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# Asynchronous neural maturation predicts transition to psychosis

Anton Iftimovici, Julie Bourgin, Josselin Houenou, Olivier Gay, Antoine Grigis, Julie Victor, Boris Chaumette, Marie-Odile Krebs, Edouard Duchesnay

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## To cite this version:

Anton Iftimovici, Julie Bourgin, Josselin Houenou, Olivier Gay, Antoine Grigis, et al. Asynchronous neural maturation predicts transition to psychosis. Psychiatry and Clinical Neurosciences, 2023, 78 (2), pp.104-112. 10.1111/pcn.13612. hal-04995751

HAL Id: hal-04995751

https://hal.science/hal-04995751v1

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# Asynchronous neural maturation predicts transition to psychosis

Anton Iftimovici, MD, PhD,<fnref n="1" />,<fnref n="2" />,<fnref n="3" />* Julie Bourgin, MD, PhD,<fnref n="4" /> Josselin Houenou, MD, PhD,<fnref n="1" />,<fnref n="5" /> Olivier Gay, MD, PhD,<fnref n="2" />
Antoine Grigis, PhD,<fnref n="1" /> Julie Victor, MS,<fnref n="1" /> Boris Chaumette, MD, PhD,<fnref n="2" />,<fnref n="3" /> Marie-Odile Krebs, MD, PhD,<fnref n="2" />,<fnref n="3" />
Edouard Duchesnay, PhD <fnref n="1" /> and for the ICAAR-plus Study Group

**Aim:** Neuroimaging-based machine-learning predictions of psychosis onset rely on the hypothesis that structural brain anomalies may reflect the underlying pathophysiology. Yet, current predictors remain difficult to interpret in light of brain structure. Here, we combined an advanced interpretable supervised algorithm and a model of neuroanatomical age to identify the level of brain maturation of the regions most predictive of psychosis.

**Methods:** We used the voxel-based morphometry of a healthy control dataset (N = 2024) and a prospective longitudinal UHR cohort (N = 82), of which 27 developed psychosis after one year. In UHR, psychosis was predicted at one year using Elastic-Net-Total-Variation (Enet-TV) penalties within a five-fold cross-validation, providing an interpretable map of distinct predictive regions. Using both the whole brain and each predictive region separately, a brain age predictor was then built and validated in 1605 controls, externally tested in 419 controls from an independent cohort, and applied in UHR. Brain age gaps were computed as the

difference between chronological and predicted age, providing a proxy of whole-brain and regional brain maturation.

**Results:** Psychosis prediction was performant with 80 ± 4% of area-under-curve and 69 ± 5% of balanced accuracy (P < 0.001), and mainly leveraged volumetric increases in the ventromedial prefrontal cortex and decreases in the left precentral gyrus and the right orbitofrontal cortex. These regions were predicted to have delayed and accelerated maturation patterns, respectively.

**Conclusion:** By combining an interpretable supervised model of conversion to psychosis with a brain age predictor, we showed that inter-regional asynchronous brain maturation underlines the predictive signature of psychosis.

**Keywords:** brain age, machine-learning, neurodevelopment, psychosis, voxel-based morphometry.

http://onlinelibrary.wiley.com/doi/10.1111/pcn.13612/full

Psychosis is a common illness occurring in adolescence and young adulthood,<fnref n="1" /> the socio-functional and economic impact of which can be prevented by early intervention.<fnref n="2" />⁻<fnref n="4" /> However, although subjects at ultra-high-risk for psychosis (UHR) can be routinely screened in clinical practice,<fnref n="5" /> it is not possible to distinguish between those who will convert to psychosis, 25% at 3 years,⁶ and those who will not. Biomarker development for outcome prediction has therefore become an important focus of research in the last decade.⁷

In this context, structural neuroimaging constitutes a promising tool because variations in brain development may reflect the gene–environment interactions underlying psychosis.⁸ Widespread lower cortical thickness is found among UHR subjects compared to healthy controls, with thinner fusiform, superior temporal, and paracentral cortices among future converters compared to non-converters.⁹ However, these group-level differences describe only an average patient modeled from variation in an entire sample. Interindividual heterogeneity precludes their use alone to differentiate outcomes.¹⁰ Moreover, it is unclear whether such brain anomalies result from abnormal dysmaturational

trajectories that started early on,¹¹,¹² or neuroprogressive processes related to more recent accelerated aging due to biological stress.¹³,¹⁴ Machine-learning strategies may provide new means of exploring these questions by leveraging the best combination of features to predict individual outcome.¹⁵ Only a handful of structural imaging studies used these techniques to determine prognosis in UHR populations,¹⁶⁻²⁰ while others aimed at predicting a functional or symptomatic course.¹⁶,²¹⁻²³ The most robust predictor of psychosis to date, applied to voxel-based morphometry (VBM), showed a 70.7% balanced accuracy (BACC) and 70% area-under-the-curve (AUC) in a sample with 25 converters and 217 non-converters, including both clinical high risk subjects (N = 167) and recent-onset depression, in the “PRONIA plus” cohort.¹⁶

Beyond accuracy, the relevance of searching for interpretable biomarkers has been debated against pragmatic approaches focusing on prediction performance only.²⁴,²⁵ The development of interpretable neuroanatomical-based predictors could uncover structural networks underlying disease pathophysiology, but the resulting weighted brain maps are made of thousands of features that are difficult to interpret.

<footnote n="1">¹ NeuroSpin, CEA, Université Paris-Saclay, Gif-sur Yvette, France</footnote>

<footnote n="2">² Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Team “Pathophysiology of Psychiatric Disorders”, GDR 3557-Institut de Psychiatrie, Paris, France</footnote>

<footnote n="3">³ GHU Paris Psychiatrie et Neurosciences, Pôle hospitalo-universitaire d’évaluation, prévention, et innovation thérapeutique (PEPIT), Paris, France</footnote>

<footnote n="4">⁴ GHNE – Site de Orsay. Domaine du Grand Mesnil, Bures-sur-Yvette, France</footnote>

<footnote n="5">⁵ APHP, CHU Mondor, DMU Impact, INSERM U955 Team 15 “Neuropsychiatrie Translationnelle”, IMRB, Créteil, France</footnote>

<footnote>* Correspondence: Email: anton.iftimovici@inserm.fr</footnote>
Dimensionality reduction, such as principal component analysis, has been used to decrease collinearity, but it does not improve interpretability, as each of the discrete principal components results from a weighted combination of all features.²⁶ Another solution is to keep features in their initial space and apply regularization through penalties limiting model complexity, by shrinking feature weight magnitude (ridge penalty – L2), selecting a reduced set of features (lasso penalty – L1),²⁷ or by exploiting biological priors resulting from the neighborhood structure of the voxels naturally encoded in the three-dimensional image acquisition grid. This third regularization enforces spatial smoothness, while simultaneously segmenting predictive regions from the background.²⁸ This can be obtained with the “total variation” penalty combined with L1-L2 (a.k.a. ElasticNet) penalties into logistic regression (Enet-TV-LR) to produce coefficient maps of clearly delimited regions of adjacent voxels.²⁹,³⁰

However, interpretation of any predictive brain map of weights is not straightforward: to amplify the signal of interest (i.e. disease outcome), the model increases weights in regions that are specifically associated with the disease but also in regions that can be used to suppress non-specific sources of variability (age, etc.). Large positive or negative weights could then represent noise suppression instead of pathophysiologically relevant signals.³¹ Haufe and colleagues therefore distinguished *backward models*, expressing a variable of interest as a function of the data (e.g. multivariate classifiers) and whose parameters are not directly interpretable, from *forward models*

expressing the data as a function of underlying variables (e.g. general linear models), with interpretable parameters. They demonstrated that it is possible to obtain forward interpretable maps from backward predictive patterns.³¹ Such interpretable models may provide insight into the mechanisms of the emergence of psychosis.

In this work, first we predicted conversion to psychosis using VBM in a longitudinal UHR cohort (N = 82), of whom 27 converted after one year. We used a supervised model (Enet-TV-LR) to automatically identify regions predictive of conversion to psychosis. We then did a backward to forward transformation to obtain interpretable areas. Second, we analyzed the pathophysiological relevance of the resulting forward maps, one region of interest relative to another, and tested their dependence on psychosis in light of possible non-specific effects of depression, cannabis, tobacco, and medication on brain structure.³²⁻³⁴ We then built a predictor of brain age using a large dataset of healthy controls (N = 2024), to compare neural maturation between future converters and non-converters, both at whole-brain level and in each region predictive of psychosis (Fig. 1).

## Methods

### Participants

At-risk individuals below 30 years old were enrolled in a prospective multicenter cohort from Parisian clinical settings (ICAAR-plus, PI Pr MO Krebs). All individuals were assessed with the Comprehensive

<figure><img src="image_5.png" /><figcaption>**Fig. 1** Study design. (1) Prediction of conversion to psychosis using voxel-based morphometry (VBM) in UHR, with a resulting signature of anatomically meaningful patterns. (2) VBM-based prediction of whole-brain age. (3) Applying the brain age predictor to the whole-brain of UHR provided a whole brain age gap for each UHR subject. (4) After filtering using the anatomical patterns predictive of psychosis, regional brain age predictors of these patterns were also built. (5) Applying the regional brain age predictors to the UHR provided a regional brain age gap for each psychosis-predictive region of each UHR subject. Color bars correspond to z-scores.</figcaption></figure>
Assessment of At Risk Mental States (CAARMS).³⁵ After baseline assessment, a consensus meeting for best estimated diagnosis was held, and help-seekers were classified as at-risk for psychosis stage IA or stage IB according to the staging model distinction.³⁶ Stage IA included patients with mild or non-specific symptoms of psychosis or severe mood disorder, and mild functional change. Stage IB included patients with moderate subthreshold symptoms and moderate functional change. Inclusion criteria were an age less than 30 years old, alterations in global functioning (Social and Occupational Functioning Assessment Scale score < 70) during the past year, which were associated with psychiatric symptoms and/or subjective cognitive complaints. Exclusion criteria included manifest symptoms of psychosis (fulfilling DSM-IV criteria), or other established psychiatric diagnoses (pervasive developmental disorder, bipolar disorder, obsessive compulsive disorder), serious or non-stabilized somatic and neurological disorders, head injury and IQ below 70. Psychotic conversion was characterized using the CAARMS-defined psychosis onset threshold (i.e., supra-threshold psychotic symptoms – thought content, perceptual abnormalities and/or disorganized speech – present for more than one week). Individuals who reached the threshold during the one-year follow-up were considered converters and individuals who recovered or displayed persistent subthreshold symptoms were called non-converters. The study was approved by the institutional ethics committees (Comités de protection des personnes, Ile-de-France III and IV, Paris, France) and it conforms to the provisions of the Declaration of Helsinki. Written informed consent was obtained from all participants or their legal representatives. Demographic and clinical data is described in Table 1. UHR T1-weighted MRI was obtained on 3T scanners. T1-weighted structural brain imaging of healthy controls aged between 15 and 90 years old was gathered from four public datasets: LOCALIZER (N = 71),³⁷ IXI (N = 557) (brain-development.org/ixi-dataset/), Schizconnect (N = 419),³⁸ and the International Consortium for Brain Mapping (N = 977).³⁹ VBM preprocessing is detailed in the Supporting Information.

## Univariate analysis

T-tests assessed associations between each voxel and outcome, and between regional mean voxels' peaks and outcome. They were corrected with non-parametric permutation-based estimations of adjusted *P*-values obtained with the max-T procedure, resulting in family-wise error rate (FWER)-corrected *P*-values.⁴⁰

## Supervised analysis

Classification of outcome among UHR, between future converters and non-converters to psychosis was first performed with L2-regularized linear logistic regression (L2-LR) and L1-regularized linear logistic regression (L1-LR), implemented in the scikit-learn python library and applied to the entire voxel-based morphometry maps.⁴¹ Hyperparameters selection was conducted using an inner 5-fold cross-validation (5CV) loop, on training samples only, over a grid of parameters. For Enet-TV-LR, the three regularization parameters (controlling L1, L2, and TV penalties) were reparameterized as a fixed global penalty of 0.1 acting on two ratios: (i) L1-over-L2 ratio controlling the amount of sparsity and (ii) TV-over-L2 ratio controlling spatial smoothness. L1-over-L2 ratio was fixed to 0.1 and TV-over-L2 ratio to 1. This parameter setting was determined by a previous sensitivity analysis conducted on independent datasets.⁴² All processing steps (including residualization for site, age, and sex) were nested inside the cross-validation (Supporting Information and Fig. S1.). Classifier performances were assessed by computing the 5-fold average BACC and AUC. To measure the significance of prediction scores against chance level, we used (i) a one-sample t-test (with a null hypothesis of 0.5) of the average across folds and (ii) we compared the observed AUC to the AUC obtained over 20 runs of the entire procedure on randomly permuted targets, simulating the null hypothesis. The Enet-TV-LR algorithm can provide coefficient maps of well delimited regions most predictive of psychosis, which are used for further analysis.

<table><caption>**Table 1.** Demographic and clinical characteristics of the cross-sectional baseline brain imaging dataset.</caption><thead><tr><th rowspan="3">Measure</th><th colspan="2">UHR baseline cohort</th><th colspan="2">T-test X<sup>2</sup> for proportions</th></tr><tr><th>Converters (27)</th><th>Non-Converters (55)</th><th rowspan="2">Test statistic</th><th rowspan="2">P-value</th></tr><tr><th>mean ± std</th><th>mean ± std</th></tr></thead><tbody><tr><td><strong>Demographics</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Age at baseline</td><td>20.7 ± 1.9</td><td>22.8 ± 3.0</td><td>-3.44</td><td><strong>0.001</strong></td></tr><tr><td>Sex (F to M)</td><td>8 to 19</td><td>24 to 31</td><td>2.68</td><td>0.10</td></tr><tr><td><strong>Symptoms</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>MADRS</td><td>21.0 ± 7.5</td><td>21.6 ± 10.2</td><td>-0.28</td><td>0.78</td></tr><tr><td>PANSS total</td><td>72.8 ± 14.9</td><td>64.8 ± 17.4</td><td>2.06</td><td><strong>0.042</strong></td></tr><tr><td>Positive</td><td>15.6 ± 5.6</td><td>12.1 ± 4.5</td><td>3.12</td><td><strong>0.003</strong></td></tr><tr><td>Negative</td><td>16.5 ± 6.6</td><td>15.1 ± 6.8</td><td>0.89</td><td>0.36</td></tr><tr><td>Desorganization</td><td>6.6 ± 2.5</td><td>5.3 ± 2.1</td><td>2.47</td><td><strong>0.016</strong></td></tr><tr><td>SOFAS</td><td>49.0 ± 9.7</td><td>48.8 ± 9.9</td><td>0.11</td><td>0.91</td></tr><tr><td><strong>Treatment</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Antipsychotic use (%)</td><td>22.2 (6/27)</td><td>12.7 (7/55)</td><td>2.49</td><td>0.11</td></tr><tr><td>Chlorpromazine eq. (mean)</td><td>87.7 ± 44.4</td><td>94.8 ± 44.0</td><td>0.84</td><td>0.41</td></tr><tr><td><strong>Substance use</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Alcohol last month (%)</td><td>48.1 (13/27)</td><td>34.5 (19/55)</td><td>2.24</td><td>0.13</td></tr><tr><td>Tobacco last month (%)</td><td>59.3 (16/27)</td><td>38.2 (21/55)</td><td>4.56</td><td><strong>0.032</strong></td></tr><tr><td>Cannabis last month (%)</td><td>48.1 (13/27)</td><td>23.6 (12/53)</td><td>8.37</td><td><strong>0.004</strong></td></tr></tbody></table>

Chlorpromazine eq., Chlorpromazine equivalent; MADRS, Montgomery-Åsberg Depression Rating Scale; PANSS, Positive And Negative Syndrome Scale; SOFAS, Social and Occupational Functioning Assessment Scale.
## Predictive “backward” maps

To highlight the most stable voxels, we computed the Z-score map of predictive coefficients across folds. A very stringent threshold of 3 was applied to the Z-score map, identifying voxels with 300% signal-to-noise ratio, and coefficients' probability to be null lower than 1%. This Z-score map constituted the final stable predictive signature.

## Feature importance

Region importance was defined as the predictive power of each region compared to the others. We first computed AUC after using only that region's voxels as input in the 5CV ("AUC reg"). Secondly, we computed AUC of the 5CV prediction while masking voxels in this region ("AUC loss"). Thirdly, a Lasso Least Angle Regression (Lasso-LARS) algorithm was used to construct a path of nested models selecting an increasing number of regions with less importance. The average selection rank ("LARS Rank") across the 5CV provided an insight of the region's importance.

## Interpretable “forward” maps

For each backward model $\hat{Y} = \beta X$ ($\hat{Y}$ being the predicted scores used in the logistic function, X the matrix of voxels, and β the vector of weights),

we obtained the parameters of its unique corresponding forward model by computing the covariances between columns of X and $\hat{Y}$.³¹ Each forward weighted region was regressed on psychosis outcome, with age, sex, site, cannabis use, medication, and depression as covariates. Reversely, conversion was regressed on the combination of regions and the same covariates to test the contribution of each region to psychosis risk. Independence of the regions as explanatory variables was tested with pairwise Pearson correlations. Normal distribution of residuals was assessed with the Jarque-Bera test.⁴³

## Regional maturation

To construct a normative model of global and regional brain volumes, we built a whole-brain-based and five region-based brain age predictors, each leveraging exclusively the brain volume corresponding to one of the five predictive regions. For each of the six predictors, the training set included 1605 controls (ICBM, IXI, LOCALIZER), and the testing set 419 controls (SCHIZCONNECT) (Fig. S2). Preprocessing steps (residualization for sex and site, and standard scaling) were done separately in the training and testing set. We applied a penalized ridge regression with an in-built cross-validation to tune the hyper-parameter, trying values from $10^{-5}$ to $10^5$ with ×10 step, in the training set. Goodness-of-fit in the control testing set was

<figure><img src="image_6.png" /><figcaption>**Fig. 2** Coefficient maps. Z-scores of predictive coefficient maps (average on 5 CV loops) in the three models, (a) with L1 penalty (scattered pattern), (b) with L2 penalty (dense pattern), (c) with Enet-TV penalty. (d, e) Comparison between the observed AUC and BACC obtained with the Enet-TV algorithm ("True") and the AUC and BACC over 20 runs after random permutations of outcome labels simulating the null hypothesis. (f) Feature importance. The more a region is important, the more to the left it is. From left to right, AUC after prediction based on each separate region independently (represented as 1 - AUC), AUC after prediction based on all regions but the one of interest (loss of ROC-AUC), and the LARS rank. Color bars correspond to z-scores.</figcaption></figure>
measured with Pearson's correlation (R), mean absolute error (MAE), and root mean squared error (RMSE). The average prediction error in control test subjects under 30 was used as a constant that was subtracted from all predictions in the UHR to account for regression toward the mean (Supporting Information).44-46 Brain age gaps (BAG) were computed as the difference between chronological and predicted age. BAG were compared with T-tests between bootstrapped distributions in each group. The contribution of cannabis, tobacco, and medication to BAG variation was tested following "BAG ~ intercept + outcome + cannabis + tobacco + medication", with quantile regression because of no residual normality on the Jarque-Bera test. For each region and each subject across time, the distribution of the peak voxel linked to disease signal was illustrated through locally weighted scatterplot smoothing (lowess).

# Results

## Prediction performance and feature importance

Enet-TV-LR prediction performed with an area-under-the-curve (AUC) of 80 ± 4% and BACC of 69 ± 5% (*P* < 0.001). Average AUC after 20 random label permutations was 49 ± 9% and BACC 49 ± 7%, i.e. no better than chance. Enet-TV-LR significantly outperformed L1-penalized (AUC = 71 ± 18%, BACC = 67 ± 16%) and L2-penalized logistic regression (AUC = 66 ± 11%, BACC = 61 ± 11%) (Fig. 2a-e). Enet-TV highlighted five regions of interest, defined by their maximum peaks: ventromedial prefrontal cortex (vmPFC), right orbitofrontal cortex (rOFC), left precentral gyrus (IPG), left posterior cingulate gyrus (lpCG), and right putamen (rP). Univariate analysis of association between voxels and outcome did not yield any significant result after correction for multiple testing (Table 2). All measures of feature importance highlighted the primary role of vmPFC and IPC, and to a lesser extent, of rOFC to predict the transition. lpCG and rP involvement was not significant (Fig. 2f, Table 2).

## Interpretation of forward features

Mean forward weights showed that psychosis was predicted by a gray matter (GM) increase in vmPFC and lpCG, and a GM decrease in IPG, rOFC and rP (Table 2, Fig. S3). The associations between psychosis and variations in each region, except for rP, remained significant after correction for age, sex, site, medication, cannabis, and depression (Table S1., Fig. S4). None of the five regions was significantly correlated to the others, after correction for multiple testing (Fig. S5). All regions, except for rP, independently explained conversion to psychosis by increases in vmPFC and lpCG, and decreases in IPG and rOFC (Table S2).

## Regional maturation study

We focused only on the whole brain and on the three regions significantly involved in the prediction after feature importance analysis. In the healthy control test set, all regions allowed to build accurate predictors of age. The whole-brain based predictor yielded the highest performance (*r* = 0.86, *P* < 2.22e-16, MAE = 4.82, RMSE = 6.05), followed by the vmPFC-based predictor (*r* = 0.73, *P* = 2.22e-16, MAE = 7.22, MSE = 8.95), the IPG-based predictor (*r* = 0.62, *P* = 2.22e-16, MAE = 7.55, MSE = 7.46), and the rOFC-based predictor (*r* = 0.54, *P* = 2.22e-16, MAE = 10.64, MSE = 12.81). Brain age gaps (BAG) were computed as the difference between chronological and predicted age. At the whole-brain level, converters showed an accelerated brain maturation (BAG = +0.88 years) and non-converters a delayed maturation (BAG = -0.28 years), compared to controls who had a BAG closer to null (+0.31 years). All pairwise differences between groups were significant (*P* < 1e-20) (Fig. 3 and Table 2).

At the regional brain level, results are summarized in Fig. 4 and Table 2. A similar accelerated brain maturation was found at the regional level in converters compared to non-converters in IPG

<table><caption>**Table 2.** Feature importance for prediction and analysis of regional neural maturation</caption><thead><tr><th></th><th>Measure</th><th>vmPFC</th><th>IPG</th><th>rOFC</th><th>rP</th><th>lpCG</th><th>Whole brain</th></tr></thead><tbody><tr><td>Feature importance</td><td>Size</td><td>4763</td><td>586</td><td>1288</td><td>950</td><td>662</td><td>330,850</td></tr><tr><td rowspan="3">MNI coord. (mm)</td><td>x</td><td>0</td><td>-42</td><td>27</td><td>17</td><td>-10</td><td>-</td></tr><tr><td>y</td><td>16</td><td>-16</td><td>32</td><td>19</td><td>-39</td><td>-</td></tr><tr><td>z</td><td>-13</td><td>45</td><td>-16</td><td>2</td><td>53</td><td>-</td></tr><tr><td rowspan="2">Univariate analysis</td><td>T</td><td>4.29</td><td>-3.23</td><td>-2.13</td><td>-1.09</td><td>3.08</td><td>-</td></tr><tr><td>P-value</td><td>0.34</td><td>0.99</td><td>1</td><td>1</td><td>1</td><td>-</td></tr><tr><td rowspan="2">Multivariate analysis</td><td>AUC Loss</td><td>-0.15</td><td>-0.02</td><td>-0.01</td><td>-0.0006</td><td>0.00</td><td>-</td></tr><tr><td>LARS</td><td>0.00 ± 0.00</td><td>1.17 ± 0.41</td><td>2.00 ± 0.63</td><td>2.83 ± 0.41</td><td>4.00 ± 0.00</td><td>-</td></tr><tr><td rowspan="2">Backward model weights</td><td>Peak ± std</td><td>14e-4 ± 2e-4</td><td>-7e-4 ± 1e-4</td><td>-1e-4 ± 0.00</td><td>-2e-04 ± 0.00</td><td>3e-06 ± 0.00</td><td>-</td></tr><tr><td>Mean ± std</td><td>6e-4 ± 1e-4</td><td>-6e-4 ± 1e-4</td><td>-1e-4 ± 0.00</td><td>-2e-04 ± 0.00</td><td>3e-06 ± 0.00</td><td>-</td></tr><tr><td rowspan="2">Forward model weights</td><td>Peak ± std</td><td>0.95 ± 0.06</td><td>-0.80 ± 0.05</td><td>-0.59 ± 0.07</td><td>-0.51 ± 0.03</td><td>0.82 ± 0.03</td><td>-</td></tr><tr><td>Mean ± std</td><td>0.29 ± 0.06</td><td>-0.27 ± 0.05</td><td>-0.12 ± 0.05</td><td>0.02 ± 0.04</td><td>0.15 ± 0.06</td><td>-</td></tr><tr><td rowspan="2">Brain age gap analysis</td><td rowspan="2">UHR-C Brain age gap</td><td>Median</td><td>-0.39</td><td>2.23</td><td>6.46</td><td>3.02</td><td>-1.59</td><td>0.88</td></tr><tr><td>95%CI</td><td>-1.86:2.99</td><td>0.96:3.94</td><td>3.08:7.68</td><td>-1.76:6.68</td><td>-5.06:1.52</td><td>-0.37:1.8</td></tr><tr><td rowspan="2">UHR-NC Brain age gap</td><td>Median</td><td>3.04</td><td>-0.86</td><td>2.31</td><td>0.75</td><td>-1.32</td><td>-0.28</td></tr><tr><td>95%CI</td><td>0.2:6.11</td><td>-5.59:1.42</td><td>-1.07:3.65</td><td>-0.9:2.93</td><td>-3.95:0.27</td><td>-1.27:0.65</td></tr><tr><td rowspan="2">CTRL Brain age gap</td><td>Median</td><td>0.43</td><td>0.07</td><td>-0.38</td><td>-0.26</td><td>0.93</td><td>0.31</td></tr><tr><td>95%CI</td><td>-1.04:1.40</td><td>-0.75:1.72</td><td>-1.58:0.86</td><td>-0.75:1.75</td><td>-1.05:0.82</td><td>-0.59:0.94</td></tr><tr><td rowspan="3">UHR-C > UHR-NC</td><td>T</td><td>-50.64</td><td>65.40</td><td>69.27</td><td>29.20</td><td>3.22<sup>†</sup></td><td>43.86</td></tr><tr><td>T</td><td>-7.3</td><td>57.42</td><td>140.53</td><td>48.69</td><td>-48.17</td><td>22.53</td></tr><tr><td>T</td><td>53.49</td><td>-27.93</td><td>47.72</td><td>24.37</td><td>-57.29</td><td>-28.67</td></tr><tr><td colspan="8">†All statistic tests were significant with *P* < 10<sup>-3</sup> except for T UHR-C > UHR-NC (*P* = 0.18).</td></tr></tbody></table>
<figure><img src="image_7.png" /><figcaption>**Fig. 3** Whole brain maturation. Whole brain maturation was measured as brain age gap in controls (CTRL), UHR non-converters (UHR-NC), and UHR converters (UHR-C). **** *P*-value <1e−¹⁰.</figcaption></figure>

(+2.23 vs. −0.86 years respectively, with control values in-
between) and rOFC (+6.46 vs. +2.31 years respectively, with con-
trol values below both patient groups). However, there was a del-
ayed brain maturation in converters compared to non-converters in
the vmPFC (−0.39 vs. +3.04 years respectively, with control
values in-between).

These associations between brain maturation and outcome
remained significant after adjusting for confounders (cannabis,
tobacco, medication). Cannabis use was independently associated
with a maturation delay in the whole brain and the IPG. Medication
or tobacco did not significantly affect maturation in the three regions
of interest (Table S3).

**Discussion**

We presented the first *interpretable* supervised algorithm to predict conversion to psychosis at one year, in 82 UHR subjects. Prediction performance showed a good 80% AUC, and its 69% BACC was similar to the performance of the most recent predictor applied to a UHR population.¹⁶ Enet-TV logistic regression provided maps with better readability compared to scattered lasso-based or dense ridge-based regressions. Unlike maps obtained after dimension reduction, Enet-TV features corresponded to localizations in the natural space of the brain, therefore offering a substrate for further regional analysis. Using three measures of feature importance, we hierarchized their relative contribution to prediction (primarily an increased vmPFC, followed by a decreased IPG and a decreased rOFC), before transforming them into clinically interpretable maps.³¹ The resulting weights were used in a linear regression to study the relationship between regions and transition to psychosis. Notably, although the right putamen region served to predict psychosis, disease did not explain direct variation of this region, suggesting that its contribution may be to correct some cohort-related noise. This highlighted the risk of misinterpreting predictive weighted maps, and the importance of their transformation into interpretable patterns. Moreover, we showed that vmPFC, IPG, and rOFC variations each contributed independently to psychosis outcome, while accounting for factors known to affect brain structure: age, sex, medication, cannabis, and depression.³²,³³,⁴⁷

Psychosis was predicted by opposite volumetric variations in
different regions, similarly to recent observations based on
machine-learning strategies using dimensional reduction.¹⁶
Decreased volumes associated with psychosis are common find-
ings at all stages of disease, with a progressive cortical-
to-subcortical and left-to-right GMV loss from UHR stage to

recently diagnosed schizophrenia and chronic disease.⁴⁸ Thinner
paracentral cortex is among three regions strongly associated with
transition to psychosis in UHR subjects,⁹ which is in line with our
results. Orbitofrontal GMV reduction has also consistently been
described across studies of first-episode psychosis, and correlated
with symptom severity and duration of illness.⁴⁹ The ventromedial
prefrontal cortex, whose *increase* was found here to predict psy-
chosis, is also a region whose greater connectivity inside the
default mode network is related to poorer clinical insight in UHR
subjects.⁵⁰ Ventromedial prefrontal GMV increase was further
negatively correlated with executive performance.⁵¹ Perturbation
of this area is particularly meaningful as it is central to the reward
system, and its dysfunction underpinned blunted reward response
and prediction error signals in UHR subjects.⁵²

Finally, to explore the inhomogeneous structural patterns predictive
of psychosis, we used brain age gap analysis. Because variations in vol-
ume due to synaptic pruning or interneuron GABAergic maturation still
occur until late adolescence and early adulthood,⁵³,⁵⁴ we interpreted the
brain age gaps (reflecting volumetric differences) as a proxy of neural
maturation. Converters showed a whole-brain maturational acceleration of
0.88 years, while non-converters had a maturational delay of −0.28 years.
This result extends in late adolescence and young adulthood the only
other similar observation, previously made in adolescents younger than
18 years old.⁵⁵ It also supports the hypothesis that at whole-brain level,
protracted brain maturation may be protective.⁵⁶ However, our results also
showed that there is an inter-regional asynchronous brain maturation asso-
ciated with psychosis, notably with less mature vmPFC and more
mature IPG.

Several considerations need to be made. It has been shown that
small sample sizes may lead to large prediction errors.⁵⁷ However,
our predictor in a sample size of 82 subjects provided a performance
well above the random 50%, with small standard errors. The number
of converters is similar to the most recent studies and reflects the dif-
iculty of building longitudinal prospective cohorts. We also followed
best practice recommendations to prevent overoptimistic results:
nested cross-validation of all processing steps, performance testing in
completely independent left-out sets, balanced class weighting to
account for the higher frequency of non-converters, and random label
permutation testing to check for overfit. The level of interpretation
provided by our model further adds to its external validity. Moreover,
the brain age predictor, which was built on a large independent
dataset of healthy controls, validated the structural differences
between converters and non-converters that were highlighted by the
algorithm predicting psychosis. These volumetric differences could
therefore be interpreted in terms of differential brain aging, and the
fact that some appeared to be delayed while others advanced in age
tentatively suggests a more complex brain maturational trajectory,
which we defined as *asynchronous neural maturation*, rather than
generalized gray matter volumetric decrease. Finally, there is an
apparent paradox in the fact that the higher volumes we found coding
for psychosis risk were described having significant GMV decreases
at later stages of chronic psychosis.⁵⁸ This may be explained by non-
constant brain maturation across disease course,⁵⁹ and the possibility
of common networks between neurodevelopment and neu-
rodegeneration, the regions developing the latest being also the ones
most at-risk of early degeneration.⁶⁰ This may especially hold true for
the ventromedial prefrontal cortex as its decrease may be driven by
greater age, a relationship that was not found in healthy controls.⁵⁸

In conclusion, this study of psychosis prediction demonstrated,
for the first time to our knowledge, that it is possible to obtain pre-
dictions that are both performant and *interpretable*. Bridging the
gap between pragmatic prognostic biomarkers and mechanistically
interpretable ones, we provided a data-driven tool that may bring
pathophysiological insight to classical machine-learning black
boxes, which is crucial for early prevention, detection, and develop-
ment of new therapies. Thus, we showed that inter-regional asyn-
chronous brain maturational patterns were most predictive of
psychosis.
<figure><img src="image_8.png" /><figcaption>**Fig. 4** Regional brain maturation. For each region (left column) and each subject across time, the distribution of the peak voxel linked to disease signal was illustrated compared to the normal variation modeled through locally weighted scatterplot smoothing (lowess) in controls (middle figure). Regional brain maturation was measured as brain age gap in controls (CTRL), UHR non-converters (UHR-NC), and UHR converters (UHR-C) in the five regions predictive of psychosis (right column). IpCG, left posterior cingulate gyrus; IPG, left precentral gyrus; rOFC, right orbitofrontal cortex; rP, right putamen; vmPFC, ventromedial prefrontal cortex. Color bars correspond to z-scores. **** *P*-value <1e-10.</figcaption></figure>

## Acknowledgements

We thank the patients and their families for their participation and their trust. We thank the collaborators of the ICAAR-plus Study Group involved in this work: Claire Daban Huard, Olivier Gay, Emilie Magaud, Célia Jantac Mam-Lam-Fook, Fabrice Rivollier, Marion Plaze, Gilles Martinez, Mathilde Kazes. A special thanks to

Stéphanie Lion for her help in handling the imaging data. Anton Iftimovici received a scholarship from the Fondation pour la Recherche Médicale (FRM FDM201806006059). Boris Chaumette received a grant from the Fondation Bettencourt Schueller (CCA INSERM Bettencourt). This work has also been supported by the French government's "Investissements d'Avenir" programme
(ANR-18-RHUS-0014 PsyCARE), and the French Ministry grant PHRC AOM07-118 (ICAAR), PHRC AOM13-681 (START). The cohorts were promoted by the GHU Paris Psychiatry and Neuroscience. This work also received funding from the French National Research Agency for the project Big2Small (Chair in AI, ANR-19-CHIA-0010-01) and the European Union's Horizon 2020 for the project R-LINK (H2020-SC1-2017, 754907).

## Disclosure statement

The authors declare no conflict of interest.

## Author contributions

A.I., M.O.K. and E.D. designed the study. M.O.K. and E.D. obtained the funding and supervised the study. A.I., J.B., B.C., O.G., J.H., B.C., M.O.K., and E.D. collected the data. J.V. and A.G. performed the preprocessing of the data. A.I. and E.D. analyzed and interpreted the data. A.I. and E.D. drafted the manuscript. All authors contributed to and have approved the final manuscript.

## References

1. Solmi M, Radua J, Olivola M et al. Age at onset of mental disorders worldwide: Large-scale meta-analysis of 192 epidemiological studies. *Mol. Psychiatry* 2021; **27**: 281–295.

2. Zheng Y, Xu T, Zhu Y et al. Cognitive behavioral therapy for prodromal stage of psychosis—Outcomes for transition, functioning, distress, and quality of life: A systematic review and meta-analysis. *Schizophr. Bull.* 2022; **48**: 8–19.

3. Frawley E, Cowman M, Lepage M, Donohoe G. Social and occupational recovery in early psychosis: A systematic review and meta-analysis of psychosocial interventions. *Psychol. Med.* 2023; **53**: 1787–1798.

4. McDaid D, Park A-L, Wahlbeck K. The economic case for the prevention of mental illness. *Annu. Rev. Public Health* 2019; **40**: 373–389.

5. Fusar-Poli P, Salazar de Pablo G, Correll CU et al. Prevention of psychosis: Advances in detection, prognosis, and intervention. *JAMA Psychiatry* 2020; **77**: 755–765.

6. Salazar de Pablo G, Radua J, Pereira J et al. Probability of transition to psychosis in individuals at clinical high risk: An updated meta-analysis. *JAMA Psychiatry* 2021; **78**: 970–978.

7. Lieberman JA, Small SA, Girgis RR. Early detection and preventive intervention in schizophrenia: From fantasy to reality. *Am. J. Psychiatry* 2019; **176**: 794–810.

8. McCutcheon RA, Reis Marques T, Howes OD. Schizophrenia—An Overview. *JAMA Psychiatry* 2019; **77**: 201–210.

9. ENIGMA Clinical High Risk for Psychosis Working Group. Association of Structural Magnetic Resonance Imaging Measures with Psychosis Onset in individuals at clinical high risk for developing psychosis: An ENIGMA working group mega-analysis. *JAMA Psychiatry* 2021; **78**: 753–766.

10. Wolfers T, Doan NT, Kaufmann T et al. Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. *JAMA Psychiatry* 2018; **75**: 1146–1155.

11. Patel PK, Leathem LD, Currin DL, Karlsgodt KH. Adolescent neurodevelopment and vulnerability to psychosis. *Biol. Psychiatry* 2021; **89**: 184–193.

12. Weinberger DR. Future of days past: Neurodevelopment and schizophrenia. *Schizophr. Bull.* 2017; **43**: 1164–1168.

13. Reckziegel R, Czepielewski LS, Hasse-Sousa M et al. Heterogeneous trajectories in schizophrenia: Insights from neurodevelopment and neuroprogression models. *Braz. J. Psychiatry* 2022; **44**: 74–80.

14. Hajek T, Franke K, Kolenic M et al. Brain age in early stages of bipolar disorders or schizophrenia. *Schizophr. Bull.* 2017; **45**: 190–198.

15. Bzdok D, Meyer-Lindenberg A. Machine learning for precision psychiatry: Opportunities and challenges. *Biol. Psychiatry Cogn. Neurosci. Neuroimaging* 2018; **3**: 223–230.

16. Koutsouleris N, Dwyer DB, Degenhardt F et al. Multimodal machine learning workflows for prediction of psychosis in patients with clinical high-risk syndromes and recent-onset depression. *JAMA Psychiatry* 2021; **78**: 195–209.

17. Zarogianni E, Storkey AJ, Borgwardt S et al. Individualized prediction of psychosis in subjects with an at-risk mental state. *Schizophr. Res.* 2019; **214**: 18–23.

18. Koutsouleris N, Riecher-Rössler A, Meisenzahl EM et al. Detecting the psychosis Prodrome across high-risk populations using neuroanatomical biomarkers. *Schizophr. Bull.* 2015; **41**: 471–482.

19. Koutsouleris N, Davatzikos C, Bottlender R et al. Early recognition and disease prediction in the At-risk mental states for psychosis using neurocognitive pattern classification. *Schizophr. Bull.* 2012; **38**: 1200–1215.

20. Koutsouleris N, Meisenzahl EM, Davatzikos C et al. Use of neuroanatomical pattern classification to identify subjects in At-risk mental states of psychosis and predict disease transition. *Arch. Gen. Psychiatry* 2009; **66**: 700–712.

21. de Wit S, Ziermans TB, Nieuwenhuis M et al. Individual prediction of long-term outcome in adolescents at ultra-high risk for psychosis: Applying machine learning techniques to brain imaging data: Individual outcome prediction with MRI. *Hum. Brain Mapp.* 2017; **38**: 704–714.

22. Kambeitz-Ilankovic L, Meisenzahl EM, Cabral C et al. Prediction of outcome in the psychosis prodrome using neuroanatomical pattern classification. *Schizophr. Res.* 2016; **173**: 159–165.

23. Tognin S, Pettersson-Yeo W, Valli I et al. Using structural neuroimaging to make quantitative predictions of symptom progression in individuals at ultra-high risk for psychosis. *Front. Psych.* 2014; **4**: 187.

24. Pine DS, Leibenluft E. Biomarkers with a mechanistic focus. *JAMA Psychiatry* 2015; **72**: 633–634.

25. Paulus MP. Pragmatism instead of mechanism: A call for impactful biological psychiatry. *JAMA Psychiatry* 2015; **72**: 631–632.

26. Nielsen AN, Barch DM, Petersen SE, Schlaggar BL, Greene DJ. Machine learning with neuroimaging: Evaluating its applications in psychiatry. *Biol. Psychiatry Cogn. Neurosci. Neuroimaging* 2020; **5**: 791–798.

27. Lever J, Krzywinski M, Altman N. Regularization. *Nat. Methods* 2016; **13**: 803–804.

28. Dubois M, Hadj-Selem F, Lofstedt T et al. Predictive support recovery with TV-elastic net penalty and logistic regression: An application to structural MRI. *In: International Workshop on Pattern Recognition in Neuroimaging*. Tübingen, Germany, IEEE, 2014; 1–4.

29. de Pierrefeu A, Lofstedt T, Laidi C et al. Interpretable and stable prediction of schizophrenia on a large multisite dataset using machine learning with structured sparsity. *In: 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI)*. Singapore, IEEE, 2018; 1–4.

30. de Pierrefeu A, Fovet T, Hadj-Selem F et al. Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity. *Hum. Brain Mapp.* 2018; **39**: 1777–1788.

31. Haufe S, Meinecke F, Görgen K et al. On the interpretation of weight vectors of linear models in multivariate neuroimaging. *Neuroimage* 2014; **87**: 96–110.

32. Albaugh MD, Ottino-Gonzalez J, Sidwell A et al. Association of Cannabis use during Adolescence with Neurodevelopment. *JAMA Psychiatry* 2021; **78**: 1–11.

33. Chopra S, Fornito A, Francey SM et al. Differentiating the effect of antipsychotic medication and illness on brain volume reductions in first-episode psychosis: A longitudinal, randomised, triple-blind, placebo-controlled MRI study. *Neuropsychopharmacology* 2021; **46**: 1494–1501.

34. Sutherland MT, Riedel MC, Flannery JS et al. Chronic cigarette smoking is linked with structural alterations in brain regions showing acute nicotinic drug-induced functional modulations. *Behav. Brain Funct. BBF* 2016; **12**: 16.

35. Yung AR, Yung AR, Pan Yuen H et al. Mapping the onset of psychosis: The comprehensive assessment of At-risk mental states. *Aust. N. Z. J. Psychiatry* 2005; **39**: 964–971.

36. McGorry PD, Purcell R, Hickie IB, Yung AR, Pantelis C, Jackson HJ. Clinical staging: A heuristic model for psychiatry and youth mental health. *Med. J. Aust.* 2007; **187**: S40–S42.

37. Papadopoulos Orfanos D, Michel V, Schwartz Y et al. The Brainomics/Localizer database. *Neuroimage* 2017; **144**: 309–314.

38. Wang L, Alpert KI, Calhoun VD et al. SchizConnect: Mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration. *Neuroimage* 2016; **124**: 1155–1167.

39. Mazziotta J, Toga A, Evans A et al. A probabilistic atlas and reference system for the human brain: International consortium for brain mapping (ICBM). *Philos. Trans. R. Soc. Lond. B Biol. Sci.* 2001; **356**: 1293–1322.

40. Dudoit S, Shaffer JP, Block JC. Multiple hypothesis testing in microarray experiments. *Stat. Sci.* 2003; **18**: 71–103.
41. Pedregosa F, Varoquaux G, Gramfort A et al. Scikit-learn: Machine learning in python. *J. Mach. Learn. Res.* 2011; **85**: 2825–2830.

42. Duchesnay E. Neuroimaging signatures of brain disorders: Fighting overfitting in predictive models. *HAL Arch. Ouvert*. 2020; **144**: 1–143. https://hal-cea.archives-ouvertes.fr/tel-03408786.

43. Jarque CM, Bera AK. Efficient tests for normality, homoscedasticity and serial independence of regression residuals. *Econ. Lett.* 1980; 255–259.

44. Barnett AG. Regression to the mean: What it is and how to deal with it. *Int. J. Epidemiol.* 2004; **34**: 215–220.

45. Le TT, Kuplicki RT, McKinne BA et al. A nonlinear simulation framework supports adjusting for age when analyzing BrainAGE. *Front. Aging Neurosci.* 2018; **10**: 317.

46. Liang H, Zhang F, Niu X. Investigating systematic bias in brain age estimation with application to post-traumatic stress disorders. *Hum. Brain Mapp.* 2019; **40**: 3143–3152.

47. Howes OD, Murray RM. Schizophrenia: an integrated sociodevelopmental-cognitive model. *Lancet* 2014; **383**: 1677–1687.

48. Liloia D, Brasso C, Cauda F et al. Updating and characterizing neuroanatomical markers in high-risk subjects, recently diagnosed and chronic patients with schizophrenia: A revised coordinate-based meta-analysis. *Neurosci. Biobehav. Rev.* 2021; **123**: 83–103.

49. Vieira S, Gong Q, Scarpazza C et al. Neuroanatomical abnormalities in first-episode psychosis across independent samples: A multi-Centre mega-analysis. Psychological medicine. 51(2):340–350. *Psychol. Med.* 2021; **51**: 340–350.

50. Clark SV, Mittal VA, Bernard JA, Ahmadi A, King TZ, Turner JA. Stronger default mode network connectivity is associated with poorer clinical insight in youth at ultra high-risk for psychotic disorders. *Schizophr. Res.* 2018; **193**: 244–250.

51. Koutsouleris N, Patschurek-Kliche K, Scheuerecker J et al. Neuroanatomical correlates of executive dysfunction in the at-risk mental state for psychosis. *Schizophr. Res.* 2010; **123**: 160–174.

52. Millman ZB, Gallagher K, Demro C et al. Evidence of reward system dysfunction in youth at clinical high-risk for psychosis from two event-related fMRI paradigms. *Schizophr. Res.* 2020; **226**: 111–119.

53. Marín O. Developmental timing and critical windows for the treatment of psychiatric disorders. *Nat. Med.* 2016; **22**: 1229–1238.

54. Uhlhaas PJ, Singer W. Abnormal neural oscillations and synchrony in schizophrenia. *Nat. Rev. Neurosci.* 2010; **11**: 100–113.

55. Chung Y, Addington J, Bearden CE et al. Use of machine learning to determine deviance in neuroanatomical maturity associated with future psychosis in youths at clinically high risk. *JAMA Psychiatry* 2018; **75**: 960–968.

56. Tooley UA, Bassett DS, Mackey AP. Environmental influences on the pace of brain development. *Nat. Rev. Neurosci.* 2021; **22**: 372–384.

57. Varoquaux G. Cross-validation failure: Small sample sizes lead to large error bars. *Neuroimage* 2018; **180**: 68–77.

58. Zhang W, Deng W, Yao L et al. Brain structural abnormalities in a Group of Never-Medicated Patients with Long-Term Schizophrenia. *Am. J. Psychiatry* 2015; **172**: 995–1003.

59. Schnack HG, van Haren NEM, Nieuwenhuis M, Hulshoff Pol HE, Cahn W, Kahn RS. Accelerated brain aging in schizophrenia: A longitudinal pattern recognition study. *Am. J. Psychiatry* 2016; **173**: 607–616.

60. Douaud G, Groves AR, Tamnes CK et al. A common brain network links development, aging, and vulnerability to disease. *Proc. Natl. Acad. Sci.* 2014; **111**: 17648–17653.

## Supporting Information

Additional supporting information can be found online in the Supporting Information section at the end of this article.