></tbody></table>

Note: The best results are highlighted in red.
Abbreviations: BACC, balanced accuracy; SEN, sensitivity; SPE, specificity.
*p value < 0.05 (significantly higher results).

sensitivities, with 87% for *lifespan tree* and 86% for the SVM-based method.

Figure 4 depicts the *motor tree* with the four branches representing PD and three causes of parkinsonism. The cut tree illustrates the proximity of DLB and PD branches at 60 years old, PD being characterized by the lowest atrophic pattern within the population compared to normal aging (see Figure 5). In contrast, PSP and MSA exhibited more distinct anatomical patterns. However, both methods, especially SVM, encountered difficulties in distinguishing MSA patients from PD patients (see confusion matrices in Figure S2), resulting in low Top-1 sensitivity for MSA (ranging from 27% for the SVM-based method to 42% for the *lifespan tree* of brain anatomy) although our *lifespan tree* produced higher sensitivity for this population. Similar to the *cognitive tree* experiment, all methods achieved their highest sensitivity on the PSP population, which possesses a clear and specific MRI phenotype compared to other classes (see Figure 5).
<figure><img src="image_7.png" /><figcaption>**FIGURE 4** | Left: The 3D motor tree composed by four branches for Parkinson's Disease (PD), progressive supranuclear palsy (PSP), dementia with Lewy bodies (DLB) and multiple system atrophy (MSA). Each branch is formed by the mean trajectory of simulated samples (solid colors), with individual simulated samples shown as surrounding semi-transparent dot clouds. Test subjects are represented by solid-colored dots. Right: A 2D tree cut at 60y (including test subjects between 58y and 62y). Branches are shown as large solid-colored circles, while test subjects appear as small solid-colored dots.</figcaption></figure>

4 | Discussion

So far, the large majority of AI-enhanced MRI analysis has been proposed for binary classification tasks between a specific pathology and healthy conditions (see Tables S6 and S7 for validation of the *cognitive and motor trees* on binary tasks) and mainly on AD diagnosis and prognosis (see Tables S3–S5 and Figure S3 for validation of our method on AD tasks). However, this oversimplification of diagnostic decision-making does not reflect everyday clinical practice. In this paper, we introduced a novel framework called *lifespan tree* that we used here to model the progression of brain anatomy in neurodegenerative disorders. This framework offers an elegant representation of the anatomical changes associated with neurodegeneration. We conducted two experiments evaluating both motor and cognitive disorders, and showed that the lifespan tree provides interpretable differential diagnosis across multiple disorders simultaneously. While there remains room for improvement in terms of accuracy, the method demonstrates performance that is comparable to, or better than, existing approaches in this complex multiclass setting. Additionally, the tree's shapes can provide new insights into the MRI phenotypes' similarities among different disorders. Moreover, as demonstrated in previous studies (Coupé et al. 2019, 2023; Planche et al. 2022, 2024; Planche, Mansencal, et al. 2023), lifespan modeling allows measuring the distances (i.e., the severity of atrophy progression) between normal aging and different pathological models to be visualized on an atlas (see Figures 3 and 5). This enables easy visualization of the arachetypal progression of each disorder according to age, which can be compared to the anatomy of each patient (see cut tree in Figure 1). Throughout our experiments, we demonstrated that the cognitive tree consistently outperformed the state-of-the-art

SVM-based method for differentiating cognitive neurodegenerative diseases. For motor disorders, the motor tree achieved a higher Top-1 balanced accuracy, but overall performance between the two approaches was more comparable.

First, we applied our framework for the differential diagnosis of seven diseases or syndromes by constructing a *cognitive tree*. We demonstrated that this seven-branch tree was capable of distinguishing CN, AD, DLB, bvFTD, PNFA, semantic dementia, and PSP with a Top-1 BACC of 55% (randomness being 14%) on completely external datasets, compared to 46% for SVM on volumes, which is considered the state of the art (Lampe et al. 2023). Moreover, the lifespan tree was able to detect the presence of a cognitive disorder (i.e., controls vs. patients) with a sensitivity of 83%, compared to 54% for the SVM-based method. However, the SVM-based method achieved a higher specificity (97% vs. 74%).

While there is no directly comparable experiment in the literature, previous studies have proposed methods for the differential diagnosis of various types of cognitive disorders. For the three-class classification problem of CN vs. AD vs. FTD, SVM on volumes achieved Top-1 BACCs from 75% to 80% (Ma et al. 2020; Nguyen et al. 2023), while recent end-to-end deep learning methods achieved BACC from 67% to 83% (Nguyen et al. 2024b; Li et al. 2024). These results suggest that machine learning methods remain competitive compared with deep learning methods for such differential diagnosis tasks. In five-class classification problems, machine learning on brain volumes achieved Top-1 BACCs from 51% (Koikkalainen et al. 2016) to 66% (Tolonen et al. 2018) for CN vs. AD vs. FTD vs. DLB vs. vascular dementia, and 59% for subjective
<figure><img src="image_8.png" /><figcaption>**FIGURE 5** | Atlas of the progression of the distance to normal aging (i.e., atrophy severity) for each neurodegenerative diseases reported in the motor tree. The four columns represent the anatomical progression of each disease at 60y, 70y, 80y and 90y. DLB=dementia with Lewy bodies, MSA=multiple system atrophy, PD=Parkinson's Disease, PSP=progressive supranuclear palsy. The distances are estimated between CN trajectories and pathological trajectories over the training dataset.</figcaption></figure>

memory complaint vs. AD vs. FTD vs. DLB vs. vascular dementia (Tong et al. 2017). More recently, a seven-class method dedicated to comparing AD vs. bvFTD vs. semantic dementia vs. PNFA vs. PSP vs. corticobasal syndrome vs. logopenic variant of primary progressive aphasias has been proposed (Lampe et al. 2023). For this seven-class differential diagnosis task, SVM on volumes obtained 47% of Top-1 BACC, which is similar to the 46% presented here using SVM on volumes on a slightly different seven-class differential diagnosis problem. This comparison highlights the good performance (55% of Top-1 BACC) of our *lifespan tree* of brain anatomy for seven-class differential diagnosis in the field of cognitive neurology. Furthermore, it is crucial to note that, unlike all the mentioned methods, which were validated using cross-validation on the same databases (potentially leading to overoptimistic performances; Varoquaux 2018), our method was validated on completely external datasets, highlighting its robustness in clinical real-world scenarios.

During our experiments with the *cognitive tree*, our method achieved Top-1 sensitivities of 44% for AD, 74% for CN, 32% for DLB, 43% for bvFTD, 33% for PNFA, 91% for semantic dementia, and 71% for PSP (see Table 2). The populations with the lowest sensitivity were DLB and PNFA. For DLB, our results were consistent with (Tong et al. 2017), where DLB achieved the lowest sensitivity (38%) compared to other populations (subjective memory complaint, AD, FTD, and vascular dementia) within their five-class problem. This low sensitivity may arise from the proximity of MRI phenotypes between AD and DLB (see Figure 3), the mild severity of brain atrophy usually reported in DLB (see Figure 3) (Whitwell et al. 2007; Planche et al. 2021), the fact that a significant number of DLB patients might have AD as co-pathology (Coughlin et al. 2019), and the heterogeneity of the DLB population (Oppedal et al. 2019). Regarding PNFA, our results are similar to those of Lampe et al. (2023), where the authors obtained a sensitivity of 36% in their seven-class problem. This is probably explained by
the heterogeneity of the underlying neuropathology explain-
ing this syndromic presentation of progressive aphasia, which
may overlap distinct tauopathies, such as PSP (Lebouvier
et al. 2017). In their study, they also demonstrated that se-
mantic dementia is the easiest variant of FTD to classify, a
finding confirmed in our results (see Table 2). Furthermore,
they obtained similar Top-1 sensitivity results to ours for AD
(42%). These comparisons on cognitive disorders highlight the
consistency of our results with existing literature and further
validate the enhanced effectiveness of our lifespan tree model
of brain anatomy for differential diagnosis among multiple
cognitive neurodegenerative diseases, compared to state-of-
the-art SVM methods.

Second, we proposed a four-class differential diagnosis ap-
proach for atypical parkinsonism by designing a *motor tree*. This
experiment aimed to demonstrate the capability of our *lifespan
tree* of brain anatomy method to discriminate between PD, DLB,
PSP, and MSA, illustrating the potential of this novel framework
to be applied across a broad range of scenarios. In this exper-
iment, the motor lifespan tree achieved a Top-1 BACC of 62%
(with chance level at 25%), whereas the SVM-based method
using brain volumes reached a significantly lower Top-1 BACC
of 56% on fully independent external datasets (see Table 3). The
lifespan tree showed higher sensitivity for DLB and MSA, while
the SVM-based method demonstrated better sensitivity for PD.
Additionally, both methods achieved comparable sensitivity
(approximately 86%) for detecting atypical parkinsonism (i.e.,
distinguishing PD from PSP, MSA, and DLB), although the
SVM-based method showed higher specificity. To date, there are
no directly comparable experiments in the literature. However,
for the easiest three-class classification problem of CN vs. PD vs.
PSP, SVM on volumes has previously achieved a Top-1 BACC of
67% using cross-validation (Talai et al. 2021).

During our experiment, the *motor tree* achieved Top-1 sensi-
tivities of 66% for DLB, 58% for PD, 81% for PSP, and 42% for
MSA. Of note, the MSA diagnostic criteria (Gilman et al. 1998)
used in the training dataset were not the most recent, and the
clinical details available in the database did not allow for the
reconstruction of the current criteria proposed by the *Movement
Disorder Society* (Wenning et al. 2022), which distinguishes be-
tween MSA-P (parkinsonian type) and MSA-C (cerebellar type).
Consequently, a mixture of MSA-P and MSA-C cases may have
been used for training (see divergence of cerebellum compared
to normal aging in MSA model on Figure 5), unlike the test
dataset, where only patients were diagnosed with MSA-P. The
lower sensitivity for MSA, which is a rare disease, might also
be explained by the sample size of the training dataset (only 21
patients).

In this study, we exclusively used volumes extracted from struc-
tural MRI as features, as a first proof of concept. However, it is
well recognized that incorporating additional variables—such
as other imaging modalities, clinical scores, or fluid biomark-
ers—can significantly improve classification performance (Talai
et al. 2021; Ma et al. 2024; Nguyen et al. 2024a; Tong et al. 2017;
Koikkalainen et al. 2016; Tolonen et al. 2018). Our lifespan tree
method can readily accommodate such multimodal data. One
main challenge remains finding datasets that provide consis-
tent imaging modalities, clinical scores, and fluid biomarkers

for all subjects. Given that T1-weighted MRI data are widely
available in clinical settings and across the datasets used here,
we chose to focus initially on this classical feature type. Future
work will focus on extending our motor and cognitive trees to
integrate multimodal imaging and clinical scores, aiming to im-
prove the accuracy and robustness of the differential diagnosis
framework.

In addition, it is important to contextualize the obtained re-
sults by comparing them to human performance under sim-
ilar conditions. For instance, when using MRI alone, Harper
et al. (2016) reported that human experts using the best avail-
able visual rating scales achieved a BACC of 77% [69%–83%] for
distinguishing AD from CN on post-mortem confirmed cases,
whereas our method achieved a BACC of 80% [77%–82%] for
the same comparison (see Table S6). Similarly, their reported
BACC for distinguishing DLB from CN was 70% [58%–80%],
compared to 74% [67%–80%] for our method. For FTD, al-
though Harper et al. did not analyze subtypes separately, we
computed an average BACC across bvFTD, PNFA, and SD,
yielding a mean BACC of 86% [80%–90%], closely matching
the 85% [77%–91%] BACC reported for human experts.

Finally, transitioning from binary (as usually done in the lit-
erature) to multiclass classification presents a considerable
increase in complexity. Binary classification tasks, such as
distinguishing patients from controls, yield higher perfor-
mance measures due to their relatively simpler nature (see
the results of binary classification achieved with our frame-
work in Tables S3–S7). However, the clinical reality demands
a more nuanced approach that extends beyond binary deci-
sions. In clinical practice, neurologists and radiologists deal
with complex and/or diffuse atrophy patterns, providing little
information. Moreover, patterns of atrophy that easily differ-
entiate AD from healthy subjects (see Table S4) become less
informative when considering differential diagnoses (see
Table 1). This is typically the case with hippocampal atrophy,
which can be encountered in many neurodegenerative dis-
eases (Planche et al. 2022; Planche, Mansencal, et al. 2023;
Camicioli et al. 2003). Therefore, to achieve accurate differ-
ential diagnoses, it becomes imperative to conduct a compre-
hensive analysis of multiple brain regions and consider the
nuanced interplay of various pathologies. This is where AI
might play a crucial role. By systematically analyzing the en-
tire brain and integrating information from multiple regions,
these algorithms can assist neurologists and radiologists in
identifying the most suitable diagnosis based on the overall
atrophy pattern. *The lifespan tree* of brain anatomy serves as
both a means of analysis and a summary of this information,
making it easily understandable for clinicians. Specifically,
our method can embed volumetric data from 124 brain struc-
tures into a latent space where participants with similar atro-
phy topographies cluster together, while those with differing
atrophy patterns are positioned further apart. This enables the
summarization of complex atrophy patterns.

Author Contributions

P.C. developed the idea, the theoretical formalism, performed the ana-
lytic calculations, and performed the numerical experiments. P.C. and
V.P. conceived and planned the experiments. P.P. and B.M. prepared and processed the data. P.C. took the lead in writing the manuscript. W.G.M., T.T., J.V.M., and V.P. aided in interpreting the results and worked on the manuscript. All authors provided critical feedback and helped shape the research, analysis, and manuscript. All authors discussed the results and contributed to the final manuscript.

## Acknowledgements

This work benefited from the support of the project HoliBrain of the French National Research Agency (ANR-23-CE45-0020-01). Moreover, this project is supported by the Precision and Global Vascular Brain Health Institute, funded by the France 2030 investment plan as part of the IHU3 initiative (ANR-23-IAHU-0001). Finally, this study received financial support from the French government in the framework of the University of Bordeaux's France 2030 program/RRI "IMPACT" and the PEPR StratifyAging and Prodrom-ND. This work was also granted access to the HPC resources of IDRIS under the allocation 2022-AD011013848R1 made by GENCI. This work also benefited from the support of the project PID2023-1521270B-100 of the Ministerio de Ciencia, Innovación y Universidades de España. Moreover, this work is based on multiple samples. We wish to thank all investigators of these projects who collected these datasets and made them freely accessible. The C-MIND data used in the preparation of this article were obtained from the C-MIND Data Repository (accessed in February 2015), created by the C-MIND study of Normal Brain Development. This is a multisite, longitudinal study of typically developing children from ages newborn through young adulthood conducted by Cincinnati Children's Hospital Medical Center and UCLA and supported by the National Institute of Child Health and Human Development (Contract #HHSN275200900018C). A listing of the participating sites and a complete listing of the study investigators can be found at https://research.cchmc.org/c-mind. The NDAR data used in the preparation of this manuscript were obtained from the NIH-supported National Database for Autism Research (NDAR). NDAR is a collaborative informatics system created by the National Institutes of Health to provide a national resource to support and accelerate research in autism. The NDAR dataset includes data from the NIH Pediatric MRI Data Repository created by the NIH MRI Study of Normal Brain Development. This is a multisite, longitudinal study of typically developing children from ages newborn through young adulthood conducted by the Brain Development Cooperative Group and supported by the National Institute of Child Health and Human Development, the National Institute on Drug Abuse, the National Institute of Mental Health, and the National Institute of Neurological Disorders and Stroke (Contracts #N01-HD02-3343, N01-MH9-0002, and N01-NS-9-2314, N01-NS-9-2315, N01-NS-9-2316, N01-NS-9-2317, N01-NS-9-2319 and N01-NS-9-2320). A listing of the participating sites and a complete listing of the study investigators can be found at http://pediatricmri.nih.gov/nihpd/info/participating_centes.html. The ADNI data used in the preparation of this manuscript were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). The ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics NV, Johnson and Johnson, Eli Lilly and Co., Medpace Inc., Merck and Co. Inc., Novartis AG, Pfizer Inc., F. Hoffmann-La Roche, Schering-Plough, Synarc Inc., as well as nonprofit partners, the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to the ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study was coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for NeuroImaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation. The

OASIS data used in the preparation of this manuscript were obtained from the OASIS project funded by grants P50 AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24 RR021382, and R01 MH56584. See http://www.oasis-brains.org/ for more details. The AIBL data used in the preparation of this manuscript were obtained from the AIBL study of ageing funded by the Commonwealth Scientific Industrial Research Organization (CSIRO; a publicly funded government research organization), Science Industry Endowment Fund, National Health and Medical Research Council of Australia (project grant 1011689), Alzheimer's Association, Alzheimer's Drug Discovery Foundation, and an anonymous foundation. See www.aibl.csiro.au for further details. The ICBM data used in the preparation of this manuscript were supported by Human Brain Project (grant PO1 MH052176-11) (ICBM, P.I. Dr. John Mazziotta) and the Canadian Institutes of Health Research (grant MOP-34996). The IXI data used in the preparation of this manuscript were supported by the UK Engineering and Physical Sciences Research Council (EPSRC) (GR/S21533/02) (http://www.brain-development.org/). The ABIDE data used in the preparation of this manuscript were supported by ABIDE funding resources listed at http://fcon_1000 projects.nitrc.org/indi/abide/. ABIDE's primary support for the work by Adriana Di Martino was provided by the NIMH (K23 MH087770) and the Leon Levy Foundation. Primary support for the work by Michael P. Milham and the INDI team was provided by gifts from Joseph P. Healy and the Stavros Niarchos Foundation to the Child Mind Institute, as well as by an NIMH award to MPM (R03 MH096321) (http://fcon_1000 projects.nitrc.org/indi/abide/). Data used in the preparation of this article were obtained from the MIRIAD database. The MIRIAD investigators did not participate in the analysis or writing of this report. The MIRIAD dataset is made available through the support of the UK Alzheimer's Society (Grant RF116). The original data collection was funded through an unrestricted educational grant from GlaxoSmithKline (Grant 6GKC). The ADHAD, DLBS, and SALD data used in the preparation of this article were obtained from http://fcon_1000 projects.nitrc.org. Data used in the preparation of this article were obtained from the Parkinson's Progression Markers Initiative (PPMI) database (www.ppmi-info.org). PPMI—a public-private partnership—was funded by The Michael J. Fox Foundation for Parkinson's Research and funding partners that can be found at https://www.ppmi-info.org/about-ppmi/who-we-are/study-sponsor. Data collection and sharing for this project were provided by the Cambridge Centre for Ageing and Neuroscience (CamCAN, https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/). CamCAN funding was provided by the UK Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1), together with support from the UK Medical Research Council and the University of Cambridge, UK. The 4-repeat tauopathy neuroimaging initiative (4RTNI) and Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) were funded through the National Institute of Aging and started in 2010. The primary goals of FTLDNI were to identify neuroimaging modalities and methods of analysis for tracking frontotemporal lobar degeneration (FTLD) and to assess the value of imaging versus other biomarkers in diagnostic roles. The Principal Investigator of NIFD was Dr. Howard Rosen, MD, at the University of California, San Francisco. The data are the result of collaborative efforts at three sites in North America. For up-to-date information on participation and protocol, please visit http://memory.ucsf.edu/research/studies/nifd. Data collection and sharing for this project were funded by the Frontotemporal Lobar Degeneration Neuroimaging Initiative (National Institutes of Health). The study is coordinated through the University of California, San Francisco, Memory and Aging Center. FTLDNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. The NACC database was funded by NIA/NIH Grants listed at https://naccdata.org/publish-project/authors-checklist#acknowledgment. This research has been conducted using the UK Biobank Resource under application number 80509. See https://www.ukbiobank.ac.uk for further details. The Amsterdam open MRI collection AOMIC ID-1000/PIOP1/PIOP2 data used in the preparation of this article were obtained from https://nilab-uva.github.io/AOMIC.github.io/ (Snoek et al. 2021). The Calgary pre-school MRI dataset was available at https://osf.io/axz5r/ and supported by the University of Calgary and CIHR (IHD-134090 and MOP-136797).
The Pixar database and related fundings were available at https://openn
euro.org/datasets/ds000228/versions/1.1.0. Data and biospecimens
used in preparation of this manuscript were obtained from the
Parkinson's Disease Biomarkers Program (PDBP) Consortium, sup-
ported by the National Institute of Neurological Disorders and Stroke at
the National Institutes of Health. Investigators include: Roger Albin,
Roy Alcalay, Alberto Ascherio, Thomas Beach, Sarah Berman, Bradley
Boeve, F. DuBois Bowman, Shu Chen, Alice Chen-Plotkin, William
Dauer, Ted Dawson, Paula Desplats, Richard Dewey, Ray Dorsey, Jori
Fleisher, Kirk Frey, Douglas Galasko, James Galvin, Dwight German,
Lawrence Honig, Xuemei Huang, David Irwin, Kejal Kantarci,
Anumantha Kanthasamy, Daniel Kaufer, James Leverenz, Carol Lippa,
Irene Litvan, Oscar Lopez, Jian Ma, Lara Mangravite, Karen Marder,
Laurie Orzelius, Vladislav Petyuk, Judith Potashkin, Liana Rosenthal,
Rachel Saunders-Pullman, Clemens Scherzer, Michael Schwarzschild,
Tanya Simuni, Andrew Singleton, David Standaert, Debby Tsuang,
David Vaillancourt, David Walt, Andrew West, Cyrus Zabetian, Jing
Zhang, and Wenquan Zou. The PDBP Investigators have not partici-
pated in reviewing the data analysis or content of the manuscript. Data
collection and dissemination of the data presented in this manuscript
was supported by the ALLFTD Consortium (U19 AG063911, funded by
the National Institute on Aging and the National Institute of
Neurological Diseases and Stroke) and the former ARTFL and LEFFTDS
Consortia (ARTFL: U54 NS092089, funded by the National Institute of
Neurological Diseases and Stroke and National Center for Advancing
Translational Sciences; LEFFTDS: U01 AG045390, funded by the
National Institute on Aging and the National Institute of Neurological
Diseases and Stroke). The authors acknowledge the invaluable contribu-
tions of the study participants and families as well as the assistance of
the support staff at each of the participating sites.

Data Availability Statement

Data sharing is not applicable to this article as no data sets were gener-
ated or analyzed during the current study.

Endnotes

¹https://www.mathworks.com/matlabcentral/fileexchange/71902.

²https://www.mathworks.com/help/stats/classificationecoc.html.

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## Supporting Information

Additional supporting information can be found online in the Supporting Information section. **Table S1:** Description of the databases used for the construction (training) of the cognitive and motor trees after quality control. The table provides the number of subjects (N) per class, the sex distribution (female/male), the mean age [age range], as well as the number of acquisition sites and the magnetic field strengths. During our experiments, we used brain MRI from cognitively normal subjects (CN), patients with Alzheimer's disease (AD), dementia with Lewy bodies (DLB), progressive supranuclear palsy (PSP), behavioral variant frontotemporal dementia (bvFTD), progressive non-fluent aphasia (PNFA) and semantic dementia (SD), Parkinson's disease (PD), and multiple system atrophy (MSA). The links to dataset websites are provided in the Acknowledgments section of the paper, and the references, when available, are provided in Section 2 of the paper. **Table S2:** Description of the databases used for the testing of the cognitive and motor trees. Testing comes from totally independent databases from training. The table provides the number of subjects (N) per class, the sex distribution (female/male), the mean age [age range], as well as the number of acquisition sites and the magnetic field strengths. During our experiments, we used brain MRI from cognitively normal subjects (CN), patients with Alzheimer's disease (AD), dementia with Lewy bodies (DLB), progressive supranuclear palsy (PSP), behavioral variant frontotemporal dementia (bvFTD), progressive non-fluent aphasia (PNFA) and semantic dementia (SD), Parkinson's disease (PD) and multiple system atrophy (MSA). The links to dataset websites are provided in the acknowledgment section of the paper, and the references, when available, are provided in Section 2 of the paper. **Figure S1:** Confusion matrices obtained for the cognitive neurodegenerative diseases experiment. **Figure S2:** Confusion matrices obtained for the motor diseases

experiment. **Table S3:** Comparison with state-of-the-art for AD diagnosis and prognosis on external databases. Balanced accuracy (Balanced ACC) in % is provided for each method for both datasets. For Deep Grading, CNN-based methods and HAVAs, the results published on the same datasets (Wen et al. 2020; Coupé et al. 2022; Nguyen et al. 2023) are used. **Table S3:** The results for AD diagnosis (AD vs. CN) and AD prognosis (pMCI vs. sMCI) tasks on external datasets, measured in terms of balanced accuracy (BACC). In both tasks, all methods were trained exclusively on AD and CN subjects, with the AD prognostic task demonstrating the methods' generalization capabilities on unseen tasks. **Table S4:** Comparison of the proposed methods on external datasets for AD diagnosis on external databases. During training, we used 377AD and 702 CN older than 55 years for training from the ADNI and OASIS databases. In addition, we used 1874 CN younger than 55 years for building lifespan trajectories as detailed in (Coupé et al. 2022). During testing, we used 47 AD and 232 CN from AIBL. **Table S5:** Comparison of the proposed methods on external datasets for AD prognosis on external databases. During training, we used 377AD and 702 CN older than 55 for training from the ADNI and OASIS databases. During testing, we used 235 pMCI and 255 sMCI from ADNI. The best results are indicated in red and the second best in green. **Figure S3:** 3D Lifespan tree composed of two branches, one for AD and one for CN. The cyan branch represents the CN model surrounded by the cloud of synthetic samples resulting from the Monte Carlo simulation. The yellow branch represents the AD model surrounded by the cloud of synthetic samples resulting from the Monte Carlo simulation. Diamonds represent the testing subjects projected into the tree space. Left: the testing subjects are the AD and CN from AIBL. Right: the testing subjects are the sMCI and pMCI from ADNI. **Table S6:** Results of binary classification task on cognitive disorders using the lifespan tree method. For training, we used 31,181 CN older than 44 years, 461 AD, 155 bvFTD, 39 SD, 41 PNFA, 91 PSP, and 126 DLB (see Table 1). For testing on external datasets, we used 528 CN, 488 AD, 90 bvFTD, 44 SD, 40 PNFA, 67 PSP-RS, and 47 DLB (see Table 1). The cognitive tree used here is identical to that of the multiclass experiment; however, for each binary comparison, only the distances to the relevant branches were computed in order to convert the outputs into binary classification scores (i.e., patient vs. control). **Table S7:** Results of binary classification task on motor disorders using our lifespan tree method. For training, we used 91 PSP, 126 DLB, 133 PD, and 21 MSA (see Table 1). For testing on external datasets, we used 67 PSP, 47 DLB, 333 PD, and 117 MSA (see Table 1). The motor tree used here is identical to that of the multiclass experiment; however, for each binary comparison, only the distances to the relevant branches were computed in order to convert the outputs into binary classification scores (i.e., patient vs. control).