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Altered small-world brain networks in schizophrenia patients during working memory performance.

ABSTRACT: Impairment of working memory (WM) performance in schizophrenia patients (SZ) is well-established. Compared to healthy controls (HC), SZ patients show aberrant blood oxygen level dependent (BOLD) activations and disrupted functional connectivity during WM performance. In this study, we examined the small-world network metrics computed from functional magnetic resonance imaging (fMRI) data collected as 35 HC and 35 SZ performed a Sternberg Item Recognition Paradigm (SIRP) at three WM load levels. Functional connectivity networks were built by calculating the partial correlation on preprocessed time courses of BOLD signal between task-related brain regions of interest (ROIs) defined by group independent component analysis (ICA). The networks were then thresholded within the small-world regime, resulting in undirected binarized small-world networks at different working memory loads. Our results showed: 1) at the medium WM load level, the networks in SZ showed a lower clustering coefficient and less local efficiency compared with HC; 2) in SZ, most network measures altered significantly as the WM load level increased from low to medium and from medium to high, while the network metrics were relatively stable in HC at different WM loads; and 3) the altered structure at medium WM load in SZ was related to their performance during the task, with longer reaction time related to lower clustering coefficient and lower local efficiency. These findings suggest brain connectivity in patients with SZ was more diffuse and less strongly linked locally in functional network at intermediate level of WM when compared to HC. SZ show distinctly inefficient and variable network structures in response to WM load increase, comparing to stable highly clustered network topologies in HC.

SUBMITTER: He H

PROVIDER: S-EPMC3368895 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Altered small-world brain networks in schizophrenia patients during working memory performance.

He Hao H Sui Jing J Yu Qingbao Q Turner Jessica A JA Ho Beng-Choon BC Sponheim Scott R SR Manoach Dara S DS Clark Vincent P VP Calhoun Vince D VD

PloS one 20120606 6

Impairment of working memory (WM) performance in schizophrenia patients (SZ) is well-established. Compared to healthy controls (HC), SZ patients show aberrant blood oxygen level dependent (BOLD) activations and disrupted functional connectivity during WM performance. In this study, we examined the small-world network metrics computed from functional magnetic resonance imaging (fMRI) data collected as 35 HC and 35 SZ performed a Sternberg Item Recognition Paradigm (SIRP) at three WM load levels. ...[more]

PMID: 22701611

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Project description:Working memory (WM) is impaired in psychotic disorders and linked to functional outcome. Most neurobiological models emphasize prefrontal cortex (PFC) dysfunction in the etiology of WM impairment. However, WM is composed of multiple processes, including encoding and maintenance, and the delineation of the neurobiology of these sub-processes has not been well characterized in schizophrenia and psychotic bipolar disorder. Functional MRI was obtained during an event-related spatial delayed match-to-sample task from 58 healthy individuals, 72 individuals with schizophrenia and 41 people with bipolar I disorder with psychotic features in order to: 1) characterize neural responses during encoding, maintenance and retrieval stages of WM using complementary region-of-interest and whole brain approaches; 2) determine whether schizophrenia and psychotic bipolar disorder exhibit similar abnormalities in WM-related brain function; and 3) elucidate the associations between WM-related brain function, task performance, and neuropsychological functioning. Both schizophrenia and psychotic bipolar disorder groups showed encoding- and maintenance-related impairments in the posterior parietal cortex (PPC) and frontal eye fields (FEF). BOLD response in the PPC and FEF, during encoding and maintenance respectively, was associated with task performance independent of group. Additionally, encoding-related activation in the PPC correlated with general neuropsychological functioning independent of group. Only encoding-related activation in the right ventral striatum differed between schizophrenia and psychotic bipolar disorder; individuals with schizophrenia showed significantly lower activation than both psychotic bipolar disorder and healthy groups. Our results are consistent with emerging evidence implicating PPC dysfunction in WM impairment and suggest interventions targeting neural activation in PPC may improve WM and neuropsychological functioning across psychotic disorders.

Project description:IntroductionPatients with schizophrenia typically exhibit deficits in working memory (WM) associated with abnormalities in brain activity. Alterations in the encoding, maintenance and retrieval phases of sequential WM tasks are well established. However, due to the heterogeneity of symptoms and complexity of its neurophysiological underpinnings, differential diagnosis remains a challenge. We conducted an electroencephalographic (EEG) study during a visual WM task in fifteen schizophrenia patients and fifteen healthy controls. We hypothesized that EEG abnormalities during the task could be identified, and patients successfully classified by an interpretable machine learning algorithm.MethodsWe tested a custom dense attention network (DAN) machine learning model to discriminate patients from control subjects and compared its performance with simpler and more commonly used machine learning models. Additionally, we analyzed behavioral performance, event-related EEG potentials, and time-frequency representations of the evoked responses to further characterize abnormalities in patients during WM.ResultsThe DAN model was significantly accurate in discriminating patients from healthy controls, ACC = 0.69, SD = 0.05. There were no significant differences between groups, conditions, or their interaction in behavioral performance or event-related potentials. However, patients showed significantly lower alpha suppression in the task preparation, memory encoding, maintenance, and retrieval phases F(1,28) = 5.93, p = 0.022, η2 = 0.149. Further analysis revealed that the two highest peaks in the attention value vector of the DAN model overlapped in time with the preparation and memory retrieval phases, as well as with two of the four significant time-frequency ROIs.DiscussionThese results highlight the potential utility of interpretable machine learning algorithms as an aid in diagnosis of schizophrenia and other psychiatric disorders presenting oscillatory abnormalities.

Dataset Information

Altered small-world brain networks in schizophrenia patients during working memory performance.

Publications

Altered small-world brain networks in schizophrenia patients during working memory performance.

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