Schizophrenia and the brain's control network: aberrant within- and between-network connectivity of the frontoparietal network in schizophrenia

Estimation of complete mutual information exploiting nonlinear magnitude-phase dependence: Application to spatial FNC for complex-valued fMRI data

BACKGROUND

Real-valued mutual information (MI) has been used in spatial functional network connectivity (FNC) to measure high-order and nonlinear dependence between spatial maps extracted from magnitude-only functional magnetic resonance imaging (fMRI). However, real-valued MI cannot fully capture the group differences in spatial FNC from complex-valued fMRI data with magnitude and phase dependence.

METHODS

We propose a complete complex-valued MI method according to the chain rule of MI. We fully exploit the dependence among magnitudes and phases of two complex-valued signals using second and fourth-order joint entropies, and propose to use a Gaussian copula transformation with a lower bound property to avoid inaccurate estimation of joint probability density function when computing the joint entropies.

RESULTS

The proposed method achieves more accurate MI estimates than the two histogram-based (normal and symbolic approaches) and kernel density estimation methods for simulated signals, and enhances group differences in spatial functional network connectivity for experimental complex-valued fMRI data.

COMPARISON WITH EXISTING METHODS

Compared with the simplified complex-valued MI and real-valued MI, the proposed method yields higher MI estimation accuracy, leading to 17.4 % and 145.5 % wider MI ranges, and more significant connectivity differences between healthy controls and schizophrenia patients. A unique connection between executive control network (EC) and right frontal parietal areas, and three additional connections mainly related to EC are detected than the simplified complex-valued MI.

CONCLUSIONS

With capability in quantifying MI fully and accurately, the proposed complex-valued MI is promising in providing qualified FNC biomarkers for identifying mental disorders such as schizophrenia.

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.

Subregional thalamic functional connectivity abnormalities and cognitive impairments in first-episode schizophrenia

BACKGROUND

Previous studies have documented thalamic functional connectivity (FC) abnormalities in schizophrenia, typically examining the thalamus as a whole. The specific link between subregional thalamic FC and cognitive deficits in first-episode schizophrenia (FES) remains unexplored.

METHODS

Using data from resting-state functional magnetic resonance imaging, we compared whole-brain FC with thalamic subregions between patients and HCs, and analyzed FC changes in drug-naïve patients separately. We then examined correlations between FC abnormalities with both cognitive impairment and clinical symptoms.

RESULTS

A total of 33 FES patients (20 drug-naïve) and 32 age- and sex-matched healthy controls (HCs) were included. Compared to HCs, FES patients exhibited increased FC between specific thalamic subregions and cortical regions, particularly bilateral middle temporal lobe and cuneus gyrus, left medial superior frontal gyrus, and right inferior/superior occipital gyrus. Decreased FC was observed between certain thalamic subregions and the left inferior frontal triangle. These findings were largely consistent in drug-naïve patients. Notably, deficits in social cognition and visual learning in FES patients correlated with increased FC between certain thalamic subregions and cortical regions involving the right superior occipital gyrus and cuneus gyrus. The severity of negative symptoms was associated with increased FC between a thalamic subregion and the left middle temporal gyrus.

CONCLUSION

Our findings suggest FC abnormalities between thalamic subregions and cortical areas in FES patients. Increased FC correlated with cognitive deficits and negative symptoms, highlighting the importance of thalamo-cortical connectivity in the pathophysiology of schizophrenia.

Serum S100B protein and white matter changes in schizophrenia before and after medication

Schizophrenia patients have abnormalities in white matter (WM) integrity in brain regions. S100B has been shown to be a marker protein for glial cells. The atypical antipsychotics have neuroprotective effects on the brain. It is not clear whether antipsychotics can induce S100B changes and improve symptoms by protecting oligodendrocytes. To investigate WM and S100B changes and associations and determine the effect of quetiapine on WM and S100B in schizophrenia patients, we determined serum S100B levels with solid phase immunochromatography and fractional anisotropy（FA）values of 36 patients and 40 healthy controls. Patients exhibited significantly higher serum concentrations of S100B and decreased FA values in left postcentral，right superior frontal，right thalamus, and left inferior occipital gyrus, while higher in right temporal cortex WM compared with healthy controls. Following treatment with quetiapine, patients had decreased S100B and higher FA values in right cerebellum，right superior frontal，right thalamus, and left parietal cortex，and decreased FA values in right temporal cortex WM compared with pre-treatment values. Furthermore, S100B were negatively correlated with PANSS positive scores and positively correlated with FA values in the left postcentral cortex. In addition，the percentage change in FA values in the right temporal cortex was positively correlated with the percentage change in the S100B, percentage reduction in PANSS scores, and percentage reduction in PANSS-positive scores. Our findings demonstrated abnormalities in S100B and WM microstructure in patients with schizophrenia. These abnormalities may be partly reversed by quetiapine treatment.

Sex differences in neuronal oscillatory activity and memory in the methylazoxymethanol acetate model of schizophrenia

The methylazoxymethanol acetate (MAM) rodent model is used to study aspects of schizophrenia. However, numerous studies that have employed this model have used only males, resulting in a dearth of knowledge on sex differences in brain function and behaviour. The purpose of this study was to determine whether differences exist between male and female MAM rats in neuronal oscillatory function within and between the prefrontal cortex (PFC), ventral hippocampus (vHIP) and thalamus, behaviour, and in proteins linked to schizophrenia neuropathology. We showed that female MAM animals exhibited region-specific alterations in theta power, elevated low and high gamma power in all regions, and elevated PFC-thalamus high gamma coherence. Male MAM rats had elevated beta and low gamma power in PFC, and elevated vHIP-thalamus coherence. MAM females displayed impaired reversal learning whereas MAM males showed impairments in spatial memory. Glycogen synthase kinase-3 (GSK-3) was altered in the thalamus, with female MAM rats displaying elevated GSK-3α phosphorylation. Male MAM rats showed higher expression and phosphorylation GSK-3α, and higher expression of GSK-β. Sex-specific changes in phosphorylated Tau levels were observed in a region-specific manner. These findings demonstrate there are notable sex differences in behaviour, oscillatory network function, and GSK-3 signaling in MAM rats, thus highlighting the importance of inclusion of both sexes when using this model to study schizophrenia.

Striatal Functional Hypoconnectivity in Patients With Schizophrenia Suffering From Negative Symptoms, Longitudinal Findings

BACKGROUND

Negative symptoms in schizophrenia (SZ), such as apathy and diminished expression, have limited treatments and significantly impact daily life. Our study focuses on the functional division of the striatum: limbic-motivation and reward, associative-cognition, and sensorimotor-sensory and motor processing, aiming to identify potential biomarkers for negative symptoms.

STUDY DESIGN

This longitudinal, 2-center resting-state-fMRI (rsfMRI) study examines striatal seeds-to-whole-brain functional connectivity. We examined connectivity aberrations in patients with schizophrenia (PwSZ), focusing on stable group differences across 2-time points using intra-class-correlation and associated these with negative symptoms and measures of cognition. Additionally, in PwSZ, we used negative symptoms to predict striatal connectivity aberrations at the baseline and used the striatal aberration to predict symptoms 9 months later.

STUDY RESULTS

A total of 143 participants (77 PwSZ, 66 controls) from 2 centers (Berlin/Geneva) participated. We found sensorimotor-striatum and associative-striatum hypoconnectivity. We identified 4 stable hypoconnectivity findings over 3 months, revealing striatal-fronto-parietal-cerebellar hypoconnectivity in PwSZ. From those findings, we found hypoconnectivity in the bilateral associative striatum with the bilateral paracingulate-gyrus and the anterior cingulate cortex in PwSZ. Additionally, hypoconnectivity between the associative striatum and the superior frontal gyrus was associated with lower cognition scores in PwSZ, and weaker sensorimotor striatum connectivity with the superior parietal lobule correlated negatively with diminished expression and could predict symptom severity 9 months later.

CONCLUSIONS

Importantly, patterns of weaker sensorimotor striatum and superior parietal lobule connectivity fulfilled the biomarker criteria: clinical significance, reflecting underlying pathophysiology, and stability across time and centers.

Neural circuit disruptions of eye gaze processing in autism spectrum disorder and schizophrenia: An activation likelihood estimation meta-analysis

BACKGROUND

Impairment in social cognition, particularly eye gaze processing, is a shared feature common to autism spectrum disorder (ASD) and schizophrenia. However, it is unclear if a convergent neural mechanism also underlies gaze dysfunction in these conditions. The present study examined whether this shared eye gaze phenotype is reflected in a profile of convergent neurobiological dysfunction in ASD and schizophrenia.

METHODS

Activation likelihood estimation (ALE) meta-analyses were conducted on peak voxel coordinates across the whole brain to identify spatial convergence. Functional coactivation with regions emerging as significant was assessed using meta-analytic connectivity modeling. Functional decoding was also conducted.

RESULTS

Fifty-six experiments (n = 30 with schizophrenia and n = 26 with ASD) from 36 articles met inclusion criteria, which comprised 354 participants with ASD, 275 with schizophrenia and 613 healthy controls (1242 participants in total). In ASD, aberrant activation was found in the left amygdala relative to unaffected controls during gaze processing. In schizophrenia, aberrant activation was found in the right inferior frontal gyrus and supplementary motor area. Across ASD and schizophrenia, aberrant activation was found in the right inferior frontal gyrus and right fusiform gyrus during gaze processing. Functional decoding mapped the left amygdala to domains related to emotion processing and cognition, the right inferior frontal gyrus to cognition and perception, and the right fusiform gyrus to visual perception, spatial cognition, and emotion perception. These regions also showed meta-analytic connectivity to frontoparietal and frontotemporal circuitry.

CONCLUSION

Alterations in frontoparietal and frontotemporal circuitry emerged as neural markers of gaze impairments in ASD and schizophrenia. These findings have implications for advancing transdiagnostic biomarkers to inform targeted treatments for ASD and schizophrenia.

The relationship between gray/white matter contrast and cognitive performance in first-episode schizophrenia

Previous postmortem brain studies have revealed disturbed myelination in the intracortical regions in patients with schizophrenia, possibly reflecting anomalous brain maturational processes. However, it currently remains unclear whether this anomalous myelination is already present in early illness stages and/or progresses during the course of the illness. In this magnetic resonance imaging study, we examined gray/white matter contrast (GWC) as a potential marker of intracortical myelination in 63 first-episode schizophrenia (FESz) patients and 77 healthy controls (HC). Furthermore, we investigated the relationships between GWC findings and clinical/cognitive variables in FESz patients. GWC in the bilateral temporal, parietal, occipital, and insular regions was significantly higher in FESz patients than in HC, which was partly associated with the durations of illness and medication, the onset age, and lower executive and verbal learning performances. Because higher GWC implicates lower myelin in the deeper layers of the cortex, these results suggest that schizophrenia patients have less intracortical myelin at the time of their first psychotic episode, which underlies lower cognitive performance in early illness stages.

Graph neural network and machine learning analysis of functional neuroimaging for understanding schizophrenia

BACKGROUND

Graph representational learning can detect topological patterns by leveraging both the network structure as well as nodal features. The basis of our exploration involves the application of graph neural network architectures and machine learning to resting-state functional Magnetic Resonance Imaging (rs-fMRI) data for the purpose of detecting schizophrenia. Our study uses single-site data to avoid the shortcomings in generalizability of neuroimaging data obtained from multiple sites.

RESULTS

The performance of our graph neural network models is on par with that of our machine learning models, each of which is trained using 69 graph-theoretical measures computed from functional correlations between various regions of interest (ROI) in a brain graph. Our deep graph convolutional neural network (DGCNN) demonstrates a promising average accuracy score of 0.82 and a sensitivity score of 0.84.

CONCLUSIONS

This study provides insights into the role of advanced graph theoretical methods and machine learning on fMRI data to detect schizophrenia by harnessing changes in brain functional connectivity. The results of this study demonstrate the capabilities of using both traditional ML techniques as well as graph neural network-based methods to detect schizophrenia using features extracted from fMRI data. The study also proposes two methods to obtain potential biomarkers for the disease, many of which are corroborated by research in this area and can further help in the understanding of schizophrenia as a mental disorder.

The effect of using group-averaged or individualized brain parcellations when investigating connectome dysfunction in psychosis

Functional magnetic resonance imaging (fMRI) is widely used to investigate functional coupling (FC) disturbances in a range of clinical disorders. Most analyses performed to date have used group-based parcellations for defining regions of interest (ROIs), in which a single parcellation is applied to each brain. This approach neglects individual differences in brain functional organization and may inaccurately delineate the true borders of functional regions. These inaccuracies could inflate or underestimate group differences in case-control analyses. We investigated how individual differences in brain organization influence group comparisons of FC using psychosis as a case study, drawing on fMRI data in 121 early psychosis patients and 57 controls. We defined FC networks using either a group-based parcellation or an individually tailored variant of the same parcellation. Individualized parcellations yielded more functionally homogeneous ROIs than did group-based parcellations. At the level of individual connections, case-control FC differences were widespread, but the group-based parcellation identified approximately 7.7% more connections as dysfunctional than the individualized parcellation. When considering differences at the level of functional networks, the results from both parcellations converged. Our results suggest that a substantial fraction of dysconnectivity previously observed in psychosis may be driven by the parcellation method, rather than by a pathophysiological process related to psychosis.

Revisiting Functional Dysconnectivity: a Review of Three Model Frameworks in Schizophrenia

PURPOSE OF REVIEW

Over the last decade, evidence suggests that a combination of behavioral and neuroimaging findings can help illuminate changes in functional dysconnectivity in schizophrenia. We review the recent connectivity literature considering several vital models, considering connectivity findings, and relationships with clinical symptoms. We reviewed resting state fMRI studies from 2017 to 2023. We summarized the role of two sets of brain networks (cerebello-thalamo-cortical (CTCC) and the triple network set) across three hypothesized models of schizophrenia etiology (neurodevelopmental, vulnerability-stress, and neurotransmitter hypotheses).

RECENT FINDINGS

The neurotransmitter and neurodevelopmental models best explained CTCC-subcortical dysfunction, which was consistently connected to symptom severity and motor symptoms. Triple network dysconnectivity was linked to deficits in executive functioning, and the salience network (SN)-default mode network dysconnectivity was tied to disordered thought and attentional deficits. This paper links behavioral symptoms of schizophrenia (symptom severity, motor, executive functioning, and attentional deficits) to various hypothesized mechanisms.

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.

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.

Brain Network Topology in Deficit and Non-Deficit Schizophrenia: Application of Graph Theory to Local and Global Indices

Patients with deficit schizophrenia (SZD) suffer from primary and enduring negative symptoms. Limited pieces of evidence and neuroimaging studies indicate they differ from patients with non-deficit schizophrenia (SZND) in neurobiological aspects, but the results are far from conclusive. We applied for the first time, graph theory analyses to discriminate local and global indices of brain network topology in SZD and SZND patients compared with healthy controls (HC). High-resolution T1-weighted images were acquired for 21 SZD patients, 21 SZND patients, and 21 HC to measure cortical thickness from 68 brain regions. Graph-based metrics (i.e., centrality, segregation, and integration) were computed and compared among groups, at both global and regional networks. When compared to HC, at the regional level, SZND were characterized by temporoparietal segregation and integration differences, while SZD showed widespread alterations in all network measures. SZD also showed less segregated network topology at the global level in comparison to HC. SZD and SZND differed in terms of centrality and integration measures in nodes belonging to the left temporoparietal cortex and to the limbic system. SZD is characterized by topological features in the network architecture of brain regions involved in negative symptomatology. Such results help to better define the neurobiology of SZD (SZD: Deficit Schizophrenia; SZND: Non-Deficit Schizophrenia; SZ: Schizophrenia; HC: healthy controls; CC: clustering coefficient; L: characteristic path length; E: efficiency; D: degree; CC_node: CC of a node; CC_glob: the global CC of the network; E_loc: efficiency of the information transfer flow either within segregated subgraphs or neighborhoods nodes; E_glob: efficiency of the information transfer flow among the global network; FDA: Functional Data Analysis; and D_min: estimated minimum densities).

Widespread Intra- and Inter-Network Dysconnectivity among Large-Scale Resting State Networks in Schizophrenia

Schizophrenia is characterized by the distributed dysconnectivity of resting-state multiple brain networks. However, the abnormalities of intra- and inter-network functional connectivity (FC) in schizophrenia and its relationship to symptoms remain unknown. The aim of the present study is to compare the intra- and inter-connectivity of the intrinsic networks between a large sample of patients with schizophrenia and healthy controls. Using the Region of interest (ROI) to ROI FC analyses, the intra- and inter-network FC of the eight resting state networks [default mode network (DMN); salience network (SN); frontoparietal network (FPN); dorsal attention network (DAN); language network (LN); visual network (VN); sensorimotor network (SMN); and cerebellar network (CN)] were investigated in 196 schizophrenia and 169-healthy controls. Compared to the healthy control group, the schizophrenia group exhibited increased intra-network FC in the DMN and decreased intra-network FC in the CN. Additionally, the schizophrenia group showed the decreased inter-network FC mainly involved the SN-DMN, SN-LN and SN-CN while increased inter-network FC in the SN-SMN and SN-DAN (p < 0.05, FDR-corrected). Our study suggests widespread intra- and inter-network dysconnectivity among large-scale RSNs in schizophrenia, mainly involving the DMN, SN and SMN, which may further contribute to the dysconnectivity hypothesis of schizophrenia.

Genetic and environmental factors influencing neonatal resting-state functional connectivity

Functional magnetic resonance imaging has been used to identify complex brain networks by examining the correlation of blood-oxygen-level-dependent signals between brain regions during the resting state. Many of the brain networks identified in adults are detectable at birth, but genetic and environmental influences governing connectivity within and between these networks in early infancy have yet to be explored. We investigated genetic influences on neonatal resting-state connectivity phenotypes by generating intraclass correlations and performing mixed effects modeling to estimate narrow-sense heritability on measures of within network and between-network connectivity in a large cohort of neonate twins. We also used backwards elimination regression and mixed linear modeling to identify specific demographic and medical history variables influencing within and between network connectivity in a large cohort of typically developing twins and singletons. Of the 36 connectivity phenotypes examined, only 6 showed narrow-sense heritability estimates greater than 0.10, with none being statistically significant. Demographic and obstetric history variables contributed to between- and within-network connectivity. Our results suggest that in early infancy, genetic factors minimally influence brain connectivity. However, specific demographic and medical history variables, such as gestational age at birth and maternal psychiatric history, may influence resting-state connectivity measures.

Identification of shared and distinct patterns of brain network abnormality across mental disorders through individualized structural covariance network analysis

BACKGROUND

Mental disorders, including depression, obsessive compulsive disorder (OCD), and schizophrenia, share a common neuropathy of disturbed large-scale coordinated brain maturation. However, high-interindividual heterogeneity hinders the identification of shared and distinct patterns of brain network abnormalities across mental disorders. This study aimed to identify shared and distinct patterns of altered structural covariance across mental disorders.

METHODS

Subject-level structural covariance aberrance in patients with mental disorders was investigated using individualized differential structural covariance network. This method inferred structural covariance aberrance at the individual level by measuring the degree of structural covariance in patients deviating from matched healthy controls (HCs). T1-weighted anatomical images of 513 participants (105, 98, 190 participants with depression, OCD and schizophrenia, respectively, and 130 age- and sex-matched HCs) were acquired and analyzed.

RESULTS

Patients with mental disorders exhibited notable heterogeneity in terms of altered edges, which were otherwise obscured by group-level analysis. The three disorders shared high difference variability in edges attached to the frontal network and the subcortical-cerebellum network, and they also exhibited disease-specific variability distributions. Despite notable variability, patients with the same disorder shared disease-specific groups of altered edges. Specifically, depression was characterized by altered edges attached to the subcortical-cerebellum network; OCD, by altered edges linking the subcortical-cerebellum and motor networks; and schizophrenia, by altered edges related to the frontal network.

CONCLUSIONS

These results have potential implications for understanding heterogeneity and facilitating personalized diagnosis and interventions for mental disorders.

Impact of low-frequency repetitive transcranial magnetic stimulation on functional network connectivity in schizophrenia patients with auditory verbal hallucinations

Auditory verbal hallucinations (AVH) are a key symptom of schizophrenia. Low-frequency repetitive transcranial magnetic stimulation (rTMS) has shown potential in the treatment of AVH. However, the underlying neural mechanismof rTMS in the treatment of AVH remains largely unknown. In this study, we used a static and dynamic functional network connectivity approach to investigate the connectivity changes among the brain functional networks in schizophrenia patients with AVH receiving 1 Hz rTMS treatment. The static functional network connectivity (sFNC) analysis revealed that patients at baseline had significantly decreased connectivity between the default mode network (DMN) and language network (LAN), and within the executive control network (ECN) as well as within the auditory network (AUD) compared to controls. However, the abnormal network connectivity patterns were normalized or restored after rTMS treatment in patients, instead of increased connectivity between the ECN and LAN, as well as within the AUD. Moreover, the dynamic functional network connectivity (dFNC) analysis showed that the patients at baseline spent more time in this state that was characterized by strongly negative connectivity between the ENC and AUD, as well as within the AUD relative to controls. While after rTMS treatment, the patients showed a higher occurrence rate in this state that was characterized by strongly positive connectivity among the LAN, DMN, and ENC, as well as within the ECN. In addition, the altered static and dynamic connectivity properties were associated with reduced severity of clinical symptoms. Both sFNC and dFNC analyses provided complementary information and suggested that low-frequency rTMS treatment could induce intrinsic functional network alternations and contribute to improvements in clinical symptoms in patients with AVH.

Using Nonhuman Primate Models to Reverse-Engineer Prefrontal Circuit Failure Underlying Cognitive Deficits in Schizophrenia

In this chapter, I review studies in nonhuman primates that emulate the circuit failure in prefrontal cortex responsible for working memory and cognitive control deficits in schizophrenia. These studies have characterized how synaptic malfunction, typically induced by blockade of NMDAR, disrupts neural function and computation in prefrontal networks to explain errors in cognitive tasks that are seen in schizophrenia. This work is finding causal relationships between pathogenic events of relevance to schizophrenia at vastly different levels of scale, from synapses, to neurons, local, circuits, distributed networks, computation, and behavior. Pharmacological manipulation, the dominant approach in primate models, has limited construct validity for schizophrenia pathogenesis, as the disease results from a complex interplay between environmental, developmental, and genetic factors. Genetic manipulation replicating schizophrenia risk is more advanced in rodent models. Nonetheless, gene manipulation in nonhuman primates is rapidly advancing, and primate developmental models have been established. Integration of large scale neural recording, genetic manipulation, and computational modeling in nonhuman primates holds considerable potential to provide a crucial schizophrenia model moving forward. Data generated by this approach is likely to fill several crucial gaps in our understanding of the causal sequence leading to schizophrenia in humans. This causal chain presents a vexing problem largely because it requires understanding how events at very different levels of scale relate to one another, from genes to circuits to cognition to social interactions. Nonhuman primate models excel here. They optimally enable discovery of causal relationships across levels of scale in the brain that are relevant to cognitive deficits in schizophrenia. The mechanistic understanding of prefrontal circuit failure they promise to provide may point the way to more effective therapeutic interventions to restore function to prefrontal networks in the disease.

The relationships between dynamic resting-state networks and social behavior in autism spectrum disorder revealed by fuzzy entropy-based temporal variability analysis of large-scale network

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by a core deficit in social processes. However, it is still unclear whether the core clinical symptoms of the disorder can be reflected by the temporal variability of resting-state network functional connectivity (FC). In this article, we examined the large-scale network FC temporal variability at the local region, within-network, and between-network levels using the fuzzy entropy technique. Then, we correlated the network FC temporal variability to social-related scores. We found that the social behavior correlated with the FC temporal variability of the precuneus, parietal, occipital, temporal, and precentral. Our results also showed that social behavior was significantly negatively correlated with the temporal variability of FC within the default mode network, between the frontoparietal network and cingulo-opercular task control network, and the dorsal attention network. In contrast, social behavior correlated significantly positively with the temporal variability of FC within the subcortical network. Finally, using temporal variability as a feature, we construct a model to predict the social score of ASD. These findings suggest that the network FC temporal variability has a close relationship with social behavioral inflexibility in ASD and may serve as a potential biomarker for predicting ASD symptom severity.

Dorsal striatial hypoconnectivity predicts antipsychotic medication treatment response in first-episode psychosis and unmedicated patients with schizophrenia

INTRODUCTION

The dorsal striatum, comprised of the caudate and putamen, is implicated in the pathophysiology of psychosis spectrum disorders. Given the high concentration of dopamine receptors in the striatum, striatal dopamine imbalance is a likely cause in cortico-striatal dysconnectivity. There is great interest in understanding the relationship between striatal abnormalities in psychosis and antipsychotic treatment response, but few studies have considered differential involvement of the caudate and putamen. This study's goals were twofold. First, identify patterns of dorsal striatal dysconnectivity for the caudate and putamen separately in patients with a psychosis spectrum disorder; second, determine if these dysconnectivity patterns were predictive of treatment response.

METHODS

Using resting state functional connectivity, we evaluated dorsal striatal connectivity using separate bilateral caudate and putamen seed regions in two cohorts of subjects: a cohort of 71 medication-naïve first episode psychosis patients and a cohort of 42 unmedicated patients with schizophrenia (along with matched controls). Patient and control connectivity maps were contrasted for each cohort. After receiving 6 weeks of risperidone treatment, patients' clinical response was calculated. We used regression analyses to determine the relationship between baseline dysconnectivity and treatment response.

RESULTS

This dysconnectivity was also predictive of treatment response in both cohorts.

DISCUSSION

These findings suggest that the caudate may be more of a driving factor than the putamen in early cortico-striatal dysconnectivity.

Adult outcome of preterm birth: Implications for neurodevelopmental theories of psychosis

Preterm birth is associated with an elevated risk of developmental and adult psychiatric disorders, including psychosis. In this review, we evaluate the implications of neurodevelopmental, cognitive, motor, and social sequelae of preterm birth for developing psychosis, with an emphasis on outcomes observed in adulthood. Abnormal brain development precipitated by early exposure to the extra-uterine environment, and exacerbated by neuroinflammation, neonatal brain injury, and genetic vulnerability, can result in alterations of brain structure and function persisting into adulthood. These alterations, including abnormal regional brain volumes and white matter macro- and micro-structure, can critically impair functional (e.g. frontoparietal and thalamocortical) network connectivity in a manner characteristic of psychotic illness. The resulting executive, social, and motor dysfunctions may constitute the basis for behavioural vulnerability ultimately giving rise to psychotic symptomatology. There are many pathways to psychosis, but elucidating more precisely the mechanisms whereby preterm birth increases risk may shed light on that route consequent upon early neurodevelopmental insult.

Static and Dynamic Functional Connectivity Alterations in Alzheimer's Disease and Neuropsychiatric Diseases

To date, numerous studies have documented various alterations in resting brain activity in Alzheimer's disease (AD) and other neuropsychiatric diseases. In particular, disease-related alterations of functional connectivity (FC) in the resting state networks (RSN) have been documented. Altered FC in RSN is useful not only for interpreting the phenotype of diseases but also for diagnosing the diseases. More recently, several studies proposed the dynamics of resting-brain activity as a useful marker for detecting altered RSNs related to AD and other diseases. In contrast to previous studies, which focused on FC calculated using an entire fMRI scan (static FC), these newer studies focused the on temporal dynamics of FC within the scan (dynamic FC) to provide more sensitive measures to characterize RSNs. However, despite the increasing popularity of dFC, several studies cautioned that the results obtained in commonly used analyses for dFC require careful interpretation. In this mini-review, we review recent studies exploring alterations of static and dynamic functional connectivity in AD and other neuropsychiatric diseases. We then discuss how to utilize and interpret dFC for studying resting brain activity in diseases.

Altered thalamic subregion functional networks in patients with treatment-resistant schizophrenia

BACKGROUND

The thalamus plays a key role in filtering information and has extensive interconnectivity with other brain regions. A large body of evidence points to impaired functional connectivity (FC) of the thalamocortical pathway in schizophrenia. However, the functional network of the thalamic subregions has not been investigated in patients with treatment-resistant schizophrenia (TRS).

AIM

To identify the neural mechanisms underlying TRS, we investigated FC of thalamic sub-regions with cortical networks and voxels, and the associations of this FC with clinical symptoms. We hypothesized that the FC of thalamic sub-regions with cortical networks and voxels would differ between TRS patients and HCs.

METHODS

In total, 50 patients with TRS and 61 healthy controls (HCs) matched for age, sex, and education underwent resting-state functional magnetic resonance imaging (rs-fMRI) and clinical evaluation. Based on the rs-fMRI data, we conducted a FC analysis between thalamic subregions and cortical functional networks and voxels, and within thalamic subregions and cortical functional networks, in the patients with TRS. A functional parcellation atlas was used to segment the thalamus into nine subregions. Correlations between altered FC and TRS symptoms were explored.

RESULTS

We found differences in FC within thalamic subregions and cortical functional networks between patients with TRS and HCs. In addition, increased FC was observed between thalamic subregions and the sensorimotor cortex, frontal medial cortex, and lingual gyrus. These abnormalities were associated with the pathophysiology of TRS.

CONCLUSION

Our findings suggest that disrupted FC within thalamic subregions and cortical functional networks, and within the thalamocortical pathway, has potential as a marker for TRS. Our findings also improve our understanding of the relationship between the thalamocortical pathway and TRS symptoms.

Impact of low-frequency rTMS on functional connectivity of the dentate nucleus subdomains in schizophrenia patients with auditory verbal hallucination

Despite low-frequency repetitive transcranial magnetic stimulation (rTMS) is effective in treating schizophrenia patients with auditory verbal hallucinations (AVH), the underlying neural mechanisms of the effect still need to be clarified. Using the cerebellar dentate nucleus (DN) subdomain (dorsal and versal DN) as seeds, the present study investigated resting state functional connectivity (FC) alternations of the seeds with the whole brain and their associations with clinical responses in schizophrenia patients with AVH receiving 1 Hz rTMS treatment. The results showed that the rTMS treatment improved the psychiatric symptoms (e.g., AVH and positive symptoms) and certain neurocognitive functions (e.g., visual learning and verbal learning) in the patients. In addition, the patients at baseline showed increased FC between the DN subdomains and temporal lobes (e.g., right superior temporal gyrus and right middle temporal gyrus) and decreased FC between the DN subdomains and the left superior frontal gyrus, right postcentral gyrus, left supramarginal gyrus and regional cerebellum (e.g., lobule 4-5) compared to controls. Furthermore, these abnormal DN subdomain connectivity patterns did not persist and decreased FC of DN subdomains with cerebellum lobule 4-5 were reversed in patients after rTMS treatment. Linear regression analysis showed that the FC difference values of DN subdomains with the temporal lobes, supramarginal gyrus and cerebellum 4-5 between the patients at baseline and posttreatment were associated with clinical improvements (e.g., AVH and verbal learning) after rTMS treatment. The results suggested that rTMS treatment may modulate the neural circuits of the DN subdomains and hint to underlying neural mechanisms for low-frequency rTMS treating schizophrenia with AVH.

Dynamic and progressive changes in thalamic functional connectivity over the first five years of psychosis

The early stage of psychosis (ESP) is a critical period where effective intervention has the most favorable impact on outcomes. Thalamic connectivity abnormalities have been consistently found in psychosis, and are associated with clinical symptoms and cognitive deficits. However, most studies consider ESP patients as a homogeneous population and fail to take the duration of illness into account. In this study, we aimed to capture the progression of thalamic connectivity changes over the first five years of psychosis. Resting-state functional MRI scans were collected from 156 ESP patients (44 with longitudinal data) and 82 healthy controls (24 with longitudinal data). We first performed a case-control analysis comparing thalamic connectivity with 13 networks in the cortex and cerebellum. Next, we modelled the shape (flat, linear, curvilinear) of thalamic connectivity trajectories by comparing flexible non-linear versus linear models. We then tested the significance of the duration of illness and diagnosis in trajectories that changed over time. Connectivity changed over the ESP period between the thalamus and default mode network (DMN) and fronto-parietal network (FPN) nodes in both the cortex and cerebellum. Three models followed a curvilinear trajectory (early increase followed by a subsequent decrease), while thalamo-cerebellar FPN connectivity followed a linear trajectory of steady reductions over time, indicating different rates of change. Finally, diagnosis significantly predicted thalamic connectivity. Thalamo-cortical and thalamo-cerebellar connectivity change in a dynamic fashion during the ESP period. A better understanding of these changes may provide insights into the compensatory and progressive changes in functional connectivity in the early stages of illness.

Cognitive Implications of Correlated Structural Network Changes in Schizophrenia

Background

Schizophrenia is a brain disorder characterized by diffuse, diverse, and wide-spread changes in gray matter volume (GM) and white matter structure (fractional anisotropy, FA), as well as cognitive impairments that greatly impact an individual’s quality of life. While the relationship of each of these image modalities and their links to schizophrenia status and cognitive impairment has been investigated separately, a multimodal fusion via parallel independent component analysis (pICA) affords the opportunity to explore the relationships between the changes in GM and FA, and the implications these network changes have on cognitive performance.

Methods

Images from 73 subjects with schizophrenia (SZ) and 82 healthy controls (HC) were drawn from an existing dataset. We investigated 12 components from each feature (FA and GM). Loading coefficients from the images were used to identify pairs of features that were significantly correlated and showed significant group differences between HC and SZ. MANCOVA analysis uncovered the relationships the identified spatial maps had with age, gender, and a global cognitive performance score.

Results

Three component pairs showed significant group differences (HC > SZ) in both gray and white matter measurements. Two of the component pairs identified networks of gray matter that drove significant relationships with cognition (HC > SZ) after accounting for age and gender. The gray and white matter structural networks identified in these three component pairs pull broadly from many regions, including the right and left thalamus, lateral occipital cortex, multiple regions of the middle temporal gyrus, precuneus cortex, postcentral gyrus, cingulate gyrus/cingulum, lingual gyrus, and brain stem.

Conclusion

The results of this multimodal analysis adds to our understanding of how the relationship between GM, FA, and cognition differs between HC and SZ by highlighting the correlated intermodal covariance of these structural networks and their differential relationships with cognitive performance. Previous unimodal research has found similar areas of GM and FA differences between these groups, and the cognitive deficits associated with SZ have been well documented. This study allowed us to evaluate the intercorrelated covariance of these structural networks and how these networks are involved the differences in cognitive performance between HC and SZ.

Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study

Interest in the application of machine learning (ML) techniques to multimodal magnetic resonance imaging (MRI) data for the diagnosis of schizophrenia (SZ) at the individual level is growing. However, a few studies have applied the features of structural and functional brain networks derived from multimodal MRI data to the discriminative analysis of SZ patients at different clinical stages. In this study, 205 normal controls (NCs), 61 first-episode drug-naive SZ (FESZ) patients, and 79 chronic SZ (CSZ) patients were recruited. We acquired their structural MRI, diffusion tensor imaging, and resting-state functional MRI data and constructed brain networks for each participant, including the gray matter network (GMN), white matter network (WMN), and functional brain network (FBN). We then calculated 3 nodal properties for each brain network, including degree centrality, nodal efficiency, and betweenness centrality. Two classifications (SZ vs. NC and FESZ vs. CSZ) were performed using five ML algorithms. We found that the SVM classifier with the input features of the combination of nodal properties of both the GMN and FBN achieved the best performance to discriminate SZ patients from NCs [accuracy, 81.2%; area under the receiver operating characteristic curve (AUC), 85.2%; p &lt; 0.05]. Moreover, the SVM classifier with the input features of the combination of the nodal properties of both the GMN and WMN achieved the best performance to discriminate FESZ from CSZ patients (accuracy, 86.2%; AUC, 92.3%; p &lt; 0.05). Furthermore, the brain areas in the subcortical/cerebellum network and the frontoparietal network showed significant importance in both classifications. Together, our findings provide new insights to understand the neuropathology of SZ and further highlight the potential advantages of multimodal network properties for identifying SZ patients at different clinical stages.

Real-time cognitive performance and positive symptom expression in schizophrenia

Deficits in cognitive functions are frequent in schizophrenia and are often conceptualized as stable characteristics of this disorder. However, cognitive capacities may fluctuate over the course of a day and it is unknown if such variation may be linked to the dynamic expression of psychotic symptoms. This investigation used Ecological Momentary Assessment (EMA) to provide mobile tests of cognitive functions and positive symptoms in real time. Thirty-three individuals with schizophrenia completed five EMA assessments per day for a one-week period that included real-time assessments of cognitive performance and psychotic symptoms. A subsample of patients and 31 healthy controls also completed a functional MRI examination. Relative to each individual's average score, moments of worsened cognitive performance on the mobile tests were associated with an increased probability of positive symptom occurrence over subsequent hours of the day (coef = 0.06, p < 0.05), adjusting for the presence of psychotic symptoms at the moment of mobile test administration. These prospective associations varied as a function of graph theory indices in MRI analyses. These findings demonstrate that cognitive performance is prospectively linked to psychotic symptom expression in daily life, and that underlying brain markers may be observed in the Executive Control Network. While the potential causal nature of this association remains to be investigated, our results offer promising prospects for a better understanding of the underlying mechanisms of symptom expression in schizophrenia.

A Case for Thalamic Mechanisms of Schizophrenia: Perspective From Modeling 22q11.2 Deletion Syndrome

Schizophrenia is a severe, chronic psychiatric disorder that devastates the lives of millions of people worldwide. The disease is characterized by a constellation of symptoms, ranging from cognitive deficits, to social withdrawal, to hallucinations. Despite decades of research, our understanding of the neurobiology of the disease, specifically the neural circuits underlying schizophrenia symptoms, is still in the early stages. Consequently, the development of therapies continues to be stagnant, and overall prognosis is poor. The main obstacle to improving the treatment of schizophrenia is its multicausal, polygenic etiology, which is difficult to model. Clinical observations and the emergence of preclinical models of rare but well-defined genomic lesions that confer substantial risk of schizophrenia (e.g., 22q11.2 microdeletion) have highlighted the role of the thalamus in the disease. Here we review the literature on the molecular, cellular, and circuitry findings in schizophrenia and discuss the leading theories in the field, which point to abnormalities within the thalamus as potential pathogenic mechanisms of schizophrenia. We posit that synaptic dysfunction and oscillatory abnormalities in neural circuits involving projections from and within the thalamus, with a focus on the thalamocortical circuits, may underlie the psychotic (and possibly other) symptoms of schizophrenia.

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

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

Corpus callosum morphology in major mental disorders: a magnetic resonance imaging study

Mental disorders diagnosis is based on specific clinical criteria. However, clinical studies found similarities and overlapping phenomenology across a variety of disorders, which suggests a common neurobiological substrate. Thus, there is a need to measure disease-related neuroanatomical similarities and differences across conditions. While structural alterations of the corpus callosum have been investigated in obsessive-compulsive disorder, schizophrenia, major depressive disorder and bipolar disorder, no study has addressed callosal aberrations in all diseases in a single study. Moreover, results from pairwise comparisons (patients vs. controls) show some inconsistencies, possibly related to the parcellation methods to divide the corpus callosum into subregions. The main aim of the present paper was to uncover highly localized callosal characteristics for each condition (i.e. obsessive-compulsive disorder, schizophrenia, major depressive disorder and bipolar disorder) as compared either to healthy control subjects or to each other. For this purpose, we did not rely on any sub-callosal parcellation method, but applied a well-validated approach measuring callosal thickness at 100 equidistant locations along the whole midline of the corpus callosum. One hundred and twenty patients (30 in each disorder) as well as 30 controls were recruited for the study. All groups were closely matched for age and gender, and the analyses were performed controlling for the impact of antipsychotic treatment and illness duration. There was a significant main effect of group along the whole callosal surface. Pairwise post hoc comparisons revealed that, compared to controls, patients with obsessive-compulsive disorder had the thinnest corpora callosa with significant effects almost on the entire callosal structure. Patients with schizophrenia also showed thinner corpora callosa than controls but effects were confined to the isthmus and the anterior part of the splenium. No significant differences were found in both major depressive disorder and bipolar disorder patients compared to controls. When comparing the disease groups to each other, the corpus callosum was thinner in obsessive-compulsive disorder patients than in any other group. The effect was evident across the entire corpus callosum, with the exception of the posterior body. Altogether, our study suggests that the corpus callosum is highly changed in obsessive-compulsive disorder, selectively changed in schizophrenia and not changed in bipolar disorder and major depressive disorder. These results shed light on callosal similarities and differences among mental disorders providing valuable insights regarding the involvement of the major brain commissural fibre tract in the pathophysiology of each specific mental illness.

Baseline Cortical Thickness Reductions in Clinical High Risk for Psychosis: Brain Regions Associated with Conversion to Psychosis Versus Non-Conversion as Assessed at One-Year Follow-Up in the Shanghai-At-Risk-for-Psychosis (SHARP) Study

OBJECTIVE

To assess cortical thickness (CT) and surface area (SA) of frontal, temporal, and parietal brain regions in a large clinical high risk for psychosis (CHR) sample, and to identify cortical brain abnormalities in CHR who convert to psychosis and in the whole CHR sample, compared with the healthy controls (HC).

METHODS

Magnetic resonance imaging, clinical, and cognitive data were acquired at baseline in 92 HC, 130 non-converters, and 22 converters (conversion assessed at 1-year follow-up). CT and SA at baseline were calculated for frontal, temporal, and parietal subregions. Correlations between regions showing group differences and clinical scores and age were also obtained.

RESULTS

CT but not SA was significantly reduced in CHR compared with HC. Two patterns of findings emerged: (1) In converters, CT was significantly reduced relative to non-converters and controls in the banks of superior temporal sulcus, Heschl's gyrus, and pars triangularis and (2) CT in the inferior parietal and supramarginal gyrus, and at trend level in the pars opercularis, fusiform, and middle temporal gyri was significantly reduced in all high-risk individuals compared with HC. Additionally, reduced CT correlated significantly with older age in HC and in non-converters but not in converters.

CONCLUSIONS

These results show for the first time that fronto-temporo-parietal abnormalities characterized all CHR, that is, both converters and non-converters, relative to HC, while CT abnormalities in converters relative to CHR-NC and HC were found in core auditory and language processing regions.

A new era for executive function research: On the transition from centralized to distributed executive functioning

"Executive functions" (EFs) is an umbrella term for higher cognitive control functions such as working memory, inhibition, and cognitive flexibility. One of the most challenging problems in this field of research has been to explain how the wide range of cognitive processes subsumed as EFs are controlled without an all-powerful but ill-defined central executive in the brain. Efforts to localize control mechanisms in circumscribed brain regions have not led to a breakthrough in understanding how the brain controls and regulates itself. We propose to re-conceptualize EFs as emergent consequences of highly distributed brain processes that communicate with a pool of highly connected hub regions, thus precluding the need for a central executive. We further discuss how graph-theory driven analysis of brain networks offers a unique lens on this problem by providing a reference frame to study brain connectivity in EFs in a holistic way and helps to refine our understanding of the mechanisms underlying EFs by providing new, testable hypotheses and resolves empirical and theoretical inconsistencies in the EF literature.

The Relationship Between Abnormal Resting-State Functional Connectivity of the Left Superior Frontal Gyrus and Cognitive Impairments in Youth-Onset Drug-Naïve Schizophrenia

Objective: Age of onset is one of the heterogeneous factors in schizophrenia, and an earlier onset of the disease indicated a worse prognosis. The left superior frontal gyrus (SFG) is involved in numerous cognitive and motor control tasks. Hence, we explored the relationship between abnormal changes in SFG resting-state functional connectivity (rsFC) and cognitive function in the peak age of incidence to understand better the pathophysiological mechanism in youth-onset drug-naïve schizophrenia to search for reliable biomarkers. Methods: About 66 youth-onset drug-naïve schizophrenia patients and 59 healthy controls (HCs) were included in this study. Abnormal connectivity changes in the left SFG and whole brain were measured using the region of interest (ROI) rsFC analysis method. The cognitive function was assessed using the MATRICS Consensus Cognitive Battery (MCCB), and the severity of the clinical symptoms was evaluated by positive and negative syndrome scale (PANSS). Furthermore, we analyzed the relationships among abnormal FC values, cognition scores, and clinical symptoms. Results: We found decreased FC between left SFG and bilateral precuneus (PCUN), right hippocampus, right parahippocampal gyrus, left thalamus, left caudate, insula, and right superior parietal lobule (SPL), whereas increased FC was seen between the left SFG and right middle frontal gyrus (MFG) in the youth-onset drug-naïve schizophrenia group, compared with HCs. Meanwhile, the T-scores were lower in each cognitive domain than HCs. Moreover, in the youth-onset drug-naive schizophrenia group, the insula was negatively correlated with processing speed. No significant correlations were found between the FC-value and PANSS score. Conclusions: Our findings suggest widespread FC network abnormalities in the left SFG and widespread cognitive impairments in the early stages of schizophrenia. The dysfunctional connectivity of the left SFG may be a potential pathophysiological mechanism in youth-onset drug-naïve schizophrenia.

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.

Thalamus-related anomalies as candidate mechanism-based biomarkers for psychosis

Identification of reliable biomarkers of prognosis in subjects with high risk to psychosis is an essential step to improve care and treatment of this population of help-seekers. Longitudinal studies highlight some clinical criteria, cognitive deficits, patterns of gray matter alterations and profiles of blood metabolites that provide some levels of prediction regarding the conversion to psychosis. Further effort is warranted to validate these results and implement these types of approaches in clinical settings. Such biomarkers may however fall short in entangling the biological mechanisms underlying the disease progression, an essential step in the development of novel therapies. Circuit-based approaches, which map on well-identified cerebral functions, could meet these needs. Converging evidence indicates that thalamus abnormalities are central to schizophrenia pathophysiology, contributing to clinical symptoms, cognitive and sensory deficits. This review highlights the various thalamus-related anomalies reported in individuals with genetic risks and in the different phases of the disorder, from prodromal to chronic stages. Several anomalies are potent endophenotypes, while others exist in clinical high-risk subjects and worsen in those who convert to full psychosis. Aberrant functional coupling between thalamus and cortex, low glutamate content and readouts from resting EEG carry predictive values for transition to psychosis or functional outcome. In this context, thalamus-related anomalies represent a valuable entry point to tackle circuit-based alterations associated with the emergence of psychosis. This review also proposes that longitudinal surveys of neuroimaging, EEG readouts associated with circuits encompassing the mediodorsal, pulvinar in high-risk individuals could unveil biological mechanisms contributing to this psychiatric disorder.

Association between functional and structural connectivity of the corticostriatal network in people with schizophrenia and unaffected first-degree relatives

BACKGROUND

Dysfunction of the corticostriatal network has been implicated in the pathophysiology of schizophrenia, but findings are inconsistent within and across imaging modalities. We used multimodal neuroimaging to analyze functional and structural connectivity in the corticostriatal network in people with schizophrenia and unaffected first-degree relatives.

METHODS

We collected resting-state functional magnetic resonance imaging and diffusion tensor imaging scans from people with schizophrenia (n = 47), relatives (n = 30) and controls (n = 49). We compared seed-based functional and structural connectivity across groups within striatal subdivisions defined a priori.

RESULTS

Compared with controls, people with schizophrenia had altered connectivity between the subdivisions and brain regions in the frontal and temporal cortices and thalamus; relatives showed different connectivity between the subdivisions and the right anterior cingulate cortex (ACC) and the left precuneus. Post-hoc t tests revealed that people with schizophrenia had decreased functional connectivity in the ventral loop (ventral striatum-right ACC) and dorsal loop (executive striatum-right ACC and sensorimotor striatum-right ACC), accompanied by decreased structural connectivity; relatives had reduced functional connectivity in the ventral loop and the dorsal loop (right executive striatum-right ACC) and no significant difference in structural connectivity compared with the other groups. Functional connectivity among people with schizophrenia in the bilateral ventral striatum-right ACC was correlated with positive symptom severity.

LIMITATIONS

The number of relatives included was moderate. Striatal subdivisions were defined based on a relatively low threshold, and structural connectivity was measured based on fractional anisotropy alone.

CONCLUSION

Our findings provide insight into the role of hypoconnectivity of the ventral corticostriatal system in people with schizophrenia.

Organized Resting-state Functional Dysconnectivity of the Prefrontal Cortex in Patients with Schizophrenia

Schizophrenia has prominent functional dysconnectivity, especially in the prefrontal cortex (PFC). However, it is unclear whether in the same group of patients with schizophrenia, PFC functional dysconnectivity appears in an organized manner or is stochastically located in different subregions. By investigating the resting-state functional connectivity (rsFC) of each PFC subregion from the Brainnetome atlas in 40 schizophrenia patients and 40 healthy subjects, we found 24 altered connections in schizophrenia, and the connections were divided into four categories by a clustering analysis: increased connections within the PFC, increased connections between the inferior PFC and the thalamus/striatum, reduced connections between the PFC and the motor control areas, and reduced connections between the orbital PFC and the emotional perception regions. In addition, the four categories of rsFC showed distinct cognitive engagement patterns. Our findings suggest that PFC subregions have specific functional dysconnectivity patterns in schizophrenia and may reflect heterogeneous symptoms and cognitive deficits in schizophrenia.

Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker

The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework's ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer's disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.

Color Trails Test: A New Set of Data on Cognitive Flexibility and Processing Speed in Schizophrenia

BACKGROUND

Although schizophrenia patients have been reported to manifest deficits in cognitive flexibility and lower processing speed (measured with i.a., the Color Trails Test, CTT), there still remain a few matters that require further investigation. We have therefore formulated three research aims: 1) to examine the factor structure of CTT in schizophrenia patients and healthy controls, 2) to compare different CTT performance measures in the two groups, 3) to investigate the relationship between these measures and selected psychopathological symptoms in the patient group.

METHODS

Sixty-seven patients with paranoid schizophrenia and 67 healthy controls, matched for gender, age, number of years of education, and overall cognitive functioning underwent assessment of cognitive flexibility and processing speed with the CTT.

RESULTS

Factor analysis of CTT variables based on the principal component method revealed a four-factor solution in both groups. Compared with healthy controls, the patients performed poorer on CTT 1 time, CTT 2 time, 2-1 difference, prompts in CTT 2, and had higher regression factor scores for Factor 1 (reflecting the slower speed of perceptual tracking). Furthermore, significant links were found between some CTT measures, and negative and disorganization symptoms.

CONCLUSIONS

Schizophrenia patients exhibit problems with speed of perceptual tracking and executive processes dependent on processing speed. Our results may be useful for the development of neuropsychological diagnostic methods for schizophrenia patients. It seems that, compared to other CTT indices, CTT 1 time, CTT 2 time, and 2-1 difference are more appropriate measures of cognitive performance in schizophrenia patients.

Identifying Schizo-Obsessive Comorbidity by Tract-Based Spatial Statistics and Probabilistic Tractography

A phenomenon in schizophrenia patients that deserves attention is the high comorbidity rate with obsessive-compulsive disorder (OCD). Little is known about the neurobiological basis of schizo-obsessive comorbidity (SOC). We aimed to investigate whether specific changes in white matter exist in patients with SOC and the relationship between such abnormalities and clinical parameters. Twenty-eight patients with SOC, 28 schizophrenia patients, 30 OCD patients, and 30 demographically matched healthy controls were recruited. Using Tract-based Spatial Statistics and Probabilistic Tractography, we examined the pattern of white matter abnormalities in these participants. We also used ANOVA and Support Vector Classification of various white matter indices and structural connection probability to further examine white matter changes among the 4 groups. We found that patients with SOC had decreased fractional anisotropy (FA) and increased radial diffusivity in the right sagittal stratum and the left crescent of the fornix/stria terminalis compared with healthy controls. We also found changed connection probability in the Default Mode Network, the Subcortical Network, the Attention Network, the Task Control Network, the Visual Network, the Somatosensory Network, and the cerebellum in the SOC group compared with the other 3 groups. The classification results further revealed that FA features could differentiate the SOC group from the other 3 groups with an accuracy of .78. These findings highlight the specific white matter abnormalities found in patients with SOC.

Aberrant Executive Control and Auditory Networks in Recent-Onset Schizophrenia

PURPOSE

Despite a large number of resting-state functional MRI (rsfMRI) studies in schizophrenia, current evidence on the abnormalities of functional connectivity (FC) of resting-state networks shows high variability, and the findings on recent-onset schizophrenia are insufficient compared to those on chronic schizophrenia.

PATIENTS AND METHODS

We performed a rsfMRI in 46 patients with recent-onset schizophrenia and 22 healthy controls. Group independent component brainmap and dual regression were performed for voxel-wise comparisons between the groups. Correlation of the symptom severity, cognitive function, duration of illness, and a total antipsychotics dose with FC was evaluated with Spearman's rho correlation.

RESULTS

The patient group had areas with a significantly decreased FC compared to that of the control group in which it existed in the left supplementary motor cortex and supramarginal gyrus (the executive control network) and the right postcentral gyrus (the auditory network). The patient group had a significant correlation of the total antipsychotics dose with the FC of the cluster in the left supplementary motor cortex in the executive control network.

CONCLUSION

Patients with recent-onset schizophrenia have decreased FC of the executive control and auditory networks compared to healthy controls.

Antidepressant and antisuicidal effects of ketamine on the functional connectivity of prefrontal cortex-related circuits in treatment-resistant depression: A double-blind, placebo-controlled, randomized, longitudinal resting fMRI study

BACKGROUND

Increasing evidence suggests that infusion of a subanesthetic dose of ketamine exerts antidepressant and antisuicidal effects in patients with treatment-resistant depression (TRD).

AIMS

In this investigation, we used the resting functional connectivity magnetic resonance imaging (fcMRI) to determine the effects of ketamine on the functional connectivity (FC) of prefrontal cortex (PFC)-related circuits in patients with TRD.

METHODS

Forty-eight patients with TRD were recruited and randomly divided into three groups on the basis of ketamine infusion dose: 0.5 mg/kg (standard dose), 0.2 mg/kg (low dose), or normal saline (a placebo infusion). Resting functional MRI data and clinical data were recorded at the baseline and on the third day after ketamine infusion treatment.

RESULTS

The standard-dose group showed a reduction in the FC of the left dorsal anterior cingulate cortex (dACC) and right dorsolateral (dl)PFC with the other frontal regions. The low-dose group demonstrated a more pervasive reduction of FC in the bilateral dACC with other frontal and parietal regions. A negative correlation was observed between the reduction in suicidal ideation and the reduction in the FC between the left dACC and right ACC regions in the standard-dose group, whereas a positive correlation was observed between the reduction in suicidal ideation and the increase in the FC between the right dlPFC and left superior parietal region in the low-dose group.

CONCLUSIONS

Our results support the hypothesis that PFC-related circuit modulation is crucial to the antidepressant and antisuicidal effects of the ketamine infusion treatment.

Functional network connectivity impairments and core cognitive deficits in schizophrenia

Cognitive deficits contribute to functional disability in patients with schizophrenia and may be related to altered functional networks that serve cognition. We evaluated the integrity of major functional networks and assessed their role in supporting two cognitive functions affected in schizophrenia: processing speed (PS) and working memory (WM). Resting-state functional magnetic resonance imaging (rsfMRI) data, N = 261 patients and 327 controls, were aggregated from three independent cohorts and evaluated using Enhancing NeuroImaging Genetics through Meta Analysis rsfMRI analysis pipeline. Meta- and mega-analyses were used to evaluate patient-control differences in functional connectivity (FC) measures. Canonical correlation analysis was used to study the association between cognitive deficits and FC measures. Patients showed consistent patterns of cognitive and resting-state FC (rsFC) deficits across three cohorts. Patient-control differences in rsFC calculated using seed-based and dual-regression approaches were consistent (Cohen's d: 0.31 ± 0.09 and 0.29 ± 0.08, p < 10^-4 ). RsFC measures explained 12-17% of the individual variations in PS and WM in the full sample and in patients and controls separately, with the strongest correlations found in salience, auditory, somatosensory, and default-mode networks. The pattern of association between rsFC (within-network) and PS (r = .45, p = .07) and WM (r = .36, p = .16), and rsFC (between-network) and PS (r = .52, p = 8.4 × 10^-3 ) and WM (r = .47, p = .02), derived from multiple networks was related to effect size of patient-control differences in the functional networks. No association was detected between rsFC and current medication dose or psychosis ratings. Patients demonstrated significant reduction in several FC networks that may partially underlie some of the core neurocognitive deficits in schizophrenia. The strength of connectivity-cognition relationships in different networks was strongly associated with network's vulnerability to schizophrenia.

Resting-State Functional Connectivity and Psychotic-like Experiences in Childhood: Results From the Adolescent Brain Cognitive Development Study

BACKGROUND

Psychotic-like experiences (PLEs) during childhood are associated with greater risk of developing a psychotic disorder (and other mental disorders), highlighting the importance of identifying neural correlates of childhood PLEs. Three major cortical networks-the cingulo-opercular network (CON), default mode network (DMN), and frontoparietal network-are consistently implicated in psychosis and PLEs in adults. However, it is unclear whether variation in functional connectivity is associated with PLEs in school-aged children.

METHODS

Using hierarchical linear models, we examined the relationships between childhood PLEs and resting-state functional connectivity of the CON, DMN, and frontoparietal network, as well as the other networks, using an a priori network parcellation, using data from 9- to 11-year-olds (n = 3434) in the ABCD (Adolescent Brain Cognitive Development) study. We examined within-network, between-network, and subcortical connectivity.

RESULTS

Decreased CON and DMN connectivity, as well as cinguloparietal (CPAR) network connectivity, were associated with greater PLEs, even after accounting for family history of psychotic disorders, internalizing symptoms, and cognitive performance. Decreased DMN connectivity was more strongly associated with increased delusional ideation, whereas decreased CON connectivity was more strongly associated with increased perceptual distortions. Increased CON-cerebellar and decreased CPAR-cerebellar connectivity were also associated with increased PLEs, and CPAR-cerebellar connectivity was more strongly associated with increased perceptual distortions.

CONCLUSIONS

Consistent with hypotheses about the dimensionality of psychosis, our results provide novel evidence that neural correlates of PLEs, such as reduced functional connectivity of higher-order cognitive networks, are present even in school-aged children. The results provide further validation for continuity of PLEs across the life span.

An Activation Likelihood Estimate Meta-analysis of Thalamocortical Dysconnectivity in Psychosis

BACKGROUND

Thalamocortical dysconnectivity is hypothesized to underlie the pathophysiology of psychotic disorders, including schizophrenia and bipolar disorder, and individuals at clinical high risk. Numerous studies have examined connectivity networks seeding from the thalamus during rest, revealing a pattern of thalamo-fronto-cerebellar hypoconnectivity and thalamosensory hyperconnectivity. However, given variability in these networks, as well as their relationships with clinical and cognitive symptoms, thalamocortical connectivity's status as a biomarker and treatment target for psychotic disorders remains unclear.

METHODS

A literature search was performed to identify thalamic seed-based connectivity studies conducted in patients with psychotic disorders. Activation likelihood estimate analysis examined the reported coordinates for hypoconnectivity (healthy control participants > patients with psychosis) and hyperconnectivity (patients with psychosis > healthy control participants). The relationship between hypoconnectivity and hyperconnectivity, as well as their relationships with clinical and cognitive measures, was meta-analyzed.

RESULTS

Each activation likelihood estimate included 20 experiments (from 17 publications). Thalamocortical hypoconnectivity was observed in middle frontal, cingulate, and thalamic regions, while hyperconnectivity was observed in motor, somatosensory, temporal, occipital, and insular cortical regions. Meta-analysis of the studies reporting correlations between hypo- and hyperconnectivity showed a strong negative relationship. Meta-analysis of studies reporting correlations between hyperconnectivity and symptoms showed small but significant positive relationships.

CONCLUSIONS

Activation likelihood estimates of thalamocortical hypoconnectivity revealed a network of prefrontal and thalamic regions, while hyperconnections identified sensory areas. The strong negative relationship between these thalamocortical deflections suggests that they arrive from a common mechanism and may account for aspects of psychosis. These findings identify reliable thalamocortical networks that may guide future studies and serve as crucial treatment targets for psychotic disorders.

Dysconnectivity of Multiple Brain Networks in Schizophrenia: A Meta-Analysis of Resting-State Functional Connectivity

Background: Seed-based studies on resting-state functional connectivity (rsFC) in schizophrenia have shown disrupted connectivity involving a number of brain networks; however, the results have been controversial. Methods: We conducted a meta-analysis based on independent component analysis (ICA) brain templates to evaluate dysconnectivity within resting-state brain networks in patients with schizophrenia. Seventy-six rsFC studies from 70 publications with 2,588 schizophrenia patients and 2,567 healthy controls (HCs) were included in the present meta-analysis. The locations and activation effects of significant intergroup comparisons were extracted and classified based on the ICA templates. Then, multilevel kernel density analysis was used to integrate the results and control bias. Results: Compared with HCs, significant hypoconnectivities were observed between the seed regions and the areas in the auditory network (left insula), core network (right superior temporal cortex), default mode network (right medial prefrontal cortex, and left precuneus and anterior cingulate cortices), self-referential network (right superior temporal cortex), and somatomotor network (right precentral gyrus) in schizophrenia patients. No hyperconnectivity between the seed regions and any other areas within the networks was detected in patients, compared with the connectivity in HCs. Conclusions: Decreased rsFC within the self-referential network and default mode network might play fundamental roles in the malfunction of information processing, while the core network might act as a dysfunctional hub of regulation. Our meta-analysis is consistent with diffuse hypoconnectivities as a dysregulated brain network model of schizophrenia.

Long-range gamma phase synchronization as a compensatory strategy during working memory in high-performing patients with schizophrenia

Working memory deficits in schizophrenia may be associated with impairments in the integration of neural activity across a distributed network of cortical areas. However, evaluation of the contribution of this integration to working memory impairments in patients is severely confounded by behavioral performance. In the present multidimensional-neuroimaging study, measures of neural oscillations at baseline and during a working memory task, baseline gamma-aminobutyric acid (GABA) level in the left dorsolateral prefrontal cortex (DLPFC), and behavioral performance were obtained. Controlling behavioral performance by recruiting only "high-performing" patients with schizophrenia, we investigated whether the strength of cross-area communications differs between patients with schizophrenia and healthy participants under accurate and equivalent behavioral performance. Results of phase-locking value indicated that these high-performing patients recruited significantly more between frontal and occipital regions in the left hemisphere, t(13) = -2.16, p = .05, Cohen's d = -1.20, and between frontal and temporal regions in the right hemisphere, t(13) = -2.63, p = .02, Cohen's d = -1.46. These cross-area communication patterns may be associated with visuoverbal and visuospatial working memory networks of the left and right hemispheres, respectively. Moreover, correlations of patient's cross-area communication with in vivo GABA levels of the left DLPFC revealed a significant positive relationship (r = .77, p = .04), demonstrating that the critical role of GABA functions in gamma band oscillations may go beyond local neuronal assemblies in the left DLPFC. Altogether, these exploratory findings point to the heterogeneity among schizophrenia patients and highlight the notion that high-performing patients may engage in potential compensatory mechanisms and may represent a subgroup of patients that may be categorically or dimensionally divergent in psychopathology.