Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia-related genes

Neuroscience Needs Network Science

The brain is a complex system comprising a myriad of interacting neurons, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such interconnected systems, offering a framework for integrating multiscale data and complexity. To date, network methods have significantly advanced functional imaging studies of the human brain and have facilitated the development of control theory-based applications for directing brain activity. Here, we discuss emerging frontiers for network neuroscience in the brain atlas era, addressing the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease. We underscore the importance of fostering interdisciplinary opportunities through workshops, conferences, and funding initiatives, such as supporting students and postdoctoral fellows with interests in both disciplines. By bringing together the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way toward a deeper understanding of the brain and its functions, as well as offering new challenges for network science.

Menstrually-related migraine shapes the structural similarity network integration of brain

Menstrually-related migraine (MM) is a primary migraine in women of reproductive age. The underlying neural mechanism of MM was still unclear. In this study, we aimed to reveal the case-control differences in network integration and segregation for the morphometric similarity network of MM. Thirty-six patients with MM and 29 healthy females were recruited and underwent MRI scanning. The morphometric features were extracted in each region to construct the single-subject interareal cortical connection using morphometric similarity. The network topology characteristics, in terms of integration and segregation, were analyzed. Our results revealed that, in the absence of morphology differences, disrupted cortical network integration was found in MM patients compared to controls. The patients with MM showed a decreased global efficiency and increased characteristic path length compared to healthy controls. Regional efficiency analysis revealed the decreased efficiency in the left precentral gyrus and bilateral superior temporal gyrus contributed to the decreased network integration. The increased nodal degree centrality in the right pars triangularis was positively associated with the attack frequency in MM. Our results suggested MM would reorganize the morphology in the pain-related brain regions and reduce the parallel information processing capacity of the brain.

Spatial transcriptomic patterns underlying regional vulnerability to amyloid-β and tau pathologies and their relationships to cognitive dysfunction in Alzheimer's disease

Amyloid-β (Aβ) and tau proteins accumulate within distinct neuronal systems in Alzheimer's disease (AD). Although it is not clear why certain brain regions are more vulnerable to Aβ and tau pathologies than others, gene expression may play a role. We studied the association between brain-wide gene expression profiles and regional vulnerability to Aβ (gene-to-Aβ associations) and tau (gene-to-tau associations) pathologies leveraging two large independent cohorts (n = 715) of participants along the AD continuum. We identified several AD susceptibility genes and gene modules in a gene co-expression network with expression profiles related to regional vulnerability to Aβ and tau pathologies in AD. In particular, we found that the positive APOE -to-tau association was only seen in the AD cohort, whereas patients with AD and frontotemporal dementia shared similar positive MAPT -to-tau association. Some AD candidate genes showed sex-dependent negative gene-to-Aβ and gene-to-tau associations. In addition, we identified distinct biochemical pathways associated with the gene-to-Aβ and the gene-to-tau associations. Finally, we proposed a novel analytic framework, linking the identified gene-to-pathology associations to cognitive dysfunction in AD at the individual level, suggesting potential clinical implication of the gene-to-pathology associations. Taken together, our study identified distinct gene expression profiles and biochemical pathways that may explain the discordance between regional Aβ and tau pathologies, and filled the gap between gene-to-pathology associations and cognitive dysfunction in individual AD patients that may ultimately help identify novel personalized pathogenetic biomarkers and therapeutic targets.

One Sentence Summary

We identified replicable cognition-related associations between regional gene expression profiles and selectively regional vulnerability to amyloid-β and tau pathologies in AD.

Robust estimation of cortical similarity networks from brain MRI

Structural similarity is a growing focus for magnetic resonance imaging (MRI) of connectomes. Here we propose Morphometric INverse Divergence (MIND), a new method to estimate within-subject similarity between cortical areas based on the divergence between their multivariate distributions of multiple MRI features. Compared to the prior approach of morphometric similarity networks (MSNs) on n > 11,000 scans spanning three human datasets and one macaque dataset, MIND networks were more reliable, more consistent with cortical cytoarchitectonics and symmetry and more correlated with tract-tracing measures of axonal connectivity. MIND networks derived from human T1-weighted MRI were more sensitive to age-related changes than MSNs or networks derived by tractography of diffusion-weighted MRI. Gene co-expression between cortical areas was more strongly coupled to MIND networks than to MSNs or tractography. MIND network phenotypes were also more heritable, especially edges between structurally differentiated areas. MIND network analysis provides a biologically validated lens for cortical connectomics using readily available MRI data.

Progressive trajectories of schizophrenia across symptoms, genes, and the brain

BACKGROUND

Schizophrenia is characterized by complex psychiatric symptoms and unclear pathological mechanisms. Most previous studies have focused on the morphological changes that occur over the development of the disease; however, the corresponding functional trajectories remain unclear. In the present study, we aimed to explore the progressive trajectories of patterns of dysfunction after diagnosis.

METHODS

Eighty-six patients with schizophrenia and 120 healthy controls were recruited as the discovery dataset. Based on multiple functional indicators of resting-state brain functional magnetic resonance imaging, we conducted a duration-sliding dynamic analysis framework to investigate trajectories in association with disease progression. Neuroimaging findings were associated with clinical symptoms and gene expression data from the Allen Human Brain Atlas database. A replication cohort of patients with schizophrenia from the University of California, Los Angeles, was used as the replication dataset for the validation analysis.

RESULTS

Five stage-specific phenotypes were identified. A symptom trajectory was characterized by positive-dominated, negative ascendant, negative-dominated, positive ascendant, and negative surpassed stages. Dysfunctional trajectories from primary and subcortical regions to higher-order cortices were recognized; these are associated with abnormal external sensory gating and a disrupted internal excitation-inhibition equilibrium. From stage 1 to stage 5, the importance of neuroimaging features associated with behaviors gradually shifted from primary to higher-order cortices and subcortical regions. Genetic enrichment analysis identified that neurodevelopmental and neurodegenerative factors may be relevant as schizophrenia progresses and highlighted multiple synaptic systems.

CONCLUSIONS

Our convergent results indicate that progressive symptoms and functional neuroimaging phenotypes are associated with genetic factors in schizophrenia. Furthermore, the identification of functional trajectories complements previous findings of structural abnormalities and provides potential targets for drug and non-drug interventions in different stages of schizophrenia.

Longitudinal trajectories in negative symptoms and changes in brain cortical thickness: 10-year follow-up study

BACKGROUND

Understanding the evolution of negative symptoms in first-episode psychosis (FEP) requires long-term longitudinal study designs that capture the progression of this condition and the associated brain changes.

AIMS

To explore the factors underlying negative symptoms and their association with long-term abnormal brain trajectories.

METHOD

We followed up 357 people with FEP over a 10-year period. Factor analyses were conducted to explore negative symptom dimensionality. Latent growth mixture modelling (LGMM) was used to identify the latent classes. Analysis of variance (ANOVA) was conducted to investigate developmental trajectories of cortical thickness. Finally, the resulting ANOVA maps were correlated with a wide set of regional molecular profiles derived from public databases.

RESULTS

Three trajectories (stable, decreasing and increasing) were found in each of the three factors (expressivity, experiential and attention) identified by the factor analyses. Patients with an increasing trajectory in the expressivity factor showed cortical thinning in caudal middle frontal, pars triangularis, rostral middle frontal and superior frontal regions from the third to the tenth year after the onset of the psychotic disorder. The F-statistic map of cortical thickness expressivity differences was associated with a receptor density map derived from positron emission tomography data.

CONCLUSIONS

Stable and decreasing were the most common trajectories. Additionally, cortical thickness abnormalities found at relatively late stages of FEP onset could be exploited as a biomarker of poor symptom outcome in the expressivity dimension. Finally, the brain areas with less density of receptors spatially overlap areas that discriminate the trajectories of the expressivity dimension.

Cortical anatomical variations, gene expression profiles, and clinical phenotypes in patients with schizophrenia

BACKGROUND AND HYPOTHESIS

Schizophrenia (SZ) patients display significant structural brain abnormalities; nevertheless, the genetic mechanisms regulating cortical anatomical variations and their correlation with the disease phenotype are still ambiguous.

STUDY DESIGN

We characterized anatomical variation using a surface-based method derived from structural magnetic resonance imaging of patients with SZ and age- and sex-matched healthy controls (HCs). Partial least-squares regression was performed across cortex regions between anatomical variation and average transcriptional profiles of SZ risk genes and all qualified genes from the Allen Human Brain Atlas. The morphological features of each brain region were correlated to symptomology variables in patients with SZ using partial correlation analysis.

STUDY RESULTS

A total of 203 SZ and 201 HCs were included in the final analysis. We observed significant variation of 55 regions of cortical thickness, 23 regions of volume, 7 regions of area, and 55 regions of local gyrification index (LGI) between SZ and HC groups. Expression profiles of 4 SZ risk genes and 96 genes from all qualified genes showed a correlation to anatomical variability, however, after multiple comparisons, the correlations were no longer significant. LGI variability in multiple frontal subregions was associated with specific symptoms of SZ, whereas cognitive function involving attention/vigilance was linked to LGI variability across nine brain regions.

CONCLUSIONS

Cortical anatomical variation of patients with schizophrenia is associated with gene transcriptome profiles as well as clinical phenotypes.

Transcriptomic and Macroscopic Architectures of Multimodal Covariance Network Reveal Molecular-Structural-Functional Co-alterations

Human cognition is usually underpinned by intrinsic structure and functional neural co-activation in spatially distributed brain regions. Owing to lacking an effective approach to quantifying the covarying of structure and functional responses, how the structural-functional circuits interact and how genes encode the relationships, to deepen our knowledge of human cognition and disease, are still unclear. Here, we propose a multimodal covariance network (MCN) construction approach to capture interregional covarying of the structural skeleton and transient functional activities for a single individual. We further explored the potential association between brain-wide gene expression patterns and structural-functional covarying in individuals involved in a gambling task and individuals with major depression disorder (MDD), adopting multimodal data from a publicly available human brain transcriptomic atlas and 2 independent cohorts. MCN analysis showed a replicable cortical structural-functional fine map in healthy individuals, and the expression of cognition- and disease phenotype-related genes was found to be spatially correlated with the corresponding MCN differences. Further analysis of cell type-specific signature genes suggests that the excitatory and inhibitory neuron transcriptomic changes could account for most of the observed correlation with task-evoked MCN differences. In contrast, changes in MCN of MDD patients were enriched for biological processes related to synapse function and neuroinflammation in astrocytes, microglia, and neurons, suggesting its promising application in developing targeted therapies for MDD patients. Collectively, these findings confirmed the correlations of MCN-related differences with brain-wide gene expression patterns, which captured genetically validated structural-functional differences at the cellular level in specific cognitive processes and psychiatric patients.

Hierarchical fluctuation shapes a dynamic flow linked to states of consciousness

Consciousness arises from the spatiotemporal neural dynamics, however, its relationship with neural flexibility and regional specialization remains elusive. We identified a consciousness-related signature marked by shifting spontaneous fluctuations along a unimodal-transmodal cortical axis. This simple signature is sensitive to altered states of consciousness in single individuals, exhibiting abnormal elevation under psychedelics and in psychosis. The hierarchical dynamic reflects brain state changes in global integration and connectome diversity under task-free conditions. Quasi-periodic pattern detection revealed that hierarchical heterogeneity as spatiotemporally propagating waves linking to arousal. A similar pattern can be observed in macaque electrocorticography. Furthermore, the spatial distribution of principal cortical gradient preferentially recapitulated the genetic transcription levels of the histaminergic system and that of the functional connectome mapping of the tuberomammillary nucleus, which promotes wakefulness. Combining behavioral, neuroimaging, electrophysiological, and transcriptomic evidence, we propose that global consciousness is supported by efficient hierarchical processing constrained along a low-dimensional macroscale gradient.

Convergent imaging-transcriptomic evidence for disturbed iron homeostasis in Gilles de la Tourette syndrome

Gilles de la Tourette syndrome (GTS) is a neuropsychiatric movement disorder with reported abnormalities in various neurotransmitter systems. Considering the integral role of iron in neurotransmitter synthesis and transport, it is hypothesized that iron exhibits a role in GTS pathophysiology. As a surrogate measure of brain iron, quantitative susceptibility mapping (QSM) was performed in 28 patients with GTS and 26 matched controls. Significant susceptibility reductions in the patient cohort, consistent with reduced local iron content, were obtained in subcortical regions known to be implicated in GTS. Regression analysis revealed a significant negative association of tic scores and striatal susceptibility. To interrogate genetic mechanisms that may drive these reductions, spatially specific relationships between susceptibility and gene-expression patterns extracted from the Allen Human Brain Atlas were assessed. Correlations in the striatum were enriched for excitatory, inhibitory, and modulatory neurochemical signaling mechanisms in the motor regions, mitochondrial processes driving ATP production and iron-sulfur cluster biogenesis in the executive subdivision, and phosphorylation-related mechanisms that affect receptor expression and long-term potentiation. This link between susceptibility reductions and normative transcriptional profiles suggests that disruptions in iron regulatory mechanisms are involved in GTS pathophysiology and may lead to pervasive abnormalities in mechanisms regulated by iron-containing enzymes.

Neuroscience needs Network Science

The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such intricate systems, offering a framework for integrating multiscale data and complexity. Here, we discuss the application of network science in the study of the brain, addressing topics such as network models and metrics, the connectome, and the role of dynamics in neural networks. We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities. We underscore the importance of fostering interdisciplinary opportunities through funding initiatives, workshops, and conferences, as well as supporting students and postdoctoral fellows with interests in both disciplines. By uniting the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way towards a deeper understanding of the brain and its functions.

Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder

The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD) are not well understood. Using a large neuroimaging dataset, we identified three latent dimensions of functional brain network connectivity that predicted individual differences in ASD behaviors and were stable in cross-validation. Clustering along these three dimensions revealed four reproducible ASD subgroups with distinct functional connectivity alterations in ASD-related networks and clinical symptom profiles that were reproducible in an independent sample. By integrating neuroimaging data with normative gene expression data from two independent transcriptomic atlases, we found that within each subgroup, ASD-related functional connectivity was explained by regional differences in the expression of distinct ASD-related gene sets. These gene sets were differentially associated with distinct molecular signaling pathways involving immune and synapse function, G-protein-coupled receptor signaling, protein synthesis and other processes. Collectively, our findings delineate atypical connectivity patterns underlying different forms of ASD that implicate distinct molecular signaling mechanisms.

Transcriptional signal and cell specificity of genes related to cortical structural differences of post-traumatic stress disorder

Due to the diversity of traumatic events, the diagnosis of Post-traumatic Stress Disorder is heterogeneous. The pathogenesis has been explored in the fields of brain imaging and genomics separately, but the results are inconsistent. Previous research evidenced that there existed structural differences between PTSD and healthy controls in multiple brain regions. This study further looked into the differences of brain structure in PTSD at the whole brain level and analyzed the difference-related genomes. The brain structure imaging data of 36 patients and 32 healthy controls were taken as morphological indexes. Partial least squares regression and transcriptome data were used to extract genomes related to structural differences. Additional data sets were used to study transcription characteristics of genome. Morphological differences were found in cingulate gyrus between patients and control group. Differentially expressed genes related to Morphometric similarity networks difference space were also observed. The obtained genes (i.e., RORA, PRKG1 and FKBP5) were proved to be related to the disorder with no significant correlation with other mental illnesses. In the subsequent cell type analysis, astrocytes, excitatory neurons and inhibitory neurons were evidenced to have the most significant correlation with these genes. This study found morphologically different brain regions related to PTSD. The related genome transcription analysis connects the structural differences and molecular mechanisms.

Transcriptional substrates of brain structural and functional impairments in drug-naive first-episode patients with major depressive disorder

BACKGROUND

Despite remarkable success in identifying genetic risk factors for depression, there are still open questions about the exact genetic mechanisms underlying certain disease phenotypes, such as brain structural and functional impairments.

METHODS

Comprehensive multi-modal neuroimaging meta-analyses were conducted to examine changes in brain structure and function in drug-naive first-episode patients with major depressive disorder (DF-MDD). Combined with the Allen Human Brain Atlas, transcriptome-neuroimaging spatial association analyses were performed to identify genes whose expression related to these brain structural and functional changes, followed by a range of gene functional signature analyses.

RESULTS

Meta-analyses revealed gray matter atrophy in the insula, temporal pole, cerebellum and postcentral gyrus, and a complex pattern of hyper-function in the temporal pole and hypo-function in the cuneus/precuneus, angular gyrus and lingual gyrus in DF-MDD. Moreover, these brain structural and functional changes were spatially associated with the expression of 1194 and 1733 genes, respectively. Importantly, there were commonalities and differences in the two gene sets and their functional signatures including functional enrichment, specific expression, behavioral relevance, and constructed protein-protein interaction networks.

LIMITATIONS

The results merit further verification using a large sample of DF-MDD.

CONCLUSIONS

Our findings not only corroborate the polygenic nature of depression, but also suggest common and distinct genetic modulations of brain structural and functional impairments in this disorder.

Neuroplasticity-Related Genes and Dopamine Receptors Associated with Regional Cortical Thickness Increase Following Electroconvulsive Therapy for Major Depressive Disorder

Electroconvulsive therapy (ECT) is an effective neuromodulatory therapy for major depressive disorder (MDD). Treatment is associated with regional changes in brain structure and function, indicating activation of neuroplastic processes. To investigate the underlying neurobiological mechanism of macroscopic reorganization following ECT, we longitudinally (before and after ECT in two centers) collected magnetic resonance images for 96 MDD patients. Similar patterns of cortical thickness (CT) changes following ECT were observed in two centers. These CT changes were spatially colocalized with a weighted combination of genes enriched for neuroplasticity-related ontology terms and pathways (e.g., synaptic pruning) as well as with a higher density of D2/3 dopamine receptors. A multiple linear regression model indicated that the region-specific gene expression and receptor density patterns explained 40% of the variance in CT changes after ECT. In conclusion, these findings suggested that dopamine signaling and neuroplasticity-related genes are associated with the ECT-induced morphological reorganization.

A cross-sectional and longitudinal study of human brain development: The integration of cortical thickness, surface area, gyrification index, and cortical curvature into a unified analytical framework

Brain maturation studies typically examine relationships linking a single morphometric feature with cognition, behavior, age, or other demographic characteristics. However, the coordinated spatiotemporal arrangement of morphological features across development and their associations with behavior are unclear. Here, we examine covariation across multiple cortical features (cortical thickness [CT], surface area [SA], local gyrification index [GI], and mean curvature [MC]) using magnetic resonance images from the NIMH developmental cohort (ages 5-25). Neuroanatomical covariance was examined using non-negative matrix factorization (NMF), which decomposes covariance resulting in a parts-based representation. Cross-sectionally, we identified six components of covariation which demonstrate differential contributions of CT, GI, and SA in hetero- vs. unimodal areas. Using this technique to examine covariance in rates of change to identify longitudinal sources of covariance highlighted preserved SA in unimodal areas and changes in CT and GI in heteromodal areas. Using behavioral partial least squares (PLS), we identified a single latent variable (LV) that recapitulated patterns of reduced CT, GI, and SA related to older age, with limited contributions of IQ and SES. Longitudinally, PLS revealed three LVs that demonstrated a nuanced developmental pattern that highlighted a higher rate of maturational change in SA and CT in higher IQ and SES females. Finally, we situated the components in the changing architecture of cortical gradients. This novel characterization of brain maturation provides an important understanding of the interdependencies between morphological measures, their coordinated development, and their relationship to biological sex, cognitive ability, and the resources of the local environment.

Morphometric dis-similarity between cortical and subcortical areas underlies cognitive function and psychiatric symptomatology: a preadolescence study from ABCD

Preadolescence is a critical period characterized by dramatic morphological changes and accelerated cortico-subcortical development. Moreover, the coordinated development of cortical and subcortical regions underlies the emerging cognitive functions during this period. Deviations in this maturational coordination may underlie various psychiatric disorders that begin during preadolescence, but to date these deviations remain largely uncharted. We constructed a comprehensive whole-brain morphometric similarity network (MSN) from 17 neuroimaging modalities in a large preadolescence sample (N = 8908) from Adolescent Brain Cognitive Development (ABCD) study and investigated its association with 10 cognitive subscales and 27 psychiatric subscales or diagnoses. Based on the MSNs, each brain was clustered into five modules with distinct cytoarchitecture and evolutionary relevance. While morphometric correlation was positive within modules, it was negative between modules, especially between isocortical and paralimbic/subcortical modules; this developmental dissimilarity was genetically linked to synapse and neurogenesis. The cortico-subcortical dissimilarity becomes more pronounced longitudinally in healthy children, reflecting developmental differentiation of segregated cytoarchitectonic areas. Higher cortico-subcortical dissimilarity (between the isocortical and paralimbic/subcortical modules) were related to better cognitive performance. In comparison, children with poor modular differentiation between cortex and subcortex displayed higher burden of externalizing and internalizing symptoms. These results highlighted cortical-subcortical morphometric dissimilarity as a dynamic maturational marker of cognitive and psychiatric status during the preadolescent stage and provided insights into brain development.

Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease

Parkinson's disease (PD) is a highly heterogeneous disorder that is difficult to diagnose. Therefore, reliable biomarkers are needed. We implemented a method constructing a regional radiomics similarity network (R2SN) based on the amplitude of low-frequency fluctuation (ALFF). We classified patients with PD and healthy individuals by using a machine learning approach in accordance with the R2SN connectome. The ALFF-based R2SN exhibited great reproducibility with different brain atlases and datasets. Great classification performances were achieved both in primary (AUC = 0.85 ± 0.02 and accuracy = 0.81 ± 0.03) and independent external validation (AUC = 0.77 and accuracy = 0.70) datasets. The discriminative R2SN edges correlated with the clinical evaluations of patients with PD. The nodes of discriminative R2SN edges were primarily located in the default mode, sensorimotor, executive control, visual and frontoparietal network, cerebellum and striatum. These findings demonstrate that ALFF-based R2SN is a robust potential neuroimaging biomarker for PD and could provide new insights into connectome reorganization in PD.

Altered global signal topography in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disease associated with widespread disruptions in intrinsic local specialization and global integration in the functional system of the brain. These changes in integration may further disrupt the global signal (GS) distribution, which might represent the local relative contribution to global activity in functional magnetic resonance imaging (fMRI).

METHODS

fMRI scans from a discovery dataset (n = 809) and a validated dataset (n = 542) were used in the analysis. We investigated the alteration of GS topography using the GS correlation (GSCORR) in patients with mild cognitive impairment (MCI) and AD. The association between GS alterations and functional network properties was also investigated based on network theory. The underlying mechanism of GSCORR alterations was elucidated using imaging-transcriptomics.

FINDINGS

Significantly increased GS topography in the frontal lobe and decreased GS topography in the hippocampus, cingulate gyrus, caudate, and middle temporal gyrus were observed in patients with AD (P_adj < 0.05). Notably, topographical GS changes in these regions correlated with cognitive ability (P < 0.05). The changes in GS topography also correlated with the changes in functional network segregation (ρ = 0.5). Moreover, the genes identified based on GS topographical changes were enriched in pathways associated with AD and neurodegenerative diseases.

INTERPRETATION

Our findings revealed significant changes in GS topography and its molecular basis, confirming the informative role of GS in AD and further contributing to the understanding of the relationship between global and local neuronal activities in patients with AD.

FUNDING

Beijing Natural Science Funds for Distinguished Young Scholars, China; Fundamental Research Funds for the Central Universities, China; National Natural Science Foundation, China.

Gene Expression has Distinct Associations with Brain Structure and Function in Major Depressive Disorder

Major depressive disorder (MDD) is associated with structural and functional brain abnormalities. MDD as well as brain anatomy and function are influenced by genetic factors, but the role of gene expression remains unclear. Here, this work investigates how cortical gene expression contributes to structural and functional brain abnormalities in MDD. This work compares the gray matter volume and resting-state functional measures in a Chinese sample of 848 MDD patients and 749 healthy controls, and these case-control differences are then associated with cortical variation of gene expression. While whole gene expression is positively associated with structural abnormalities, it is negatively associated with functional abnormalities. This work observes the relationships of expression levels with brain abnormalities for individual genes, and found that transcriptional correlates of brain structure and function show opposite relations with gene dysregulation in postmortem cortical tissue from MDD patients. This work further identifies genes that are positively or negatively related to structural abnormalities as well as functional abnormalities. The MDD-related genes are enriched for brain tissue, cortical cells, and biological pathways. These findings suggest that distinct genetic mechanisms underlie structural and functional brain abnormalities in MDD, and highlight the importance of cortical gene expression for the development of cortical abnormalities.

Resting-state brain functional alterations and their genetic mechanisms in drug-naive first-episode psychosis

Extensive research has established the presence of resting-state brain functional damage in psychosis. However, the genetic mechanisms of such disease phenotype are yet to be unveiled. We investigated resting-state brain functional alterations in patients with drug-naive first-episode psychosis (DFP) by performing a neuroimaging meta-analysis of 8 original studies comprising 500 patients and 469 controls. Combined with the Allen Human Brain Atlas, we further conducted transcriptome-neuroimaging spatial correlations to identify genes whose expression levels were linked to brain functional alterations in DFP, followed by a range of gene functional characteristic analyses. Meta-analysis revealed a mixture of increased and decreased brain function in widespread areas including the default-mode, visual, motor, striatal, and cerebellar systems in DFP. Moreover, these brain functional alterations were spatially associated with the expression of 1662 genes, which were enriched for molecular functions, cellular components, and biological processes of the cerebral cortex, as well as psychiatric disorders including schizophrenia. Specific expression analyses demonstrated that these genes were specifically expressed in the brain tissue, in cortical neurons and immune cells, and during nearly all developmental periods. Concurrently, the genes could construct a protein-protein interaction network supported by hub genes and were linked to multiple behavioral domains including emotion, attention, perception, and motor. Our findings provide empirical evidence for the notion that brain functional damage in DFP involves a complex interaction of polygenes with various functional characteristics.

Brain network deficits in breast cancer patients after early neoadjuvant chemotherapy: A longitudinal MRI study

Breast cancer (BC) patients who undergo chemotherapy are likely to develop chemotherapy-related cognitive impairment (CRCI). Recent studies of BC patients after chemotherapy have used graph theory to investigate the topological properties of the brain functional connectome. However, little is known about structural morphological networks in BC patients after early neoadjuvant chemotherapy (NAC). Brain morphological network organization in 47 female participants with BC was investigated before and after NAC. Topological properties of brain networks were ascertained based on morphological similarities in regional gray matter using a graph theory approach based on 3D T1-weighted MRI data. Nonparametric permutation testing was used to assess longitudinal-group differences in topological metrics. Compared with BC patients before NAC, BC patients after early NAC showed significantly increased global efficiency (p = .048), decreased path length (p = .033), and abnormal nodal properties and connectivity, mainly located in the central executive network (CEN). The change in the network efficiency of the right caudate was negatively correlated with the change in the Self-Rating Anxiety Scale score (r = -.435, p = .008), and the change in the nodal degree of the left superior frontal gyrus (dorsolateral part) was positively correlated with the change in the Functional Assessment of Cancer Therapy score (r = .547, p = .002). BC participants showed randomization in global properties and dysconnectivity in the CEN after early NAC. NAC may disrupt the cognitive balance of the brain morphological network in individuals with BC.

Connectivity between default mode and frontoparietal networks mediates the association between global amyloid-β and episodic memory

Βeta-amyloid (Aβ) is a neurotoxic protein that deposits early in the pathogenesis of preclinical Alzheimer's disease. We aimed to identify network connectivity that may alter the negative effect of Aβ on cognition. Following assessment of memory performance, resting-state fMRI, and mean cortical PET-Aβ, a total of 364 older adults (286 with clinical dementia rating [CDR-0], 59 with CDR-0.5 and 19 with CDR-1, mean age: 74.0 ± 6.4 years) from the OASIS-3 sample were included in the analysis. Across all participants, a partial least squares regression showed that lower connectivity between posterior medial default mode and frontoparietal networks, higher within-default mode, and higher visual-motor connectivity predict better episodic memory. These connectivities partially mediate the effect of Aβ on episodic memory. These results suggest that connectivity strength between the precuneus cortex and the superior frontal gyri may alter the negative effect of Aβ on episodic memory. In contrast, education was associated with different functional connectivity patterns. In conclusion, functional characteristics of specific brain networks may help identify amyloid-positive individuals with a higher likelihood of memory decline, with implications for AD clinical trials.

Virtual histology of morphometric similarity network after risperidone monotherapy and imaging-epigenetic biomarkers for treatment response in first-episode schizophrenia

BACKGROUND

Antipsychotic treatment has been conceived to alter brain connectivity, but it is unclear how the changes of network phenotypes relate to the underlying transcriptomics. Given DNA methylation (DNAm) may alter transcriptional levels, we further integrated an imaging-transcriptomic-epigenetic analysis to explore multi-omics treatment response biomarkers.

METHODS

Forty-two treatment-naive first-episode schizophrenia patients were scanned by TI weighted (T1W) imaging and DTI before and after 8-week risperidone monotherapy, and their peripheral blood genomic DNAm values were examined in parallel with MRI scanning. Morphometric similarity network (MSN) quantified with DTI and T1W data were used as a marker of treatment-related alterations in interareal cortical connectivity. We utilized partial least squares (PLS) to examine spatial associations between treatment-related MSN variations and cortical transcriptomic data obtained from the Allen Human Brain Atlas.

RESULTS

Longitudinal MSN alterations were related to treatment response on cognitive function and general psychopathology symptoms, while DNAm values of 59 PLS1 genes were on negative and positive symptoms. Virtual-histology transcriptomic analysis linked the MSN alterations with the neurobiological, cellular and metabolic pathways or processes, and assigned MSN-related genes to multiple cell types, specifying neurons and glial cells as contributing most to the transcriptomic associations of longitudinal changes in MSN.

CONCLUSIONS

We firstly reveal how brain-wide transcriptional levels and cell classes capture molecularly validated cortical connectivity alterations after antipsychotic treatment. Our findings represent a vital step towards the exploration of treatment response biomarkers on the basis of multiple omics rather than a single omics type as a strategy for advancing precise care.

Cortical morphometric vulnerability to generalised epilepsy reflects chromosome- and cell type-specific transcriptomic signatures

AIMS

Generalised epilepsy is thought to involve distributed brain networks. However, the molecular and cellular factors that render different brain regions more vulnerable to epileptogenesis remain largely unknown. We aimed to investigate epilepsy-related morphometric similarity network (MSN) abnormalities at the macroscale level and their relationships with microscale gene expressions at the microscale level.

METHODS

We compared the MSN of genetic generalised epilepsy with generalised tonic-clonic seizure patients (GGE-GTCS, n = 101) to demographically matched healthy controls (HC, n = 150). Cortical MSNs were estimated by combining seven morphometric features derived from structural magnetic resonance imaging for each individual. Regional gene expression profiles were derived from brain-wide microarray measurements provided by the Allen Human Brain Atlas.

RESULTS

GGE-GTCS patients exhibited decreased regional MSNs in primary motor, prefrontal and temporal regions and increases in occipital, insular and posterior cingulate cortices, when compared with the HC. These case-control neuroimaging differences were validated using split-half analyses and were not affected by medication or drug response effects. When assessing associations with gene expression, genes associated with GGE-GTCS-related MSN differences were enriched in several biological processes, including 'synapse organisation', 'neurotransmitter transport' pathways and excitatory/inhibitory neuronal cell types. Collectively, the GGE-GTCS-related cortical vulnerabilities were associated with chromosomes 4, 5, 11 and 16 and were dispersed bottom-up at the cellular, pathway and disease levels, which contributed to epileptogenesis, suggesting diverse neurobiologically relevant enrichments in GGE-GTCS.

CONCLUSIONS

By bridging the gaps between transcriptional signatures and in vivo neuroimaging, we highlighted the importance of using MSN abnormalities of the human brain in GGE-GTCS patients to investigate disease-relevant genes and biological processes.

Transcriptional signatures of the cortical morphometric similarity network gradient in first-episode, treatment-naive major depressive disorder

Recent studies have shown that major depressive disorder (MDD) is accompanied by alterations in functional and structural network gradients. However, whether changes are present in the cortical morphometric similarity (MS) network gradient, and the relationship between alterations of the gradient and gene expression remains largely unknown. In this study, the MS network was constructed, and its gradient was calculated in 71 patients with first-episode, treatment-naive MDD, and 69 demographically matched healthy controls. Between-group comparisons were performed to investigate abnormalities in the MS network gradient, and partial least squares regression analysis was conducted to explore the association between gene expression profiles and MS network gradient-based alternations in MDD. We found that the gradient was primarily significantly decreased in sensorimotor regions in patients with MDD compared with healthy controls, and increased in visual-related regions. In addition, the altered principal MS network gradient in the left postcentral cortex and right lingual cortex exhibited significant correlations with symptom severity. The abnormal gradient pattern was spatially correlated with the brain-wide expression of genes enriched for neurobiologically relevant pathways, downregulated in the MDD postmortem brain, and preferentially expressed in different cell types and cortical layers. These results demonstrated alterations of the principal MS network gradient in MDD and suggested the molecular mechanisms for structural alternations underlying MDD.

Acquired Pedophilia: international Delphi-method-based consensus guidelines

Idiopathic and acquired pedophilia are two different disorders with two different etiologies. However, the differential diagnosis is still very difficult, as the behavioral indicators used to discriminate the two forms of pedophilia are underexplored, and clinicians are still devoid of clear guidelines describing the clinical and neuroscientific investigations suggested to help them with this difficult task. Furthermore, the consequences of misdiagnosis are not known, and a consensus regarding the legal consequences for the two kinds of offenders is still lacking. The present study used the Delphi method to reach a global consensus on the following six topics: behavioral indicators/red flags helpful for differential diagnosis; neurological conditions potentially leading to acquired pedophilia; neuroscientific investigations important for a correct understanding of the case; consequences of misdiagnosis; legal consequences; and issues and future perspectives. An international and multidisciplinary board of scientists and clinicians took part in the consensus statements as Delphi members. The Delphi panel comprised 52 raters with interdisciplinary competencies, including neurologists, psychiatrists, neuropsychologists, forensic psychologists, expert in ethics, etc. The final recommendations consisted of 63 statements covering the six different topics. The current study is the first expert consensus on a delicate topic such as pedophilia. Important exploitable consensual recommendations that can ultimately be of immediate use by clinicians to help with differential diagnosis and plan and guide therapeutic interventions are described, as well as future perspectives for researchers.

Sexual offenses and the brain

Sexual offenses are a great concern worldwide due to the devastating physical and psychological consequences the victims of such crimes often experience. It is an important task to investigate potential mechanisms that may underlie sexual offending and predispose an individual for such antisocial behavior. Advanced techniques in neuroscience are increasingly used to uncover biomarkers in psychiatric disorders and organic brain disease. As this type of research is flourishing, preliminary studies with the aim to explore the neural basis of sex offenders have started to appear. To this purpose, researchers began to study the brain's structural and functional changes and the neurocognitive profiles of sex offenders, in comparison to nonoffenders and nonsex offenders, or among different subtypes of sex offenders. Most of the research to date has focused on pedophilia, with some inconsistent findings, which hampers the translation of the results into the forensic and clinical context. Any attempt to increase convergent evidence may profit from the harmonization of data analysis and avoidance of methodological inconsistencies, which can account for the different results across studies. Today, uncovering the neural basis of sex offences has to become a priority, not only for clinical interventions, but might also be important knowledge for crime prevention.

Longitudinal multi-omics alterations response to 8-week risperidone monotherapy: Evidence linking cortical thickness, transcriptomics and epigenetics

Background

Antipsychotic treatment-related alterations of cortical thickness (CT) and clinical symptoms have been previously corroborated, but less is known about whether the changes are driven by gene expression and epigenetic modifications.

Methods

Utilizing a prospective design, we recruited 42 treatment-naive first-episode schizophrenia patients (FESP) and 38 healthy controls. Patients were scanned by TI weighted imaging before and after 8-week risperidone monotherapy. CT estimation was automatically performed with the FreeSurfer software package. Participants' peripheral blood genomic DNA methylation (DNAm) status, quantified by using Infinium^® Human Methylation 450K BeadChip, was examined in parallel with T1 scanning. In total, CT measures from 118 subjects and genomic DNAm status from 114 subjects were finally collected. Partial least squares (PLS) regression was used to detect the spatial associations between longitudinal CT variations after treatment and cortical transcriptomic data acquired from the Allen Human Brain Atlas. Canonical correlation analysis (CCA) was then performed to identify multivariate associations between DNAm of PLS1 genes and patients' clinical improvement.

Results

We detected the significant PLS1 component (2,098 genes) related to longitudinal alterations of CT, and the PLS1 genes were significantly enriched in neurobiological processes, and dopaminergic- and cancer-related pathways. Combining Laplacian score and CCA analysis, we further linked DNAm of 33 representative genes from the 2,098 PLS1 genes with patients' reduction rate of clinical symptoms.

Conclusions

This study firstly revealed that changes of CT and clinical behaviors after treatment may be transcriptionally and epigenetically underlied. We define a "three-step" roadmap which represents a vital step toward the exploration of treatment- and treatment response-related biomarkers on the basis of multiple omics rather than a single omics type as a strategy for advancing precise care.

Gene Expressions Preferentially Influence Cortical Thickness of Human Connectome Project Atlas Parcellated Regions in First-Episode Antipsychotic-Naïve Psychoses

Altered gene expressions may mechanistically link genetic factors with brain morphometric alterations. Existing gene expression studies have examined selected morphometric features using low-resolution atlases in medicated schizophrenia. We examined the relationship of gene expression with cortical thickness (CT), surface area (SA), and gray matter volume (GMV) of first-episode antipsychotic-naïve psychosis patients (FEAP = 85) and 81 controls, hypothesizing that gene expressions often associated with psychosis will differentially associate with different morphometric features. We explored such associations among schizophrenia and non-schizophrenia subgroups within FEAP group compared to controls. We mapped 360 Human Connectome Project atlas-based parcellations on brain MRI on to the publicly available brain gene expression data from the Allen Brain Institute collection. Significantly correlated genes were investigated using ingenuity pathway analysis to elucidate molecular pathways. CT but not SA or GMV correlated with expression of 1137 out of 15 633 genes examined controlling for age, sex, and average CT. Among these ≈19%, ≈39%, and 8% of genes were unique to FEAP, schizophrenia, and non-schizophrenia, respectively. Variants of 10 among these 1137 correlated genes previously showed genome-wide-association with schizophrenia. Molecular pathways associated with CT were axonal guidance and sphingosine pathways (common to FEAP and controls), selected inflammation pathways (unique to FEAP), synaptic modulation (unique to schizophrenia), and telomere extension (common to NSZ and healthy controls). We demonstrate that different sets of genes and molecular pathways may preferentially influence CT in different diagnostic groups. Genes with altered expressions correlating with CT and associated pathways may be targets for pathophysiological investigations and novel treatment designs.

Brain functional activity-based classification of autism spectrum disorder using an attention-based graph neural network combined with gene expression

Autism spectrum disorder (ASD) is a complex brain neurodevelopmental disorder related to brain activity and genetics. Most of the ASD diagnostic models perform feature selection at the group level without considering individualized information. Evidence has shown the unique topology of the individual brain has a fundamental impact on brain diseases. Thus, a data-constructing method fusing individual topological information and a corresponding classification model is crucial in ASD diagnosis and biomarker discovery. In this work, we trained an attention-based graph neural network (GNN) to perform the ASD diagnosis with the fusion of graph data. The results achieved an accuracy of 79.78%. Moreover, we found the model paid high attention to brain regions mainly involved in the social-brain circuit, default-mode network, and sensory perception network. Furthermore, by analyzing the covariation between functional magnetic resonance imaging data and gene expression, current studies detected several ASD-related genes (i.e. MUTYH, AADAT, and MAP2), and further revealed their links to image biomarkers. Our work demonstrated that the ASD diagnostic framework based on graph data and attention-based GNN could be an effective tool for ASD diagnosis. The identified functional features with high attention values may serve as imaging biomarkers for ASD.

Multi-modal intermediate integrative methods in neuropsychiatric disorders: A review

The etiology of neuropsychiatric disorders involves complex biological processes at different omics layers, such as genomics, transcriptomics, epigenetics, proteomics, and metabolomics. The advent of high-throughput technology, as well as the availability of large open-source datasets, has ushered in a new era in system biology, necessitating the integration of various types of omics data. The complexity of biological mechanisms, the limitations of integrative strategies, and the heterogeneity of multi-omics data have all presented significant challenges to computational scientists. In comparison to early and late integration, intermediate integration may transform each data type into appropriate intermediate representations using various data transformation techniques, allowing it to capture more complementary information contained in each omics and highlight new interactions across omics layers. Here, we reviewed multi-modal intermediate integrative techniques based on component analysis, matrix factorization, similarity network, multiple kernel learning, Bayesian network, artificial neural networks, and graph transformation, as well as their applications in neuropsychiatric domains. We depicted advancements in these approaches and compared the strengths and weaknesses of each method examined. We believe that our findings will aid researchers in their understanding of the transformation and integration of multi-omics data in neuropsychiatric disorders.

Cortical gradient of a human functional similarity network captured by the geometry of cytoarchitectonic organization

Mapping the functional topology from a multifaceted perspective and relating it to underlying cross-scale structural principles is crucial for understanding the structural-functional relationships of the cerebral cortex. Previous works have described a sensory-association gradient axis in terms of coupling relationships between structure and function, but largely based on single specific feature, and the mesoscopic underpinnings are rarely determined. Here we show a gradient pattern encoded in a functional similarity network based on data from Human Connectome Project and further link it to cytoarchitectonic organizing principles. The spatial distribution of the primary gradient follows an inferior-anterior to superior-posterior axis. The primary gradient demonstrates converging relationships with layer-specific microscopic gene expression and mesoscopic cortical layer thickness, and is captured by the geometric representation of a myelo- and cyto-architecture based laminar differentiation theorem, involving a dual origin theory. Together, these findings provide a gradient, which describes the functional topology, and more importantly, linking the macroscale functional landscape with mesoscale laminar differentiation principles.

Individualized morphometric similarity predicts body mass index and food approach behavior in school-age children

Childhood obesity is associated with alterations in brain structure. Previous studies generally used a single structural index to characterize the relationship between body mass index(BMI) and brain structure, which could not describe the alterations of structural covariance between brain regions. To cover this research gap, this study utilized two independent datasets with brain structure profiles and BMI of 155 school-aged children. Connectome-based predictive modeling(CPM) was used to explore whether children’s BMI is reliably predictable by the novel individualized morphometric similarity network(MSN). We revealed the MSN can predict the BMI in school-age children with good generalizability to unseen dataset. Moreover, these revealed significant brain structure covariant networks can further predict children’s food approach behavior. The positive predictive networks mainly incorporated connections between the frontoparietal network(FPN) and the visual network(VN), between the FPN and the limbic network(LN), between the default mode network(DMN) and the LN. The negative predictive network primarily incorporated connections between the FPN and DMN. These results suggested that the incomplete integration of the high-order brain networks and the decreased dedifferentiation of the high-order networks to the primary reward networks can be considered as a core structural basis of the imbalance between inhibitory control and reward processing in childhood obesity.

Morphometric similarity networks discriminate patients with lumbar disc herniation from healthy controls and predict pain intensity

We used a recently advanced technique, morphometric similarity (MS), in a large sample of lumbar disc herniation patients with chronic pain (LDH-CP) to examine morphometric features derived from multimodal MRI data. To do so, we evenly allocated 136 LDH-CPs to exploratory and validation groups with matched healthy controls (HC), randomly chosen from the pool of 157 HCs. We developed three MS-based models to discriminate LDH-CPs from HCs and to predict the pain intensity of LDH-CPs. In addition, we created analogous models using resting state functional connectivity (FC) to perform the above discrimination and prediction of pain, in addition to comparing the performance of FC- and MS-based models and investigating if an ensemble model, combining morphometric features and resting-state signals, could improve performance. We conclude that 1) MS-based models were able to discriminate LDH-CPs from HCs and the MS networks (MSN) model performed best; 2) MSN was able to predict the pain intensity of LDH-CPs; 3) FC networks constructed were able to discriminate LDH-CPs from HCs, but they could not predict pain intensity; and 4) the ensemble model neither improved discrimination nor pain prediction performance. Generally, MSN is sensitive enough to uncover brain morphology alterations associated with chronic pain and provides novel insights regarding the neuropathology of chronic pain.

Genetic variants associated with psychiatric disorders are enriched at epigenetically active sites in lymphoid cells

Multiple psychiatric disorders have been associated with abnormalities in both the innate and adaptive immune systems. The role of these abnormalities in pathogenesis, and whether they are driven by psychiatric risk variants, remains unclear. We test for enrichment of GWAS variants associated with multiple psychiatric disorders (cross-disorder or trans-diagnostic risk), or 5 specific disorders (cis-diagnostic risk), in regulatory elements in immune cells. We use three independent epigenetic datasets representing multiple organ systems and immune cell subsets. Trans-diagnostic and cis-diagnostic risk variants (for schizophrenia and depression) are enriched at epigenetically active sites in brain tissues and in lymphoid cells, especially stimulated CD4⁺ T cells. There is no evidence for enrichment of either trans-risk or cis-risk variants for schizophrenia or depression in myeloid cells. This suggests a possible model where environmental stimuli activate T cells to unmask the effects of psychiatric risk variants, contributing to the pathogenesis of mental health disorders.

Regional gene expression signatures are associated with sex-specific functional connectivity changes in depression

The neural substrates of depression may differ in men and women, but the underlying mechanisms are incompletely understood. Here, we show that depression is associated with sex-specific patterns of abnormal functional connectivity in the default mode network and in five regions of interest with sexually dimorphic transcriptional effects. Regional differences in gene expression in two independent datasets explained the neuroanatomical distribution of abnormal connectivity. These gene sets varied by sex and were strongly enriched for genes implicated in depression, synapse function, immune signaling, and neurodevelopment. In an independent sample, we confirmed the prediction that individual differences in default mode network connectivity are explained by inferred brain expression levels for six depression-related genes, including PCDH8, a brain-specific protocadherin integral membrane protein implicated in activity-related synaptic reorganization. Together, our results delineate both shared and sex-specific changes in the organization of depression-related functional networks, with implications for biomarker development and fMRI-guided therapeutic neuromodulation.

The development of cortical functional hierarchy is associated with the molecular organization of prenatal/postnatal periods

The human cerebral cortex conforms to specific functional hierarchies facilitating information processing and higher-order cognition. Prior studies in adults have unveiled a dominant functional hierarchy spanning from sensorimotor regions to transmodal regions, which is also present in younger cohorts. However, how the functional hierarchy develops and the underlying molecular mechanisms remain to be investigated. Here, we set out to investigate the developmental patterns of the functional hierarchy for preschool children (#scans = 141, age = 2.41-6.90 years) using a parsimonious general linear model and the underlying biological mechanisms by combining the neuroimaging developmental pattern with two separate transcriptomic datasets (i.e. Allen Human Brain Atlas and BrainSpan Atlas). Our results indicated that transmodal regions were further segregated from sensorimotor regions and that such changes were potentially driven by two gene clusters with distinct enrichment profiles, namely prenatal gene cluster and postnatal gene cluster. Additionally, we found similar developmental profiles manifested in subsequent developmental periods by conducting identical analyses on the Human Connectome Projects in Development (#scans = 638, age = 5.58-21.92 years) and Philadelphia Neurodevelopment Cohort datasets (#scans = 795, age = 8-21 years), driven by concordant two gene clusters. Together, these findings illuminate a comprehensive developmental principle of the functional hierarchy and the underpinning molecular factors, and thus may shed light on the potential pathobiology of neurodevelopmental disorders.

The expanding horizons of network neuroscience: From description to prediction and control

The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.

Two subtypes of schizophrenia identified by an individual-level atypical pattern of tensor-based morphometric measurement

Difficulties in parsing the multiaspect heterogeneity of schizophrenia (SCZ) based on current nosology highlight the need to subtype SCZ using objective biomarkers. Here, utilizing a large-scale multisite SCZ dataset, we identified and validated 2 neuroanatomical subtypes with individual-level abnormal patterns of the tensor-based morphometric measurement. Remarkably, compared with subtype 1, which showed moderate deficits of some subcortical nuclei and an enlarged striatum and cerebellum, subtype 2, which showed cerebellar atrophy and more severe subcortical nuclei atrophy, had a higher subscale score of negative symptoms, which is considered to be a core aspect of SCZ and is associated with functional outcome. Moreover, with the neuroimaging-clinic association analysis, we explored the detailed relationship between the heterogeneity of clinical symptoms and the heterogeneous abnormal neuroanatomical patterns with respect to the 2 subtypes. And the neuroimaging-transcription association analysis highlighted several potential heterogeneous biological factors that may underlie the subtypes. Our work provided an effective framework for investigating the heterogeneity of SCZ from multilevel aspects and may provide new insights for precision psychiatry.

Local molecular and global connectomic contributions to cross-disorder cortical abnormalities

Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21,000 participants and N = 26,000 controls, collected using a harmonised processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find a relationship between molecular vulnerability and white-matter architecture that drives cortical disorder profiles. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, inferior temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local molecular attributes and global connectivity jointly shape cross-disorder cortical abnormalities.

Local dynamic spontaneous brain activity changes in first-episode, treatment-naïve patients with major depressive disorder and their associated gene expression profiles

BACKGROUND

Major depressive disorder (MDD) is a common debilitating disorder characterized by impaired spontaneous brain activity, yet little is known about its alterations in dynamic properties and the molecular mechanisms associated with these changes.

METHODS

Based on the resting-state functional MRI data of 65 first-episode, treatment-naïve patients with MDD and 66 healthy controls, we compared dynamic regional homogeneity (dReHo) of spontaneous brain activity between the two groups, and we investigated gene expression profiles associated with dReHo alterations in MDD by leveraging transcriptional data from the Allen Human Brain Atlas and weighted gene co-expression network analysis.

RESULTS

Compared with healthy controls, patients with MDD consistently showed reduced dReHo in both fusiform gyri and in the right temporal pole and hippocampus. The expression profiles of 16 gene modules were correlated with dReHo alterations in MDD. These gene modules were enriched for various biological process terms, including immune, synaptic signalling, ion channels, mitochondrial function and protein metabolism, and were preferentially expressed in different cell types.

CONCLUSIONS

Patients with MDD have reduced dReHo in brain areas associated with emotional and cognitive regulation, and these changes may be related to complex polygenetic and polypathway mechanisms.

Decoding transcriptional signatures of the association between free water and macroscale organizations in healthy adolescents

We leveraged a novel index of diffusion MRI to investigate the relationships among cortical free water, macro-organizations and gene expression in healthy adults. Few research has been conducted to investigate the role of free water in the healthy adults due to it can easily be affected also by aging diseases. High quality data of 350 subjects from Human Connectome Project were used in our study. Cortical free water was estimated by using a bi-tensor model. The free water was high in the limbic, insular and somatosensory cortex, while being lower in motor and association cortex. The negative correlation between the free water and cortical thickness has been consistently identified in almost all the cortical regions. Negative correlation between the cortical free water and structural covariance (rho=-0.38, p_spin=0.005) revealed the free water was sensitive to cortical heterogeneity. Using human gene expression dataset, we found the gene expression pattern of the relationship between the free water and cortical thickness spatially coupled with primary gradient of structural covariance network (rho=0.40, p_spin=0.004). Our findings indicated the free water was sensitive to the cortical cellular status. The relationship between free water and macroscale organization also reflected hierarchal structures of cerebral cortex.

SPAMRI: A MATLAB Toolbox for Surface-Based Processing and Analysis of Magnetic Resonance Imaging

Structural magnetic resonance imaging (MRI) has elicited increasing attention in morphological surface studies due to its stability and sensitivity to neurodegenerative processes, particularly in exploring brain aging and psychiatric disease. However, a user-friendly toolbox for the surface-based analysis of structural MRI is still lacking. On the basis of certain software functions in FreeSurfer, CAT and ANTs, a MATLAB toolbox called "surface-based processing and analysis of MRI" (SPAMRI) has been developed, which can be performed in Windows, Linux and Mac-OS. SPAMRI contains several features as follows: (1) open-source MATLAB-based package with a graphical user interface (GUI); (2) a set of images that can be generated for quality checking, such as Talairach transform, skull strip, and surface reconstruction; (3) user-friendly GUI with capabilities on statistical analysis, multiple comparison corrections, reporting of results, and surface measurement extraction; and (4) provision of a conversion tool between surface files (e.g., mesh files) and volume files (e.g., NIFTI files). SPAMRI is applied to a publicly released structural MRI dataset of 44 healthy young adults and 39 old adults. Findings showed that old people have decreased cortical thickness, especially in prefrontal cortex, relative to those of young adults, thereby suggesting a cognitive decline in the former. SPAMRI is anticipated to substantially simplify surface-based image processing and MRI dataset analyses and subsequently open new opportunities to investigate structural morphologies.

Morphometric features of drug-resistant essential tremor and recovery after stereotactic radiosurgical thalamotomy

Essential tremor (ET) is the most common movement disorder. Its neural underpinnings remain unclear. Here, we quantified structural covariance between cortical thickness (CT), surface area (SA), and mean curvature (MC) estimates in patients with ET before and 1 year after ventro-intermediate nucleus stereotactic radiosurgical thalamotomy, and contrasted the observed patterns with those from matched healthy controls. For SA, complex rearrangements within a network of motion-related brain areas characterized patients with ET. This was complemented by MC alterations revolving around the left middle temporal cortex and the disappearance of positive-valued covariance across both modalities in the right fusiform gyrus. Recovery following thalamotomy involved MC readjustments in frontal brain centers, the amygdala, and the insula, capturing nonmotor characteristics of the disease. The appearance of negative-valued CT covariance between the left parahippocampal gyrus and hippocampus was another recovery mechanism involving high-level visual areas. This was complemented by the appearance of negative-valued CT/MC covariance, and positive-valued SA/MC covariance, in the right inferior temporal cortex and bilateral fusiform gyrus. Our results demonstrate that different morphometric properties provide complementary information to understand ET, and that their statistical cross-dependences are also valuable. They pinpoint several anatomical features of the disease and highlight routes of recovery following thalamotomy.

Brain morphometric similarity and flexibility

Background

The cerebral cortex is represented through multiple multilayer morphometric similarity networks to study their modular structures. The approach introduces a novel way for studying brain networks' metrics across individuals, and can quantify network properties usually not revealed using conventional network analyses.

Methods

A total of 8 combinations or types of morphometric similarity networks were constructed – 4 combinations of the inter-regional cortical features on 2 brain atlases. The networks' modular structures were investigated by identifying those modular interactions that stay consistent across the combinations of inter-regional morphometric features and individuals.

Results

The results provide evidence of the community structures as the property of (i) cortical lobar divisions, and also as (ii) the product of different combinations of morphometric features used for the construction of the multilayer representations of the cortex. For the first time, this study has mapped out flexible and inflexible morphometric similarity hubs, and evidence has been provided about variations of the modular network topology across the multilayers with age and IQ.

Conclusions

The results contribute to understanding of intra-regional characteristics in cortical interactions, which potentially can be used to map heterogeneous neurodegeneration patterns in diseased brains.

Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia

BACKGROUND AND HYPOTHESIS

Schizophrenia is increasingly understood as a disorder of brain dysconnectivity. Recently, graph-based approaches such as graph convolutional network (GCN) have been leveraged to explore complex pairwise similarities in imaging features among brain regions, which can reveal abstract and complex relationships within brain networks.

STUDY DESIGN

We used GCN to investigate topological abnormalities of functional brain networks in schizophrenia. Resting-state functional magnetic resonance imaging data were acquired from 505 individuals with schizophrenia and 907 controls across 6 sites. Whole-brain functional connectivity matrix was extracted for each individual. We examined the performance of GCN relative to support vector machine (SVM), extracted the most salient regions contributing to both classification models, investigated the topological profiles of identified salient regions, and explored correlation between nodal topological properties of each salient region and severity of symptom.

STUDY RESULTS

GCN enabled nominally higher classification accuracy (85.8%) compared with SVM (80.9%). Based on the saliency map, the most discriminative brain regions were located in a distributed network including striatal areas (ie, putamen, pallidum, and caudate) and the amygdala. Significant differences in the nodal efficiency of bilateral putamen and pallidum between patients and controls and its correlations with negative symptoms were detected in post hoc analysis.

CONCLUSIONS

The present study demonstrates that GCN allows classification of schizophrenia at the individual level with high accuracy, indicating a promising direction for detection of individual patients with schizophrenia. Functional topological deficits of striatal areas may represent a focal neural deficit of negative symptomatology in schizophrenia.

Integrating neuroimaging and gene expression data using the imaging transcriptomics toolbox

The integration of neuroimaging and transcriptomics data, Imaging Transcriptomics, is becoming increasingly popular but standardized workflows for its implementation are still lacking. We describe the Imaging Transcriptomics toolbox, a new package that implements a full imaging transcriptomics pipeline using a user-friendly, command line interface. This toolbox allows the user to identify patterns of gene expression which correlates with a specific neuroimaging phenotype and perform gene set enrichment analyses to inform the biological interpretation of the findings using up-to-date methods. For complete details on the use and execution of this protocol, please refer to Martins et al. (2021).