Brain connectivity studies in schizophrenia: unravelling the effects of antipsychotics

Elevated intrinsic cortical curvature in treatment-resistant schizophrenia: Evidence of structural deformation in functional connectivity areas and comparison with alternate indices of structure

BACKGROUND

Research suggests structural and connectivity abnormalities in patients with treatment-resistant schizophrenia (TRS) compared to first-line responders and healthy-controls. However, measures of these abnormalities are often influenced by external factors like nicotine and antipsychotics, limiting their clinical utility. Intrinsic-cortical-curvature (ICC) presents a millimetre-scale measure of brain gyrification, highly sensitive to schizophrenia differences, and associated with TRS-like traits in early stages of the disorder. Despite this evidence, ICC in TRS remains unexplored. This study investigates ICC as a marker for treatment resistance in TRS, alongside structural indices for comparison.

METHODS

We assessed ICC in anterior cingulate, dorsolateral prefrontal, temporal, and parietal cortices of 38 first-line responders, 30 clozapine-resistant TRS, 37 clozapine-responsive TRS, and 52 healthy-controls. For comparative purposes, Fold and Curvature indices were also analyzed.

RESULTS

Adjusting for age, sex, nicotine-use, and chlorpromazine equivalence, principal findings indicate ICC elevations in the left hemisphere dorsolateral prefrontal (p < 0.001, η2partial = 0.142) and temporal cortices (LH p = 0.007, η2partial = 0.060; RH p = 0.011, η2partial = 0.076) of both TRS groups, and left anterior cingulate cortex of clozapine-resistant TRS (p = 0.026, η2partial = 0.065), compared to healthy-controls. Elevations that correlated with reduced cognition (p = 0.001) and negative symptomology (p < 0.034) in clozapine-resistant TRS. Fold and Curvature indices only detected group differences in the right parietal cortex, showing interactions with age, sex, and nicotine use. ICC showed interactions with age.

CONCLUSION

ICC elevations were found among patients with TRS, and correlated with symptom severity. ICCs relative independence from sex, nicotine-use, and antipsychotics, may support ICC's potential as a viable marker for TRS, though age interactions should be considered.

Topoisomerase IIIβ Deficiency Induces Neuro-Behavioral Changes and Brain Connectivity Alterations in Mice

Topoisomerase IIIβ (Top3β), the only dual-activity topoisomerase in mammals that can change topology of both DNA and RNA, is known to be associated with neurodevelopment and mental dysfunction in humans. However, there is no report showing clear associations of Top3β with neuropsychiatric phenotypes in mice. Here, we investigated the effect of Top3β on neuro-behavior using newly generated Top3β deficient (Top3β^-/-) mice. We found that Top3β^-/- mice showed decreased anxiety and depression-like behaviors. The lack of Top3β was also associated with changes in circadian rhythm. In addition, a clear expression of Top3β was demonstrated in the central nervous system of mice. Positron emission tomography/computed tomography (PET/CT) analysis revealed significantly altered connectivity between many brain regions in Top3β^-/- mice, including the connectivity between the olfactory bulb and the cerebellum, the connectivity between the amygdala and the olfactory bulb, and the connectivity between the globus pallidus and the optic nerve. These connectivity alterations in brain regions are known to be linked to neurodevelopmental as well as psychiatric and behavioral disorders in humans. Therefore, we conclude that Top3β is essential for normal brain function and behavior in mice and that Top3β could be an interesting target to study neuropsychiatric disorders in humans.

Maximally selective single-cell target for circuit control in epilepsy models

Neurological and psychiatric disorders are associated with pathological neural dynamics. The fundamental connectivity patterns of cell-cell communication networks that enable pathological dynamics to emerge remain unknown. Here, we studied epileptic circuits using a newly developed computational pipeline that leveraged single-cell calcium imaging of larval zebrafish and chronically epileptic mice, biologically constrained effective connectivity modeling, and higher-order motif-focused network analysis. We uncovered a novel functional cell type that preferentially emerged in the preseizure state, the superhub, that was unusually richly connected to the rest of the network through feedforward motifs, critically enhancing downstream excitation. Perturbation simulations indicated that disconnecting superhubs was significantly more effective in stabilizing epileptic circuits than disconnecting hub cells that were defined traditionally by connection count. In the dentate gyrus of chronically epileptic mice, superhubs were predominately modeled adult-born granule cells. Collectively, these results predict a new maximally selective and minimally invasive cellular target for seizure control.

Activity flow underlying abnormalities in brain activations and cognition in schizophrenia

Cognitive dysfunction is a core feature of many brain disorders, including schizophrenia (SZ), and has been linked to aberrant brain activations. However, it is unclear how these activation abnormalities emerge. We propose that aberrant flow of brain activity across functional connectivity (FC) pathways leads to altered activations that produce cognitive dysfunction in SZ. We tested this hypothesis using activity flow mapping, an approach that models the movement of task-related activity between brain regions as a function of FC. Using functional magnetic resonance imaging data from SZ individuals and healthy controls during a working memory task, we found that activity flow models accurately predict aberrant cognitive activations across multiple brain networks. Within the same framework, we simulated a connectivity-based clinical intervention, predicting specific treatments that normalized brain activations and behavior in patients. Our results suggest that dysfunctional task-evoked activity flow is a large-scale network mechanism contributing to cognitive dysfunction in SZ.

Neurocognitive disorders in the elderly: altered functional resting-state hyperconnectivities in postoperative delirium patients

Postoperative delirium (POD) represents a confusional state during days/weeks after surgery and is particularly frequent in elderly patients. Hardly any fMRI studies were conducted to understand the underlying pathophysiology of POD patients. This prospective observational cohort study aims to examine changes of specific resting-state functional connectivity networks across different time points (pre- and 3-5 months postoperatively) in delirious patients compared to no-POD patients. Two-hundred eighty-three elderly surgical patients underwent preoperative resting-state fMRI (46 POD). One-hundred seventy-eight patients completed postoperative scans (19 POD). For functional connectivity analyses, three functional connectivity networks with seeds located in the orbitofrontal cortex (OFC), nucleus accumbens (NAcc), and hippocampus were investigated. The relationship of POD and connectivity changes between both time points (course connectivity) were examined (ANOVA). Preoperatively, delirious patients displayed hyperconnectivities across the examined functional connectivity networks. In POD patients, connectivities within NAcc and OFC networks demonstrated a decrease in course connectivity [max. F = 9.03, p = 0.003; F = 4.47, p = 0.036, resp.]. The preoperative hyperconnectivity in the three networks in the patients at risk for developing POD could possibly indicate existing compensation mechanisms for subtle brain dysfunction. The observed pathophysiology of network function in POD patients at least partially involves dopaminergic pathways.

The genetic determinants of language network dysconnectivity in drug-naïve early stage schizophrenia

Schizophrenia is a neurocognitive illness of synaptic and brain network-level dysconnectivity that often reaches a persistent chronic stage in many patients. Subtle language deficits are a core feature even in the early stages of schizophrenia. However, the primacy of language network dysconnectivity and language-related genetic variants in the observed phenotype in early stages of illness remains unclear. This study used two independent schizophrenia dataset consisting of 138 and 53 drug-naïve first-episode schizophrenia (FES) patients, and 112 and 56 healthy controls, respectively. A brain-wide voxel-level functional connectivity analysis was conducted to investigate functional dysconnectivity and its relationship with illness duration. We also explored the association between critical language-related genetic (such as FOXP2) mutations and the altered functional connectivity in patients. We found elevated functional connectivity involving Broca's area, thalamus and temporal cortex that were replicated in two FES datasets. In particular, Broca's area - anterior cingulate cortex dysconnectivity was more pronounced for patients with shorter illness duration, while thalamic dysconnectivity was predominant in those with longer illness duration. Polygenic risk scores obtained from FOXP2-related genes were strongly associated with functional dysconnectivity identified in patients with shorter illness duration. Our results highlight the criticality of language network dysconnectivity, involving the Broca's area in early stages of schizophrenia, and the role of language-related genes in this aberration, providing both imaging and genetic evidence for the association between schizophrenia and the determinants of language.

Integrated metastate functional connectivity networks predict change in symptom severity in clinical high risk for psychosis

The ability to identify biomarkers of psychosis risk is essential in defining effective preventive measures to potentially circumvent the transition to psychosis. Using samples of people at clinical high risk for psychosis (CHR) and Healthy controls (HC) who were administered a task fMRI paradigm, we used a framework for labelling time windows of fMRI scans as 'integrated' FC networks to provide a granular representation of functional connectivity (FC). Periods of integration were defined using the 'cartographic profile' of time windows and k-means clustering, and sub-network discovery was carried out using Network Based Statistics (NBS). There were no network differences between CHR and HC groups. Within the CHR group, using integrated FC networks, we identified a sub-network negatively associated with longitudinal changes in the severity of psychotic symptoms. This sub-network comprised brain areas implicated in bottom-up sensory processing and in integration with motor control, suggesting it may be related to the demands of the fMRI task. These data suggest that extracting integrated FC networks may be useful in the investigation of biomarkers of psychosis risk.

The thalamic midline nucleus reuniens: potential relevance for schizophrenia and epilepsy

Anatomical, electrophysiological and behavioral studies in rodents have shown that the thalamic midline nucleus reuniens (RE) is a crucial link in the communication between hippocampal formation (HIP, i.e., CA1, subiculum) and medial prefrontal cortex (mPFC), important structures for cognitive and executive functions. A common feature in neurodevelopmental and neurodegenerative brain diseases is a dysfunctional connectivity/communication between HIP and mPFC, and disturbances in the cognitive domain. Therefore, it is assumed that aberrant functioning of RE may contribute to behavioral/cognitive impairments in brain diseases characterized by cortico-thalamo-hippocampal circuit dysfunctions. In the human brain the connections of RE are largely unknown. Yet, recent studies have found important similarities in the functional connectivity of HIP-mPFC-RE in humans and rodents, making cautious extrapolating experimental findings from animal models to humans justifiable. The focus of this review is on a potential involvement of RE in schizophrenia and epilepsy.

Effective connectivity of the right anterior insula in schizophrenia: The salience network and task-negative to task-positive transition

Triple network dysfunction theory of schizophrenia postulates that the interaction between the default-mode and the fronto-parietal executive network is disrupted by aberrant salience signals from the right anterior insula (rAI). To date, it is not clear how the proposed resting-state disruption translates to task-processing inefficiency in subjects with schizophrenia. Using a contiguous resting and 2-back task performance fMRI paradigm, we quantified the change in effective connectivity that accompanies rest-to-task state transition in 29 clinically stable patients with schizophrenia and 31 matched healthy controls. We found an aberrant task-evoked increase in the influence of the rAI to both executive (Cohen's d = 1.35, p = 2.8 × 10-6) and default-mode (Cohen's d = 1.22, p = 1.5 × 10-5) network regions occur in patients when compared to controls. In addition, the effective connectivity from middle occipital gyrus (dorsal visual cortex) to insula is also increased in patients as compared with healthy controls. Aberrant insula to executive network influence is pronounced in patients with more severe negative symptom burden. These findings suggest that control signals from rAI are abnormally elevated and directed towards both task-positive and task-negative brain regions, when task-related demands arise in schizophrenia. This aberrant, undiscriminating surge in salience signalling may disrupt contextually appropriate allocation of resources in the neuronal workspace in patients with schizophrenia.

Deep Temporal Organization of fMRI Phase Synchrony Modes Promotes Large-Scale Disconnection in Schizophrenia

Itinerant dynamics of the brain generates transient and recurrent spatiotemporal patterns in neuroimaging data. Characterizing metastable functional connectivity (FC) - particularly at rest and using functional magnetic resonance imaging (fMRI) - has shaped the field of dynamic functional connectivity (DFC). Mainstream DFC research relies on (sliding window) correlations to identify recurrent FC patterns. Recently, functional relevance of the instantaneous phase synchrony (IPS) of fMRI signals has been revealed using imaging studies and computational models. In the present paper, we identify the repertoire of whole-brain inter-network IPS states at rest. Moreover, we uncover a hierarchy in the temporal organization of IPS modes. We hypothesize that connectivity disorder in schizophrenia (SZ) is related to the (deep) temporal arrangement of large-scale IPS modes. Hence, we analyze resting-state fMRI data from 68 healthy controls (HC) and 51 SZ patients. Seven resting-state networks (and their sub-components) are identified using spatial independent component analysis. IPS is computed between subject-specific network time courses, using analytic signals. The resultant phase coupling patterns, across time and subjects, are clustered into eight IPS states. Statistical tests show that the relative expression and mean lifetime of certain IPS states have been altered in SZ. Namely, patients spend (45%) less time in a globally coherent state and a subcortical-centered state, and (40%) more time in states reflecting anticoupling within the cognitive control network, compared to the HC. Moreover, the transition profile (between states) reveals a deep temporal structure, shaping two metastates with distinct phase synchrony profiles. A metastate is a collection of states such that within-metastate transitions are more probable than across. Remarkably, metastate occupation balance is altered in SZ, in favor of the less synchronous metastate that promotes disconnection across networks. Furthermore, the trajectory of IPS patterns is less efficient, less smooth, and more restricted in SZ subjects, compared to the HC. Finally, a regression analysis confirms the diagnostic value of the defined IPS measures for SZ identification, highlighting the distinctive role of metastate proportion. Our results suggest that the proposed IPS features may be used for classification studies and for characterizing phase synchrony modes in other (clinical) populations.

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.

The dark matter of the brain

The bulk of brain energy expenditure is allocated for maintenance of perpetual intrinsic activity of neurons and neural circuits. Long-term electrophysiological and neuroimaging studies in anesthetized and behaving animals show, however, that the great majority of nerve cells in the intact brain do not fire action potentials, i.e., are permanently silent. Herein, I review emerging data suggesting massive redundancy of nerve cells in mammalian nervous system, maintained in inhibited state at high energetic costs. Acquired in the course of evolution, these collections of dormant neurons and circuits evade routine functional undertakings, and hence, keep out of the reach of natural selection. Under penetrating stress and disease, however, they occasionally switch in active state and drive a variety of neuro-psychiatric symptoms and behavioral abnormalities. The increasing evidence for widespread occurrence of silent neurons warrants careful revision of functional models of the brain and entails unforeseen reserves for rehabilitation and plasticity.

Neuroimaging markers of antipsychotic treatment response in schizophrenia: An overview of magnetic resonance imaging studies

Antipsychotic drugs are the primary treatment for psychosis, yet individual response to their administration remains variable. At present, no biological predictors of response exist to guide clinicians as they select treatments for patients, and our understanding of the neurobiology underlying the heterogeneity of outcomes remains limited. Magnetic Resonance Imaging (MRI) has been applied by numerous studies to examine the response to antipsychotic treatment, though a large gap remains between their results and our clinical practice. To advance patient care with precision medicine approaches, prior work must be accounted for and built upon with future studies. This review provides an overview of studies that relate treatment outcome to various MRI-related measures, including structural, spectroscopic, diffusion tensor, and functional imaging. Knowledge derived from these studies will be discussed along with future directions for the field.

Effects of ketamine on brain function during smooth pursuit eye movements

The uncompetitive NMDA receptor antagonist ketamine has been proposed to model symptoms of psychosis. Smooth pursuit eye movements (SPEM) are an established biomarker of schizophrenia. SPEM performance has been shown to be impaired in the schizophrenia spectrum and during ketamine administration in healthy volunteers. However, the neural mechanisms mediating SPEM impairments during ketamine administration are unknown. In a counter-balanced, placebo-controlled, double-blind, within-subjects design, 27 healthy participants received intravenous racemic ketamine (100 ng/mL target plasma concentration) on one of two assessment days and placebo (intravenous saline) on the other. Participants performed a block-design SPEM task during functional magnetic resonance imaging (fMRI) at 3 Tesla field strength. Self-ratings of psychosis-like experiences were obtained using the Psychotomimetic States Inventory (PSI). Ketamine administration induced psychosis-like symptoms, during ketamine infusion, participants showed increased ratings on the PSI dimensions cognitive disorganization, delusional thinking, perceptual distortion and mania. Ketamine led to robust deficits in SPEM performance, which were accompanied by reduced blood oxygen level dependent (BOLD) signal in the SPEM network including primary visual cortex, area V5 and the right frontal eye field (FEF), compared to placebo. A measure of connectivity with V5 and FEF as seed regions, however, was not significantly affected by ketamine. These results are similar to the deviations found in schizophrenia patients. Our findings support the role of glutamate dysfunction in impaired smooth pursuit performance and the use of ketamine as a pharmacological model of psychosis, especially when combined with oculomotor biomarkers. Hum Brain Mapp 37:4047-4060, 2016. © 2016 Wiley Periodicals, Inc.

Predictors for drug effects with brain disease: Shed new light from EEG parameters to brain connectomics

Though researchers spent a lot of effort to develop treatments for neuropsychiatric disorders, the poor translation of drug efficacy data from animals to human hampered the success of these therapeutic approaches in human. Pharmaceutical industry is challenged by low clinical success rates for new drug registration. To maximize the success in drug development, biomarkers are required to act as surrogate end points and predictors of drug effects. The pathology of brain disease could be in part due to synaptic dysfunction. Electroencephalogram (EEG), generating from the result of the postsynaptic potential discharge between cells, could be a potential measure to bridge the gaps between animal and human data. Here we discuss recent progress on using relevant EEG characteristics and brain connectomics as biomarkers to monitor drug effects and measure cognitive changes on animal models and human in real-time. It is expected that the novel approach, i.e. EEG connectomics, will offer a deeper understanding on the drug efficacy at a microcirculatory level, which will be useful to support the development of new treatments for neuropsychiatric disorders.

Altered Coupling Between Resting-State Cerebral Blood Flow and Functional Connectivity in Schizophrenia

BACKGROUND

Respective changes in resting-state cerebral blood flow (CBF) and functional connectivity in schizophrenia have been reported. However, their coupling alterations in schizophrenia remain largely unknown.

METHODS

89 schizophrenia patients and 90 sex- and age-matched healthy controls underwent resting-state functional MRI to calculate functional connectivity strength (FCS) and arterial spin labeling imaging to compute CBF. The CBF-FCS coupling of the whole gray matter and the CBF/FCS ratio (the amount of blood supply per unit of connectivity strength) of each voxel were compared between the 2 groups.

RESULTS

Whole gray matter CBF-FCS coupling was decreased in schizophrenia patients relative to healthy controls. In schizophrenia patients, the decreased CBF/FCS ratio was predominantly located in cognitive- and emotional-related brain regions, including the dorsolateral prefrontal cortex, insula, hippocampus and thalamus, whereas an increased CBF/FCS ratio was mainly identified in the sensorimotor regions, including the putamen, and sensorimotor, mid-cingulate and visual cortices.

CONCLUSION

These findings suggest that the neurovascular decoupling in the brain may be a possible neuropathological mechanism of schizophrenia.

Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms

Schizophrenia is often associated with disrupted brain connectivity. However, identifying specific neuroimaging-based patterns pathognomonic for schizophrenia and related symptom severity remains a challenging open problem requiring large-scale data-driven analyses emphasizing not only statistical significance but also stability across multiple datasets, contexts and cohorts. Accurate prediction on previously unseen subjects, or generalization, is also essential for any useful biomarker of schizophrenia. In order to build a predictive model based on functional network feature patterns, we studied whole-brain fMRI functional networks, both at the voxel level and lower-resolution supervoxel level. Targeting Auditory Oddball task data on the FBIRN fMRI dataset (n = 95), we considered node-degree and link-weight network features and evaluated stability and generalization accuracy of statistically significant feature sets in discriminating patients vs.

CONTROLS

We also applied sparse multivariate regression (elastic net) to whole-brain functional connectivity features, for the first time, to derive stable predictive features for symptom severity. Whole-brain link-weight features achieved 74% accuracy in identifying patients and were more stable than voxel-wise node-degrees. Link-weight features predicted severity of several negative and positive symptom scales, including inattentiveness and bizarre behavior. The most-significant, stable and discriminative functional connectivity changes involved increased correlations between thalamus and primary motor/primary sensory cortex, and between precuneus (BA7) and thalamus, putamen, and Brodmann areas BA9 and BA44. Precuneus, along with BA6 and primary sensory cortex, was also involved in predicting severity of several symptoms. Overall, the proposed multi-step methodology may help identify more reliable multivariate patterns allowing for accurate prediction of schizophrenia and its symptoms severity.

Practice and Learning: Spatiotemporal Differences in Thalamo-Cortical-Cerebellar Networks Engagement across Learning Phases in Schizophrenia

BACKGROUND

Understanding how practice mediates the transition of brain-behavior networks between early and later stages of learning is constrained by the common approach to analysis of fMRI data. Prior imaging studies have mostly relied on a single scan, and parametric, task-related analyses. Our experiment incorporates a multisession fMRI lexicon-learning experiment with multivariate, whole-brain analysis to further knowledge of the distributed networks supporting practice-related learning in schizophrenia (SZ).

METHODS

Participants with SZ were compared with healthy control (HC) participants as they learned a novel lexicon during two fMRI scans over a several day period. All participants were trained to equal task proficiency prior to scanning. Behavioral-Partial Least Squares, a multivariate analytic approach, was used to analyze the imaging data. Permutation testing was used to determine statistical significance and bootstrap resampling to determine the reliability of the findings.

RESULTS

With practice, HC participants transitioned to a brain-accuracy network incorporating dorsostriatal regions in late-learning stages. The SZ participants did not transition to this pattern despite comparable behavioral results. Instead, successful learners with SZ were differentiated primarily on the basis of greater engagement of perceptual and perceptual-integration brain regions.

CONCLUSION

There is a different spatiotemporal unfolding of brain-learning relationships in SZ. In SZ, given the same amount of practice, the movement from networks suggestive of effortful learning toward subcortically driven procedural one differs from HC participants. Learning performance in SZ is driven by varying levels of engagement in perceptual regions, which suggests perception itself is impaired and may impact downstream, "higher level" cognition.

Alterations of Intrinsic Brain Connectivity Patterns in Depression and Bipolar Disorders: A Critical Assessment of Magnetoencephalography-Based Evidence

Despite being the object of a thriving field of clinical research, the investigation of intrinsic brain network alterations in psychiatric illnesses is still in its early days. Because the pathological alterations are predominantly probed using functional magnetic resonance imaging (fMRI), many questions about the electrophysiological bases of resting-state alterations in psychiatric disorders, particularly among mood disorder patients, remain unanswered. Alongside important research using electroencephalography (EEG), the specific recent contributions and future promise of magnetoencephalography (MEG) in this field are not fully recognized and valued. Here, we provide a critical review of recent findings from MEG resting-state connectivity within major depressive disorder (MDD) and bipolar disorder (BD). The clinical MEG resting-state results are compared with those previously reported with fMRI and EEG. Taken together, MEG appears to be a promising but still critically underexploited technique to unravel the neurophysiological mechanisms that mediate abnormal (both hyper- and hypo-) connectivity patterns involved in MDD and BD. In particular, a major strength of MEG is its ability to provide source-space estimations of neuromagnetic long-range rhythmic synchronization at various frequencies (i.e., oscillatory coupling). The reviewed literature highlights the relevance of probing local and interregional rhythmic synchronization to explore the pathophysiological underpinnings of each disorder. However, before we can fully take advantage of MEG connectivity analyses in psychiatry, several limitations inherent to MEG connectivity analyses need to be understood and taken into account. Thus, we also discuss current methodological challenges and outline paths for future research. MEG resting-state studies provide an important window onto perturbed spontaneous oscillatory brain networks and hence supply an important complement to fMRI-based resting-state measurements in psychiatric populations.

Connectome organization is related to longitudinal changes in general functioning, symptoms and IQ in chronic schizophrenia

Emerging evidence suggests schizophrenia to involve widespread alterations in the macroscale wiring architecture of the human connectome. Recent findings of attenuated connectome alterations in unaffected siblings of schizophrenia patients suggest that altered connectome organization may relate to the vulnerability to develop the disorder, but whether it relates to progression of illness after disease onset is currently unknown. Here, we examined the interaction between connectome structure and longitudinal changes in general functioning, clinical symptoms and IQ in the 3years following MRI assessment in a group of chronically ill schizophrenia patients. Effects in patients were compared to associations between connectome organization and changes in subclinical symptoms and IQ in healthy controls and unaffected siblings of schizophrenia patients. Analyzing the patient sample revealed a relationship between structural connectivity-particularly among central 'brain hubs'-and progressive changes in general functioning (p=0.007), suggesting that more prominent impairments of hub connectivity may herald future functional decline. Our findings further indicate that affected local connectome organization relates to longitudinal increases in overall PANSS symptoms (p=0.013) and decreases in total IQ (p=0.003), independent of baseline symptoms and IQ. No significant associations were observed in controls and siblings, suggesting that the findings in patients represent effects of ongoing illness, as opposed to normal time-related changes. In all, our findings suggest connectome structure to have predictive value for the course of illness in schizophrenia.

Cognitive Effort and Schizophrenia Modulate Large-Scale Functional Brain Connectivity

Schizophrenia (SZ) is characterized by cognitive dysfunction and disorganized thought, in addition to hallucinations and delusions, and is regarded a disorder of brain connectivity. Recent efforts have been made to characterize the underlying brain network organization and interactions. However, to which degree connectivity alterations in SZ vary across different levels of cognitive effort is unknown. Utilizing independent component analysis (ICA) and methods for delineating functional connectivity measures from functional magnetic resonance imaging (fMRI) data, we investigated the effects of cognitive effort, SZ and their interactions on between-network functional connectivity during 2 levels of cognitive load in a large and well-characterized sample of SZ patients (n = 99) and healthy individuals (n = 143). Cognitive load influenced a majority of the functional connections, including but not limited to fronto-parietal and default-mode networks, reflecting both decreases and increases in between-network synchronization. Reduced connectivity in SZ was identified in 2 large-scale functional connections across load conditions, with a particular involvement of an insular network. The results document an important role of interactions between insular, default-mode, and visual networks in SZ pathophysiology. The interplay between brain networks was robustly modulated by cognitive effort, but the reduced functional connectivity in SZ, primarily related to an insular network, was independent of cognitive load, indicating a relatively general brain network-level dysfunction.

Voxel-based, brain-wide association study of aberrant functional connectivity in schizophrenia implicates thalamocortical circuitry

Background:

Wernicke’s concept of ‘sejunction’ or aberrant associations among specialized brain regions is one of the earliest hypotheses attempting to explain the myriad of symptoms in psychotic disorders. Unbiased data mining of all possible brain-wide connections in large data sets is an essential first step in localizing these aberrant circuits.

Methods:

We analyzed functional connectivity using the largest resting-state neuroimaging data set reported to date in the schizophrenia literature (415 patients vs. 405 controls from UK, USA, Taiwan, and China). An exhaustive brain-wide association study at both regional and voxel-based levels enabled a continuous data-driven discovery of the key aberrant circuits in schizophrenia.

Results:

Results identify the thalamus as the key hub for altered functional networks in patients. Increased thalamus–primary somatosensory cortex connectivity was the most significant aberration in schizophrenia (P=10⁻¹⁸). Overall, a number of thalamic links with motor and sensory cortical regions showed increased connectivity in schizophrenia, whereas thalamo–frontal connectivity was weakened. Network changes were correlated with symptom severity and illness duration, and support vector machine analysis revealed discrimination accuracies of 73.53–80.92%.

Conclusions:

Widespread alterations in resting-state thalamocortical functional connectivity is likely to be a core feature of schizophrenia that contributes to the extensive sensory, motor, cognitive, and emotional impairments in this disorder. Changes in this schizophrenia-associated network could be a reliable mechanistic index to discriminate patients from healthy controls.

Antipsychotic treatment and functional connectivity of the striatum in first-episode schizophrenia

IMPORTANCE

Previous evidence has implicated corticostriatal abnormalities in the pathophysiology of psychosis. Although the striatum is the primary target of all efficacious antipsychotics, the relationship between its functional connectivity and symptomatic reduction remains unknown.

OBJECTIVE

To explore the longitudinal effect of treatment with second-generation antipsychotics on functional connectivity of the striatum during the resting state in patients experiencing a first episode of psychosis.

DESIGN, SETTING, AND PARTICIPANTS

This prospective controlled study took place at a clinical research center and included 24 patients with first-episode psychosis and 24 healthy participants matched for age, sex, education, and handedness. Medications were administered in a double-blind randomized manner.

INTERVENTIONS

Patients were scanned at baseline and after 12 weeks of treatment with either risperidone or aripiprazole. Their symptoms were evaluated with the Brief Psychiatric Rating Scale at baseline and follow-up. Healthy participants were scanned twice within a 12-week interval.

MAIN OUTCOMES AND MEASURES

Functional connectivity of striatal regions was examined via functional magnetic resonance imaging using a seed-based approach. Changes in functional connectivity of these seeds were compared with reductions in ratings of psychotic symptoms.

RESULTS

Patients had a median exposure of 1 day to antipsychotic medication prior to being scanned (mean [SD] = 4.5 [6.1]). Eleven patients were treated with aripiprazole and 13 patients were treated with risperidone. As psychosis improved, we observed an increase in functional connectivity between striatal seed regions and the anterior cingulate, dorsolateral prefrontal cortex, and limbic regions such as the hippocampus and anterior insula (P < .05, corrected for multiple comparisons). Conversely, a negative relationship was observed between reduction in psychosis and functional connectivity of striatal regions with structures within the parietal lobe (P < .05, corrected for multiple comparisons).

CONCLUSIONS AND RELEVANCE

Our results indicated that corticostriatal functional dysconnectivity in psychosis is a state-dependent phenomenon. Increased functional connectivity of the striatum with prefrontal and limbic regions may be a biomarker for improvement in symptoms associated with antipsychotic treatment.

Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia

Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting-state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity strength between time-varying brain graphs. Our results indicate that SZs show decreased variance in the dynamic graph metrics. Consistent with prior stationary functional brain connectivity works, graph measures of identified first-level connectivity states show lower values in SZs. In addition, more first-level connectivity states are disassociated with the second-level connectivity state which resembles the stationary connectivity pattern computed by the entire scan. Collectively, the findings provide new evidence about altered dynamic brain graphs in schizophrenia, which may underscore the abnormal brain performance in this mental illness.

Reconciling abnormalities of brain network structure and function in schizophrenia

Schizophrenia is widely regarded as a disorder of abnormal brain connectivity. Magnetic resonance imaging (MRI) suggests that patients show robust reductions of structural connectivity. However, corresponding changes in functional connectivity do not always follow, with increased functional connectivity being reported in many cases. Here, we consider different methodological and mechanistic accounts that might reconcile these apparently contradictory findings and argue that increased functional connectivity in schizophrenia likely represents a pathophysiological dysregulation of brain activity arising from abnormal neurodevelopmental wiring of structural connections linking putative hub regions of association cortex to other brain areas. Elucidating the pathophysiological significance of connectivity abnormalities in schizophrenia will be contingent on better understanding how network structure shapes and constrains function.

State-related functional integration and functional segregation brain networks in schizophrenia

Altered topological properties of brain connectivity networks have emerged as important features of schizophrenia. The aim of this study was to investigate how the state-related modulations to graph measures of functional integration and functional segregation brain networks are disrupted in schizophrenia. Firstly, resting state and auditory oddball discrimination (AOD) fMRI data of healthy controls (HCs) and schizophrenia patients (SZs) were decomposed into spatially independent components (ICs) by group independent component analysis (ICA). Then, weighted positive and negative functional integration (inter-component networks) and functional segregation (intra-component networks) brain networks were built in each subject. Subsequently, connectivity strength, clustering coefficient, and global efficiency of all brain networks were statistically compared between groups (HCs and SZs) in each state and between states (rest and AOD) within group. We found that graph measures of negative functional integration brain network and several positive functional segregation brain networks were altered in schizophrenia during AOD task. The metrics of positive functional integration brain network and one positive functional segregation brain network were higher during the resting state than during the AOD task only in HCs. These findings imply that state-related characteristics of both functional integration and functional segregation brain networks are impaired in schizophrenia which provides new insight into the altered brain performance in this brain disorder.