Mapping grey matter reductions in schizophrenia: an anatomical likelihood estimation analysis of voxel-based morphometry studies

Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research

Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.

Compensatory thickening of cortical thickness in early stage of schizophrenia

Brain structural abnormality has been observed in the prodromal and early stages of schizophrenia, but the mechanism behind it is not clear. In this study, to explore the association between cortical abnormalities, metabolite levels, inflammation levels and clinical symptoms of schizophrenia, 51 drug-naive first-episode schizophrenia (FES) patients, 51 ultra-high risk for psychosis (UHR), and 51 healthy controls (HC) were recruited. We estimated gray matter volume (GMV), cortical thickness (CT), concentrations of different metabolites, and inflammatory marks among four groups (UHR converted to psychosis [UHR-C], UHR unconverted to psychosis [UHR-NC], FES, HC). UHR-C group had more CT in the right lateral occipital cortex and the right medial orbito-frontal cortex (rMOF), while a significant reduction in CT of the right fusiform cortex was observed in FES group. UHR-C group had significantly higher concentration of IL-6, while IL-17 could significantly predict CT of the right fusiform and IL-4 and IL-17 were significant predictors of CT in the rMOF. To conclude, it is reasonable to speculate that the increased CT in UHR-C group is related to the inflammatory response, and may participate in some compensatory mechanism, but might become exhaustive with the progress of the disease due to potential neurotoxic effects.

The Roles of hnRNP Family in the Brain and Brain-Related Disorders

Heterogeneous nuclear ribonucleoproteins (hnRNPs) belong to a complex family of RNA-binding proteins that are essential to control alternative splicing, mRNA trafficking, synaptic plasticity, stress granule formation, cell cycle regulation, and axonal transport. Over the past decade, hnRNPs have been associated with different brain disorders such as Alzheimer's disease, multiple sclerosis, and schizophrenia. Given their essential role in maintaining cell function and integrity, it is not surprising that dysregulated hnRNP levels lead to neurological implications. This review aims to explore the primary functions of hnRNPs in neurons, oligodendrocytes, microglia, and astrocytes, and their roles in brain disorders. We also discuss proteomics and other technologies and their potential for studying and evaluating hnRNPs in brain disorders, including the discovery of new therapeutic targets and possible pharmacological interventions.

Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities

A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.

Parkinson's disease and schizophrenia interactomes contain temporally distinct gene clusters underlying comorbid mechanisms and unique disease processes

Genome-wide association studies suggest significant overlaps in Parkinson's disease (PD) and schizophrenia (SZ) risks, but the underlying mechanisms remain elusive. The protein-protein interaction network ('interactome') plays a crucial role in PD and SZ and can incorporate their spatiotemporal specificities. Therefore, to study the linked biology of PD and SZ, we compiled PD- and SZ-associated genes from the DisGeNET database, and constructed their interactomes using BioGRID and HPRD. We examined the interactomes using clustering and enrichment analyses, in conjunction with the transcriptomic data of 26 brain regions spanning foetal stages to adulthood available in the BrainSpan Atlas. PD and SZ interactomes formed four gene clusters with distinct temporal identities (Disease Gene Networks or 'DGNs'1-4). DGN1 had unique SZ interactome genes highly expressed across developmental stages, corresponding to a neurodevelopmental SZ subtype. DGN2, containing unique SZ interactome genes expressed from early infancy to adulthood, correlated with an inflammation-driven SZ subtype and adult SZ risk. DGN3 contained unique PD interactome genes expressed in late infancy, early and late childhood, and adulthood, and involved in mitochondrial pathways. DGN4, containing prenatally-expressed genes common to both the interactomes, involved in stem cell pluripotency and overlapping with the interactome of 22q11 deletion syndrome (comorbid psychosis and Parkinsonism), potentially regulates neurodevelopmental mechanisms in PD-SZ comorbidity. Our findings suggest that disrupted neurodevelopment (regulated by DGN4) could expose risk windows in PD and SZ, later elevating disease risk through inflammation (DGN2). Alternatively, variant clustering in DGNs may produce disease subtypes, e.g., PD-SZ comorbidity with DGN4, and early/late-onset SZ with DGN1/DGN2.

Dynamic fusion of genomics and functional network connectivity in UK biobank reveals static and time-varying SNP manifolds

Many psychiatric and neurological disorders show significant heritability, indicating strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) measures functional temporal coupling between brain networks in a time-varying manner and has proven to identify disease-related changes in the brain. However, it remains largely unclear how genetic risk contributes to brain dysconnectivity that further manifests into clinical symptoms. The current work aimed to address this gap by proposing a novel joint ICA (jICA)-based "dynamic fusion" framework to identify dynamically tuned SNP manifolds by linking static SNPs to dynamic functional information of the brain. The sliding window approach was utilized to estimate four dFNC states and compute subject-level state-specific dFNC features. Each state of dFNC features were then combined with 12946 SZ risk SNPs for jICA decomposition, resulting in four parallel fusions in 32861 European ancestry individuals within the UK Biobank cohort. The identified joint SNP-dFNC components were further validated for SZ relevance in an aggregated SZ cohort, and compared for across-state similarity to indicate level of dynamism. The results supported that dynamic fusion yielded "static" and "dynamic" components (i.e., high and low across-state similarity, respectively) for SNP and dFNC modalities. As expected, the SNP components presented a mixture of static and dynamic manifolds, with the latter largely driven by fusion with dFNC. We also showed that some of the dynamic SNP manifolds uniquely elicited by fusion with state-specific dFNC features complemented each other in terms of biological interpretation. This dynamic fusion framework thus allows expanding the SNP modality to manifolds in the time dimension, which provides a unique lens to elicit unique SNP correlates of dFNC otherwise unseen, promising additional insights on how genetic risk links to disease-related dysconnectivity.

Network-Based Spreading of Gray Matter Changes Across Different Stages of Psychosis

Importance

Psychotic illness is associated with anatomically distributed gray matter reductions that can worsen with illness progression, but the mechanisms underlying the specific spatial patterning of these changes is unknown.

Objective

To test the hypothesis that brain network architecture constrains cross-sectional and longitudinal gray matter alterations across different stages of psychotic illness and to identify whether certain brain regions act as putative epicenters from which volume loss spreads.

Design, Settings, and Participants

This case-control study included 534 individuals from 4 cohorts, spanning early and late stages of psychotic illness. Early-stage cohorts included patients with antipsychotic-naive first-episode psychosis (n = 59) and a group of patients receiving medications within 3 years of psychosis onset (n = 121). Late-stage cohorts comprised 2 independent samples of people with established schizophrenia (n = 136). Each patient group had a corresponding matched control group (n = 218). A sample of healthy adults (n = 356) was used to derive representative structural and functional brain networks for modeling of network-based spreading processes. Longitudinal illness-related and antipsychotic-related gray matter changes over 3 and 12 months were examined using a triple-blind randomized placebo-control magnetic resonance imaging study of the antipsychotic-naive patients. All data were collected between April 29, 2008, and January 15, 2020, and analyses were performed between March 1, 2021, and January 14, 2023.

Main Outcomes and Measures

Coordinated deformation models were used to estimate the extent of gray matter volume (GMV) change in each of 332 parcellated areas by the volume changes observed in areas to which they were structurally or functionally coupled. To identify putative epicenters of volume loss, a network diffusion model was used to simulate the spread of pathology from different seed regions. Correlations between estimated and empirical spatial patterns of GMV alterations were used to quantify model performance.

Results

Of 534 included individuals, 354 (66.3%) were men, and the mean (SD) age was 28.4 (7.4) years. In both early and late stages of illness, spatial patterns of cross-sectional volume differences between patients and controls were more accurately estimated by coordinated deformation models constrained by structural, rather than functional, network architecture (r range, >0.46 to <0.57; P < .01). The same model also robustly estimated longitudinal volume changes related to illness (r ≥ 0.52; P < .001) and antipsychotic exposure (r ≥ 0.50; P < .004). Network diffusion modeling consistently identified, across all 4 data sets, the anterior hippocampus as a putative epicenter of pathological spread in psychosis. Epicenters of longitudinal GMV loss were apparent in posterior cortex early in the illness and shifted to the prefrontal cortex with illness progression.

Conclusion and Relevance

These findings highlight a central role for white matter fibers as conduits for the spread of pathology across different stages of psychotic illness, mirroring findings reported in neurodegenerative conditions. The structural connectome thus represents a fundamental constraint on brain changes in psychosis, regardless of whether these changes are caused by illness or medication. Moreover, the anterior hippocampus represents a putative epicenter of early brain pathology from which dysfunction may spread to affect connected areas.

Chromatic fusion: Generative multimodal neuroimaging data fusion provides multi-informed insights into schizophrenia

This work proposes a novel generative multimodal approach to jointly analyze multimodal data while linking the multimodal information to colors. We apply our proposed framework, which disentangles multimodal data into private and shared sets of features from pairs of structural (sMRI), functional (sFNC and ICA), and diffusion MRI data (FA maps). With our approach, we find that heterogeneity in schizophrenia is potentially a function of modality pairs. Results show (1) schizophrenia is highly multimodal and includes changes in specific networks, (2) non-linear relationships with schizophrenia are observed when interpolating among shared latent dimensions, and (3) we observe a decrease in the modularity of functional connectivity and decreased visual-sensorimotor connectivity for schizophrenia patients for the FA-sFNC and sMRI-sFNC modality pairs, respectively. Additionally, our results generally indicate decreased fractional corpus callosum anisotropy, and decreased spatial ICA map and voxel-based morphometry strength in the superior frontal lobe as found in the FA-sFNC, sMRI-FA, and sMRI-ICA modality pair clusters. In sum, we introduce a powerful new multimodal neuroimaging framework designed to provide a rich and intuitive understanding of the data which we hope challenges the reader to think differently about how modalities interact.

Pathways from threat exposure to psychotic symptoms in youth: The role of emotion recognition bias and brain structure

BACKGROUND

Research supports an association between threatening experiences in childhood and psychosis. It is possible that early threat exposure disrupts the development of emotion recognition (specifically, producing a bias for facial expressions relating to threat) and the brain structures subserving it, contributing to psychosis development.

METHODS

Using data from the Philadelphia Neurodevelopmental Cohort, we examined associations between threat exposure and both the misattribution of facial expressions to fear/anger in an emotion recognition task, and gray matter volumes in key emotion processing regions. Our sample comprised youth with psychosis spectrum symptoms (N = 304), control youth (N = 787), and to evaluate specificity, youth with internalizing symptoms (N = 92). The moderating effects of group and sex were examined.

RESULTS

Both the psychosis spectrum and internalizing groups had higher levels of threat exposure than controls. In the total sample, threat exposure was associated with lower left medial prefrontal cortex (mPFC) volume but not misattributions to fear/anger. The effects of threat exposure did not significantly differ by group or sex.

CONCLUSIONS

The findings of this study provide evidence for an effect of threat exposure on mPFC morphology, but do not support an association between threat exposure and a recognition bias for threat-related expressions, that is particularly pronounced in psychosis. Future research should investigate factors linking transdiagnostic alterations related to threat exposure with psychotic symptoms, and attempt to clarify the mechanisms underpinning emotion recognition misattributions in threat-exposed youth.

Chromatic fusion: generative multimodal neuroimaging data fusion provides multi-informed insights into schizophrenia

This work proposes a novel generative multimodal approach to jointly analyze multimodal data while linking the multimodal information to colors. By linking colors to private and shared information from modalities, we introduce chromatic fusion, a framework that allows for intuitively interpreting multimodal data. We test our framework on structural, functional, and diffusion modality pairs. In this framework, we use a multimodal variational autoencoder to learn separate latent subspaces; a private space for each modality, and a shared space between both modalities. These subspaces are then used to cluster subjects, and colored based on their distance from the variational prior, to obtain meta-chromatic patterns (MCPs). Each subspace corresponds to a different color, red is the private space of the first modality, green is the shared space, and blue is the private space of the second modality. We further analyze the most schizophrenia-enriched MCPs for each modality pair and find that distinct schizophrenia subgroups are captured by schizophrenia-enriched MCPs for different modality pairs, emphasizing schizophrenia's heterogeneity. For the FA-sFNC, sMRI-ICA, and sMRI-ICA MCPs, we generally find decreased fractional corpus callosum anisotropy and decreased spatial ICA map and voxel-based morphometry strength in the superior frontal lobe for schizophrenia patients. To additionally highlight the importance of the shared space between modalities, we perform a robustness analysis of the latent dimensions in the shared space across folds. These robust latent dimensions are subsequently correlated with schizophrenia to reveal that for each modality pair, multiple shared latent dimensions strongly correlate with schizophrenia. In particular, for FA-sFNC and sMRI-sFNC shared latent dimensions, we respectively observe a reduction in the modularity of the functional connectivity and a decrease in visual-sensorimotor connectivity for schizophrenia patients. The reduction in modularity couples with increased fractional anisotropy in the left part of the cerebellum dorsally. The reduction in the visual-sensorimotor connectivity couples with a reduction in the voxel-based morphometry generally but increased dorsal cerebellum voxel-based morphometry. Since the modalities are trained jointly, we can also use the shared space to try and reconstruct one modality from the other. We show that cross-reconstruction is possible with our network and is generally much better than depending on the variational prior. In sum, we introduce a powerful new multimodal neuroimaging framework designed to provide a rich and intuitive understanding of the data that we hope challenges the reader to think differently about how modalities interact.

Aberrant global and local dynamic properties in schizophrenia with instantaneous phase method based on Hilbert transform

BACKGROUND

Emerging functional imaging studies suggest that schizophrenia is associated with aberrant spatiotemporal interaction which may result in aberrant global and local dynamic properties.

METHODS

We investigated the dynamic functional connectivity (FC) by using instantaneous phase method based on Hilbert transform to detect abnormal spatiotemporal interaction in schizophrenia. Based on resting-state functional magnetic resonance imaging, two independent datasets were included, with 114 subjects from COBRE [51 schizophrenia patients (SZ) and 63 healthy controls (HCs)] and 96 from OpenfMRI (36 SZ and 60 HCs). Phase differences and instantaneous coupling matrices were firstly calculated at all time points by extracting instantaneous parameters. Global [global synchrony and intertemporal closeness (ITC)] and local dynamic features [strength of FC (sFC) and variability of FC (vFC)] were compared between two groups. Support vector machine (SVM) was used to estimate the ability to discriminate two groups by using all aberrant features.

RESULTS

We found SZ had lower global synchrony and ITC than HCs on both datasets. Furthermore, SZ had a significant decrease in sFC but an increase in vFC, which were mainly located at prefrontal cortex, anterior cingulate cortex, temporal cortex and visual cortex or temporal cortex and hippocampus, forming significant dynamic subnetworks. SVM analysis revealed a high degree of balanced accuracy (85.75%) on the basis of all aberrant dynamic features.

CONCLUSIONS

SZ has worse overall spatiotemporal stability and extensive FC subnetwork lesions compared to HCs, which to some extent elucidates the pathophysiological mechanism of schizophrenia, providing insight into time-variation properties of patients with other mental illnesses.

Neuronal dysfunction in individuals at early stage of schizophrenia, A resting-state fMRI study

Schizophrenia has been associated with abnormal intrinsic brain activity, involving various cognitive impairments. Qualitatively similar abnormalities are seen in individuals at ultra-high risk (UHR) for psychosis. In this study, resting-state fMRI (rs-fMRI) data were collected from 44 drug-naïve first-episode schizophrenia (Dn-FES) patients, 48 UHR individuals, and 40 healthy controls (HCs). The fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and functional connectivity (FC), were performed to evaluate resting brain function. A support vector machine (SVM) was applied for classification analysis. Compared to HCs, both clinical groups showed increased fALFF in the central executive network (CEN), decreased ReHo in the ventral visual pathway (VVP) and decreased FC in temporal-sensorimotor regions. Excellent performance was achieved by using fALFF value in distinguishing both FES (sensitivity=83.21%, specificity=80.58%, accuracy=81.37%, p=0.009) and UHR (sensitivity=75.88%, specificity=85.72%, accuracy=80.72%, p<0.001) from HC group. Moreover, the study highlighted the importance of frontal and temporal alteration in the pathogenesis of schizophrenia. However, no fMRI features were observed that could well distinguish Dn-FES from UHR group. To conclude, fALFF in the CEN may provide potential power for identifying individuals at the early stage of schizophrenia and the alteration in the frontal and temporal lobe may be important to these individuals.

Changes in neurofilament light chain protein (NEFL) in children and adolescents with Schizophrenia and Bipolar Disorder: Early period neurodegeneration

AIM

Neurofilament light chain protein (NEFL), is defined as a structural protein which exists particularly in axones of neurons and is released to the cerum in consequence of neuroaxonal damage. The aim of this study is to investigate the peripheral cerumNEFLlevels of children and adolescents with early onset schizophrenia and bipolar disorder.

METHOD

In this study, we evaluated serum levels of NEFL in children and adolescents (13-17 years) with schizophrenia, bipolar disorder and healthy control group. The study is conducted with 35 schizophrenia, 38 bipolar disorder manic episode patients and 40 healthy controls.

RESULTS

The median age of the patient and control groups was 16 (IQR- Interquartile Range: 2). There was no statistical difference in median age (p = 0.52) and gender distribution(p = 0.53) between groups. NEFL levels of the patients with schizophrenia were significantly higher than the controls. NEFL levels of the patients with bipolar disorder were significantly higher than the controls. Serum levels of NEFL of the schizophrenia were higher than the bipolar disorder; however, the difference was not statistically significant.

CONCLUSION

In conclusion, serum NEFL level, as a confidential marker of neural damage, is increased in the children and adolescents with bipolar disorder and schizophrenia. This result may indicatea degenerative period in neurons of children and adolescents with schizophrenia or bipolar disorder and may play a role in the pathophisiology of these disorders. This result shows that there is neuronal damage in both diseases, but neuronal damage may be more in schizophrenia.

Neuroimaging in schizophrenia: an overview of findings and their implications for synaptic changes

Over the last five decades, a large body of evidence has accrued for structural and metabolic brain alterations in schizophrenia. Here we provide an overview of these findings, focusing on measures that have traditionally been thought to reflect synaptic spine density or synaptic activity and that are relevant for understanding if there is lower synaptic density in the disorder. We conducted literature searches to identify meta-analyses or other relevant studies in patients with chronic or first-episode schizophrenia, or in people at high genetic or clinical risk for psychosis. We identified 18 meta-analyses including over 50,000 subjects in total, covering: structural MRI measures of gyrification index, grey matter volume, grey matter density and cortical thickness, neurite orientation dispersion and density imaging, PET imaging of regional glucose metabolism and magnetic resonance spectroscopy measures of N-acetylaspartate. We also review preclinical evidence on the relationship between ex vivo synaptic measures and structural MRI imaging, and PET imaging of synaptic protein 2A (SV2A). These studies show that schizophrenia is associated with lower grey matter volumes and cortical thickness, accelerated grey matter loss over time, abnormal gyrification patterns, and lower regional SV2A levels and metabolic markers in comparison to controls (effect sizes from ~ -0.11 to -1.0). Key regions affected include frontal, anterior cingulate and temporal cortices and the hippocampi. We identify several limitations for the interpretation of these findings in terms of understanding synaptic alterations. Nevertheless, taken with post-mortem findings, they suggest that schizophrenia is associated with lower synaptic density in some brain regions. However, there are several gaps in evidence, in particular whether SV2A findings generalise to other cohorts.

Hearing voices in the head: Two meta-analyses on structural correlates of auditory hallucinations in schizophrenia

Past voxel-based morphometry (VBM) studies demonstrate reduced grey matter volume (GMV) in schizophrenia (SZ) patients' brains in various cortical and subcortical regions. Probably due to SZ symptoms' heterogeneity, these results are often inconsistent and difficult to integrate. We hypothesized that focusing on auditory verbal hallucinations (AVH) - one of the most common SZ symptoms - would allow reducing heterogeneity and discovering further compelling evidence of SZ neural correlates. We carried out two voxel-based meta-analyses of past studies that investigated the structural correlates of AVH in SZ. The review of whole-brain VBM studies published until June 2022 in PubMed and PsychInfo databases yielded (a) 13 studies on correlations between GMV and AVH severity in SZ patients (n = 472; 86 foci), and (b) 11 studies involving comparisons between hallucinating SZ patients (n = 504) and healthy controls (n = 524; 74 foci). Data were analyzed using the Activation Likelihood Estimation method. AVH severity was associated with decreased GMV in patients' left superior temporal gyrus (STG) and left posterior insula. Compared with healthy controls, hallucinating SZ patients showed reduced GMV on the left anterior insula and left inferior frontal gyrus (IFG). Our findings revealed important structural dysfunctions in a left lateralized cluster of brain regions, including the insula and temporo-frontal regions, that significantly contribute to the severity and persistence of AVH. Structural atrophy found in circuits involved in generating and perceiving speech, as well as in auditory signal processing, might reasonably be considered a biological marker of AVH in SZ.

Chaos analysis of the brain topology in first-episode psychosis and clinical high risk patients

Structural MRI studies in first-episode psychosis (FEP) and in clinical high risk (CHR) patients have consistently shown volumetric abnormalities in frontal, temporal, and cingulate cortex areas. The aim of the present study was to employ chaos analysis for the identification of brain topology differences in people with psychosis. Structural MRI were acquired from 77 FEP, 73 CHR and 44 healthy controls (HC). Chaos analysis of the gray matter distribution was performed: First, the distances of each voxel from the center of mass in the gray matter image was calculated. Next, the distances multiplied by the voxel intensity were represented as a spatial-series, which then was analyzed by extracting the Largest-Lyapunov-Exponent (lambda). The lambda brain map depicts thus how the gray matter topology changes. Between-group differences were identified by (a) comparing the lambda brain maps, which resulted in statistically significant differences in FEP and CHR compared to HC; and (b) matching the lambda series with the Morlet wavelet, which resulted in statistically significant differences in the scalograms of FEP against CHR and HC. The proposed framework using spatial-series extraction enhances the between-group differences of FEP, CHR and HC subjects, verifies diagnosis-relevant features and may potentially contribute to the identification of structural biomarkers for psychosis.

Abnormal brain gray matter volume in patients with major depressive disorder: Associated with childhood trauma?

BACKGROUND

Patients with major depressive disorders (MDD) have abnormalities in the frontal-limbic structures of the brain. Childhood trauma is a risk factor for both structural brain alterations and MDD. However, the relationships among the three have not been confirmed.

METHODS

Sixty-four patients with MDD and sixty-one healthy controls (HC) were checked by using MRI, the Hamilton Depression Scale (HAMD) and Childhood Trauma Questionnaire (CTQ). Voxel-based morphometry (VBM) was used to compare gray matter volume (GMV) differences between the two groups. Moreover, partial correlation and mediation analyses were conducted to test for potential associations between CTQ scores, different GMV, and clinical variables.

RESULTS

Compared to the HC group, the MDD patients showed decreased GMV in the right middle frontal gyrus (rMFG) and right precentral gyrus (rPreCG). In the patient group, reduced GMV in rMFG was associated with CTQ scores (r = -0.30, P = 0.019) and HAMD scores (r = -0.53, P < 0.001). Finally, in the patient group, mediation analysis revealed that the indirect effect of rMFG GMV in CTQ scores and HAMD scores was 0.115 and the proportion of indirect effect to total effect was 23.86%.

LIMITATIONS

This study used a cross-sectional collection, and it is unclear whether at the longitudinal level the brain GMV mediates the relationship between childhood trauma and depression.

CONCLUSIONS

Abnormalities in the frontal GMV were presented in the MDD patients. It is possible that childhood traumatic experiences cause inefficient GMV and ultimately lead to an increased susceptibility to depression.

Alterations in the volume of thalamic nuclei in patients with schizophrenia and persistent auditory hallucinations

•

Analysis of structural MRI images using a probabilistic atlas for segmentation of several nuclei of the thalamus.

•

Comparison of chronic patients with schizophrenia, with and without auditory hallucinations and matched healthy controls.

•

Volumetric reductions in patients with AH vs controls: Medial geniculate nucleus, anterior pulvinar nucleus and lateral and medial mediodorsal nuclei.

•

In patients without AH we found reductions in the volume of the pulvinar and mediodorsal nuclei, but not in the medial geniculate nucleus.

•

Found also some significant correlations between the volume of these nuclei and the total score of the PSYRATS scale.

The thalamus is a subcortical structure formed by different nuclei that relay information to the neocortex. Several reports have already described alterations of this structure in patients of schizophrenia that experience auditory hallucinations. However, to date no study has addressed whether the volumes of specific thalamic nuclei are altered in chronic patients experiencing persistent auditory hallucinations. We have processed structural MRI images using Freesurfer, and have segmented them into 25 nuclei using the probabilistic atlas developed by Iglesias and collaborators (Iglesias et al., 2018). To homogenize the sample, we have matched patients of schizophrenia, with and without persistent auditory hallucinations, with control subjects, considering sex, age and their estimated intracranial volume. This rendered a group number of 41 patients experiencing persistent auditory hallucinations, 35 patients without auditory hallucinations, and 55 healthy controls. In addition, we have also correlated the volume of the altered thalamic nuclei with the total score of the PSYRATS, a clinical scale used to evaluate the positive symptoms of this disorder. We have found alterations in the volume of 8 thalamic nuclei in both cohorts of patients with schizophrenia: The medial and lateral geniculate nuclei, the anterior, inferior, and lateral pulvinar nuclei, the lateral complex and the lateral and medial mediodorsal nuclei. We have also found some significant correlations between the volume of these nuclei in patients experiencing auditory hallucinations, and the total score of the PSYRATS scale. Altogether our results indicate that volumetric alterations of thalamic nuclei involved in audition may be related to persistent auditory hallucinations in chronic schizophrenia patients, whereas alterations in nuclei related to association cortices are evident in all patients. Future studies should explore whether the structural alterations are cause or consequence of these positive symptoms and whether they are already present in first episodes of psychosis.

Differences in electric field strength between clinical and non-clinical populations induced by prefrontal tDCS: A cross-diagnostic, individual MRI-based modeling study

•

MDD and SCZ showed lower prefrontal tDCS-induced e-field strengths compared to HC.

•

Average e-field strengths did not significantly differ between MDD and SCZ patients.

•

Inter-individual variability of e-field intensities and distribution was prominent.

•

Inter-rater variability emphasizes the importance of standardized positioning.

Introduction

Prefrontal cortex (PFC) regions are promising targets for therapeutic applications of non-invasive brain stimulation, e.g. transcranial direct current stimulation (tDCS), which has been proposed as a novel intervention for major depressive disorder (MDD) and negative symptoms of schizophrenia (SCZ). However, the effects of tDCS vary inter-individually, and dose–response relationships have not been established. Stimulation parameters are often tested in healthy subjects and transferred to clinical populations. The current study investigates the variability of individual MRI-based electric fields (e-fields) of standard bifrontal tDCS across individual subjects and diagnoses.

Method

The study included 74 subjects, i.e. 25 patients with MDD, 24 patients with SCZ, and 25 healthy controls (HC). Individual e-fields of a common tDCS protocol (i.e. 2 mA stimulation intensity, bifrontal anode-F3/cathode-F4 montage) were modeled by two investigators using SimNIBS (2.0.1) based on structural MRI scans.

Result

On a whole-brain level, the average e-field strength was significantly reduced in MDD and SCZ compared to HC, but MDD and SCZ did not differ significantly. Regions of interest (ROI) analysis for PFC subregions showed reduced e-fields in Sallet areas 8B and 9 for MDD and SCZ compared to HC, whereas there was again no difference between MDD and SCZ. Within groups, we generally observed high inter-individual variability of e-field intensities at a higher percentile of voxels.

Conclusion

MRI-based e-field modeling revealed significant differences in e-field strengths between clinical and non-clinical populations in addition to a general inter-individual variability. These findings support the notion that dose–response relationships for tDCS cannot be simply transferred from healthy to clinical cohorts and need to be individually established for clinical groups. In this respect, MRI-based e-field modeling may serve as a proxy for individualized dosing.

Clustering of cognitive subtypes in schizophrenia patients and their siblings: relationship with regional brain volumes

Schizophrenia patients (SZH) often show impaired cognition and reduced brain structural volumes; these deficits are also detectable in healthy relatives of SZH. However, there is considerable heterogeneity: a sizable percentage of SZH are relatively cognitively intact; clustering strategies have proved useful for categorising into cognitive subgroups. We used a clustering strategy to investigate relationships between subgroup assignment and brain volumes, in 102 SZH (N = 102) and 32 siblings of SZH (SZH-SIB), alongside 92 controls (CON) and 48 of their siblings. SZH had poorer performance in all cognitive domains, and smaller brain volumes within prefrontal and temporal regions compared to controls. We identified three distinct cognitive clusters (‘neuropsychologically normal’, ‘intermediate’, ‘cognitively impaired’) based on age- and gender-adjusted cognitive domain scores. The majority of SZH (60.8%) were assigned to the cognitively impaired cluster, while the majority of SZH-SIB (65.6%) were placed in the intermediate cluster. Greater right middle temporal volume distinguished the normal cluster from the more impaired clusters. Importantly, the observed brain volume differences between SZH and controls disappeared after adjustment for cluster assignment. This suggests an intimate link between cognitive performance levels and regional brain volume differences in SZH. This highlights the importance of accounting for heterogeneity in cognitive performance within SZH populations when attempting to characterise the brain structural abnormalities associated with the disease.

Structural and diffusion MRI based schizophrenia classification using 2D pretrained and 3D naive Convolutional Neural Networks

The ability of automatic feature learning makes Convolutional Neural Network (CNN) potentially suitable to uncover the complex and widespread brain changes in schizophrenia. Despite that, limited studies have been done on schizophrenia identification using interpretable deep learning approaches on multimodal neuroimaging data. Here, we developed a deep feature approach based on pre-trained 2D CNN and naive 3D CNN models trained from scratch for schizophrenia classification by integrating 3D structural and diffusion magnetic resonance imaging (MRI) data. We found that the naive 3D CNN models outperformed the pretrained 2D CNN models and the handcrafted feature-based machine learning approach using support vector machine during both cross-validation and testing on an independent dataset. Multimodal neuroimaging-based models accomplished performance superior to models based on a single modality. Furthermore, we identified brain grey matter and white matter regions critical for illness classification at the individual- and group-level which supported the salience network and striatal dysfunction hypotheses in schizophrenia. Our findings underscore the potential of CNN not only to automatically uncover and integrate multimodal 3D brain imaging features for schizophrenia identification, but also to provide relevant neurobiological interpretations which are crucial for developing objective and interpretable imaging-based probes for prognosis and diagnosis in psychiatric disorders.

Neuroimaging correlates of insight in non-affective psychosis: A systematic review and meta-analysis

OBJECTIVE

Neurological correlates of impaired insight in non-affective psychosis remain unclear. This study aimed to review and meta-analyze the studies assessing the grey matter volumetric correlates of impaired insight in non-affective psychosis.

METHODS

This study consisted of a systematic review of 23 studies, and a meta-analysis with SDM-PSI of the 11 studies that were whole-brain and reported maps or peaks of correlation of studies investigating the grey matter volumetric correlates of insight assessments of non-affective psychosis, PubMed and OVID datasets were independently reviewed for articles reporting neuroimaging correlates of insight in non-affective psychosis. Quality assessment was realized following previous methodological approaches for the ABC quality assessment test of imaging studies, based on two main criteria: the statistical power and the multidimensional assessment of insight. Study peaks of correlation between grey matter volume and insight were used to recreate brain correlation maps.

RESULTS

A total of 418 records were identified through database searching. Of these records, twenty-three magnetic resonance imaging (MRI) studies that used different insight scales were included. The quality of the evidence was high in 11 studies, moderate in nine, and low in three. Patients with reduced insight showed decreases in the frontal, temporal (specifically in superior temporal gyrus), precuneus, cingulate, insula, and occipital lobes cortical grey matter volume. The meta-analysis indicated a positive correlation between grey matter volume and insight in the right insula (i.e., the smaller the grey matter, the lower the insight).

CONCLUSION

Several brain areas might be involved in impaired insight in patients with non-affective psychoses. The methodologies employed, such as the applied insight scales, may have contributed to the considerable discrepancies in the findings.

The Limits between Schizophrenia and Bipolar Disorder: What Do Magnetic Resonance Findings Tell Us?

Schizophrenia and bipolar disorder, two of the most severe psychiatric illnesses, have historically been regarded as dichotomous entities but share many features of the premorbid course, clinical profile, genetic factors and treatment approaches. Studies focusing on neuroimaging findings have received considerable attention, as they plead for an improved understanding of the brain regions involved in the pathophysiology of schizophrenia and bipolar disorder. In this review, we summarize the main magnetic resonance imaging findings in both disorders, aiming at exploring the neuroanatomical and functional similarities and differences between the two. The findings show that gray and white matter structural changes and functional dysconnectivity predominate in the frontal and limbic areas and the frontotemporal circuitry of the brain areas involved in the integration of executive, cognitive and affective functions, commonly affected in both disorders. Available evidence points to a considerable overlap in the affected regions between the two conditions, therefore possibly placing them at opposite ends of a psychosis continuum.

A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium

Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia.

Prefrontal contributions to the stability and variability of thought and conscious experience

The human prefrontal cortex is a structurally and functionally heterogenous brain region, including multiple subregions that have been linked to different large-scale brain networks. It contributes to a broad range of mental phenomena, from goal-directed thought and executive functions to mind-wandering and psychedelic experience. Here we review what is known about the functions of different prefrontal subregions and their affiliations with large-scale brain networks to examine how they may differentially contribute to the diversity of mental phenomena associated with prefrontal function. An important dimension that distinguishes across different kinds of conscious experience is the stability or variability of mental states across time. This dimension is a central feature of two recently introduced theoretical frameworks-the dynamic framework of thought (DFT) and the relaxed beliefs under psychedelics (REBUS) model-that treat neurocognitive dynamics as central to understanding and distinguishing between different mental phenomena. Here, we bring these two frameworks together to provide a synthesis of how prefrontal subregions may differentially contribute to the stability and variability of thought and conscious experience. We close by considering future directions for this work.

The Thought From the Machine: Neural Basis of Thoughts With a Coherent and Diminished Sense of Authorship

Patients with schizophrenia who experience inserted thoughts report a diminished sense of thought authorship. Based on its elusive neural basis, this functional neuroimaging study used a novel setup to convince healthy participants that a technical device triggers thoughts in their stream of consciousness. Self-reports indicate that participants experienced their thoughts as self-generated when they believed the (fake) device was deactivated, and attributed their thoughts externally when they believed the device was activated-an experience usually only reported by patients diagnosed with schizophrenia. Distinct activations in the medial prefrontal cortex (mPFC) were observed: ventral mPFC activation was linked to a sense of thought authorship and dorsal mPFC activation to a diminished sense of thought authorship. This functional differentiation corresponds to research on self- and other-oriented reflection processes and on patients with schizophrenia who show abnormal mPFC activation. Results thus support the notion that the mPFC might be involved in thought authorship as well as anomalous self-experiences.

Modular-level alterations of single-subject gray matter networks in schizophrenia

Schizophrenia is often regarded as a psychiatric disorder caused by disrupted connections in the brain. Evidence suggests that the gray matter of schizophrenia patients is damaged in a modular pattern. Recently, abnormal topological organization was observed in the gray matter networks of patients with schizophrenia. However, the modular-level alteration of gray matter networks in schizophrenia remains unclear. In this study, single-subject gray matter networks were constructed for a total of 217 subjects (116 patients with schizophrenia and 101 controls). We analyzed the topological characteristics of the brain network and the strengths of connections between and within modules. Compared with the outcomes in the control group, the global efficiency and participation coefficient values of the single-subject gray matter networks in schizophrenic patients were significantly reduced. The nodal participation coefficient of the regions involving the frontoparietal attention network, default mode network and subcortical network were significantly decreased in subjects with schizophrenia. The intermodule connections between the frontoparietal attention network and visual network and between the default mode network and subcortical network, in the frontoparietal attention network were significantly reduced in the patient group. In the frontoparietal attention network, the intramodule nodal connection strength of the left orbital inferior frontal gyrus and right inferior parietal gyrus was significantly decreased in schizophrenia patients. Reduced intermodule nodal connection strength between the frontoparietal attention network and visual network was associated with the severity of schizophrenia symptoms. These findings suggest that abnormal intramodule and intermodule connections in the structural brain network may a biomarker of schizophrenia symptoms.

An ALE Meta-Analysis of Specific Functional MRI Studies on Subcortical Vascular Cognitive Impairment

Background: Subcortical vascular cognitive impairment (sVCI), caused by cerebral small vessel disease, accounts for the majority of vascular cognitive impairment, and is characterized by an insidious onset and impaired memory and executive function. If not recognized early, it inevitably develops into vascular dementia. Several quantitative studies have reported the consistent results of brain regions in sVCI patients that can be used to predict dementia conversion. The purpose of the study was to explore the exact abnormalities within the brain in sVCI patients by combining the coordinates reported in previous studies.

Methods: The PubMed, Embase, and Web of Science databases were thoroughly searched to obtain neuroimaging articles on the amplitude of low-frequency fluctuation, regional homogeneity, and functional connectivity in sVCI patients. According to the activation likelihood estimation (ALE) algorithm, a meta-analysis based on coordinate and functional connectivity modeling was conducted.

Results: The quantitative meta-analysis included 20 functional imaging studies on sVCI patients. Alterations in specific brain regions were mainly concentrated in the frontal lobes including the middle frontal gyrus, superior frontal gyrus, medial frontal gyrus, and precentral gyrus; parietal lobes including the precuneus, angular gyrus, postcentral gyrus, and inferior parietal lobule; occipital lobes including the lingual gyrus and cuneus; temporal lobes including the fusiform gyrus and middle temporal gyrus; and the limbic system including the cingulate gyrus. These specific brain regions belonged to important networks known as the default mode network, the executive control network, and the visual network.

Conclusion: The present study determined specific abnormal brain regions in sVCI patients, and these brain regions with specific changes were found to belong to important brain functional networks. The findings objectively present the exact abnormalities within the brain, which help further understand the pathogenesis of sVCI and identify them as potential imaging biomarkers. The results may also provide a basis for new approaches to treatment.

Neural Substrates of Psychotic Depression: Findings From the Global ECT-MRI Research Collaboration

Psychotic major depression (PMD) is hypothesized to be a distinct clinical entity from nonpsychotic major depression (NPMD). However, neurobiological evidence supporting this notion is scarce. The aim of this study is to identify gray matter volume (GMV) differences between PMD and NPMD and their longitudinal change following electroconvulsive therapy (ECT). Structural magnetic resonance imaging (MRI) data from 8 independent sites in the Global ECT-MRI Research Collaboration (GEMRIC) database (n = 108; 56 PMD and 52 NPMD; mean age 71.7 in PMD and 70.2 in NPMD) were analyzed. All participants underwent MRI before and after ECT. First, cross-sectional whole-brain voxel-wise GMV comparisons between PMD and NPMD were conducted at both time points. Second, in a flexible factorial model, a main effect of time and a group-by-time interaction were examined to identify longitudinal effects of ECT on GMV and longitudinal differential effects of ECT between PMD and NPMD, respectively. Compared with NPMD, PMD showed lower GMV in the prefrontal, temporal and parietal cortex before ECT; PMD showed lower GMV in the medial prefrontal cortex (MPFC) after ECT. Although there was a significant main effect of time on GMV in several brain regions in both PMD and NPMD, there was no significant group-by-time interaction. Lower GMV in the MPFC was consistently identified in PMD, suggesting this may be a trait-like neural substrate of PMD. Longitudinal effect of ECT on GMV may not explain superior ECT response in PMD, and further investigation is needed.

Non-linear associations between retinal nerve fibre layer (RNFL) and positive and negative symptoms among men with acute and chronic schizophrenia spectrum disorder

BACKGROUND

Schizophrenia Spectrum Disorder (SSD) is a chronic psychiatric disorder with modest treatment outcomes. Changes in neuronal morphology may be associated with the symptomatology of SSD. In the present study, we compared the retinal nerve fibre layer thickness (RNFLT) of typically developed adults with that of individuals with SSD in both acute and chronic stages.

METHODS

Fifteen healthy adult males (mean age: 36.40 years) and 30 individuals with SSD (mean age: 37.9 years) took part in the study. Among the latter, 15 had a chronic mean SSD for 15.33 years, while 15 were in an acute psychotic phase with a mean illness duration of 12.20 years. Experts rated positive and negative symptoms of SSD. Retinal nerve fibre layer thickness (RNFLT) of all participants was measured with optical coherence tomography (OCT).

RESULTS

Compared to healthy controls, individuals with acute SSD had the lowest macula thickness in the right eye. For nerve fiber layer atrophy, participants with acute SSD showed the largest atrophy (right eye, inferior quadrant). For retinal thickness and macular volume cube, compared to healthy controls, participants with acute SSD had the lowest thickness in the subfield of the right eye. Non-linear associations were observed between RNFL and positive and negative symptoms: e.g., for macula central and subfoveal thickness (left and right eye) and for participants with both acute and chronic SSD, exclusively positive and exclusively negative symptoms (as opposed to prevalently negative with some positive symptoms or prevalently positive with some negative symptoms) were associated with lower volumes. In participants with acute SSD, a longer disease duration was associated with thicker RNFL, while in participants with a chronic SSD a longer disease duration was associated with a thinner RNFL.

CONCLUSION

The present results confirm previous findings that specific neuronal morphological abnormalities can be observed among individuals with SSD. The non-linear associations between neuronal alterations and positive and negative symptomatology suggested that higher pronounced SSD severity appears to be particularly related to morphological changes. Disease duration and RNFL thickness were linearly associated, though, in opposite directions depending on the chronic or acute state.

Fronto-thalamic structural and effective connectivity and delusions in schizophrenia: a combined DTI/DCM study

BACKGROUND

Schizophrenia (SZ) is a complex disorder characterized by a range of behavioral and cognitive symptoms as well as structural and functional alterations in multiple cortical and subcortical structures. SZ is associated with reduced functional network connectivity involving core regions such as the anterior cingulate cortex (ACC) and the thalamus. However, little is known whether effective coupling, the directed influence of one structure over the other, is altered during rest in the ACC-thalamus network.

METHODS

We collected resting-state fMRI and diffusion-weighted MRI data from 18 patients and 20 healthy controls. We analyzed fronto-thalamic effective connectivity using dynamic causal modeling for cross-spectral densities in a network consisting of the ACC and the left and right medio-dorsal thalamic regions. We studied structural connectivity using fractional anisotropy (FA).

RESULTS

We found decreased coupling strength from the right thalamus to the ACC and from the right thalamus to the left thalamus, as well as increased inhibitory intrinsic connectivity in the right thalamus in patients relative to controls. ACC-to-left thalamus coupling strength correlated with the Positive and Negative Syndrome Scale (PANSS) total positive syndrome score and with delusion score. Whole-brain structural analysis revealed several tracts with reduced FA in patients, with a maximum decrease in white matter tracts containing fronto-thalamic and cingulo-thalamic fibers.

CONCLUSIONS

We found altered effective and structural connectivity within the ACC-thalamus network in SZ. Our results indicate that ACC-thalamus network activity at rest is characterized by reduced thalamus-to-ACC coupling. We suggest that positive symptoms may arise as a consequence of compensatory measures to imbalanced fronto-thalamic coupling.

Dissecting autism and schizophrenia through neuroimaging genomics

Neuroimaging genomic studies of autism spectrum disorder and schizophrenia have mainly adopted a 'top-down' approach, beginning with the behavioural diagnosis, and moving down to intermediate brain phenotypes and underlying genetic factors. Advances in imaging and genomics have been successfully applied to increasingly large case-control studies. As opposed to diagnostic-first approaches, the bottom-up strategy begins at the level of molecular factors enabling the study of mechanisms related to biological risk, irrespective of diagnoses or clinical manifestations. The latter strategy has emerged from questions raised by top-down studies: why are mutations and brain phenotypes over-represented in individuals with a psychiatric diagnosis? Are they related to core symptoms of the disease or to comorbidities? Why are mutations and brain phenotypes associated with several psychiatric diagnoses? Do they impact a single dimension contributing to all diagnoses? In this review, we aimed at summarizing imaging genomic findings in autism and schizophrenia as well as neuropsychiatric variants associated with these conditions. Top-down studies of autism and schizophrenia identified patterns of neuroimaging alterations with small effect-sizes and an extreme polygenic architecture. Genomic variants and neuroimaging patterns are shared across diagnostic categories suggesting pleiotropic mechanisms at the molecular and brain network levels. Although the field is gaining traction; characterizing increasingly reproducible results, it is unlikely that top-down approaches alone will be able to disentangle mechanisms involved in autism or schizophrenia. In stark contrast with top-down approaches, bottom-up studies showed that the effect-sizes of high-risk neuropsychiatric mutations are equally large for neuroimaging and behavioural traits. Low specificity has been perplexing with studies showing that broad classes of genomic variants affect a similar range of behavioural and cognitive dimensions, which may be consistent with the highly polygenic architecture of psychiatric conditions. The surprisingly discordant effect sizes observed between genetic and diagnostic first approaches underscore the necessity to decompose the heterogeneity hindering case-control studies in idiopathic conditions. We propose a systematic investigation across a broad spectrum of neuropsychiatric variants to identify putative latent dimensions underlying idiopathic conditions. Gene expression data on temporal, spatial and cell type organization in the brain have also considerable potential for parsing the mechanisms contributing to these dimensions' phenotypes. While large neuroimaging genomic datasets are now available in unselected populations, there is an urgent need for data on individuals with a range of psychiatric symptoms and high-risk genomic variants. Such efforts together with more standardized methods will improve mechanistically informed predictive modelling for diagnosis and clinical outcomes.

Neuroimaging correlates of insight in non-affective psychosis: A systematic review and meta-analysis

OBJECTIVE

Neurological correlates of impaired insight in non-affective psychosis remain unclear. This study aimed to review and meta-analyze the studies assessing the grey matter volumetric correlates of impaired insight in non-affective psychosis.

METHODS

This study consisted of a systematic review of 23 studies, and a meta-analysis with SDM-PSI of the 11 studies that were whole-brain and reported maps or peaks of correlation of studies investigating the grey matter volumetric correlates of insight assessments of non-affective psychosis, PubMed and OVID datasets were independently reviewed for articles reporting neuroimaging correlates of insight in non-affective psychosis. Quality assessment was realized following previous methodological approaches for the ABC quality assessment test of imaging studies, based on two main criteria: the statistical power and the multidimensional assessment of insight. Study peaks of correlation between grey matter volume and insight were used to recreate brain correlation maps.

RESULTS

A total of 418 records were identified through database searching. Of these records, twenty-three magnetic resonance imaging (MRI) studies that used different insight scales were included. The quality of the evidence was high in 11 studies, moderate in nine, and low in three. Patients with reduced insight showed decreases in the frontal, temporal (specifically in superior temporal gyrus), precuneus, cingulate, insula, and occipital lobes cortical grey matter volume. The meta-analysis indicated a positive correlation between grey matter volume and insight in the right insula (i.e., the smaller the grey matter, the lower the insight).

CONCLUSION

Several brain areas might be involved in impaired insight in patients with non-affective psychoses. The methodologies employed, such as the applied insight scales, may have contributed to the considerable discrepancies in the findings.

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

Changes in brain volume are a common finding in Magnetic Resonance Imaging (MRI) studies of people with psychosis and numerous longitudinal studies suggest that volume deficits progress with illness duration. However, a major unresolved question concerns whether these changes are driven by the underlying illness or represent iatrogenic effects of antipsychotic medication. In this study, 62 antipsychotic-naïve patients with first-episode psychosis (FEP) received either a second-generation antipsychotic (risperidone or paliperidone) or a placebo pill over a treatment period of 6 months. Both FEP groups received intensive psychosocial therapy. A healthy control group (n = 27) was also recruited. Structural MRI scans were obtained at baseline, 3 months and 12 months. Our primary aim was to differentiate illness-related brain volume changes from medication-related changes within the first 3 months of treatment. We secondarily investigated long-term effects at the 12-month timepoint. From baseline to 3 months, we observed a significant group x time interaction in the pallidum (p < 0.05 FWE-corrected), such that patients receiving antipsychotic medication showed increased volume, patients on placebo showed decreased volume, and healthy controls showed no change. Across the entire patient sample, a greater increase in pallidal grey matter volume over 3 months was associated with a greater reduction in symptom severity. Our findings indicate that psychotic illness and antipsychotic exposure exert distinct and spatially distributed effects on brain volume. Our results align with prior work in suggesting that the therapeutic efficacy of antipsychotic medications may be primarily mediated through their effects on the basal ganglia.

Orexin Modulation of VTA Dopamine Neuron Activity: Relevance to Schizophrenia

BACKGROUND

The hippocampus is a region consistently implicated in schizophrenia and has been advanced as a therapeutic target for positive, negative, and cognitive deficits associated with the disease. Recently, we reported that the paraventricular nucleus of the thalamus (PVT) works in concert with the ventral hippocampus to regulate dopamine system function; however, the PVT has yet to be investigated as target for the treatment of the disease. Given the dense expression of orexin receptors in the thalamus, we believe these to be a possible target for pharmacological regulation of PVT activity.

METHODS

Here we used the methylazoxymethanol acetate (MAM) rodent model, which displays pathological alterations consistent with schizophrenia to determine whether orexin receptor blockade can restore ventral tegmental area dopamine system function. We measured dopamine neuron population activity, using in vivo electrophysiology, following administration of the dual orexin antagonist, TCS 1102 (both intraperitoneal and intracranial into the PVT in MAM- and saline-treated rats), and orexin A and B peptides (intracranial into the PVT in naïve rats).

RESULTS

Aberrant dopamine system function in MAM-treated rats was normalized by the systemic administration of TCS 1102. To investigate the potential site of action, the orexin peptides A and B were administered directly into the PVT, where they significantly increased ventral tegmental area dopamine neuron population activity in control rats. In addition, the direct administration of TCS 1102 into the PVT reproduced the beneficial effects seen with the systemic administration in MAM-treated rats.

CONCLUSION

Taken together, these data suggest the orexin system may represent a novel site of therapeutic intervention for psychosis via an action in the PVT.

Resting-state functional hypoconnectivity of amygdala in clinical high risk state and first-episode schizophrenia

Resting-state functional hypoconnectivity of the amygdala with several brain regions has been identified in patients with schizophrenia. However, little is known about it in individuals at clinical high risk state. Treatment-seeking, drug-naive young adults were recruited for the study. The participants included 33 adults at Clinical High Risk (CHRs), 31 adults with first-episode schizophrenia (FSZs), and 37 age-, gender-, and education-matched healthy controls. All the participants were subjected to resting-state functional magnetic resonance imaging scans. Seed-based voxel-wise amygdala/whole-brain functional connectivity (FC) was calculated and compared. In the CHR group, the right amygdala showed decreased FC with clusters located in the left orbital, right temporal, insular, and bilateral frontal and cingulate areas. In the FSZ group, the right amygdala showed decreased FC with clusters located in the right temporal, insular, cingulate, and frontal areas. Exactly 30% of the voxels showing decreased FC in the FSZ group coincided with those in the CHR group. No difference in FC was identified between the CHR and FSZ groups. Voxel-wise FC values with the left or right amygdala in the bilateral occipital cortex were negatively correlated with the PANSS total score in the FSZ group. Resting-state functional hypoconnectivity of the amygdala is a valuable risk phenotype of schizophrenia, and its distribution, rather than degree, distinguishes CHR state from schizophrenia. This particular hypoconnectivity in CHRs and FSZs is relatively independent of the symptomatology and may reflect a dysfunctional dopamine system.

Updating and characterizing neuroanatomical markers in high-risk subjects, recently diagnosed and chronic patients with schizophrenia: A revised coordinate-based meta-analysis

Characterizing neuroanatomical markers of different stages of schizophrenia (SZ) to assess pathophysiological models of how the disorder develops is an important target for the clinical practice. We performed a meta-analysis of voxel-based morphometry studies of genetic and clinical high-risk subjects (g-/c-HR), recently diagnosed (RDSZ) and chronic SZ patients (ChSZ). We quantified gray matter (GM) changes associated with these four conditions and compared them with contrast and conjunctional data. We performed the behavioral analysis and networks decomposition of alterations to obtain their functional characterization. Results reveal a cortical-subcortical, left-to-right homotopic progression of GM loss. The right anterior cingulate is the only altered region found altered among c-HR, RDSZ and ChSZ. Contrast analyses show left-lateralized insular, amygdalar and parahippocampal GM reduction in RDSZ, which appears bilateral in ChSZ. Functional decomposition shows involvement of the salience network, with an enlargement of the sensorimotor network in RDSZ and the thalamus-basal nuclei network in ChSZ. These findings support the current neuroprogressive models of SZ and integrate this deterioration with the clinical evolution of the disease.

Reduced Brain Gray Matter Volume in Patients With First-Episode Major Depressive Disorder: A Quantitative Meta-Analysis

Background: The findings of many neuroimaging studies in patients with first-episode major depressive disorder (MDD), and even those of previous meta-analysis, are divergent. To quantitatively integrate these studies, we performed a meta-analysis of gray matter volumes using voxel-based morphometry (VBM). Methods: We performed a comprehensive literature search for relevant studies and traced the references up to May 1, 2021 to select the VBM studies between first-episode MDD and healthy controls (HC). A quantitative meta-analysis of VBM studies on first-episode MDD was performed using the Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) method, which allows a familywise error rate (FWE) correction for multiple comparisons of the results. Meta-regression was used to explore the effects of demographics and clinical characteristics. Results: Nineteen studies, with 22 datasets comprising 619 first-episode MDD and 707 HC, were included. The pooled and subgroup meta-analysis showed robust gray matter reductions in the left insula, the bilateral parahippocampal gyrus extending into the bilateral hippocampus, the right gyrus rectus extending into the right striatum, the right superior frontal gyrus (dorsolateral part), the left superior frontal gyrus (medial part) and the left superior parietal gyrus. Meta-regression analyses showed that higher HDRS scores were significantly more likely to present reduced gray matter volumes in the right amygdala, and the mean age of MDD patients in each study was negatively correlated with reduced gray matter in the left insula. Conclusions: The present meta-analysis revealed that structural abnormalities in the fronto-striatal-limbic and fronto-parietal networks are essential characteristics in first-episode MDD patients, which may become a potential target for clinical intervention.

A Multimodal Fusion Analysis of Pretreatment Anatomical and Functional Cortical Abnormalities in Responsive and Non-responsive Schizophrenia

Background: Antipsychotic medications provide limited long-term benefit to ~30% of schizophrenia patients. Multimodal magnetic resonance imaging (MRI) data have been used to investigate brain features between responders and nonresponders to antipsychotic treatment; however, these analytical techniques are unable to weigh the interrelationships between modalities. Here, we used multiset canonical correlation and joint independent component analysis (mCCA + jICA) to fuse MRI data to examine the shared and specific multimodal features between the patients and healthy controls (HCs) and between the responders and non-responders. Method: Resting-state functional and structural MRI data were collected from 55 patients with drug-naïve first-episode schizophrenia (FES) and demographically matched HCs. Based on the decrease in Positive and Negative Syndrome Scale scores from baseline to the 1-year follow-up, FES patients were divided into a responder group (RG) and a non-responder group (NRG). Gray matter volume (GMV), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) maps were used as features in mCCA + jICA. Results: Between FES patients and HCs, there were three modality-specific discriminative independent components (ICs) showing the difference in mixing coefficients (GMV-IC7, GMV-IC8, and fALFF-IC5). The fusion analysis indicated one modality-shared IC (GMV-IC2 and ReHo-IC2) and three modality-specific ICs (GMV-IC1, GMV-IC3, and GMV-IC6) between the RG and NRG. The right postcentral gyrus showed a significant difference in GMV features between FES patients and HCs and modality-shared features (GMV and ReHo) between responders and nonresponders. The modality-shared component findings were highlighted by GMV, mainly in the bilateral temporal gyrus and the right cerebellum associated with ReHo in the right postcentral gyrus. Conclusions: This study suggests that joint anatomical and functional features of the cortices may reflect an early pathophysiological mechanism that is related to a 1-year treatment response.

Altered cortical thickness development in 22q11.2 deletion syndrome and association with psychotic symptoms

Schizophrenia has been extensively associated with reduced cortical thickness (CT), and its neurodevelopmental origin is increasingly acknowledged. However, the exact timing and extent of alterations occurring in preclinical phases remain unclear. With a high prevalence of psychosis, 22q11.2 deletion syndrome (22q11DS) is a neurogenetic disorder that represents a unique opportunity to examine brain maturation in high-risk individuals. In this study, we quantified trajectories of CT maturation in 22q11DS and examined the association of CT development with the emergence of psychotic symptoms. Longitudinal structural MRI data with 1-6 time points were collected from 324 participants aged 5-35 years (N = 148 22q11DS, N = 176 controls), resulting in a total of 636 scans (N = 334 22q11DS, N = 302 controls). Mixed model regression analyses were used to compare CT trajectories between participants with 22q11DS and controls. Further, CT trajectories were compared between participants with 22q11DS who developed (N = 61, 146 scans), or remained exempt of (N = 47; 98 scans) positive psychotic symptoms during development. Compared to controls, participants with 22q11DS showed widespread increased CT, focal reductions in the posterior cingulate gyrus and superior temporal gyrus (STG), and accelerated cortical thinning during adolescence, mainly in frontotemporal regions. Within 22q11DS, individuals who developed psychotic symptoms showed exacerbated cortical thinning in the right STG. Together, these findings suggest that genetic predisposition for psychosis is associated with increased CT starting from childhood and altered maturational trajectories of CT during adolescence, affecting predominantly frontotemporal regions. In addition, accelerated thinning in the STG may represent an early biomarker associated with the emergence of psychotic symptoms.

Comparisons of Voxel-Based Morphometric Brain Volumes of Individuals with Methamphetamine-Induced Psychotic Disorder and Schizophrenia Spectrum Disorder and Healthy Controls

BACKGROUND

Several psychological and neurological pathways are described to explain the emergence and maintenance of psychiatric disorders, and changes in brain volumes and brain activity are observed as correlates of psychiatric disorders. In the present study, we investigated if and to what extent specific voxel-based morphometric brain volume differences could be observed among individuals with methamphetamine-induced psychosis (MAIP) and schizophrenia spectrum disorder (SSD) compared to healthy controls.

METHODS

A total of 69 individuals took part in the present study. Of those, 26 were diagnosed with MAIP, 23 with SSD, and 20 were healthy controls. After a thorough psychiatric assessment, participants underwent brain volume measurement.

RESULTS

Compared to healthy controls, participants with MAIP had smaller volumes for left caudate and left and right parahippocampal gyrus. Compared to healthy controls, participants with SSD had smaller volumes for the gray and white matter, left amygdala, left hippocampus, left parahippocampal gyrus, left putamen, and the total volume. Compared to individuals with MAIP, individuals with SSD had a lower white matter brain volume.

CONCLUSIONS

The pattern of results suggests that individuals with MAIP and SSD showed specific and regional brain atrophies on the left hemisphere, always compared to healthy controls. Given the cross-sectional design, it remains undisclosed if specific and regional brain atrophies were the cause or the consequence of the psychiatric issues.

Chemotherapy-induced grey matter abnormalities in cancer survivors: a voxel-wise neuroimaging meta-analysis

BACKGROUND

Findings regarding chemotherapy-induced grey matter abnormalities are heterogeneous, and no meta-analysis has quantitatively assessed brain structural alterations in cancer survivors treated with chemotherapy.

PURPOSE

To investigate the grey matter abnormalities in non-CNS (central nervous system) cancer survivors treated with chemotherapy using Anisotropic Effect Size Signed Differential Mapping (AES-SDM) software.

METHOD

We identified studies published up to Sep 2018 that compared grey matter in non-CNS cancer survivors treated with chemotherapy (CT+, 10 data sets including 433 individuals) and cancer survivors not treated with chemotherapy (CT-, 7 data sets including 210 individuals) or healthy controls (HC, 3 data sets including 407 individuals) using whole-brain VBM. We used statistical maps from the studies included where available and reported peak coordinates otherwise.

RESULTS

Compared with both CT- and HC, the CT + groups exhibited a reduced grey matter volume (GMV), mainly in the prefrontal and anterior cingulate cortex (ACC) and right fusiform gyrus (FG). A smaller GMV in the FG and prefrontal cortex were found in the CT + compared with the CT-groups and in the CT + groups with impaired cognition. GMV in two areas was positively associated with the time since chemotherapy.

CONCLUSIONS

The present results suggest that non-CNS cancer survivors treated with chemotherapy exhibit grey matter abnormalities in the brain, especially in the prefrontal and ACC cortex. Grey matter volume changes after chemotherapy may contribute to cognitive impairments in cancer survivors that can be observed after chemotherapy.

Altered cerebellocerebral structural covariance in individuals with attenuated psychosis syndrome

BACKGROUND

Reduced cerebellar volumes and altered cerebellocerebral structural covariance have been reported in patients with schizophrenia. However, it is not clear whether such altered cerebellocerebral structural covariance would be observed before the onset of psychosis. We examined brain structural changes, including cerebral and cerebellar volumes, and cerebellocerebral structural covariance in individuals with attenuated psychosis syndrome (APS).

MATERIALS AND METHODS

Twenty-one individuals with APS and 24 healthy controls (HC) were recruited and underwent structural MRI brain scan. Differences in voxel-based grey matter (GM) volume and cerebellar volume between the APS and HC groups were examined. The correlation between cerebellar volumes and voxel-based cerebral GM volumes were calculated to measure cerebellocerebral structural covariance in each group followed by group comparisons.

RESULTS

Compared with HC, individuals with APS exhibited extensive GM volume reduction in the frontal and striatal areas and reduced cerebellar volume. Structural covariance analysis indicated that the anterior and posterior parts of the cerebellum showed disparate correlation with cerebral voxel-based GM volumes. Abnormal cerebellar-cerebral correlation was also found in individuals with APS at the medial prefrontal gyrus.

CONCLUSIONS

Our findings suggest that prefrontal and striatal structural changes as well as cerebellar structural covariance at the medial prefrontal gyrus may underpin the risk for psychosis and may serve as a potential target for early intervention in individuals at-risk for psychosis.

A meta-analytic approach to mapping co-occurrent grey matter volume increases and decreases in psychiatric disorders

Numerous studies have investigated grey matter (GM) volume changes in diverse patient groups. Reports of disorder-related GM reductions are common in such work, but many studies also report evidence for GM volume increases in patients. It is unclear whether these GM increases and decreases are independent or related in some way. Here, we address this question using a novel meta-analytic network mapping approach. We used a coordinate-based meta-analysis of 64 voxel-based morphometry studies of psychiatric disorders to calculate the probability of finding a GM increase or decrease in one region given an observed change in the opposite direction in another region. Estimating this co-occurrence probability for every pair of brain regions allowed us to build a network of concurrent GM changes of opposing polarity. Our analysis revealed that disorder-related GM increases and decreases are not independent; instead, a GM change in one area is often statistically related to a change of opposite polarity in other areas, highlighting distributed yet coordinated changes in GM volume as a function of brain pathology. Most regions showing GM changes linked to an opposite change in a distal area were located in salience, executive-control and default mode networks, as well as the thalamus and basal ganglia. Moreover, pairs of regions showing coupled changes of opposite polarity were more likely to belong to different canonical networks than to the same one. Our results suggest that regional GM alterations in psychiatric disorders are often accompanied by opposing changes in distal regions that belong to distinct functional networks.

Joint Multi-modal Parcellation of the Human Striatum: Functions and Clinical Relevance

The human striatum is essential for both low- and high-level functions and has been implicated in the pathophysiology of various prevalent disorders, including Parkinson's disease (PD) and schizophrenia (SCZ). It is known to consist of structurally and functionally divergent subdivisions. However, previous parcellations are based on a single neuroimaging modality, leaving the extent of the multi-modal organization of the striatum unknown. Here, we investigated the organization of the striatum across three modalities-resting-state functional connectivity, probabilistic diffusion tractography, and structural covariance-to provide a holistic convergent view of its structure and function. We found convergent clusters in the dorsal, dorsolateral, rostral, ventral, and caudal striatum. Functional characterization revealed the anterior striatum to be mainly associated with cognitive and emotional functions, while the caudal striatum was related to action execution. Interestingly, significant structural atrophy in the rostral and ventral striatum was common to both PD and SCZ, but atrophy in the dorsolateral striatum was specifically attributable to PD. Our study revealed a cross-modal convergent organization of the striatum, representing a fundamental topographical model that can be useful for investigating structural and functional variability in aging and in clinical conditions.

Multivariate patterns of gray matter volume in thalamic nuclei are associated with positive schizotypy in healthy individuals

BACKGROUND

Previous models suggest biological and behavioral continua among healthy individuals (HC), at-risk condition, and full-blown schizophrenia (SCZ). Part of these continua may be captured by schizotypy, which shares subclinical traits and biological phenotypes with SCZ, including thalamic structural abnormalities. In this regard, previous findings have suggested that multivariate volumetric patterns of individual thalamic nuclei discriminate HC from SCZ. These results were obtained using machine learning, which allows case-control classification at the single-subject level. However, machine learning accuracy is usually unsatisfactory possibly due to phenotype heterogeneity. Indeed, a source of misclassification may be related to thalamic structural characteristics of those HC with high schizotypy, which may resemble structural abnormalities of SCZ. We hypothesized that thalamic structural heterogeneity is related to schizotypy, such that high schizotypal burden would implicate misclassification of those HC whose thalamic patterns resemble SCZ abnormalities.

METHODS

Following a previous report, we used Random Forests to predict diagnosis in a case-control sample (SCZ = 131, HC = 255) based on thalamic nuclei gray matter volumes estimates. Then, we investigated whether the likelihood to be classified as SCZ (π-SCZ) was associated with schizotypy in 174 HC, evaluated with the Schizotypal Personality Questionnaire.

RESULTS

Prediction accuracy was 72.5%. Misclassified HC had higher positive schizotypy scores, which were correlated with π-SCZ. Results were specific to thalamic rather than whole-brain structural features.

CONCLUSIONS

These findings strengthen the relevance of thalamic structural abnormalities to SCZ and suggest that multivariate thalamic patterns are correlates of the continuum between schizotypy in HC and the full-blown disease.

Stable biomarker identification for predicting schizophrenia in the human connectome

Schizophrenia, as a psychiatric disorder, has recognized brain alterations both at the structural and at the functional magnetic resonance imaging level. The developing field of connectomics has attracted much attention as it allows researchers to take advantage of powerful tools of network analysis in order to study structural and functional connectivity abnormalities in schizophrenia. Many methods have been proposed to identify biomarkers in schizophrenia, focusing mainly on improving the classification performance or performing statistical comparisons between groups. However, the stability of biomarkers selection has been for long overlooked in the connectomics field. In this study, we follow a machine learning approach where the identification of biomarkers is addressed as a feature selection problem for a classification task. We perform a recursive feature elimination and support vector machines (RFE-SVM) approach to identify the most meaningful biomarkers from the structural, functional, and multi-modal connectomes of healthy controls and patients. Furthermore, the stability of the retrieved biomarkers is assessed across different subsamplings of the dataset, allowing us to identify the affected core of the pathology. Considering our technique altogether, it demonstrates a principled way to achieve both accurate and stable biomarkers while highlighting the importance of multi-modal approaches to brain pathology as they tend to reveal complementary information.

Cortical Volume Differences in Subjects at Risk for Psychosis Are Driven by Surface Area

In subjects at risk for psychosis, the studies on gray matter volume (GMV) predominantly reported volume loss compared with healthy controls (CON). However, other important morphological measurements such as cortical surface area (CSA) and cortical thickness (CT) were not systematically compared. So far, samples mostly comprised subjects at genetic risk or at clinical risk fulfilling an ultra-high risk (UHR) criterion. No studies comparing UHR subjects with at-risk subjects showing only basic symptoms (BS) investigated the differences in CSA or CT. Therefore, we aimed to unravel the contribution of the 2 morphometrical measures constituting the cortical volume (CV) and to test whether these groups inhere different morphometric features. We conducted a surface-based morphometric analysis in 34 CON, 46 BS, and 39 UHR to examine between-group differences in CV, CSA, and CT vertex-wise across the whole cortex. Compared with BS and CON, UHR individuals presented increased CV in frontal and parietal regions, which was driven by larger CSA. These groups did not differ in CT. Yet, at-risk subjects who later developed schizophrenia showed thinning in the occipital cortex. Furthermore, BS presented increased CSA compared with CON. Our results suggest that volumetric differences in UHR subjects are driven by CSA while CV loss in converters seems to be based on cortical thinning. We attribute the larger CSA in UHR to aberrant pruning representing a vulnerability to develop psychotic symptoms reflected in different levels of vulnerability for BS and UHR, and cortical thinning to a presumably stress-related cortical decomposition.

Spatial Patterning of Tissue Volume Loss in Schizophrenia Reflects Brain Network Architecture

BACKGROUND

There is growing recognition that connectome architecture shapes cortical and subcortical gray matter atrophy across a spectrum of neurological and psychiatric diseases. Whether connectivity contributes to tissue volume loss in schizophrenia in the same manner remains unknown.

METHODS

Here, we relate tissue volume loss in patients with schizophrenia to patterns of structural and functional connectivity. Gray matter deformation was estimated in a sample of 133 individuals with chronic schizophrenia (48 women, mean age 34.7 ± 12.9 years) and 113 control subjects (64 women, mean age 23.5 ± 8.4 years). Deformation-based morphometry was used to estimate cortical and subcortical gray matter deformation from T1-weighted magnetic resonance images. Structural and functional connectivity patterns were derived from an independent sample of 70 healthy participants using diffusion spectrum imaging and resting-state functional magnetic resonance imaging.

RESULTS

We found that regional deformation is correlated with the deformation of structurally and functionally connected neighbors. Distributed deformation patterns are circumscribed by specific functional systems (the ventral attention network) and cytoarchitectonic classes (limbic class), with an epicenter in the anterior cingulate cortex.

CONCLUSIONS

Altogether, the present study demonstrates that brain tissue volume loss in schizophrenia is conditioned by structural and functional connectivity, accounting for 25% to 35% of regional variance in deformation.