Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders

Brainwide Anatomical Connectivity and Prediction of Longitudinal Outcomes in Antipsychotic-Naïve First-Episode Psychosis

BACKGROUND

Disruptions of axonal connectivity are thought to be a core pathophysiological feature of psychotic illness, but whether they are present early in the illness, prior to antipsychotic exposure, and whether they can predict clinical outcome remain unknown.

METHODS

We acquired diffusion-weighted magnetic resonance images to map structural connectivity between each pair of 319 parcellated brain regions in 61 antipsychotic-naïve individuals with first-episode psychosis (15-25 years, 46% female) and a demographically matched sample of 27 control participants. Clinical follow-up data were also acquired in patients 3 and 12 months after the scan. We used connectome-wide analyses to map disruptions of inter-regional pairwise connectivity and connectome-based predictive modeling to predict longitudinal change in symptoms and functioning.

RESULTS

Individuals with first-episode psychosis showed disrupted connectivity in a brainwide network linking all brain regions compared with controls (familywise error-corrected p = .03). Baseline structural connectivity significantly predicted change in functioning over 12 months (r = 0.44, familywise error-corrected p = .041), such that lower connectivity within fronto-striato-thalamic systems predicted worse functional outcomes.

CONCLUSIONS

Brainwide reductions of structural connectivity exist during the early stages of psychotic illness and cannot be attributed to antipsychotic medication. Moreover, baseline measures of structural connectivity can predict change in patient functional outcomes up to 1 year after engagement with treatment services.

Structural brain connectivity does not associate with childhood trauma in individuals with schizophrenia

BACKGROUND

Schizophrenia is a brain dysconnectivity disorder. However, it is not well understood whether the experience of childhood trauma (CT) affects dysconnectivity in individuals with schizophrenia (SZ). Using a network-based approach, we examined whether self-reported CT would explain additional variance compared to whole-brain topology and structural connectivity changes in SZ versus healthy controls (HC).

MATERIAL AND METHODS

CT was assessed in 51 SZ (mean age ± standard deviation 44 ± 11 years) and 140 HC (34.0 ± 12 years). Structural brain networks were constructed from T1-weighted MR and diffusion-MRI scans using non-tensor based tractography. Group differences in whole-brain topology and permutation-based statistics were examined and corrected for age and sex.

RESULTS

SZ showed reductions in efficiency, strength, clustering and density (p < 0.01) as well as increases in path length (F(range) = 4.71-18.1, p < 0.03) when compared to HC. We also observed hypoconnectivity in a subnetwork of frontotemporal, frontoparietal and occipital regions in SZ relative to HC (T > 4.0, p < 0.001). However, we did not find that high CT levels were related to structural network differences or structural connectivity changes in SZ.

CONCLUSIONS

CT did not impact on topology or subnetwork connectivity changes in SZ. High CT levels were also not associated with any differences in network organisation irrespective of diagnosis. However, our findings confirm that SZ showed both network-level reductions and increases in a subnetwork. These findings suggest that the patterns of neuroanatomical dysconnectivity in established schizophrenia may not be influenced by CT. Future studies are needed to investigate the association between CT and structural dysconnectivity in schizophrenia.

A review of diffusion MRI in mood disorders: mechanisms and predictors of treatment response

By measuring the molecular diffusion of water molecules in brain tissue, diffusion MRI (dMRI) provides unique insight into the microstructure and structural connections of the brain in living subjects. Since its inception, the application of dMRI in clinical research has expanded our understanding of the possible biological bases of psychiatric disorders and successful responses to different therapeutic interventions. Here, we review the past decade of diffusion imaging-based investigations with a specific focus on studies examining the mechanisms and predictors of therapeutic response in people with mood disorders. We present a brief overview of the general application of dMRI and key methodological developments in the field that afford increasingly detailed information concerning the macro- and micro-structural properties and connectivity patterns of white matter (WM) pathways and their perturbation over time in patients followed prospectively while undergoing treatment. This is followed by a more in-depth summary of particular studies using dMRI approaches to examine mechanisms and predictors of clinical outcomes in patients with unipolar or bipolar depression receiving pharmacological, neurostimulation, or behavioral treatments. Limitations associated with dMRI research in general and with treatment studies in mood disorders specifically are discussed, as are directions for future research. Despite limitations and the associated discrepancies in findings across individual studies, evolving research strongly indicates that the field is on the precipice of identifying and validating dMRI biomarkers that could lead to more successful personalized treatment approaches and could serve as targets for evaluating the neural effects of novel treatments.

Brain structural associations of syntactic complexity and diversity across schizophrenia spectrum and major depressive disorders, and healthy controls

Deviations in syntax production have been well documented in schizophrenia spectrum disorders (SSD). Recently, we have shown evidence for transdiagnostic subtypes of syntactic complexity and diversity. However, there is a lack of studies exploring brain structural correlates of syntax across diagnoses. We assessed syntactic complexity and diversity of oral language production using four Thematic Apperception Test pictures in a sample of N = 87 subjects (n = 24 major depressive disorder (MDD), n = 30 SSD patients both diagnosed according to DSM-IV-TR, and n = 33 healthy controls (HC)). General linear models were used to investigate the association of syntax with gray matter volume (GMV), fractional anisotropy (FA), axial (AD), radial (RD), and mean diffusivity (MD). Age, sex, total intracranial volume, group, interaction of group and syntax were covariates of no interest. Syntactic diversity was positively correlated with the GMV of the right medial pre- and postcentral gyri and with the FA of the left superior-longitudinal fasciculus (temporal part). Conversely, the AD of the left cingulum bundle and the forceps minor were negatively correlated with syntactic diversity. The AD of the right inferior-longitudinal fasciculus was positively correlated with syntactic complexity. Negative associations were observed between syntactic complexity and the FA of the left cingulum bundle, the right superior-longitudinal fasciculus, and the AD of the forceps minor and the left uncinate fasciculus. Our study showed brain structural correlates of syntactic complexity and diversity across diagnoses and HC. This contributes to a comprehensive understanding of the interplay between linguistic and neural substrates in syntax production in psychiatric disorders and HC.

Functional brain networks in clinical high-risk for bipolar disorder and psychosis

Abnormal connectivity in the brain has been linked to the pathophysiology of severe mental illnesses, including bipolar disorder and schizophrenia. The current study aimed to investigate large-scale functional networks and global network metrics in clinical high-risk for bipolardisorder (CHR-BD, n = 25), clinical high-risk for psychosis (CHR-P, n = 30), and healthy controls (HCs, n = 19). Help-seeking youth at CHR-BD and CHR-P were recruited from the early intervention program at Dokuz Eylul University, Izmir, Turkey. Resting-state functional magnetic resonance imaging scans were obtained from youth at CHR-BD, CHR-P, and HCs. Graph theoretical analysis and network-based statistics were employed to construct and examine the topological features of the whole-brain metrics and large-scale functional networks. Connectivity was increased (i) between the visual and default mode, (ii) between the visual and salience, (iii) between the visual and cingulo-opercular networks, and decreased (i) within the default mode and (ii) between the default mode and fronto-parietal networks in the CHR-P compared to HCs. Decreased global efficiency was found in CHR-P compared to CHR-BD. Functional networks were not different between CHR-BD and HCs. Global efficiency was negatively correlated with subthreshold positive symptoms and thought disorder in the high-risk groups. The current results suggest disrupted networks in CHR-P compared to HCs and CHR-BD. Moreover, transdiagnostic psychosis features are linked to functional brain networks in the at-risk groups. However, given the small, medicated sample, results are exploratory and hypothesis-generating.

Polyconnectomic scoring of functional connectivity patterns across eight neuropsychiatric and three neurodegenerative disorders

BACKGROUND

Neuropsychiatric and neurodegenerative disorders involve diverse changes in brain functional connectivity. As an alternative to approaches searching for specific mosaic patterns of affected connections and networks, we used polyconnectomic scoring to quantify disorder-related whole-brain connectivity signatures into interpretable, personalized scores.

METHODS

The polyconnectomic score (PCS) measures the extent to which an individual's functional connectivity (FC) mirrors the whole-brain circuitry characteristics of a trait. We computed PCS for eight neuropsychiatric conditions (attention-deficit/hyperactivity disorder, anxiety-related disorders, autism spectrum disorder, obsessive-compulsive disorder, bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) and three neurodegenerative conditions (Alzheimer's disease, frontotemporal dementia, and Parkinson's disease) across 22 datasets with resting-state functional MRI of 10,667 individuals (5,325 patients, 5,342 controls). We further examined PCS in 26,673 individuals from the population-based UK Biobank cohort.

RESULTS

PCS was consistently higher in out-of-sample patients across six of the eight neuropsychiatric and across all three investigated neurodegenerative disorders ([min, max]: AUC = [0.55, 0.73], pFDR = [1.8 x 10-16, 4.5 x 10-2]). Individuals with elevated PCS levels for neuropsychiatric conditions exhibited higher neuroticism (pFDR < 9.7 x 10-5), lower cognitive performance (pFDR < 5.3 x 10-5), and lower general wellbeing (pFDR < 9.7 x 10-4).

CONCLUSIONS

Our findings reveal generalizable whole-brain connectivity alterations in brain disorders. PCS effectively aggregates disorder-related signatures across the entire brain into an interpretable, subject-specific metric. A toolbox is provided for PCS computation.

Biased Information Routing Through the Basolateral Amygdala, Altered Valence Processing, and Impaired Affective States Associated With Psychiatric Illnesses

Accumulating evidence supports a role for altered circuit function in impaired valence processing and altered affective states as a core feature of psychiatric illnesses. We review the circuit mechanisms underlying normal valence processing and highlight evidence supporting altered function of the basolateral amygdala, valence processing, and affective states across psychiatric illnesses. The mechanisms controlling network activity that governs valence processing are reviewed in the context of potential pathophysiological mechanisms mediating circuit dysfunction and impaired valence processing in psychiatric illnesses. Finally, we review emerging data demonstrating experience-dependent, biased information routing through the basolateral amygdala promoting negative valence processing and discuss the potential relevance to impaired affective states and psychiatric illnesses.

Study protocol TransTAM: Transdiagnostic research into emotional disorders and cognitive-behavioral therapy of the adaptive mind

BACKGROUND

Emotional disorders such as depression and anxiety disorders share substantial similarities in their etiology and treatment. In recent decades, these commonalities have been increasingly recognized in classification systems and treatment programs crossing diagnostic boundaries.

METHODS

To examine the prospective effects of different transdiagnostic markers on relevant treatment outcomes, we plan to track a minimum of N = 200 patients with emotional disorders during their routine course of cognitive behavioral therapy at two German outpatient clinics. We will collect a wide range of transdiagnostic markers, ranging from basic perceptual processes and self-report measures to complex behavioral and neurobiological indicators, before entering therapy. Symptoms and psychopathological processes will be recorded before entering therapy, between the 20th and 24th therapy session, and at the end of therapy.

DISCUSSION

Our results could help to identify transdiagnostic markers with high predictive power, but also provide deeper insights into which patient groups with which symptom clusters are less likely to benefit from therapy, and for what reasons.

TRIAL REGISTRATION

The trial was preregistered at the German Clinical Trial Register (DRKS-ID: DRKS00031206; 2023-05-09).

The interplay between polygenic score for tumor necrosis factor-α, brain structural connectivity, and processing speed in major depression

Reduced processing speed is a core deficit in major depressive disorder (MDD) and has been linked to altered structural brain network connectivity. Ample evidence highlights the involvement of genetic-immunological processes in MDD and specific depressive symptoms. Here, we extended these findings by examining associations between polygenic scores for tumor necrosis factor-α blood levels (TNF-α PGS), structural brain connectivity, and processing speed in a large sample of MDD patients. Processing speed performance of n = 284 acutely depressed, n = 177 partially and n = 198 fully remitted patients, and n = 743 healthy controls (HC) was estimated based on five neuropsychological tests. Network-based statistic was used to identify a brain network associated with processing speed. We employed general linear models to examine the association between TNF-α PGS and processing speed. We investigated whether network connectivity mediates the association between TNF-α PGS and processing speed. We identified a structural network positively associated with processing speed in the whole sample. We observed a significant negative association between TNF-α PGS and processing speed in acutely depressed patients, whereas no association was found in remitted patients and HC. The mediation analysis revealed that brain connectivity partially mediated the association between TNF-α PGS and processing speed in acute MDD. The present study provides evidence that TNF-α PGS is associated with decreased processing speed exclusively in patients with acute depression. This association was partially mediated by structural brain connectivity. Using multimodal data, the current findings advance our understanding of cognitive dysfunction in MDD and highlight the involvement of genetic-immunological processes in its pathomechanisms.

Age Trajectories of the Structural Connectome in Child and Adolescent Offspring of Individuals With Bipolar Disorder or Schizophrenia

Background

Offspring of parents with severe mental illness (e.g., bipolar disorder or schizophrenia) are at elevated risk of developing psychiatric illness owing to both genetic predisposition and increased burden of environmental stress. Emerging evidence indicates a disruption of brain network connectivity in young offspring of patients with bipolar disorder and schizophrenia, but the age trajectories of these brain networks in this high-familial-risk population remain to be elucidated.

Methods

A total of 271 T1-weighted and diffusion-weighted scans were obtained from 174 offspring of at least 1 parent diagnosed with bipolar disorder (n = 74) or schizophrenia (n = 51) and offspring of parents without severe mental illness (n = 49). The age range was 8 to 23 years; 97 offspring underwent 2 scans. Anatomical brain networks were reconstructed into structural connectivity matrices. Network analysis was performed to investigate anatomical brain connectivity.

Results

Offspring of parents with schizophrenia had differential trajectories of connectivity strength and clustering compared with offspring of parents with bipolar disorder and parents without severe mental illness, of global efficiency compared with offspring of parents without severe mental illness, and of local connectivity compared with offspring of parents with bipolar disorder.

Conclusions

The findings of this study suggest that familial high risk of schizophrenia is related to deviations in age trajectories of global structural connectome properties and local connectivity strength.

Diagnostic value of structural, functional and effective connectivity in bipolar disorder

INTRODUCTION

The aim of this systematic review is to assess the functional magnetic resonance imaging (fMRI) studies of bipolar disorder (BD) patients that characterize differences in terms of structural, functional, and effective connectivity between the patients with BD, patients with other psychiatric disorders and healthy controls as possible biomarkers for diagnosing the disorder using neuroimaging.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), guidelines a systematic search for recent (since 2015) original studies on connectivity in bipolar disorder was conducted in PUBMED and SCOPUS.

RESULTS

A total of 60 studies were included in this systematic review: 20 of the structural connectivity, 33 of the functional connectivity, and only 7 of the studies focused on effective connectivity complied with the inclusion and exclusion criteria.

DISCUSSION

Despite the great heterogeneity in the findings, there are several trends that emerge. In structural connectivity studies, the main abnormalities in bipolar disorder patients were in the frontal gyrus, anterior, as well as posterior cingulate cortex and differences in emotion and reward-related networks. Cerebellum (vermis) to cerebrum functional connectivity was found to be the most common finding in BD. Moreover, prefrontal cortex and amygdala connectivity as part of the rich-club hubs were often reported to be disrupted. The most common findings based on effective connectivity were alterations in salience network, default mode network and executive control network. Although more studies with larger sample sizes are needed to ascertain altered brain connectivity as diagnostic biomarker, there is a perspective that the method could be used as a single marker of diagnosis in the future, and the process of adoption could be accelerated by using approaches such as semiunsupervised machine learning.

Factor analysis of lifetime psychopathology and its brain morphometric and genetic correlates in a transdiagnostic sample

There is a lack of knowledge regarding the relationship between proneness to dimensional psychopathological syndromes and the underlying pathogenesis across major psychiatric disorders, i.e., Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizoaffective Disorder (SZA), and Schizophrenia (SZ). Lifetime psychopathology was assessed using the OPerational CRITeria (OPCRIT) system in 1,038 patients meeting DSM-IV-TR criteria for MDD, BD, SZ, or SZA. The cohort was split into two samples for exploratory and confirmatory factor analyses. All patients were scanned with 3-T MRI, and data was analyzed with the CAT-12 toolbox in SPM12. Psychopathological factor scores were correlated with gray matter volume (GMV) and cortical thickness (CT). Finally, factor scores were used for exploratory genetic analyses including genome-wide association studies (GWAS) and polygenic risk score (PRS) association analyses. Three factors (paranoid-hallucinatory syndrome, PHS; mania, MA; depression, DEP) were identified and cross-validated. PHS was negatively correlated with four GMV clusters comprising parts of the hippocampus, amygdala, angular, middle occipital, and middle frontal gyri. PHS was also negatively associated with the bilateral superior temporal, left parietal operculum, and right angular gyrus CT. No significant brain correlates were observed for the two other psychopathological factors. We identified genome-wide significant associations for MA and DEP. PRS for MDD and SZ showed a positive effect on PHS, while PRS for BD showed a positive effect on all three factors. This study investigated the relationship of lifetime psychopathological factors and brain morphometric and genetic markers. Results highlight the need for dimensional approaches, overcoming the limitations of the current psychiatric nosology.

Dynamic structure-function coupling across three major psychiatric disorders

BACKGROUND

Convergent evidence has suggested atypical relationships between brain structure and function in major psychiatric disorders, yet how the abnormal patterns coincide and/or differ across different disorders remains largely unknown. Here, we aim to investigate the common and/or unique dynamic structure-function coupling patterns across major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ).

METHODS

We quantified the dynamic structure-function coupling in 452 patients with psychiatric disorders (MDD/BD/SZ = 166/168/118) and 205 unaffected controls at three distinct brain network levels, such as global, meso-, and local levels. We also correlated dynamic structure-function coupling with the topological features of functional networks to examine how the structure-function relationship facilitates brain information communication over time.

RESULTS

The dynamic structure-function coupling is preserved for the three disorders at the global network level. Similar abnormalities in the rich-club organization are found in two distinct functional configuration states at the meso-level and are associated with the disease severity of MDD, BD, and SZ. At the local level, shared and unique alterations are observed in the brain regions involving the visual, cognitive control, and default mode networks. In addition, the relationships between structure-function coupling and the topological features of functional networks are altered in a manner indicative of state specificity.

CONCLUSIONS

These findings suggest both transdiagnostic and illness-specific alterations in the dynamic structure-function relationship of large-scale brain networks across MDD, BD, and SZ, providing new insights and potential biomarkers into the neurodevelopmental basis underlying the behavioral and cognitive deficits observed in these disorders.

Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study

Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenia's alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia.

Causal relationship between resting-state networks and depression: a bidirectional two-sample mendelian randomization study

BACKGROUND

Cerebral resting-state networks were suggested to be strongly associated with depressive disorders. However, the causal relationship between cerebral networks and depressive disorders remains controversial. In this study, we aimed to investigate the effect of resting-state networks on depressive disorders using a bidirectional Mendelian randomization (MR) design.

METHODS

Updated summary-level genome-wide association study (GWAS) data correlated with resting-state networks were obtained from a meta-analysis of European-descent GWAS from the Complex Trait Genetics Lab. Depression-related GWAS data were obtained from the FinnGen study involving participants with European ancestry. Resting-state functional magnetic resonance imaging and multiband diffusion imaging of the brain were performed to measure functional and structural connectivity in seven well-known networks. Inverse-variance weighting was used as the primary estimate, whereas the MR-Pleiotropy RESidual Sum and Outliers (PRESSO), MR-Egger, and weighted median were used to detect heterogeneity, sensitivity, and pleiotropy.

RESULTS

In total, 20,928 functional and 20,573 structural connectivity data as well as depression-related GWAS data from 48,847 patients and 225,483 controls were analyzed. Evidence for a causal effect of the structural limbic network on depressive disorders was found in the inverse variance-weighted limbic network (odds ratio, [Formula: see text]; 95% confidence interval, [Formula: see text]; [Formula: see text]), whereas the causal effect of depressive disorders on SC LN was not found(OR=1.0025; CI,1.0005-1.0046; P=0.012). No significant associations between functional connectivity of the resting-state networks and depressive disorders were found in this MR study.

CONCLUSIONS

These results suggest that genetically determined structural connectivity of the limbic network has a causal effect on depressive disorders and may play a critical role in its neuropathology.

Duration of the psychosis prodrome and its relationship to duration of untreated psychosis across all 12 DSM-IV psychotic diagnoses: Evidence for a trans-diagnostic process associated with resilience

While duration of the psychosis prodrome (DPP) attracts attention in relation to the developmental trajectory of psychotic illness and service models, fundamental issues endure in the context of dimensional-spectrum models of psychosis. Among 205 epidemiologically representative subjects in the Cavan-Monaghan First Episode Psychosis Study, DPP was systematically quantified and compared, for the first time, across all 12 DSM-IV psychotic diagnoses. DPP was also compared with duration of untreated psychosis (DUP) and each was then analysed in relation to premorbid features across three age ranges: <12, 12-15 and 16-18 years. For each diagnosis, medians for both DPP and DUP were shorter than means, indicating common right-skewed distributions. Rank orders for both DPP and DUP were longest for schizophrenia, intermediate for other schizophrenia-spectrum psychoses, psychotic depression and psychotic disorder not otherwise specified, and shortest for brief psychotic disorder, bipolar disorder and substance-induced psychotic disorder, though with overlapping right-skewed distributions. DPP was longer than DUP for all diagnoses except substance-induced psychotic disorder. Across functional psychotic diagnoses, longer DPP was predicted by higher premorbid intelligence and better premorbid adjustment during age 16-18 years. These findings indicate that, trans-diagnostically, DPP and DUP share right-skewed continuities, in accordance with a dimensional-spectrum model of psychotic illness, and may reflect a unitary process that has been dichotomized at a subjective threshold along its trajectory. Better premorbid functioning during age 16-18 years appears to confer resilience by delaying progression to overt psychotic symptoms and may constitute a particular target period for psychosocial interventions.

Individual-level brain morphological similarity networks: Current methodologies and applications

AIMS

The human brain is an extremely complex system in which neurons, clusters of neurons, or regions are connected to form a complex network. With the development of neuroimaging techniques, magnetic resonance imaging (MRI)-based brain networks play a key role in our understanding of the intricate architecture of human brain. Among them, the structural MRI-based brain morphological network approach has attracted increasing attention due to the advantages in data acquisition, image quality, and in revealing the structural organizing principles intrinsic to the brain. This review is to summarize the methodology and related applications of individual-level morphological networks.

BACKGROUND

There have been a growing number of studies related to brain morphological similarity networks. Conventional morphological networks are intersubject covariance networks constructed using a certain morphological indicator of a group of subjects; individual-level morphological networks, on the other hand, measure the morphological similarity between brain regions for individual brains and can reflect the morphological information of single subjects. In recent years, individual morphological networks have demonstrated significant worth in exploring the topological changes of the human brain under both normal and disease conditions. Such studies provided novel perspectives for understanding human brain development and exploring the pathological mechanisms of neuropsychiatric disorders.

CONCLUSION

This paper mainly focuses on the studies of brain morphological networks at the individual level, introduces several ways for network construction, reviews representative work in this field, and finally points out current problems and future directions.

Shared and specific neurobiology in bipolar disorder and unipolar disorder: Evidence based on the connectome gradient and a transcriptome-connectome association study

BACKGROUND

Distinguishing bipolar disorder (BD) and unipolar disorder (UD) remains challenging. To identify the common and diagnosis-specific neuropathological alterations and their potential molecular mechanisms in patients with UD and BD (with a current depressive episode).

METHODS

Resting-state functional magnetic resonance imaging was obtained from 279 participants (95 BD patients, 107 UD patients and 77 health controls). Connectome gradients analysis was performed to explore the shared and diagnosis-specific gradient alterations in BD and UD. The Allen Human Brain Atlas data was used to explore the potential gene mechanisms of the gradient alterations.

RESULTS

BD and UD had shared hierarchical disorganisation, including downgrading and contraction from the unimodal sensory networks (vision and sensorimotor) to the transmodal cognitive networks (limbic, frontoparietal, dorsal attention, and default) (all P < 0.05, FDR corrected) in gradient 1 and gradient 2. The BD patients had specific connectome gradient dysfunction in the subcortical network. Moreover, the hierarchical disorganisation was closely correlated with profiles of gene expression specific to the neuroglial cells in the prefrontal cortex in BD and UD, while the most correlated gene ontology biological processes and function were concentrated in synaptic signalling, calcium ion binding, and transmembrane transporter activity.

CONCLUSION

These findings reveal the shared and diagnosis-specific neurobiological mechanism underlying BD and UD patients, which advances our understanding of the neuromechanisms of these disorders.

Repetitive Negative Thinking-Specific and -Nonspecific White Matter Tracts Engaged by Historical Psychosurgical Targets for Depression

BACKGROUND

Repetitive negative thinking (RNT) is a frequent symptom of major depressive disorder (MDD) that is associated with poor outcomes and treatment resistance. While most studies on RNT have focused on structural and functional characteristics of gray matter, this study aimed to examine the association between white matter (WM) tracts and interindividual variability in RNT.

METHODS

A probabilistic tractography approach was used to characterize differences in the size and anatomical trajectory of WM fibers traversing psychosurgery targets historically useful in the treatment of MDD (anterior capsulotomy, anterior cingulotomy, and subcaudate tractotomy) in patients with MDD and low (n = 53) or high (n = 52) RNT, and healthy control subjects (n = 54). MDD samples were propensity matched on depression and anxiety severity and demographics.

RESULTS

WM tracts traversing left hemisphere targets and reaching the ventral anterior body of the corpus callosum (thus extending to contralateral regions) were larger in the high-RNT MDD group compared with low-RNT (effect size D = 0.27, p = .042) and healthy control (D = 0.23, p = .02) groups. MDD was associated with greater size of tracts that converge onto the right medial orbitofrontal cortex regardless of RNT intensity. Other RNT-nonspecific findings in MDD involved tracts reaching the left primary motor and right primary somatosensory cortices.

CONCLUSIONS

This study provides the first evidence to our knowledge that WM connectivity patterns, which could become targets of intervention, differ between high- and low-RNT participants with MDD. These WM differences extend to circuits that are not specific to RNT, possibly subserving reward mechanisms and psychomotor activity.

Quantifying brain connectivity signatures by means of polyconnectomic scoring

A broad range of neuropsychiatric disorders are associated with alterations in macroscale brain circuitry and connectivity. Identifying consistent brain patterns underlying these disorders by means of structural and functional MRI has proven challenging, partly due to the vast number of tests required to examine the entire brain, which can lead to an increase in missed findings. In this study, we propose polyconnectomic score (PCS) as a metric designed to quantify the presence of disease-related brain connectivity signatures in connectomes. PCS summarizes evidence of brain patterns related to a phenotype across the entire landscape of brain connectivity into a subject-level score. We evaluated PCS across four brain disorders (autism spectrum disorder, schizophrenia, attention deficit hyperactivity disorder, and Alzheimer's disease) and 14 studies encompassing ~35,000 individuals. Our findings consistently show that patients exhibit significantly higher PCS compared to controls, with effect sizes that go beyond other single MRI metrics ([min, max]: Cohen's d = [0.30, 0.87], AUC = [0.58, 0.73]). We further demonstrate that PCS serves as a valuable tool for stratifying individuals, for example within the psychosis continuum, distinguishing patients with schizophrenia from their first-degree relatives (d = 0.42, p = 4 × 10-3, FDR-corrected), and first-degree relatives from healthy controls (d = 0.34, p = 0.034, FDR-corrected). We also show that PCS is useful to uncover associations between brain connectivity patterns related to neuropsychiatric disorders and mental health, psychosocial factors, and body measurements.

Syntactic complexity and diversity of spontaneous speech production in schizophrenia spectrum and major depressive disorders

Syntax, the grammatical structure of sentences, is a fundamental aspect of language. It remains debated whether reduced syntactic complexity is unique to schizophrenia spectrum disorder (SSD) or whether it is also present in major depressive disorder (MDD). Furthermore, the association of syntax (including syntactic complexity and diversity) with language-related neuropsychology and psychopathological symptoms across disorders remains unclear. Thirty-four SSD patients and thirty-eight MDD patients diagnosed according to DSM-IV-TR as well as forty healthy controls (HC) were included and tasked with describing four pictures from the Thematic Apperception Test. We analyzed the produced speech regarding its syntax delineating measures for syntactic complexity (the total number of main clauses embedding subordinate clauses) and diversity (number of different types of complex sentences). We performed cluster analysis to identify clusters based on syntax and investigated associations of syntactic, to language-related neuropsychological (verbal fluency and verbal episodic memory), and psychopathological measures (positive and negative formal thought disorder) using network analyses. Syntax in SSD was significantly reduced in comparison to MDD and HC, whereas the comparison of HC and MDD revealed no significant differences. No associations were present between speech measures and current medication, duration and severity of illness, age or sex; the single association accounted for was education. A cluster analysis resulted in four clusters with different degrees of syntax across diagnoses. Subjects with less syntax exhibited pronounced positive and negative symptoms and displayed poorer performance in executive functioning, global functioning, and verbal episodic memory. All cluster-based networks indicated varying degrees of domain-specific and cross-domain connections. Measures of syntactic complexity were closely related while syntactic diversity appeared to be a separate node outside of the syntactic network. Cross-domain associations were more salient in more complex syntactic production.

Intrinsic Organization of Occipital Hubs Predicts Depression: A Resting-State fNIRS Study

Dysfunctional brain networks have been found in patients with major depressive disorder (MDD). In this study, to verify this in a more straightforward way, we investigated the intrinsic organization of brain networks in MDD by leveraging the resting-state functional near-infrared spectroscopy (rs-fNIRS). Thirty-four MDD patients (24 females, 38.41 ± 13.14 years old) and thirty healthy controls (22 females, 34.43 ± 5.03 years old) underwent a 10 min rest while their brain activity was recorded via fNIRS. The results showed that MDD patients and healthy controls exhibited similar resting-state functional connectivity. Moreover, the depression group showed lower small-world Lambda (1.12 ± 0.04 vs. 1.16 ± 0.10, p = 0.04) but higher global efficiency (0.51 ± 0.03 vs. 0.48 ± 0.05, p = 0.03) than the control group. Importantly, MDD patients, as opposed to healthy controls, showed a significantly lower nodal local efficiency at the left middle occipital gyrus (0.56 ± 0.36 vs. 0.81 ± 0.20, pFDR < 0.05), which predicted the level of depression in MDD (r = 0.45, p = 0.01, R2 = 0.15). In sum, we found a more integrated brain network in MDD patients with a lower nodal local efficiency at the occipital hub, which could predict depressive symptoms.