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GABA level, gamma oscillation, and working memory performance in schizophrenia.

ABSTRACT: A relationship between working memory impairment, disordered neuronal oscillations, and abnormal prefrontal GABA function has been hypothesized in schizophrenia; however, in vivo GABA measurements and gamma band neural synchrony have not yet been compared in schizophrenia. This case-control pilot study (N = 24) compared baseline and working memory task-induced neuronal oscillations acquired with high-density electroencephalograms (EEGs) to GABA levels measured in vivo with magnetic resonance spectroscopy. Working memory performance, baseline GABA level in the left dorsolateral prefrontal cortex (DLPFC), and measures of gamma oscillations from EEGs at baseline and during a working memory task were obtained. A major limitation of this study is a relatively small sample size for several analyses due to the integration of diverse methodologies and participant compliance. Working memory performance was significantly lower for patients than for controls. During the working memory task, patients (n = 7) had significantly lower amplitudes in gamma oscillations than controls (n = 9). However, both at rest and across working memory stages, there were significant correlations between gamma oscillation amplitude and left DLPFC GABA level. Peak gamma frequency during the encoding stage of the working memory task (n = 16) significantly correlated with GABA level and working memory performance. Despite gamma band amplitude deficits in patients across working memory stages, both baseline and working memory-induced gamma oscillations showed strong dependence on baseline GABA levels in patients and controls. These findings suggest a critical role for GABA function in gamma band oscillations, even under conditions of system and cognitive impairments as seen in schizophrenia.

SUBMITTER: Chen CM

PROVIDER: S-EPMC3989525 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

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GABA level, gamma oscillation, and working memory performance in schizophrenia.

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