Variable global dysconnectivity and individual differences in schizophrenia

Unique Functional Neuroimaging Signatures of Genetic Versus Clinical High Risk for Psychosis

BACKGROUND

22q11.2 deletion syndrome (22qDel) is a copy number variant that is associated with psychosis and other neurodevelopmental disorders. Adolescents who are at clinical high risk for psychosis (CHR) are identified based on the presence of subthreshold psychosis symptoms. Whether common neural substrates underlie these distinct high-risk populations is unknown. We compared functional brain measures in 22qDel and CHR cohorts and mapped the results to biological pathways.

METHODS

We analyzed 2 large multisite cohorts with resting-state functional magnetic resonance imaging data: 1) a 22qDel cohort (n = 164, 47% female) and typically developing (TD) control participants (n = 134, 56% female); and 2) a cohort of CHR individuals (n = 240, 41% female) and TD control participants (n = 149, 46% female) from the NAPLS-2 (North American Prodrome Longitudinal Study-2). We computed global brain connectivity (GBC), local connectivity (LC), and brain signal variability (BSV) across cortical regions and tested case-control differences for 22qDel and CHR separately. Group difference maps were related to published brain maps using autocorrelation-preserving permutation.

RESULTS

BSV, LC, and GBC were significantly disrupted in individuals with 22qDel compared with TD control participants (false discovery rate-corrected q < .05). Spatial maps of BSV and LC differences were highly correlated with each other, unlike GBC. In the CHR group, only LC was significantly altered versus the control group, with a different spatial pattern than the 22qDel group. Group differences mapped onto biological gradients, with 22qDel effects being strongest in regions with high predicted blood flow and metabolism.

CONCLUSIONS

22qDel carriers and CHR individuals exhibited different effects on functional magnetic resonance imaging temporal variability and multiscale functional connectivity. In 22qDel carriers, strong and convergent disruptions in BSV and LC that were not seen in CHR individuals suggest distinct functional brain alterations.

Individualized Texture Similarity Network in Schizophrenia

BACKGROUND

Structural covariance network disruption has been considered an important pathophysiological indicator for schizophrenia. Here, we introduced a novel individualized structural covariance network measure, referred to as a texture similarity network (TSN), and hypothesized that the TSN could reliably reveal unique intersubject heterogeneity and complex dysconnectivity patterns in schizophrenia.

METHODS

The TSN was constructed by measuring the covariance of 180 three-dimensional voxelwise gray-level co-occurrence matrix feature maps between brain areas in each participant. We first tested the validity and reproducibility of the TSN in characterizing the intersubject variability in 2 longitudinal test-retest healthy cohorts. The TSN was further applied to elucidate intersubject variability and dysconnectivity patterns in 10 schizophrenia case-control datasets (609 schizophrenia cases vs. 579 controls) as well as in a first-episode depression dataset (69 patients with depression vs. 69 control participants).

RESULTS

The test-retest analysis demonstrated higher TSN intersubject than intrasubject variability. Moreover, the TSN reliably revealed higher intersubject variability in both chronic and first-episode schizophrenia, but not in depression. The TSN also reproducibly detected coexistent increased and decreased TSN strength in widespread brain areas, increased global small-worldness, and the coexistence of both structural hyposynchronization in the central networks and hypersynchronization in peripheral networks in patients with schizophrenia but not in patients with depression. Finally, aberrant intersubject variability and covariance strength patterns revealed by the TSN showed a missing or weak correlation with other individualized structural covariance network measures, functional connectivity, and regional volume changes.

CONCLUSIONS

These findings support the reliability of a TSN in revealing unique structural heterogeneity and complex dysconnectivity in patients with schizophrenia.

Mapping Brain Synergy Dysfunction in Schizophrenia: Understanding Individual Differences and Underlying Molecular Mechanisms

To elucidate the brain-wide information interactions that vary and contribute to individual differences in schizophrenia (SCZ), an information-resolved method is employed to construct individual synergistic and redundant interaction matrices based on regional pairwise BOLD time-series from 538 SCZ and 540 normal controls (NC). This analysis reveals a stable pattern of regionally-specific synergy dysfunction in SCZ. Furthermore, a hierarchical Bayesian model is applied to deconstruct the patterns of whole-brain synergy dysfunction into three latent factors that explain symptom heterogeneity in SCZ. Factor 1 exhibits a significant positive correlation with Positive and Negative Syndrome Scale (PANSS) positive scores, while factor 3 demonstrates significant negative correlations with PANSS negative and general scores. By integrating the neuroimaging data with normative gene expression information, this study identifies that each of these three factors corresponded to a subset of the SCZ risk gene set. Finally, by combining data from NeuroSynth and open molecular imaging sources, along with a spatially heterogeneous mean-field model, this study delineates three SCZ synergy factors corresponding to distinct symptom profiles and implicating unique cognitive, neurodynamic, and neurobiological mechanisms.

A connectome-wide association study of altered functional connectivity in schizophrenia based on resting-state fMRI

BACKGROUND

Aberrant resting-state functional connectivity is a neuropathological feature of schizophrenia (SCZ). Prior investigations into functional connectivity abnormalities have primarily employed seed-based connectivity analysis, necessitating predefined seed locations. To address this limitation, a data-driven multivariate method known as connectome-wide association study (CWAS) has been proposed for exploring whole-brain functional connectivity.

METHODS

We conducted a CWAS analysis involving 46 patients with SCZ and 40 age- and sex-matched healthy controls. Multivariate distance matrix regression (MDMR) was utilized to identify key nodes in the brain. Subsequently, we conducted a follow-up seed-based connectivity analysis to elucidate specific connectivity patterns between regions of interest (ROIs). Additionally, we explored the spatial correlation between changes in functional connectivity and underlying molecular architectures by examining correlations between neurotransmitter/transporter distribution densities and functional connectivity.

RESULTS

MDMR revealed the right medial frontal gyrus and the left calcarine sulcus as two key nodes. Follow-up analysis unveiled hypoconnectivity between the right medial frontal superior gyrus and the right fusiform gyrus, as well as hypoconnectivity between the left calcarine sulcus and the right lingual gyrus in SCZ. Notably, a significant association between functional connectivity strength and positive symptom severity was identified. Furthermore, altered functional connectivity patterns suggested potential dysfunctions in the dopamine, serotonin, and gamma-aminobutyric acid systems.

CONCLUSIONS

This study elucidated reduced functional connectivity both within and between the medial frontal regions and the occipital cortex in patients with SCZ. Moreover, it indicated potential alterations in molecular architecture, thereby expanding current knowledge regarding neurobiological changes associated with SCZ.

Sex-biasing influence of autism-associated Ube3a gene overdosage at connectomic, behavioral, and transcriptomic levels

Genomic mechanisms enhancing risk in males may contribute to sex bias in autism. The ubiquitin protein ligase E3A gene (Ube3a) affects cellular homeostasis via control of protein turnover and by acting as transcriptional coactivator with steroid hormone receptors. Overdosage of Ube3a via duplication or triplication of chromosomal region 15q11-13 causes 1 to 2% of autistic cases. Here, we test the hypothesis that increased dosage of Ube3a may influence autism-relevant phenotypes in a sex-biased manner. We show that mice with extra copies of Ube3a exhibit sex-biasing effects on brain connectomics and autism-relevant behaviors. These effects are associated with transcriptional dysregulation of autism-associated genes, as well as genes differentially expressed in 15q duplication and in autistic people. Increased Ube3a dosage also affects expression of genes on the X chromosome, genes influenced by sex steroid hormone, and genes sex-differentially regulated by transcription factors. These results suggest that Ube3a overdosage can contribute to sex bias in neurodevelopmental conditions via influence on sex-differential mechanisms.

Neural dysfunction underlying working memory processing at different stages of the illness course in schizophrenia: a comparative meta-analysis

Schizophrenia, as a chronic and persistent disorder, exhibits working memory deficits across various stages of the disorder, yet the neural mechanisms underlying these deficits remain elusive with inconsistent neuroimaging findings. We aimed to compare the brain functional changes of working memory in patients at different stages: clinical high risk, first-episode psychosis, and long-term schizophrenia, using meta-analyses of functional magnetic resonance imaging studies. Following a systematic literature search, 56 whole-brain task-based functional magnetic resonance imaging studies (15 for clinical high risk, 16 for first-episode psychosis, and 25 for long-term schizophrenia) were included. The separate and pooled neurofunctional mechanisms among clinical high risk, first-episode psychosis, and long-term schizophrenia were generated by Seed-based d Mapping toolbox. The clinical high risk and first-episode psychosis groups exhibited overlapping hypoactivation in the right inferior parietal lobule, right middle frontal gyrus, and left superior parietal lobule, indicating key lesion sites in the early phase of schizophrenia. Individuals with first-episode psychosis showed lower activation in left inferior parietal lobule than those with long-term schizophrenia, reflecting a possible recovery process or more neural inefficiency. We concluded that SCZ represent as a continuum in the early stage of illness progression, while the neural bases are inversely changed with the development of illness course to long-term course.

Heterogeneity in functional connectivity: Dimensional predictors of individual variability during rest and task fMRI in psychosis

BACKGROUND

Individuals with schizophrenia spectrum disorders (SSD) often demonstrate cognitive impairments, associated with poor functional outcomes. While neurobiological heterogeneity has posed challenges when examining social cognition in SSD, it provides a unique opportunity to explore brain-behavior relationships. The aim of this study was to investigate the relationship between individual variability in functional connectivity during resting state and the performance of a social task and social and non-social cognition in a large sample of controls and individuals diagnosed with SSD.

METHODS

Neuroimaging and behavioral data were analyzed for 193 individuals with SSD and 155 controls (total n = 348). Individual variability was quantified through mean correlational distance (MCD) of functional connectivity between participants; MCD was defined as a global 'variability score'. Pairwise correlational distance was calculated as 1 - the correlation coefficient between a given pair of participants, and averaging distance from one participant to all other participants provided the mean correlational distance metric. Hierarchical regressions were performed on variability scores derived from resting state and Empathic Accuracy (EA) task functional connectivity data to determine potential predictors (e.g., age, sex, neurocognitive and social cognitive scores) of individual variability.

RESULTS

Group comparison between SSD and controls showed greater SSD MCD during rest (p = 0.00038), while no diagnostic differences were observed during task (p = 0.063). Hierarchical regression analyses demonstrated the persistence of a significant diagnostic effect during rest (p = 0.008), contrasting with its non-significance during the task (p = 0.50), after social cognition was added to the model. Notably, social cognition exhibited significance in both resting state and task conditions (both p = 0.01).

CONCLUSIONS

Diagnostic differences were more prevalent during unconstrained resting scans, whereas the task pushed participants into a more common pattern which better emphasized transdiagnostic differences in cognitive abilities. Focusing on variability may provide new opportunities for interventions targeting specific cognitive impairments to improve functional outcomes.

Exploring global and local processes underlying alterations in resting-state functional connectivity and dynamics in schizophrenia

Introduction

We examined changes in large-scale functional connectivity and temporal dynamics and their underlying mechanisms in schizophrenia (ScZ) through measurements of resting-state functional magnetic resonance imaging (rs-fMRI) data and computational modelling.

Methods

The rs-fMRI measurements from patients with chronic ScZ (n=38) and matched healthy controls (n=43), were obtained through the public schizConnect repository. Computational models were constructed based on diffusion-weighted MRI scans and fit to the experimental rs-fMRI data.

Results

We found decreased large-scale functional connectivity across sensory and association areas and for all functional subnetworks for the ScZ group. Additionally global synchrony was reduced in patients while metastability was unaltered. Perturbations of the computational model revealed that decreased global coupling and increased background noise levels both explained the experimentally found deficits better than local changes to the GABAergic or glutamatergic system.

Discussion

The current study suggests that large-scale alterations in ScZ are more likely the result of global rather than local network changes.

The schizophrenia syndrome, circa 2024: What we know and how that informs its nature

With new data about different aspects of schizophrenia being continually generated, it becomes necessary to periodically revisit exactly what we know. Along with a need to review what we currently know about schizophrenia, there is an equal imperative to evaluate the construct itself. With these objectives, we undertook an iterative, multi-phase process involving fifty international experts in the field, with each step building on learnings from the prior one. This review assembles currently established findings about schizophrenia (construct, etiology, pathophysiology, clinical expression, treatment) and posits what they reveal about its nature. Schizophrenia is a heritable, complex, multi-dimensional syndrome with varying degrees of psychotic, negative, cognitive, mood, and motor manifestations. The illness exhibits a remitting and relapsing course, with varying degrees of recovery among affected individuals with most experiencing significant social and functional impairment. Genetic risk factors likely include thousands of common genetic variants that each have a small impact on an individual's risk and a plethora of rare gene variants that have a larger individual impact on risk. Their biological effects are concentrated in the brain and many of the same variants also increase the risk of other psychiatric disorders such as bipolar disorder, autism, and other neurodevelopmental conditions. Environmental risk factors include but are not limited to urban residence in childhood, migration, older paternal age at birth, cannabis use, childhood trauma, antenatal maternal infection, and perinatal hypoxia. Structural, functional, and neurochemical brain alterations implicate multiple regions and functional circuits. Dopamine D-2 receptor antagonists and partial agonists improve psychotic symptoms and reduce risk of relapse. Certain psychological and psychosocial interventions are beneficial. Early intervention can reduce treatment delay and improve outcomes. Schizophrenia is increasingly considered to be a heterogeneous syndrome and not a singular disease entity. There is no necessary or sufficient etiology, pathology, set of clinical features, or treatment that fully circumscribes this syndrome. A single, common pathophysiological pathway appears unlikely. The boundaries of schizophrenia remain fuzzy, suggesting the absence of a categorical fit and need to reconceptualize it as a broader, multi-dimensional and/or spectrum construct.

Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering

BACKGROUND

Similarities among schizophrenia (SZ), schizoaffective disorder (SAD) and bipolar disorder (BP) including clinical phenotypes, brain alterations and risk genes, make it challenging to perform reliable separation among them. However, previous subtype identification that transcend traditional diagnostic boundaries were based on group-level neuroimaging features, ignoring individual-level inferences.

METHODS

455 psychoses (178 SZs, 134 SADs and 143 BPs), their first-degree relatives (N = 453) and healthy controls (HCs, N = 220) were collected from Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP I) consortium. Individualized covariance structural differential networks (ICSDNs) were constructed for each patient and multi-site clustering was used to identify psychosis subtypes. Group differences between subtypes in clinical phenotypes and voxel-wise fractional amplitude of low frequency fluctuation (fALFF) were calculated, as well as between the corresponding relatives.

RESULTS

Two psychosis subtypes were identified with increased whole brain structural covariance, with decreased connectivity between amygdala-hippocampus and temporal-occipital cortex in subtype I (S-I) compared to subtype II (S-II), which was replicated under different clustering methods, number of edges and across datasets (B-SNIP II) and different brain atlases. S-I had higher emotional-related symptoms than S-II and showed significant fALFF decrease in temporal and occipital cortex, while S-II was more similar to HC. This pattern was consistently validated on relatives of S-I and S-II in both fALFF and clinical symptoms.

CONCLUSIONS

These findings reconcile categorical and dimensional perspectives of psychosis neurobiological heterogeneity, indicating that relatives of S-I might have greater predisposition in developing psychosis, while relatives of S-II are more likely to be healthy. This study contributes to the development of neuroimaging informed diagnostic classifications within psychosis spectrum.

Brain Networks With Modified Connectivity in Patients With Neuropathic Pain and Spinal Cord Injury

Background. Neuropathic pain (NP) following spinal cord injury (SCI) affects the quality of life of almost 40% of the injured population. The modified brain connectivity was reported under different NP conditions. Therefore, brain connectivity was studied in the SCI population with and without NP with the aim to identify networks that are altered due to injury, pain, or both. Methods. The study cohort is classified into 3 groups, SCI patients with NP, SCI patients without NP, and able-bodied. EEG of each participant was recorded during motor imagery (MI) of paralyzed and painful, and nonparalyzed and nonpainful limbs. Phased locked value was calculated using Hilbert transform to study altered functional connectivity between different regions. Results. The posterior region connectivity with frontal, fronto-central, and temporal regions is strongly decreased mainly during MI of dominant upper limb (nonparalyzed and nonpainful limbs) in SCI no pain group. This modified connectivity is prominent in the alpha and high-frequency bands (beta and gamma). Moreover, oscillatory modified global connectivity is observed in the pain group during MI of painful and paralyzed limb which is more evident between fronto-posterior, frontocentral-posterior, and within posterior and within frontal regions in the theta and SMR frequency bands. Cluster coefficient and local efficiency values are reduced in patients with no reported pain group while increased in the PWP group. Conclusion. The altered theta band connectivity found in the fronto-parietal network along with a global increase in local efficiency is a consequence of pain only, while altered connectivity in the beta and gamma bands along with a decrease in cluster coefficient values observed in the sensory-motor network is dominantly a consequence of injury only. The outcomes of this study may be used as a potential diagnostic biomarker for the NP. Further, the expected insight holds great clinical relevance in the design of neurofeedback-based neurorehabilitation and connectivity-based brain-computer interfaces for SCI patients.

A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis

BACKGROUND

Psychotic disorders are characterized by structural and functional abnormalities in brain networks. Neuroimaging techniques map and characterize such abnormalities using unique features (e.g., structural integrity, coactivation). However, it is unclear if a specific method, or a combination of modalities, is particularly effective in identifying differences in brain networks of someone with a psychotic disorder.

METHODS

A systematic meta-analysis evaluated machine learning classification of schizophrenia spectrum disorders in comparison to healthy control participants using various neuroimaging modalities (i.e., T1-weighted imaging (T1), diffusion tensor imaging (DTI), resting state functional connectivity (rs-FC), or some combination (multimodal)). Criteria for manuscript inclusion included whole-brain analyses and cross-validation to provide a complete picture regarding the predictive ability of large-scale brain systems in psychosis. For this meta-analysis, we searched Ovid MEDLINE, PubMed, PsychInfo, Google Scholar, and Web of Science published between inception and March 13th 2023. Prediction results were averaged for studies using the same dataset, but parallel analyses were run that included studies with pooled sample across many datasets. We assessed bias through funnel plot asymmetry. A bivariate regression model determined whether differences in imaging modality, demographics, and preprocessing methods moderated classification. Separate models were run for studies with internal prediction (via cross-validation) and external prediction.

RESULTS

93 studies were identified for quantitative review (30 T1, 9 DTI, 40 rs-FC, and 14 multimodal). As a whole, all modalities reliably differentiated those with schizophrenia spectrum disorders from controls (OR = 2.64 (95%CI = 2.33 to 2.95)). However, classification was relatively similar across modalities: no differences were seen across modalities in the classification of independent internal data, and a small advantage was seen for rs-FC studies relative to T1 studies in classification in external datasets. We found large amounts of heterogeneity across results resulting in significant signs of bias in funnel plots and Egger's tests. Results remained similar, however, when studies were restricted to those with less heterogeneity, with continued small advantages for rs-FC relative to structural measures. Notably, in all cases, no significant differences were seen between multimodal and unimodal approaches, with rs-FC and unimodal studies reporting largely overlapping classification performance. Differences in demographics and analysis or denoising were not associated with changes in classification scores.

CONCLUSIONS

The results of this study suggest that neuroimaging approaches have promise in the classification of psychosis. Interestingly, at present most modalities perform similarly in the classification of psychosis, with slight advantages for rs-FC relative to structural modalities in some specific cases. Notably, results differed substantially across studies, with suggestions of biased effect sizes, particularly highlighting the need for more studies using external prediction and large sample sizes. Adopting more rigorous and systematized standards will add significant value toward understanding and treating this critical population.

Resolving heterogeneity in depression using individualized structural covariance network analysis

BACKGROUND

Elucidating individual aberrance is a critical first step toward precision medicine for heterogeneous disorders such as depression. The neuropathology of depression is related to abnormal inter-regional structural covariance indicating a brain maturational disruption. However, most studies focus on group-level structural covariance aberrance and ignore the interindividual heterogeneity. For that reason, we aimed to identify individualized structural covariance aberrance with the help of individualized differential structural covariance network (IDSCN) analysis.

METHODS

T1-weighted anatomical images of 195 first-episode untreated patients with depression and matched healthy controls (n = 78) were acquired. We obtained IDSCN for each patient and identified subtypes of depression based on shared differential edges.

RESULTS

As a result, patients with depression demonstrated tremendous heterogeneity in the distribution of differential structural covariance edges. Despite this heterogeneity, altered edges within subcortical-cerebellum network were often shared by most of the patients. Two robust neuroanatomical subtypes were identified. Specifically, patients in subtype 1 often shared decreased motor network-related edges. Patients in subtype 2 often shared decreased subcortical-cerebellum network-related edges. Functional annotation further revealed that differential edges in subtype 2 were mainly implicated in reward/motivation-related functional terms.

CONCLUSIONS

In conclusion, we investigated individualized differential structural covariance and identified that decreased edges within subcortical-cerebellum network are often shared by patients with depression. The identified two subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of depression.

Altered MANF and RYR2 concentrations associated with hypolipidemia in the serum of patients with schizophrenia

Schizophrenia (SCZ) is associated with abnormal serum lipid profiles, but their relationship is poorly understood. Mesencephalic astrocyte-derived neurotrophic factor (MANF) is an important regulator of lipid metabolism. Previous studies have shown its involvement in the pathogenesis of numerous neuropsychiatric disorders, while its role in SCZ is still unknown. Therefore, this study was conducted to examine serum MANF levels in patients with SCZ, and to investigate the potential relationship between MANF, serum lipid levels and SCZ. The results showed that total cholesterol (TC) levels were significantly lower in 225 patients with SCZ than in 233 healthy controls (HCs). According to Ingenuity Pathway Analysis, hypolipidemia is associated with SCZ via MANF/ryanodine receptor 2 (RYR2) pathway. This theory was supported by another sample set, which showed significantly lower MANF levels and higher RYR2 levels in the serum of 170 SCZ patients compared to 80 HCs. Moreover, MANF and RYR2 levels both were significantly correlated with the severity of psychotic symptoms and TC levels. In addition, a model consisting of MANF and RYR2 was found to be effective in distinguishing SCZ patients from HCs. These findings suggested that the MANF/RYR2 pathway might serve as a bridge between hypolipidemia and SCZ, and MANF and RYR2 held promise as biomarkers for SCZ.

One ring to rule them all: The unifying role of prefrontal cortex in steering task-related brain dynamics

Surviving and thriving in a complex world require intricate balancing of higher order brain functions with essential survival-related behaviours. Exactly how this is achieved is not fully understood but a large body of work has shown that different regions in the prefrontal cortex (PFC) play key roles for diverse cognitive and emotional tasks including emotion, control, response inhibition, mental set shifting and working memory. We hypothesised that the key regions are hierarchically organised and we developed a framework for discovering the driving brain regions at the top of the hierarchy, responsible for steering the brain dynamics of higher brain function. We fitted a time-dependent whole-brain model to the neuroimaging data from large-scale Human Connectome Project with over 1,000 participants and computed the entropy production for rest and seven tasks (covering the main domains of cognition). This thermodynamics framework allowed us to identify the main common, unifying drivers steering the orchestration of brain dynamics during difficult tasks; located in key regions of the PFC (inferior frontal gyrus, lateral orbitofrontal cortex, rostral and caudal frontal cortex and rostral anterior cingulate cortex). Selectively lesioning these regions in the whole-brain model demonstrated their causal mechanistic importance. Overall, this shows the existence of a 'ring' of specific PFC regions ruling over the orchestration of higher brain function.

Impaired large-scale cortico-hippocampal network connectivity, including the anterior temporal and posterior medial systems, and its associations with cognition in patients with first-episode schizophrenia

Background and objective

The cortico-hippocampal network is an emerging neural framework with striking evidence that it supports cognition in humans, especially memory; this network includes the anterior temporal (AT) system, the posterior medial (PM) system, the anterior hippocampus (aHIPPO), and the posterior hippocampus (pHIPPO). This study aimed to detect aberrant patterns of functional connectivity within and between large-scale cortico-hippocampal networks in first-episode schizophrenia patients compared with a healthy control group via resting-state functional magnetic resonance imaging (rs-fMRI) and to explore the correlations of these aberrant patterns with cognition.

Methods

A total of 86 first-episode, drug-naïve schizophrenia patients and 102 healthy controls (HC) were recruited to undergo rs-fMRI examinations and clinical evaluations. We conducted large-scale edge-based network analysis to characterize the functional architecture of the cortico-hippocampus network and investigate between-group differences in within/between-network functional connectivity. Additionally, we explored the associations of functional connectivity (FC) abnormalities with clinical characteristics, including scores on the Positive and Negative Syndrome Scale (PANSS) and cognitive scores.

Results

Compared with the HC group, schizophrenia patients exhibited widespread alterations to within-network FC of the cortico-hippocampal network, with decreases in FC involving the precuneus (PREC), amygdala (AMYG), parahippocampal cortex (PHC), orbitofrontal cortex (OFC), perirhinal cortex (PRC), retrosplenial cortex (RSC), posterior cingulate cortex (PCC), angular gyrus (ANG), aHIPPO, and pHIPPO. Schizophrenia patients also showed abnormalities in large-scale between-network FC of the cortico-hippocampal network, in the form of significantly decreased FC between the AT and the PM, the AT and the aHIPPO, the PM and the aHIPPO, and the aHIPPO and the pHIPPO. A number of these signatures of aberrant FC were correlated with PANSS score (positive, negative, and total score) and with scores on cognitive test battery items, including attention/vigilance (AV), working memory (WM), verbal learning and memory (Verb_Lrng), visual learning and memory (Vis_Lrng), reasoning and problem-solving (RPS), and social cognition (SC).

Conclusion

Schizophrenia patients show distinct patterns of functional integration and separation both within and between large-scale cortico-hippocampal networks, reflecting a network imbalance of the hippocampal long axis with the AT and PM systems, which regulate cognitive domains (mainly Vis_Lrng, Verb_Lrng, WM, and RPS), and particularly involving alterations to FC of the AT system and the aHIPPO. These findings provide new insights into the neurofunctional markers of schizophrenia.

Neural substrates of verbal memory impairment in schizophrenia: A multimodal connectomics study

While verbal memory is among the most compromised cognitive domains in schizophrenia (SZ), its neural substrates remain elusive. Here, we explored the structural and functional brain network correlates of verbal memory impairment in SZ. We acquired diffusion and resting-state functional MRI data of 49 SZ patients, classified as having preserved (VMP, n = 22) or impaired (VMI, n = 26) verbal memory based on the List Learning task, and 55 healthy controls (HC). Structural and functional connectivity matrices were obtained and analyzed to assess associations with disease status (SZ vs. HC) and verbal memory impairment (VMI vs. VMP) using two complementary data-driven approaches: threshold-free network-based statistics (TFNBS) and hybrid connectivity independent component analysis (connICA). TFNBS showed altered connectivity in SZ patients compared with HC (p < .05, FWER-corrected), with distributed structural changes and functional reorganization centered around sensorimotor areas. Specifically, functional connectivity was reduced within the visual and somatomotor networks and increased between visual areas and associative and subcortical regions. Only a tiny cluster of increased functional connectivity between visual and bilateral parietal attention-related areas correlated with verbal memory dysfunction. Hybrid connICA identified four robust traits, representing fundamental patterns of joint structural-functional connectivity. One of these, mainly capturing the functional connectivity profile of the visual network, was significantly associated with SZ (HC vs. SZ: Cohen's d = .828, p < .0001) and verbal memory impairment (VMP vs. VMI: Cohen's d = -.805, p = .01). We suggest that aberrant connectivity of sensorimotor networks may be a key connectomic signature of SZ and a putative biomarker of SZ-related verbal memory impairment, in consistency with bottom-up models of cognitive disruption.

Individualized functional connectome identified generalizable biomarkers for psychiatric symptoms in transdiagnostic patients

Substantial clinical heterogeneity and comorbidity inherent amongst mental disorders limit the identification of neuroimaging biomarkers that can reliably track clinical symptoms. Strategies that enable generation of meaningful and replicable neurobiological markers at the individual level will push the field of neuropsychiatry forward in developing efficacious personalized treatment. The current study included 142 adult patients with a primary diagnosis of schizophrenia (SCZ), bipolar (BP), or attention deficit/hyperactivity disorder (ADHD), and 67 patient ratings across four behavioral measures. Using functional connectivity derived from a personalized fMRI approach, we identified several candidate imaging markers related to dimensional phenotypes across disorders, assessed the internal and external generalizability of these markers, and compared the probability of replicating findings across datasets using individual and group-averaged defined functional regions. We identified subject-specific connections related to three different clinical domains (attention deficit, appetite-energy, psychosis-positive) in a discovery dataset. Importantly, these connectivity biomarkers were robust and were reproduced in an independent validation dataset. For markers related to neurovegetative symptoms (attention deficit, appetite-energy symptoms), the brain connections involved showed similar connectivity patterns across the different diagnoses. However, psychosis-positive symptoms were associated with connections of varying strength across disorders. Finally, we found that markers for symptom domains were replicable for individually-specified connections, but not for group template-derived connections. Our personalized strategies allowed us to identify meaningful and generalizable imaging markers for symptom domains in patients who exhibit high levels of heterogeneity. These biomarkers may shed new light on the connectivity underpinnings of psychiatric symptoms and lead to personalized interventions.

The thalamus in psychosis spectrum disorder

Psychosis spectrum disorder (PSD) affects 1% of the world population and results in a lifetime of chronic disability, causing devastating personal and economic consequences. Developing new treatments for PSD remains a challenge, particularly those that target its core cognitive deficits. A key barrier to progress is the tenuous link between the basic neurobiological understanding of PSD and its clinical phenomenology. In this perspective, we focus on a key opportunity that combines innovations in non-invasive human neuroimaging with basic insights into thalamic regulation of functional cortical connectivity. The thalamus is an evolutionary conserved region that forms forebrain-wide functional loops critical for the transmission of external inputs as well as the construction and update of internal models. We discuss our perspective across four lines of evidence: First, we articulate how PSD symptomatology may arise from a faulty network organization at the macroscopic circuit level with the thalamus playing a central coordinating role. Second, we discuss how recent animal work has mechanistically clarified the properties of thalamic circuits relevant to regulating cortical dynamics and cognitive function more generally. Third, we present human neuroimaging evidence in support of thalamic alterations in PSD, and propose that a similar "thalamocortical dysconnectivity" seen in pharmacological imaging (under ketamine, LSD and THC) in healthy individuals may link this circuit phenotype to the common set of symptoms in idiopathic and drug-induced psychosis. Lastly, we synthesize animal and human work, and lay out a translational path for biomarker and therapeutic development.

The mouse claustrum synaptically connects cortical network motifs

Spatially distant areas of the cerebral cortex coordinate their activity into networks that are integral to cognitive processing. A common structural motif of cortical networks is co-activation of frontal and posterior cortical regions. The neural circuit mechanisms underlying such widespread inter-areal cortical coordination are unclear. Using a discovery based functional magnetic resonance imaging (fMRI) approach in mouse, we observe frontal and posterior cortical regions that demonstrate significant functional connectivity with the subcortical nucleus, the claustrum. Examining whether the claustrum synaptically supports such frontoposterior cortical network architecture, we observe cortico-claustro-cortical circuits reflecting the fMRI data: significant trans-claustral synaptic connectivity from frontal cortices to posteriorly lying sensory and sensory association cortices contralaterally. These data reveal discrete cortical pathways through the claustrum that are positioned to support cortical network motifs central to cognitive control functions and add to the canon of major extended cortico-subcortico-cortical systems in the mammalian brain.

Impaired dynamic functional brain properties and their relationship to symptoms in never treated first-episode patients with schizophrenia

Studies of dynamic functional connectivity (dFC) and topology can provide novel insights into the neurophysiology of brain dysfunction in schizophrenia and its relation to core symptoms of psychosis. Limited investigations of these disturbances have been conducted with never-treated first-episode patients to avoid the confounds of treatment or chronic illness. Therefore, we recruited 95 acutely ill, first-episode, never-treated patients with schizophrenia and examined brain dFC patterns relative to healthy controls using resting-state functional magnetic resonance imaging and a sliding-window approach. We compared the dynamic attributes at the group level and found patients spent more time in a hypoconnected state and correspondingly less time in a hyperconnected state. Patients demonstrated decreased dynamics of nodal efficiency and eigenvector centrality (EC) in the right medial prefrontal cortex, which was associated with psychosis severity reflected in Positive and Negative Syndrome Scale ratings. We also observed increased dynamics of EC in temporal and sensorimotor regions. These findings were supported by validation analysis. To supplement the group comparison analyses, a support vector classifier was used to identify the dynamic attributes that best distinguished patients from controls at the individual level. Selected features for case-control classification were highly coincident with the properties having significant between-group differences. Our findings provide novel neuroimaging evidence about dynamic characteristics of brain physiology in acute schizophrenia. The clinically relevant atypical pattern of dynamic shifting between brain states in schizophrenia may represent a critical aspect of illness pathophysiology underpinning its defining cognitive, behavioral, and affective features.

Functional connectivity associated with improvement in emotion management after cognitive enhancement therapy in early-course schizophrenia

BACKGROUND

The ability to manage emotions is an important social-cognitive domain impaired in schizophrenia and linked to functional outcome. The goal of our study was to examine the impact of cognitive enhancement therapy (CET) on the ability to manage emotions and brain functional connectivity in early-course schizophrenia.

METHODS

Participants were randomly assigned to CET (n = 55) or an enriched supportive therapy (EST) control group (n = 45). The resting-state functional magnetic resonance imaging scans and measures of emotion management performances were collected at baseline, 9, and 18 months follow-up. The final sample consisted of 37 CET and 25 EST participants, including 19 CET and 12 EST participants with imaging data. Linear mixed-effects models investigated the impact of treatment on emotion management and functional connectivity from the amygdala to ventrolateral and dorsolateral prefrontal cortex (dlPFC).

RESULTS

The CET group showed significant improvement over time in emotion management compared to EST. Neither functional connectivity changes nor main group differences were observed following treatment. However, a significant between-group interaction showed that improved emotion management ability was associated with increased functional connectivity between the left amygdala and the left dlPFC in the CET group exclusively.

CONCLUSION

Our results replicate the previous work demonstrating that CET is effective at improving some aspects of social cognition in schizophrenia. We found evidence that improvement in emotion management may be associated with a change in amygdala-dlPFC connectivity. This fronto-limbic circuit may provide a mechanistic link between the biology of emotion management processes that can be enhanced in individuals with schizophrenia.

Regularized Functional Connectivity in Schizophrenia

Regularization may be used as an alternative to dimensionality reduction when the number of variables in a model is much larger than the number of available observations. In a recent study from our group regularized regression was employed to quantify brain functional connectivity in a sample of healthy controls using a brain parcellation and resting state fMRI images. Here regularization is applied to evaluate resting state connectivity abnormalities at the voxel level in a sample of patients with schizophrenia. Specifically, ridge regression is implemented with different degrees of regularization. Results are compared to those delivered by the weighted global brain connectivity method (GBC), which is based on averaged bivariate correlations and from the non-redundant connectivity method (NRC), a dimensionality reduction approach that applies supervised principal component regressions. Ridge regression is able to detect a larger set of abnormally connected regions than both GBC and NRC methods, including schizophrenia related connectivity reductions in fronto-medial, somatosensory and occipital structures. Due to its multivariate nature, the proposed method is much more sensitive to group abnormalities than the GBC, but it also outperforms the NRC, which is multivariate too. Voxel based regularized regression is a simple and sensitive alternative for quantifying brain functional connectivity.

Path Analysis: A Method to Estimate Altered Pathways in Time-varying Graphs of Neuroimaging Data

Graph-theoretical methods have been widely used to study human brain networks in psychiatric disorders. However, the focus has primarily been on global graphic metrics with little attention to the information contained in paths connecting brain regions. Details of disruption of these paths may be highly informative for understanding disease mechanisms. To detect the absence or addition of multistep paths in the patient group, we provide an algorithm estimating edges that contribute to these paths with reference to the control group. We next examine where pairs of nodes were connected through paths in both groups by using a covariance decomposition method. We apply our method to study resting-state fMRI data in schizophrenia versus controls. Results show several disconnectors in schizophrenia within and between functional domains, particularly within the default mode and cognitive control networks. Additionally, we identify new edges generating additional paths. Moreover, although paths exist in both groups, these paths take unique trajectories and have a significant contribution to the decomposition. The proposed path analysis provides a way to characterize individuals by evaluating changes in paths, rather than just focusing on the pairwise relationships. Our results show promise for identifying path-based metrics in neuroimaging data.

Two distinct subtypes of obsessive compulsive disorder revealed by heterogeneity through discriminative analysis

Neurobiological heterogeneity in obsessive compulsive disorder (OCD) is understudied leading to conflicting neuroimaging findings. Therefore, we investigated objective neuroanatomical subtypes of OCD by adopting a newly proposed method based on gray matter volumes (GMVs). GMVs were derived from T1‐weighted anatomical images of patients with OCD (n = 100) and matched healthy controls (HCs; n = 106). We first inquired whether patients with OCD presented higher interindividual variability HCs in terms of GMVs. Then, we identified distinct subtypes of OCD by adopting heterogeneity through discriminative analysis (HYDRA), where regional GMVs were treated as features. Patients with OCD presented higher interindividual variability than HCs, suggesting a high structural heterogeneity of OCD. HYDRA identified two distinct robust subtypes of OCD presenting opposite neuroanatomical aberrances compared with HCs, while sharing indistinguishable clinical and demographic features. Specifically, Subtype 1 exhibited widespread increased GMVs in cortical and subcortical regions, including the orbitofrontal gyrus, right anterior insula, bilateral hippocampus, and bilateral parahippocampus and cerebellum. Subtype 2 demonstrated overall decreased GMVs in regions such as the orbitofrontal gyrus, right anterior insula, and precuneus. When mixed together, none of patients presented significant differences compared with HCs. In addition, the total intracranial volume of Subtype 2 was significantly correlated with the total score of the Yale–Brown Obsessive Compulsive Scale while that of Subtype 1 was not. These results identified two distinct neuroanatomical subtypes, providing a possible explanation for conflicting neuroimaging findings, and proposed a potential objective taxonomy contributing to precise clinical diagnosis and treatment in OCD.

Intrinsic Connectivity Networks of Glutamate-Mediated Antidepressant Response: A Neuroimaging Review

Conventional monoamine-based pharmacotherapy, considered the first-line treatment for major depressive disorder (MDD), has several challenges, including high rates of non-response. To address these challenges, preclinical and clinical studies have sought to characterize antidepressant response through monoamine-independent mechanisms. One striking example is glutamate, the brain's foremost excitatory neurotransmitter: since the 1990s, studies have consistently reported altered levels of glutamate in MDD, as well as antidepressant effects following molecular targeting of glutamatergic receptors. Therapeutically, this has led to advances in the discovery, testing, and clinical application of a wide array of glutamatergic agents, particularly ketamine. Notably, ketamine has been demonstrated to rapidly improve mood symptoms, unlike monoamine-based interventions, and the neurobiological basis behind this rapid antidepressant response is under active investigation. Advances in brain imaging techniques, including functional magnetic resonance imaging, magnetic resonance spectroscopy, and positron emission tomography, enable the identification of the brain network-based characteristics distinguishing rapid glutamatergic modulation from the effect of slow-acting conventional monoamine-based pharmacology. Here, we review brain imaging studies that examine brain connectivity features associated with rapid antidepressant response in MDD patients treated with glutamatergic pharmacotherapies in contrast with patients treated with slow-acting monoamine-based treatments. Trends in recent brain imaging literature suggest that the activity of brain regions is organized into coherent functionally distinct networks, termed intrinsic connectivity networks (ICNs). We provide an overview of major ICNs implicated in depression and explore how treatment response following glutamatergic modulation alters functional connectivity of limbic, cognitive, and executive nodes within ICNs, with well-characterized anti-anhedonic effects and the enhancement of "top-down" executive control. Alterations within and between the core ICNs could potentially exert downstream effects on the nodes within other brain networks of relevance to MDD that are structurally and functionally interconnected through glutamatergic synapses. Understanding similarities and differences in brain ICNs features underlying treatment response will positively impact the trajectory and outcomes for adults suffering from MDD and will facilitate the development of biomarkers to enable glutamate-based precision therapeutics.

Improving Between-Group Effect Size for Multi-Site Functional Connectivity Data via Site-Wise De-Meaning

Background: Multi-site functional MRI (fMRI) databases are becoming increasingly prevalent in the study of neurodevelopmental and psychiatric disorders. However, multi-site databases are known to introduce site effects that may confound neurobiological and measures such as functional connectivity (FC). Although studies have been conducted to mitigate site effects, these methods often result in reduced effect size in FC comparisons between controls and patients.

Methods: We present a site-wise de-meaning (SWD) strategy in multi-site FC analysis and compare its performance with two common site-effect mitigation methods, i.e., generalized linear model (GLM) and Combining Batches (ComBat) Harmonization. For SWD, after FC was calculated and Fisher z-transformed, the site-wise FC mean was removed from each subject before group-level statistical analysis. The above methods were tested on two multi-site psychiatric consortiums [Autism Brain Imaging Data Exchange (ABIDE) and Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP)]. Preservation of consistent FC alterations in patients were evaluated for each method through the effect sizes (Hedge’s g) of patients vs. controls.

Results: For the B-SNIP dataset, SWD improved the effect size between schizophrenic and control subjects by 4.5–7.9%, while GLM and ComBat decreased the effect size by 22.5–42.6%. For the ABIDE dataset, SWD improved the effect size between autistic and control subjects by 2.9–5.3%, while GLM and ComBat decreased the effect size by up to 11.4%.

Conclusion: Compared to the original data and commonly used methods, the SWD method demonstrated superior performance in preserving the effect size in FC features associated with disorders.

Atypical Antipsychotics Mediate Dynamics of Intrinsic Brain Activity in Early-Stage Schizophrenia? A Preliminary Study

OBJECTIVE

Abnormalities of static brain activity have been reported in schizophrenia, but it remains to be clarified the temporal variability of intrinsic brain activities in schizophrenia and how atypical antipsychotics affect it.

METHODS

We employed a resting-state functional magnetic resonance imaging (rs-fMRI) and a sliding-window analysis of dynamic amplitude of low-frequency fluctuation (dALFF) to evaluate the dynamic brain activities in schizophrenia (SZ) patients before and after 8-week antipsychotic treatment. Twenty-six schizophrenia individuals and 26 matched healthy controls (HC) were included in this study.

RESULTS

Compared with HC, SZ showed stronger dALFF in the right inferior temporal gyrus (ITG.R) at baseline. After medication, the SZ group exhibited reduced dALFF in the right middle occipital gyrus (MOG.R) and increased dALFF in the left superior frontal gyrus (SFG.L), right middle frontal gyrus (MFG.R), and right inferior parietal lobule (IPL.R). Dynamic ALFF in IPL.R was found to significant negative correlate with the Scale for the Assessment of Negative Symptoms (SANS) scores at baseline.

CONCLUSION

Our results showed dynamic intrinsic brain activities altered in schizophrenia after short term antipsychotic treatment. The findings of this study support and expand the application of dALFF method in the study of the pathological mechanism in psychosis in the future.

Abnormal nodal and global network organization in resting state functional MRI from subjects with the 22q11 deletion syndrome

The 22q11 deletion syndrome is a genetic disorder associated with a high risk of developing psychosis, and is therefore considered a neurodevelopmental model for studying the pathogenesis of schizophrenia. Studies have shown that localized abnormal functional brain connectivity is present in 22q11 deletion syndrome like in schizophrenia. However, it is less clear whether these abnormal cortical interactions lead to global or regional network disorganization as seen in schizophrenia. We analyzed from a graph-theory perspective fMRI data from 40 22q11 deletion syndrome patients and 67 healthy controls, and reconstructed functional networks from 105 brain regions. Between-group differences were examined by evaluating edge-wise strength and graph theoretical metrics of local (weighted degree, nodal efficiency, nodal local efficiency) and global topological properties (modularity, local and global efficiency). Connectivity strength was globally reduced in patients, driven by a large network comprising 147 reduced connections. The 22q11 deletion syndrome network presented with abnormal local topological properties, with decreased local efficiency and reductions in weighted degree particularly in hub nodes. We found evidence for abnormal integration but intact segregation of the 22q11 deletion syndrome network. Results suggest that 22q11 deletion syndrome patients present with similar aberrant local network organization as seen in schizophrenia, and this network configuration might represent a vulnerability factor to psychosis.

White matter microstructural associates of apathy-avolition in schizophrenia

Apathy is present at the onset in nearly half the patients with schizophrenia. Current therapies lack the efficiency to improve apathy in patients. The presence of apathy is also associated with poorer outcomes. Despite its clinical importance, the underlying mechanism of apathy in schizophrenia is unclear, but it seems frontostriatal connections play a role. In this study, we investigated whole-brain white matter microstructural properties associated with the severity of apathy-avolition in schizophrenia. We included 80 schizophrenia patients (60 Male, 20 Female) from the Mind Clinical Imaging Consortium database and associated Apathy-Avolition score of "Scale for Assessment of Negative Symptoms" with fiber integrity measures derived from diffusion-weighted imaging using Tract-Based Spatial Statistics (TBSS). We also did tractography on eight tracts, including bilateral superior longitudinal fasciculus, uncinate fasciculus, cingulum, genu and splenium of the corpus callosum. Age, gender, years of education, chlorpromazine equivalent cumulative dose, and acquisition site were inserted as covariates. We showed a widespread association between lower fiber integrity (by measures of increased mean diffusivity and decreased fractional anisotropy) and increased apathy-avolition in TBSS, which we also validated in tractography. Moreover, mean diffusivity, and not fractional anisotropy, was associated with apathy independent of disease severity. In conclusion, we propose diffuse white-matter pathology, within the corpus callosum, limbic system, and the frontostriatal circuit is involved in apathy-avolition in schizophrenia. Also, we suggest that diffuse neuroinflammatory processes may play a part in apathy-avolition, independent of disease severity.

Altered brain activity in the bilateral frontal cortices and neural correlation with cognitive impairment in schizophrenia

Cognitive impairments are core aspects of schizophrenia and are highly related to poor outcomes. However, the effect of therapy on cognitive impairments remains unsatisfactory as its biological mechanisms are not fully understood. The purpose of this study was to investigate the disrupted intrinsic neural activity of the frontal areas and to further examine the functional connectivity of frontal areas related to cognitive impairments in schizophrenia. We collected brain imaging data using a 3T Siemens Prisma MRI system in 32 patients with schizophrenia and 34 age- and sex-matched healthy controls. The mean fractional amplitude of low-frequency fluctuation (mfALFF) in the frontal regions was calculated and analyzed to evaluate regional neural activity alterations in schizophrenia. Seed regions were generated from clusters showing significant changes in mfALFF in schizophrenia, and its resting-state functional connectivity (rs-FC) with other brain regions were estimated to detect possible aberrant rs-FC indicating cognitive impairments in schizophrenia. We found that mfALFF in the bilateral frontal cortices was increased in schizophrenia. mfALFF-based rs-FC revealed that decreased rs-FC between left middle frontal gyrus (MFG) and left medial superior frontal gyrus (MFSG) was associated with poor delayed memory (r = 0.566, Bonferroni-corrected p = 0.012). These findings demonstrate increased neural activity in the frontal cortices in schizophrenia. FC analysis revealed a diminished rs-FC pattern between the left MFG and left MSFG that was associated with cognitive impairments. These findings have provided deeper insight into the alterations in brain function related to specific domains of cognitive impairment and may provide evidence for precise interventions for cognitive deficits in schizophrenia.

The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

Network neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization's basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions.

Transcriptomics-informed large-scale cortical model captures topography of pharmacological neuroimaging effects of LSD

Psychoactive drugs can transiently perturb brain physiology while preserving brain structure. The role of physiological state in shaping neural function can therefore be investigated through neuroimaging of pharmacologically induced effects. Previously, using pharmacological neuroimaging, we found that neural and experiential effects of lysergic acid diethylamide (LSD) are attributable to agonism of the serotonin-2A receptor (Preller et al., 2018). Here, we integrate brain-wide transcriptomics with biophysically based circuit modeling to simulate acute neuromodulatory effects of LSD on human cortical large-scale spatiotemporal dynamics. Our model captures the inter-areal topography of LSD-induced changes in cortical blood oxygen level-dependent (BOLD) functional connectivity. These findings suggest that serotonin-2A-mediated modulation of pyramidal-neuronal gain is a circuit mechanism through which LSD alters cortical functional topography. Individual-subject model fitting captures patterns of individual neural differences in pharmacological response related to altered states of consciousness. This work establishes a framework for linking molecular-level manipulations to systems-level functional alterations, with implications for precision medicine.

Mapping brain-behavior space relationships along the psychosis spectrum

Difficulties in advancing effective patient-specific therapies for psychiatric disorders highlight a need to develop a stable neurobiologically grounded mapping between neural and symptom variation. This gap is particularly acute for psychosis-spectrum disorders (PSD). Here, in a sample of 436 PSD patients spanning several diagnoses, we derived and replicated a dimensionality-reduced symptom space across hallmark psychopathology symptoms and cognitive deficits. In turn, these symptom axes mapped onto distinct, reproducible brain maps. Critically, we found that multivariate brain-behavior mapping techniques (e.g. canonical correlation analysis) do not produce stable results with current sample sizes. However, we show that a univariate brain-behavioral space (BBS) can resolve stable individualized prediction. Finally, we show a proof-of-principle framework for relating personalized BBS metrics with molecular targets via serotonin and glutamate receptor manipulations and neural gene expression maps derived from the Allen Human Brain Atlas. Collectively, these results highlight a stable and data-driven BBS mapping across PSD, which offers an actionable path that can be iteratively optimized for personalized clinical biomarker endpoints.

16p11 Duplication Disrupts Hippocampal-Orbitofrontal-Amygdala Connectivity, Revealing a Neural Circuit Endophenotype for Schizophrenia

Chromosome 16p11.2 duplications dramatically increase risk for schizophrenia, but the mechanisms remain largely unknown. Here, we show that mice with an equivalent genetic mutation (16p11.2 duplication mice) exhibit impaired hippocampal-orbitofrontal and hippocampal-amygdala functional connectivity. Expression of schizophrenia-relevant GABAergic cell markers (parvalbumin and calbindin) is selectively decreased in orbitofrontal cortex, while somatostatin expression is decreased in lateral amygdala. When 16p11.2 duplication mice are tested in cognitive tasks dependent on hippocampal-orbitofrontal connectivity, performance is impaired in an 8-arm maze "N-back" working memory task and in a touchscreen continuous performance task. Consistent with hippocampal-amygdala dysconnectivity, deficits in ethologically relevant social behaviors are also observed. Overall, the cellular/molecular, brain network, and behavioral alterations markedly mirror those observed in schizophrenia patients. Moreover, the data suggest that 16p11.2 duplications selectively impact hippocampal-amygdaloid-orbitofrontal circuitry, supporting emerging ideas that dysfunction in this network is a core element of schizophrenia and defining a neural circuit endophenotype for the disease.

Shared and distinct global signal topography disturbances in subcortical and cortical networks in human epilepsy

Epilepsy is a common brain network disorder associated with disrupted large-scale excitatory and inhibitory neural interactions. Recent resting-state fMRI evidence indicates that global signal (GS) fluctuations that have commonly been ignored are linked to neural activity. However, the mechanisms underlying the altered global pattern of fMRI spontaneous fluctuations in epilepsy remain unclear. Here, we quantified GS topography using beta weights obtained from a multiple regression model in a large group of epilepsy with different subtypes (98 focal temporal epilepsy; 116 generalized epilepsy) and healthy population (n = 151). We revealed that the nonuniformly distributed GS topography across association and sensory areas in healthy controls was significantly shifted in patients. Particularly, such shifts of GS topography disturbances were more widespread and bilaterally distributed in the midbrain, cerebellum, visual cortex, and medial and orbital cortex in generalized epilepsy, whereas in focal temporal epilepsy, these networks spread beyond the temporal areas but mainly remain lateralized. Moreover, we found that these abnormal GS topography patterns were likely to evolve over the course of a longer epilepsy disease. Our study demonstrates that epileptic processes can potentially affect global excitation/inhibition balance and shift the normal GS topological distribution. These progressive topographical GS disturbances in subcortical-cortical networks may underlie pathophysiological mechanisms of global fluctuations in human epilepsy.

Conceptual disorganization and redistribution of resting-state cortical hubs in untreated first-episode psychosis: A 7T study

Network-level dysconnectivity has been studied in positive and negative symptoms of schizophrenia. Conceptual disorganization (CD) is a symptom subtype that predicts impaired real-world functioning in psychosis. Systematic reviews have reported aberrant connectivity in formal thought disorder, a construct related to CD. However, no studies have investigated whole-brain functional correlates of CD in psychosis. We sought to investigate brain regions explaining the severity of CD in patients with first-episode psychosis (FEPs) compared with healthy controls (HCs). We computed whole-brain binarized degree centrality maps of 31 FEPs, 25 HCs, and characterized the patterns of network connectivity in the 2 groups. In FEPs, we related these findings to the severity of CD. We also studied the effect of positive and negative symptoms on altered network connectivity. Compared to HCs, reduced centrality of a right superior temporal gyrus (rSTG) cluster was observed in the FEPs. In patients exhibiting high CD, increased centrality of a medial superior parietal (mSPL) cluster was observed, compared to patients exhibiting low CD. This cluster was strongly correlated with CD scores but not with other symptom scores. Our observations are congruent with previous findings of reduced but not increased centrality. We observed increased centrality of mSPL suggesting that cortical reorganization occurs to provide alternate routes for information transfer. These findings provide insight into the underlying neural processes mediating the presentation of symptoms in untreated FEP. Longitudinal tracking of the symptom course will be useful to assess the mechanisms underlying these compensatory changes.

Altered temporal, but intact spatial, features of transient network dynamics in psychosis

Contemporary models of psychosis suggest that a continuum of severity of psychotic symptoms exists, with subthreshold psychotic experiences (PEs) potentially reflecting some genetic and environmental risk factors shared with clinical psychosis. Thus, identifying abnormalities in brain activity that manifest across this continuum can shed new light on the pathophysiology of psychosis. Here, we investigated the moment-to-moment engagement of brain networks ("states") in individuals with schizophrenia (SCZ) and PEs and identified features of these states that are associated with psychosis-spectrum symptoms. Transient brain states were defined by clustering "single snapshots" of blood oxygen level-dependent images, based on spatial similarity of the images. We found that individuals with SCZ (n = 35) demonstrated reduced recruitment of three brain states compared to demographically matched healthy controls (n = 35). Of these three illness-related states, one specific state, involving primarily the visual and salience networks, also occurred at a lower rate in individuals with persistent PEs (n = 22), compared to demographically matched healthy youth (n = 22). Moreover, the occurrence rate of this marker brain state was negatively correlated with the severity of PEs (r = -0.26, p = 0.003, n = 130). In contrast, the spatial map of this state appeared to be unaffected in the SCZ or PE groups. Thus, reduced engagement of a brain state involving the visual and salience networks was demonstrated across the psychosis continuum, suggesting that early disruptions of perceptual and affective function may underlie some of the core symptoms of the illness.

Structural Covariance Reveals Alterations in Control and Salience Network Integrity in Chronic Schizophrenia

Schizophrenia (SCZ) is recognized as a disorder of distributed brain dysconnectivity. While progress has been made delineating large-scale functional networks in SCZ, little is known about alterations in grey matter integrity of these networks. We used a multivariate approach to identify the structural covariance of the salience, default, motor, visual, fronto-parietal control, and dorsal attention networks. We derived individual scores reflecting covariance in each structural image for a given network. Seed-based multivariate analyses were conducted on structural images in a discovery (n = 90) and replication (n = 74) sample of SCZ patients and healthy controls. We first validated patterns across all networks, consistent with well-established functional connectivity reports. Next, across two SCZ samples, we found reliable and robust reductions in structural integrity of the fronto-parietal control and salience networks, but not default, dorsal attention, motor and sensory networks. Well-powered exploratory analyses failed to identify relationships with symptoms. These findings provide evidence of selective structural decline in associative networks in SCZ. Such decline may be linked with recently identified functional disturbances in associative networks, providing more sensitive multi-modal network-level probes in SCZ. Absence of symptom effects suggests that identified disturbances may underlie a trait-type marker in SCZ.

Disrupted Intersubject Variability Architecture in Functional Connectomes in Schizophrenia

Schizophrenia (SCZ) is a highly heterogeneous disorder with remarkable intersubject variability in clinical presentations. Previous neuroimaging studies in SCZ have primarily focused on identifying group-averaged differences in the brain connectome between patients and healthy controls (HCs), largely neglecting the intersubject differences among patients. We acquired whole-brain resting-state functional MRI data from 121 SCZ patients and 183 HCs and examined the intersubject variability of the functional connectome (IVFC) in SCZ patients and HCs. Between-group differences were determined using permutation analysis. Then, we evaluated the relationship between IVFC and clinical variables in SCZ. Finally, we used datasets of patients with bipolar disorder (BD) and major depressive disorder (MDD) to assess the specificity of IVFC alteration in SCZ. The whole-brain IVFC pattern in the SCZ group was generally similar to that in HCs. Compared with the HC group, the SCZ group exhibited higher IVFC in the bilateral sensorimotor, visual, auditory, and subcortical regions. Moreover, altered IVFC was negatively correlated with age of onset, illness duration, and Brief Psychiatric Rating Scale scores and positively correlated with clinical heterogeneity. Although the SCZ shared altered IVFC in the visual cortex with BD and MDD, the alterations of IVFC in the sensorimotor, auditory, and subcortical cortices were specific to SCZ. The alterations of whole-brain IVFC in SCZ have potential implications for the understanding of the high clinical heterogeneity of SCZ and the future individualized clinical diagnosis and treatment of this disease.

Overlapping but Asymmetrical Relationships Between Schizophrenia and Autism Revealed by Brain Connectivity

Although the relationship between schizophrenia spectrum disorder (SSD) and autism spectrum disorder (ASD) has long been debated, it has not yet been fully elucidated. The authors quantified and visualized the relationship between ASD and SSD using dual classifiers that discriminate patients from healthy controls (HCs) based on resting-state functional connectivity magnetic resonance imaging. To develop a reliable SSD classifier, sophisticated machine-learning algorithms that automatically selected SSD-specific functional connections were applied to Japanese datasets from Kyoto University Hospital (N = 170) including patients with chronic-stage SSD. The generalizability of the SSD classifier was tested by 2 independent validation cohorts, and 1 cohort including first-episode schizophrenia. The specificity of the SSD classifier was tested by 2 Japanese cohorts of ASD and major depressive disorder. The weighted linear summation of the classifier's functional connections constituted the biological dimensions representing neural classification certainty for the disorders. Our previously developed ASD classifier was used as ASD dimension. Distributions of individuals with SSD, ASD, and HCs s were examined on the SSD and ASD biological dimensions. We found that the SSD and ASD populations exhibited overlapping but asymmetrical patterns in the 2 biological dimensions. That is, the SSD population showed increased classification certainty for the ASD dimension but not vice versa. Furthermore, the 2 dimensions were correlated within the ASD population but not the SSD population. In conclusion, using the 2 biological dimensions based on resting-state functional connectivity enabled us to discover the quantified relationships between SSD and ASD.

A Whole-Brain and Cross-Diagnostic Perspective on Functional Brain Network Dysfunction

A wide variety of mental disorders have been associated with resting-state functional network alterations, which are thought to contribute to the cognitive changes underlying mental illness. These observations appear to support theories postulating large-scale disruptions of brain systems in mental illness. However, existing approaches isolate differences in network organization without putting those differences in a broad, whole-brain perspective. Using a graph distance approach-connectome-wide similarity-we found that whole-brain resting-state functional network organization is highly similar across groups of individuals with and without a variety of mental diseases. This similarity was observed across autism spectrum disorder, attention-deficit hyperactivity disorder, and schizophrenia. Nonetheless, subtle differences in network graph distance were predictive of diagnosis, suggesting that while functional connectomes differ little across health and disease, those differences are informative. These results suggest a need to reevaluate neurocognitive theories of mental illness, with a role for subtle functional brain network changes in the production of an array of mental diseases. Such small network alterations suggest the possibility that small, well-targeted alterations to brain network organization may provide meaningful improvements for a variety of mental disorders.

Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illness

Neuroimaging studies of psychotic disorders have demonstrated abnormalities in structural and functional connectivity involving widespread brain networks. However, these group-level observations have failed to yield any biomarkers that can provide confirmatory evidence of a patient's current symptoms, predict future symptoms, or predict a treatment response. Lack of precision in both neuroanatomical and clinical boundaries have likely contributed to the inability of even well-powered studies to resolve these key relationships. Here, we employed a novel approach to defining individual-specific functional connectivity in 158 patients diagnosed with schizophrenia (n = 49), schizoaffective disorder (n = 37), or bipolar disorder with psychosis (n = 72), and identified neuroimaging features that track psychotic symptoms in a dimension- or disorder-specific fashion. Using individually specified functional connectivity, we were able to estimate positive, negative, and manic symptoms that showed correlations ranging from r = 0.35 to r = 0.51 with the observed symptom scores. Comparing optimized estimation models among schizophrenia spectrum patients, positive and negative symptoms were associated with largely non-overlapping sets of cortical connections. Comparing between schizophrenia spectrum and bipolar disorder patients, the models for positive symptoms were largely non-overlapping between the two disorder classes. Finally, models derived using conventional region definition strategies performed at chance levels for most symptom domains. Individual-specific functional connectivity analyses revealed important new distinctions among cortical circuits responsible for the positive and negative symptoms, as well as key new information about how circuits underlying symptom expressions may vary depending on the underlying etiology and illness syndrome from which they manifest.

Psilocybin Induces Time-Dependent Changes in Global Functional Connectivity

BACKGROUND

The use of psilocybin in scientific and experimental clinical contexts has triggered renewed interest in the mechanism of action of psychedelics. However, its time-dependent systems-level neurobiology remains sparsely investigated in humans.

METHODS

We conducted a double-blind, randomized, counterbalanced, crossover study comprising 23 healthy human participants who received placebo and 0.2 mg/kg of psilocybin orally on 2 different test days. Participants underwent magnetic resonance imaging at 3 time points between administration and peak effects: 20 minutes, 40 minutes, and 70 minutes after administration. Resting-state functional connectivity was quantified via a data-driven global brain connectivity method and compared with cortical gene expression maps.

RESULTS

Psilocybin reduced associative, but concurrently increased sensory, brain-wide connectivity. This pattern emerged over time from administration to peak effects. Furthermore, we showed that baseline connectivity is associated with the extent of psilocybin-induced changes in functional connectivity. Lastly, psilocybin-induced changes correlated in a time-dependent manner with spatial gene expression patterns of the 5-HT_2A (5-hydroxytryptamine 2A) and 5-HT_1A (5-hydroxytryptamine 1A) receptors.

CONCLUSIONS

These results suggest that the integration of functional connectivity in sensory regions and the disintegration in associative regions may underlie the psychedelic state and pinpoint the critical role of the serotonin 2A and 1A receptor systems. Furthermore, baseline connectivity may represent a predictive marker of the magnitude of changes induced by psilocybin and may therefore contribute to a personalized medicine approach within the potential framework of psychedelic treatment.

Plasticity of DNA methylation, functional brain connectivity and efficiency in cognitive remediation for schizophrenia

Cognitive remediation (CR) is predicated on principles of neuroplasticity, but the actual molecular and neurocircuitry changes underlying cognitive change in individuals with impaired neuroplastic processes is poorly understood. The present study examined epigenetic-neurocircuitry-behavioral outcome measures in schizophrenia, before and after participating in a CR program that targeted higher-order cognitive functions. Outcome measures included DNA methylation of genes central to synaptic plasticity (CpG sites of Reelin promoter and BDNF promoter) from buccal swabs, resting-state functional brain connectivity and topological network efficiency, and global scores of a cognitive battery from 35 inpatients in a rehabilitative ward (18 CR, 17 non-CR) with similar premorbid IQ to 15 healthy controls. Baseline group differences between healthy controls and schizophrenia, group-by-time effects of CR in schizophrenia, and associations between the outcome measures were tested. Baseline functional connectivity abnormalities within the frontal, fronto-temporal and fronto-parietal regions, and trending decreases in global efficiency, but not DNA methylation, were found in schizophrenia; the frontal and fronto-temporal connectivity, and global efficiency correlated with global cognitive performance across all individuals. Notably, CR resulted in differential changes in Reelin promoter CpG methylation levels, altered within-frontal and fronto-temporal functional connectivity, increasing global efficiency and improving cognitive performance in schizophrenia, when compared to non-CR. In the CR inpatients, positive associations between the micro to macro measures: Reelin methylation changes, higher global efficiency and improving global cognitive performance were found. Present findings provide a neurobiological insight into potential CR-led epigenetics-neurocircuitry modifications driving cognitive plasticity.

Decreased interhemispheric connectivity and increased cortical excitability in unmedicated schizophrenia: A prefrontal interleaved TMS fMRI study

BACKGROUND

Prefrontal abnormalities in schizophrenia have consistently emerged from resting state and cognitive neuroimaging studies. However, these correlative findings require causal verification via combined imaging/stimulation approaches. To date, no interleaved transcranial magnetic stimulation and functional magnetic resonance imaging study (TMS fMRI) has probed putative prefrontal cortex abnormalities in schizophrenia.

OBJECTIVE

/Hypothesis: We hypothesized that subjects with schizophrenia would show significant hyperexcitability at the site of stimulation (BA9) and decreased interhemispheric functional connectivity.

METHODS

We enrolled 19 unmedicated subjects with schizophrenia and 22 controls. All subjects underwent brain imaging using a 3T MRI scanner with a SENSE coil. They also underwent a single TMS fMRI session involving motor threshold (rMT) determination, structural imaging, and a parametric TMS fMRI protocol with 10 Hz triplet pulses at 0, 80, 100 and 120% rMT. Scanning involved a surface MR coil optimized for bilateral prefrontal cortex image acquisition.

RESULTS

Of the original 41 enrolled subjects, 8 subjects with schizophrenia and 11 controls met full criteria for final data analyses. At equal TMS intensity, subjects with schizophrenia showed hyperexcitability in left BA9 (p = 0.0157; max z-score = 4.7) and neighboring BA46 (p = 0.019; max z-score = 4.47). Controls showed more contralateral functional connectivity between left BA9 and right BA9 through increased activation in right BA9 (p = 0.02; max z-score = 3.4). GM density in subjects with schizophrenia positively correlated with normalized prefrontal to motor cortex ratio of the corresponding distance from skull to cortex ratio (S-BA9/S-MC) (r = 0.83, p = 0.004).

CONCLUSIONS

Subjects with schizophrenia showed hyperexcitability in left BA9 and impaired interhemispheric functional connectivity compared to controls. Interleaved TMS fMRI is a promising tool to investigate prefrontal dysfunction in schizophrenia.

Dysfunctional connectivity in posterior brain regions involved in cognitive control in schizophrenia: A preliminary fMRI study

Cognitive control, the ability to use goal-directed information to guide behaviour, is impaired in schizophrenia, and mainly related to dysfunctions within the fronto-posterior brain network. However, cognitive control is a broad cognitive function encompassing distinct sub-processes that, until now, studies have failed to separate and relate to specific brain regions. The goal of this preliminary fMRI study is to investigate the functional specialization of posterior brain regions, and their functional interaction with lateral prefrontal cortex (LPFC) regions, in schizophrenia. Fourteen healthy participants and 15 matched schizophrenic patients participated in this fMRI study. We used a task paradigm that differentiates two cognitive control sub-processes according to the temporal framing of information, namely the control of immediate context (present cues) vs. temporal episode (past instructions). We found that areas activated during contextual and episodic controls were in dorsal posterior regions and that activations did not significantly differ between schizophrenic patients and healthy participants. However, while processing contextual signals, patients with schizophrenia failed to show decreased connectivity between caudal LPFC and areas located in ventral posterior regions. The absence of group difference in the functional specialization of posterior regions is difficult to interpret due to our small sample size. One interpretation for our connectivity results is that patients present an inefficient extinction of posterior regions involved in attention shifting by prefrontal areas involved in the top-down control of contextual signals. Further studies with larger sample sizes will be needed to ascertain those observations.

The Amygdala in Schizophrenia and Bipolar Disorder: A Synthesis of Structural MRI, Diffusion Tensor Imaging, and Resting-State Functional Connectivity Findings

Frequently implicated in psychotic spectrum disorders, the amygdala serves as an important hub for elucidating the convergent and divergent neural substrates in schizophrenia and bipolar disorder, the two most studied groups of psychotic spectrum conditions. A systematic search of electronic databases through December 2017 was conducted to identify neuroimaging studies of the amygdala in schizophrenia and bipolar disorder, focusing on structural MRI, diffusion tensor imaging (DTI), and resting-state functional connectivity studies, with an emphasis on cross-diagnostic studies. Ninety-four independent studies were selected for the present review (49 structural MRI, 27 DTI, and 18 resting-state functional MRI studies). Also selected, and analyzed in a separate meta-analysis, were 33 volumetric studies with the amygdala as the region-of-interest. Reduced left, right, and total amygdala volumes were found in schizophrenia, relative to both healthy controls and bipolar subjects, even when restricted to cohorts in the early stages of illness. No volume abnormalities were observed in bipolar subjects relative to healthy controls. Shape morphometry studies showed either amygdala deformity or no differences in schizophrenia, and no abnormalities in bipolar disorder. In contrast to the volumetric findings, DTI studies of the uncinate fasciculus tract (connecting the amygdala with the medial- and orbitofrontal cortices) largely showed reduced fractional anisotropy (a marker of white matter microstructure abnormality) in both schizophrenia and bipolar patients, with no cross-diagnostic differences. While decreased amygdalar-orbitofrontal functional connectivity was generally observed in schizophrenia, varying patterns of amygdalar-orbitofrontal connectivity in bipolar disorder were found. Future studies can consider adopting longitudinal approaches with multimodal imaging and more extensive clinical subtyping to probe amygdalar subregional changes and their relationship to the sequelae of psychotic disorders.

Relationship between resting-state theta phase-gamma amplitude coupling and neurocognitive functioning in patients with first-episode psychosis

BACKGROUND

Although cognitive dysfunction is a core element of schizophrenia, the neurobiological underpinnings of the pathophysiology are not yet sufficiently understood. Because the resting state is crucial for cognitive functioning and electroencephalography (EEG) can reflect instantaneous neural activity, we investigated theta phase-gamma amplitude coupling (TGC) of resting-state EEG and its relationship with cognitive function in patients with first-episode psychosis (FEP) to reveal the neural correlates of cognitive dysfunction.

METHODS

A total of 59 FEP patients and 50 healthy controls (HCs) underwent resting-state, eyes-closed EEG recordings and performed the Trail Making Test Part A (TMT-A) and Part B (TMT-B) and California Verbal Learning Test (CVLT). TGC from the source signal of the resting-state EEG in default mode network (DMN)-related brain regions was compared between groups. Correlation analyses were performed between TGC and cognitive function test performance in FEP patients.

RESULTS

Mean resting-state TGC was larger for the FEP patients than for the HCs. Patients with FEP showed increased TGC in the left posterior cingulate cortex, which was correlated with better performance on the TMT-A and TMT-B and on immediate and delayed recall in the CVLT.

CONCLUSIONS

These results suggest that patients with FEP show compensatory hyperactivation of resting-state TGC in DMN-related brain regions, which may be related to the reallocation of cognitive resources to prepare for successful cognitive execution. This study not only highlights the neural underpinnings of cognitive dysfunction in FEP patients but also provides useful background to support the development of treatments for cognitive dysfunction in schizophrenia.