Dataset Information

Transient Patterns of Functional Dysconnectivity in Clinical High Risk and Early Illness Schizophrenia Individuals Compared with Healthy Controls.

ABSTRACT: Schizophrenia shows abnormal dynamic functional network connectivity (dFNC), but it is unclear whether these abnormalities are present early in the illness course or precede illness onset in individuals at clinical high risk (CHR) for psychosis. We examined dFNC from resting-state functional magnetic resonance imaging data in CHR (n?=?53), early illness schizophrenia (ESZ; n?=?58), and healthy control (HC; n?=?70) individuals. We applied a sliding temporal window approach capturing five distinct dFNC states. In ESZ patients, the likelihood of transitioning from state 4, a state that exhibited greater cortical-subcortical hyperconnectivity and also lacked typically observed anticorrelation between the default mode network and other functional networks, to a hypoconnected state was increased compared with HC and CHR groups. Furthermore, we investigated the interaction of group and state on dFNC. Overall, HC individuals showed significant changes of connectivity between states that were absent or altered in ESZ patients and CHR individuals. Connectivity differences between groups were identified primarily in two out of the five states, in particular, between HC and ESZ groups. In summary, it appears that the interaction effect was mostly driven by (1) dynamic connectivity changes in HC that were abnormal in CHR and ESZ individuals and (2) the fact that dysconnectivity between groups was only present in some states. These findings underscore the likelihood that abnormalities are present not only in static FNC but also in dFNC, in individuals at CHR for schizophrenia.

SUBMITTER: Mennigen E

PROVIDER: S-EPMC6390658 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Transient Patterns of Functional Dysconnectivity in Clinical High Risk and Early Illness Schizophrenia Individuals Compared with Healthy Controls.

Mennigen Eva E Fryer Susanna L SL Rashid Barnaly B Damaraju Eswar E Du Yuhui Y Loewy Rachel L RL Stuart Barbara K BK Calhoun Vince D VD Mathalon Daniel H DH

Brain connectivity 20180705 1

Schizophrenia shows abnormal dynamic functional network connectivity (dFNC), but it is unclear whether these abnormalities are present early in the illness course or precede illness onset in individuals at clinical high risk (CHR) for psychosis. We examined dFNC from resting-state functional magnetic resonance imaging data in CHR (n = 53), early illness schizophrenia (ESZ; n = 58), and healthy control (HC; n = 70) individuals. We applied a sliding temporal window approach capturing five distinct ...[more]

PMID: 29855202

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Project description:Schizophrenia is a psychotic disorder characterized by functional dysconnectivity or abnormal integration between distant brain regions. Recent functional imaging studies have implicated large-scale thalamo-cortical connectivity as being disrupted in patients. However, observed connectivity differences in schizophrenia have been inconsistent between studies, with reports of hyperconnectivity and hypoconnectivity between the same brain regions. Using resting state eyes-closed functional imaging and independent component analysis on a multi-site data that included 151 schizophrenia patients and 163 age- and gender matched healthy controls, we decomposed the functional brain data into 100 components and identified 47 as functionally relevant intrinsic connectivity networks. We subsequently evaluated group differences in functional network connectivity, both in a static sense, computed as the pairwise Pearson correlations between the full network time courses (5.4 minutes in length), and a dynamic sense, computed using sliding windows (44 s in length) and k-means clustering to characterize five discrete functional connectivity states. Static connectivity analysis revealed that compared to healthy controls, patients show significantly stronger connectivity, i.e., hyperconnectivity, between the thalamus and sensory networks (auditory, motor and visual), as well as reduced connectivity (hypoconnectivity) between sensory networks from all modalities. Dynamic analysis suggests that (1), on average, schizophrenia patients spend much less time than healthy controls in states typified by strong, large-scale connectivity, and (2), that abnormal connectivity patterns are more pronounced during these connectivity states. In particular, states exhibiting cortical-subcortical antagonism (anti-correlations) and strong positive connectivity between sensory networks are those that show the group differences of thalamic hyperconnectivity and sensory hypoconnectivity. Group differences are weak or absent during other connectivity states. Dynamic analysis also revealed hypoconnectivity between the putamen and sensory networks during the same states of thalamic hyperconnectivity; notably, this finding cannot be observed in the static connectivity analysis. Finally, in post-hoc analyses we observed that the relationships between sub-cortical low frequency power and connectivity with sensory networks is altered in patients, suggesting different functional interactions between sub-cortical nuclei and sensorimotor cortex during specific connectivity states. While important differences between patients with schizophrenia and healthy controls have been identified, one should interpret the results with caution given the history of medication in patients. Taken together, our results support and expand current knowledge regarding dysconnectivity in schizophrenia, and strongly advocate the use of dynamic analyses to better account for and understand functional connectivity differences.

Project description:Although individuals at clinical high risk (CHR) for psychosis exhibit a psychosis-risk syndrome involving attenuated forms of the positive symptoms typical of schizophrenia (SZ), it remains unclear whether their resting-state brain intrinsic functional networks (INs) show attenuated or qualitatively distinct patterns of functional dysconnectivity relative to SZ patients. Based on resting-state functional magnetic imaging data from 70 healthy controls (HCs), 53 CHR individuals (among which 41 subjects were antipsychotic medication-naive), and 58 early illness SZ (ESZ) patients (among which 53 patients took antipsychotic medication) within five years of illness onset, we estimated subject-specific INs using a novel group information guided independent component analysis (GIG-ICA) and investigated group differences in INs. We found that when compared to HCs, both CHR and ESZ groups showed significant differences, primarily in default mode, salience, auditory-related, visuospatial, sensory-motor, and parietal INs. Our findings suggest that widespread INs were diversely impacted. More than 25% of voxels in the identified significant discriminative regions (obtained using all 19 possible changing patterns excepting the no-difference pattern) from six of the 15 interrogated INs exhibited monotonically decreasing Z-scores (in INs) from the HC to CHR to ESZ, and the related regions included the left lingual gyrus of two vision-related networks, the right postcentral cortex of the visuospatial network, the left thalamus region of the salience network, the left calcarine region of the fronto-occipital network and fronto-parieto-occipital network. Compared to HCs and CHR individuals, ESZ patients showed both increasing and decreasing connectivity, mainly hypo-connectivity involving 15% of the altered voxels from four INs. The left supplementary motor area from the sensory-motor network and the right inferior occipital gyrus in the vision-related network showed a common abnormality in CHR and ESZ groups. Some brain regions also showed a CHR-unique alteration (primarily the CHR-increasing connectivity). In summary, CHR individuals generally showed intermediate connectivity between HCs and ESZ patients across multiple INs, suggesting that some dysconnectivity patterns evident in ESZ predate psychosis in attenuated form during the psychosis risk stage. Hence, these connectivity measures may serve as possible biomarkers to predict schizophrenia progression.

Project description:Recent evidence suggests that the aberrant signaling of salience is associated with psychotic illness. Salience, however, can take many forms in task environments. For example, salience may refer to any of the following: (1) the valence of an outcome, (2) outcomes that are unexpected, called reward prediction errors (PEs), or (3) cues associated with uncertain outcomes. Here, we measure brain responses to different forms of salience in the context of a passive PE-signaling task, testing whether patients with schizophrenia (SZ) showed aberrant signaling of particular types of salience. We acquired event-related MRI data from 29 SZ patients and 23 controls during the performance of a passive outcome prediction task. Across groups, we found that the anterior insula and posterior parietal cortices were activated to multiple different types of salience, including PE magnitude and heightened levels of uncertainty. However, BOLD activation to salient events was not significantly different between patients and controls in many regions, including the insula, posterior parietal cortices, and default mode network nodes. Such results suggest that deficiencies in salience processing in SZ may not result from an impaired ability to signal salience per se, but instead the ability to use such signals to guide future actions. Notably, no between-group differences were observed in BOLD signal changes associated with PE-signaling in the striatum. However, positive symptom severity was found to significantly correlate with the magnitudes of salience contrasts in default mode network nodes. Our results suggest that, in an observational environment, SZ patients may show an intact ability to activate striatal and cortical regions to rewarding and non-rewarding salient events. Furthermore, reduced deactivation of a hypothesized default mode network node for SZ participants with high levels of positive symptoms, following salient events, point to abnormalities in interactions of the salience network with other brain networks, and their potential importance to positive symptoms.

Dataset Information

Transient Patterns of Functional Dysconnectivity in Clinical High Risk and Early Illness Schizophrenia Individuals Compared with Healthy Controls.

Publications

Transient Patterns of Functional Dysconnectivity in Clinical High Risk and Early Illness Schizophrenia Individuals Compared with Healthy Controls.

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