Brain morphometric features predict medication response in youth with bipolar disorder: a prospective randomized clinical trial.

BACKGROUND: Identification of treatment-specific predictors of drug therapies for bipolar disorder (BD) is important because only about half of individuals respond to any specific medication. However, medication response in pediatric BD is variable and not well predicted by clinical characteristics. METHODS: A total of 121 youth with early course BD (acute manic/mixed episode) were prospectively recruited and randomized to 6 weeks of double-blind treatment with quetiapine (n = 71) or lithium (n = 50). Participants completed structural magnetic resonance imaging (MRI) at baseline before treatment and 1 week after treatment initiation, and brain morphometric features were extracted for each individual based on MRI scans. Positive antimanic treatment response at week 6 was defined as an over 50% reduction of Young Mania Rating Scale scores from baseline. Two-stage deep learning prediction model was established to distinguish responders and non-responders based on different feature sets. RESULTS: Pre-treatment morphometry and morphometric changes occurring during the first week can both independently predict treatment outcome of quetiapine and lithium with balanced accuracy over 75% (all p < 0.05). Combining brain morphometry at baseline and week 1 allows prediction with the highest balanced accuracy (quetiapine: 83.2% and lithium: 83.5%). Predictions in the quetiapine and lithium group were found to be driven by different morphometric patterns. CONCLUSIONS: These findings demonstrate that pre-treatment morphometric measures and acute brain morphometric changes can serve as medication response predictors in pediatric BD. Brain morphometric features may provide promising biomarkers for developing biologically-informed treatment outcome prediction and patient stratification tools for BD treatment development.

Connectome gradient dysfunction in major depression and its association with gene expression profiles and treatment outcomes.

Patients with major depressive disorder (MDD) exhibit concurrent deficits in both sensory and higher-order cognitive processing. Connectome studies have suggested a principal primary-to-transmodal gradient in functional brain networks, supporting the spectrum from sensation to cognition. However, whether this gradient structure is disrupted in patients with MDD and how this disruption associates with gene expression profiles and treatment outcome remain unknown. Using a large cohort of resting-state fMRI data from 2227 participants (1148 MDD patients and 1079 healthy controls) recruited at nine sites, we investigated MDD-related alterations in the principal connectome gradient. We further used Neurosynth, postmortem gene expression, and an 8-week antidepressant treatment (20 MDD patients) data to assess the meta-analytic cognitive functions, transcriptional profiles, and treatment outcomes related to MDD gradient alterations, respectively. Relative to the controls, MDD patients exhibited global topographic alterations in the principal primary-to-transmodal gradient, including reduced explanation ratio, gradient range, and gradient variation (Cohen's d = 0.16-0.21), and focal alterations mainly in the primary and transmodal systems (d = 0.18-0.25). These gradient alterations were significantly correlated with meta-analytic terms involving sensory processing and higher-order cognition. The transcriptional profiles explained 53.9% variance of the altered gradient pattern, with the most correlated genes enriched in transsynaptic signaling and calcium ion binding. The baseline gradient maps of patients significantly predicted symptomatic improvement after treatment. These results highlight the connectome gradient dysfunction in MDD and its linkage with gene expression profiles and clinical management, providing insight into the neurobiological underpinnings and potential biomarkers for treatment evaluation in this disorder.

Brain age prediction: A comparison between machine learning models using region- and voxel-based morphometric data.

Brain morphology varies across the ageing trajectory and the prediction of a person's age using brain features can aid the detection of abnormalities in the ageing process. Existing studies on such "brain age prediction" vary widely in terms of their methods and type of data, so at present the most accurate and generalisable methodological approach is unclear. Therefore, we used the UK Biobank data set (N = 10,824, age range 47-73) to compare the performance of the machine learning models support vector regression, relevance vector regression and Gaussian process regression on whole-brain region-based or voxel-based structural magnetic resonance imaging data with or without dimensionality reduction through principal component analysis. Performance was assessed in the validation set through cross-validation as well as an independent test set. The models achieved mean absolute errors between 3.7 and 4.7 years, with those trained on voxel-level data with principal component analysis performing best. Overall, we observed little difference in performance between models trained on the same data type, indicating that the type of input data had greater impact on performance than model choice. All code is provided online in the hope that this will aid future research.

Neuroanatomical abnormalities in first-episode psychosis across independent samples: a multi-centre mega-analysis.

BACKGROUND: Neuroanatomical abnormalities in first-episode psychosis (FEP) tend to be subtle and widespread. The vast majority of previous studies have used small samples, and therefore may have been underpowered. In addition, most studies have examined participants at a single research site, and therefore the results may be specific to the local sample investigated. Consequently, the findings reported in the existing literature are highly heterogeneous. This study aimed to overcome these issues by testing for neuroanatomical abnormalities in individuals with FEP that are expressed consistently across several independent samples. METHODS: Structural Magnetic Resonance Imaging data were acquired from a total of 572 FEP and 502 age and gender comparable healthy controls at five sites. Voxel-based morphometry was used to investigate differences in grey matter volume (GMV) between the two groups. Statistical inferences were made at p < 0.05 after family-wise error correction for multiple comparisons. RESULTS: FEP showed a widespread pattern of decreased GMV in fronto-temporal, insular and occipital regions bilaterally; these decreases were not dependent on anti-psychotic medication. The region with the most pronounced decrease - gyrus rectus - was negatively correlated with the severity of positive and negative symptoms. CONCLUSIONS: This study identified a consistent pattern of fronto-temporal, insular and occipital abnormalities in five independent FEP samples; furthermore, the extent of these alterations is dependent on the severity of symptoms and duration of illness. This provides evidence for reliable neuroanatomical alternations in FEP, expressed above and beyond site-related differences in anti-psychotic medication, scanning parameters and recruitment criteria.

Autobiographical memory as a latent vulnerability mechanism following childhood maltreatment: Association with future depression symptoms and prosocial behavior.

OBJECTIVES: Childhood maltreatment is associated with altered neural reactivity during autobiographical memory (ABM) recall and a pattern of overgeneral memory (OGM). Altered ABM and OGM have been linked with psychopathology and poorer social functioning. The present study investigated the association between altered ABM and subsequent socio-emotional functioning (measured two years later) in a sample of adolescents with (N = 20; maltreatment group, MT) and without (N = 17; non-MT group) documented childhood maltreatment histories. METHOD: At baseline, adolescents (aged 12.6 ± 1.45 years) were administered the Autobiographical Memory Test to measure OGM. Participants also recalled specific ABMs in response to emotionally valenced cue words during functional MRI. Adolescents in both groups underwent assessments measuring depressive symptoms and prosocial behavior at both timepoints. Regression analyses were carried out to predict outcome measures at follow-up controlling for baseline levels. RESULTS: In the MT group, greater OGM at baseline significantly predicted reduced prosocial behavior at follow-up and showed a trend level association with elevated depressive symptoms. Patterns of altered ABM-related brain activity did not significantly predict future psycho-social functioning. CONCLUSIONS: The current findings highlight the potential value of OGM as a cognitive mechanism that could be targeted to reduce risk of depression in adolescents with prior histories of maltreatment.

Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual.

Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain abnormalities. In the past few years, there has been growing interest in the application of machine learning techniques to neuroimaging data for the diagnostic and prognostic assessment of this disorder. However, the vast majority of studies published so far have used either structural or functional neuroimaging data, without accounting for the multimodal nature of the disorder. Structural MRI and resting-state functional MRI data were acquired from a total of 295 patients with schizophrenia and 452 healthy controls at five research centers. We extracted features from the data including gray matter volume, white matter volume, amplitude of low-frequency fluctuation, regional homogeneity and two connectome-wide based metrics: structural covariance matrices and functional connectivity matrices. A support vector machine classifier was trained on each dataset separately to distinguish the subjects at individual level using each of the single feature as well as their combination, and 10-fold cross-validation was used to assess the performance of the model. Functional data allow higher accuracy of classification than structural data (mean 82.75% vs. 75.84%). Within each modality, the combination of images and matrices improves performance, resulting in mean accuracies of 81.63% for structural data and 87.59% for functional data. The use of all combined structural and functional measures allows the highest accuracy of classification (90.83%). We conclude that combining multimodal measures within a single model is a promising direction for developing biologically informed diagnostic tools in schizophrenia.

Detecting schizophrenia at the level of the individual: relative diagnostic value of whole-brain images, connectome-wide functional connectivity and graph-based metrics.

BACKGROUND: Previous studies using resting-state functional neuroimaging have revealed alterations in whole-brain images, connectome-wide functional connectivity and graph-based metrics in groups of patients with schizophrenia relative to groups of healthy controls. However, it is unclear which of these measures best captures the neural correlates of this disorder at the level of the individual patient. METHODS: Here we investigated the relative diagnostic value of these measures. A total of 295 patients with schizophrenia and 452 healthy controls were investigated using resting-state functional Magnetic Resonance Imaging at five research centres. Connectome-wide functional networks were constructed by thresholding correlation matrices of 90 brain regions, and their topological properties were analyzed using graph theory-based methods. Single-subject classification was performed using three machine learning (ML) approaches associated with varying degrees of complexity and abstraction, namely logistic regression, support vector machine and deep learning technology. RESULTS: Connectome-wide functional connectivity allowed single-subject classification of patients and controls with higher accuracy (average: 81%) than both whole-brain images (average: 53%) and graph-based metrics (average: 69%). Classification based on connectome-wide functional connectivity was driven by a distributed bilateral network including the thalamus and temporal regions. CONCLUSION: These results were replicated across the three employed ML approaches. Connectome-wide functional connectivity permits differentiation of patients with schizophrenia from healthy controls at single-subject level with greater accuracy; this pattern of results is consistent with the 'dysconnectivity hypothesis' of schizophrenia, which states that the neural basis of the disorder is best understood in terms of system-level functional connectivity alterations.

Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study.

Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain-based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for resembling a "black box" that provides little or no insight into the nature of the data. In this article, we propose an alternative conceptual and practical approach for investigating brain-based disorders which aim to overcome these limitations. We used an artificial neural network known as "deep autoencoder" to create a normative model using structural magnetic resonance imaging data from 1,113 healthy people. We then used this model to estimate total and regional neuroanatomical deviation in individual patients with schizophrenia and autism spectrum disorder using two independent data sets (n = 263). We report that the model was able to generate different values of total neuroanatomical deviation for each disease under investigation relative to their control group (p < .005). Furthermore, the model revealed distinct patterns of neuroanatomical deviations for the two diseases, consistent with the existing neuroimaging literature. We conclude that the deep autoencoder provides a flexible and promising framework for assessing total and regional neuroanatomical deviations in neuropsychiatric populations.

Urban Mind: Using Smartphone Technologies to Investigate the Impact of Nature on Mental Well-Being in Real Time.

Existing evidence on the beneficial effects of nature on mental health comes from studies using cross-sectional designs. We developed a smartphone-based tool (Urban Mind; www.urbanmind.info) to examine how exposure to natural features within the built environment affects mental well-being in real time. The tool was used to monitor 108 individuals who completed 3013 assessments over a 1-week period. Significant immediate and lagged associations with mental well-being were found for several natural features. These associations were stronger in people with higher trait impulsivity, a psychological measure of one's tendency to behave with little forethought or consideration of the consequences, which is indicative of a higher risk of developing mental-health issues. Our investigation suggests that the benefits of nature on mental well-being are time-lasting and interact with an individual's vulnerability to mental illness. These findings have potential implications from the perspectives of global mental health as well as urban planning and design.

Autobiographical memory: a candidate latent vulnerability mechanism for psychiatric disorder following childhood maltreatment.

BackgroundAltered autobiographical memory (ABM) functioning has been implicated in the pathogenesis of depression and post-traumatic stress disorder and may represent one mechanism by which childhood maltreatment elevates psychiatric risk.AimsTo investigate the impact of childhood maltreatment on ABM functioning.MethodThirty-four children with documented maltreatment and 33 matched controls recalled specific ABMs in response to emotionally valenced cue words during functional magnetic resonance imaging.ResultsChildren with maltreatment experience showed reduced hippocampal and increased middle temporal and parahippocampal activation during positive ABM recall compared with peers. During negative ABM recall they exhibited increased amygdala activation, and greater amygdala connectivity with the salience network.ConclusionsChildhood maltreatment is associated with altered ABM functioning, specifically reduced activation in areas encoding specification of positive memories, and greater activation of the salience network for negative memories. This pattern may confer latent vulnerability to future depression and post-traumatic stress disorder.

Longitudinal Changes in Resting-State Cerebral Activity in Patients with First-Episode Schizophrenia: A 1-Year Follow-up Functional MR Imaging Study.

Purpose To determine whether the brain functional abnormalities of drug-naive first-episode schizophrenia are reduced after 1 year of undergoing antipsychotic treatment and whether pretreatment resting-state functional magnetic resonance (MR) imaging parameters are associated with longitudinal changes in clinical symptoms. Materials and Methods This prospective study was approved by the local ethical committee, and written informed consent was obtained from all participants. Twenty antipsychotic-naive first-episode patients with schizophrenia and 16 healthy individuals were recruited and underwent resting-state functional MR imaging at baseline and again at 1-year follow-up, by which time significant clinical improvement was seen. The amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity (FC) were analyzed with analysis of covariance. Results The amount of ALFF in the right inferior parietal lobule (IPL) and orbitofrontal cortex (OFC) and the amount of FC between the bilateral IPLs significantly increased over the follow-up period, and the amount of ALFF in the right occipital gyrus was reduced (P < .050, AlphaSim corrected [ http://afni.nimh.nih.gov/pub/dist/doc/manual/AlphaSim.pdf ]), returning toward normal levels. Furthermore, the degree of alteration in ALFF values in the right OFC (P = .037) and occipital gyrus (P = .002) at baseline was significantly correlated with the magnitude of the normalization in those regions at 1-year follow-up. In contrast, abnormalities of ALFF in the bilateral thalamus, ventral medial prefrontal cortex, precuneus, and right amygdala and of FC between the right OFC and the dorsal medial prefrontal cortex at baseline did not improve in patients at 1-year follow-up. Conclusion These findings show that some, but not all, neurophysiologic alterations that occur during the acute phase of schizophrenia are normalized in the context of clinical improvement and suggest therapeutic implications for exploration of which alterations in regional and network-level brain function evolve over time in patients with schizophrenia and which reflect persistent pathologic traits. Online supplemental material is available for this article.

Cognitive relevance of the community structure of the human brain functional coactivation network.

There is growing interest in the complex topology of human brain functional networks, often measured using resting-state functional MRI (fMRI). Here, we used a meta-analysis of the large primary literature that used fMRI or PET to measure task-related activation (>1,600 studies; 1985-2010). We estimated the similarity (Jaccard index) of the activation patterns across experimental tasks between each pair of 638 brain regions. This continuous coactivation matrix was used to build a weighted graph to characterize network topology. The coactivation network was modular, with occipital, central, and default-mode modules predominantly coactivated by specific cognitive domains (perception, action, and emotion, respectively). It also included a rich club of hub nodes, located in parietal and prefrontal cortex and often connected over long distances, which were coactivated by a diverse range of experimental tasks. Investigating the topological role of edges between a deactivated and an activated node, we found that such competitive interactions were most frequent between nodes in different modules or between an activated rich-club node and a deactivated peripheral node. Many aspects of the coactivation network were convergent with a connectivity network derived from resting state fMRI data (n = 27, healthy volunteers); although the connectivity network was more parsimoniously connected and differed in the anatomical locations of some hubs. We conclude that the community structure of human brain networks is relevant to cognitive function. Deactivations may play a role in flexible reconfiguration of the network according to cognitive demand, varying the integration between modules, and between the periphery and a central rich club.

Neuroimaging distinction between neurological and psychiatric disorders.

BACKGROUND: It is unclear to what extent the traditional distinction between neurological and psychiatric disorders reflects biological differences. AIMS: To examine neuroimaging evidence for the distinction between neurological and psychiatric disorders. METHOD: We performed an activation likelihood estimation meta-analysis on voxel-based morphometry studies reporting decreased grey matter in 14 neurological and 10 psychiatric disorders, and compared the regional and network-level alterations for these two classes of disease. In addition, we estimated neuroanatomical heterogeneity within and between the two classes. RESULTS: Basal ganglia, insula, sensorimotor and temporal cortex showed greater impairment in neurological disorders; whereas cingulate, medial frontal, superior frontal and occipital cortex showed greater impairment in psychiatric disorders. The two classes of disorders affected distinct functional networks. Similarity within classes was higher than between classes; furthermore, similarity within class was higher for neurological than psychiatric disorders. CONCLUSIONS: From a neuroimaging perspective, neurological and psychiatric disorders represent two distinct classes of disorders.

Does neuroanatomy account for superior temporal dysfunction in early psychosis? A multimodal MRI investigation.

BACKGROUND: Neuroimaging studies of ultra-high risk (UHR) and first-episode psychosis (FEP) have revealed widespread alterations in brain structure and function. Recent evidence suggests there is an intrinsic relationship between these 2 types of alterations; however, there is very little research linking these 2 modalities in the early stages of psychosis. METHODS: To test the hypothesis that functional alteration in UHR and FEP articipants would be associated with corresponding structural alteration, we examined brain function and structure in these participants as well as in a group of healthy controls using multimodal MRI. The data were analyzed using statistical parametric mapping. RESULTS: We included 24 participants in the FEP group, 18 in the UHR group and 21 in the control group. Patients in the FEP group showed a reduction in functional activation in the left superior temporal gyrus relative to controls, and the UHR group showed intermediate values. The same region showed a corresponding reduction in grey matter volume in the FEP group relative to controls. However, while the difference in grey matter volume remained significant after including functional activation as a covariate of no interest, the reduction in functional activation was no longer evident after including grey matter volume as a covariate of no interest. LIMITATIONS: Our sample size was relatively small. All participants in the FEP group and 2 in the UHR group had received antipsychotic medication, which may have impacted neurofunction and/or neuroanatomy. CONCLUSION: Our results suggest that superior temporal dysfunction in early psychosis is accounted for by a corresponding alteration in grey matter volume. This finding has important implications for the interpretation of functional alteration in early psychosis.

The hubs of the human connectome are generally implicated in the anatomy of brain disorders.

Brain networks or 'connectomes' include a minority of highly connected hub nodes that are functionally valuable, because their topological centrality supports integrative processing and adaptive behaviours. Recent studies also suggest that hubs have higher metabolic demands and longer-distance connections than other brain regions, and therefore could be considered biologically costly. Assuming that hubs thus normally combine both high topological value and high biological cost, we predicted that pathological brain lesions would be concentrated in hub regions. To test this general hypothesis, we first identified the hubs of brain anatomical networks estimated from diffusion tensor imaging data on healthy volunteers (n = 56), and showed that computational attacks targeted on hubs disproportionally degraded the efficiency of brain networks compared to random attacks. We then prepared grey matter lesion maps, based on meta-analyses of published magnetic resonance imaging data on more than 20 000 subjects and 26 different brain disorders. Magnetic resonance imaging lesions that were common across all brain disorders were more likely to be located in hubs of the normal brain connectome (P < 10(-4), permutation test). Specifically, nine brain disorders had lesions that were significantly more likely to be located in hubs (P < 0.05, permutation test), including schizophrenia and Alzheimer's disease. Both these disorders had significantly hub-concentrated lesion distributions, although (almost completely) distinct subsets of cortical hubs were lesioned in each disorder: temporal lobe hubs specifically were associated with higher lesion probability in Alzheimer's disease, whereas in schizophrenia lesions were concentrated in both frontal and temporal cortical hubs. These results linking pathological lesions to the topological centrality of nodes in the normal diffusion tensor imaging connectome were generally replicated when hubs were defined instead by the meta-analysis of more than 1500 task-related functional neuroimaging studies of healthy volunteers to create a normative functional co-activation network. We conclude that the high cost/high value hubs of human brain networks are more likely to be anatomically abnormal than non-hubs in many (if not all) brain disorders.

Sex differences in socioemotional functioning, attentional bias, and gray matter volume in maltreated children: A multilevel investigation.

While maltreatment is known to impact social and emotional functioning, threat processing, and neural structure, the potentially dimorphic influence of sex on these outcomes remains relatively understudied. We investigated sex differences across these domains in a large community sample of children aged 10 to 14 years (n = 122) comprising 62 children with verified maltreatment experience and 60 well-matched nonmaltreated peers. The maltreated group relative to the nonmaltreated comparison group exhibited poorer social and emotional functioning (more peer problems and heightened emotional reactivity). Cognitively, they displayed a pattern of attentional avoidance of threat in a visual dot-probe task. Similar patterns were observed in males and females in these domains. Reduced gray matter volume was found to characterize the maltreated group in the medial orbitofrontal cortex, bilateral middle temporal lobes, and bilateral supramarginal gyrus; sex differences were observed only in the supramarginal gyrus. In addition, a disordinal interaction between maltreatment exposure and sex was found in the postcentral gyrus. Finally, attentional avoidance to threat mediated the relationship between maltreatment and emotional reactivity, and medial orbitofrontal cortex gray matter volume mediated the relationship between maltreatment and peer functioning. Similar mediation patterns were observed across sexes. This study highlights the utility of combining multiple levels of analysis when studying the "latent vulnerability" engendered by childhood maltreatment and yields tentative findings regarding a neural basis of sex differences in long-term outcomes for maltreated children.

Multivariate pattern analysis of DTI reveals differential white matter in individuals with obsessive-compulsive disorder.

Diffusion tensor imaging (DTI) studies have revealed group differences in white matter between patients with obsessive-compulsive disorder (OCD) and healthy controls. However, the results of these studies were based on average differences between the two groups, and therefore had limited clinical applicability. The objective of this study was to investigate whether fractional anisotropy (FA) of white matter can be used to discriminate between patients with OCD and healthy controls at the level of the individual. DTI data were acquired from 28 OCD patients and 28 demographically matched healthy controls, scanned using a 3T MRI system. Differences in FA values of white matter between OCD and healthy controls were examined using a multivariate pattern classification technique known as support vector machine (SVM). SVM applied to FA images correctly identified OCD patients with a sensitivity of 86% and a specificity of 82% resulting in a statistically significant accuracy of 84% (P ≤ 0.001). This discrimination was based on a distributed network including bilateral prefrontal and temporal regions, inferior fronto-occipital fasciculus, superior fronto-parietal fasciculus, splenium of corpus callosum and left middle cingulum bundle. The present study demonstrates subtle and spatially distributed white matter abnormalities in individuals with OCD, and provides preliminary support for the suggestion that that these could be used to aid the identification of individuals with OCD in clinical practice.

Evidence of reporting biases in voxel-based morphometry (VBM) studies of psychiatric and neurological disorders.

OBJECTIVES: To evaluate whether biases may influence the findings of whole-brain structural imaging literature. METHODS: Forty-seven whole-brain voxel-based meta-analyses including voxel-based morphometry (VBM) studies in neuropsychiatric conditions were included, for a total of 324 individual VBM studies. The total sample size, the overall number of foci, and different moderators were extracted both at the level of the individual studies and at the level of the meta-analyses. RESULTS: Sample size ranged from 12 to 545 (median n = 47) per VBM study. The median number of reported foci per study was six. VBM studies with larger sample sizes reported only slightly more abnormalities than smaller studies (2% increase in the number of foci per 10-patients increase in sample size). A similar pattern was seen in several analyses according to different moderator variables with some possible modulating evidence for the statistical threshold employed, publication year and number of coauthors. Whole-brain meta-analyses (median sample size n = 534) found fewer foci (median = 3) than single studies and overall they showed no significant increase in the number of foci with increasing sample size. Meta-analyses with ≥10 VBM studies reported a median of three foci and showed a significant increase with increasing sample size, while there was no relationship between sample size and number of foci (median = 5) in meta-analyses with <10 VBM studies. CONCLUSIONS: The number of foci reported in small VBM studies and even in meta-analyses with few studies may often be inflated. This picture is consistent with reporting biases affecting small studies.

Characterization of major depressive disorder using a multiparametric classification approach based on high resolution structural images.

BACKGROUND: Major depressive disorder (MDD) is one of the most disabling mental illnesses. Previous neuroanatomical studies of MDD have revealed regional alterations in grey matter volume and density. However, owing to the heterogeneous symptomatology and complex etiology, MDD is likely to be associated with multiple morphometric alterations in brain structure. We sought to distinguish first-episode, medication-naive, adult patients with MDD from healthy controls and characterize neuroanatomical differences between the groups using a multiparameter classification approach. METHODS: We recruited medication-naive patients with first-episode depression and healthy controls matched for age, sex, handedness and years of education. High-resolution T1-weighted images were used to extract 7 morphometric parameters, including both volumetric and geometric features, based on the surface data of the entire cerebral cortex. These parameters were used to compare patients and controls using multivariate support vector machine, and the regions that informed the discrimination between the 2 groups were identified based on maximal classification weights. RESULTS: Thirty-two patients and 32 controls participated in the study. Both volumetric and geometric parameters could discriminate patients with MDD from healthy controls, with cortical thickness in the right hemisphere providing the greatest accuracy (78%, p ≤ 0.001). This discrimination was informed by a bilateral network comprising mainly frontal, temporal and parietal regions. LIMITATIONS: The sample size was relatively small and our results were based on first-episode, medication-naive patients. CONCLUSION: Our investigation demonstrates that multiple cortical features are affected in medication-naive patients with first-episode MDD. These findings extend the current understanding of the neuropathological underpinnings of MDD and provide preliminary support for the use of neuroanatomical scans in the early detection of MDD.