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Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults.

ABSTRACT: BACKGROUND:With millisecond-level resolution, electroencephalographic (EEG) recording provides a sensitive tool to assay neural dynamics of human cognition. However, selection of EEG features used to answer experimental questions is typically determined a priori. The utility of machine learning was investigated as a computational framework for extracting the most relevant features from EEG data empirically. METHODS:Schizophrenia (SZ; n = 40) and healthy community (HC; n = 12) subjects completed a Sternberg Working Memory Task (SWMT) during EEG recording. EEG was analyzed to extract 5 frequency components (theta1, theta2, alpha, beta, gamma) at 4 processing stages (baseline, encoding, retention, retrieval) and 3 scalp sites (frontal-Fz, central-Cz, occipital-Oz) separately for correctly and incorrectly answered trials. The 1-norm support vector machine (SVM) method was used to build EEG classifiers of SWMT trial accuracy (correct vs. incorrect; Model 1) and diagnosis (HC vs. SZ; Model 2). External validity of SVM models was examined in relation to neuropsychological test performance and diagnostic classification using conventional regression-based analyses. RESULTS:SWMT performance was significantly reduced in SZ (p < .001). Model 1 correctly classified trial accuracy at 84 % in HC, and at 74 % when cross-validated in SZ data. Frontal gamma at encoding and central theta at retention provided highest weightings, accounting for 76 % of variance in SWMT scores and 42 % variance in neuropsychological test performance across samples. Model 2 identified frontal theta at baseline and frontal alpha during retrieval as primary classifiers of diagnosis, providing 87 % classification accuracy as a discriminant function. CONCLUSIONS:EEG features derived by SVM are consistent with literature reports of gamma's role in memory encoding, engagement of theta during memory retention, and elevated resting low-frequency activity in schizophrenia. Tests of model performance and cross-validation support the stability and generalizability of results, and utility of SVM as an analytic approach for EEG feature selection.

SUBMITTER: Johannesen JK

PROVIDER: S-EPMC4928381 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults.

Johannesen Jason K JK Bi Jinbo J Jiang Ruhua R Kenney Joshua G JG Chen Chi-Ming A CA

Neuropsychiatric electrophysiology 20160211

<h4>Background</h4>With millisecond-level resolution, electroencephalographic (EEG) recording provides a sensitive tool to assay neural dynamics of human cognition. However, selection of EEG features used to answer experimental questions is typically determined <i>a priori</i>. The utility of machine learning was investigated as a computational framework for extracting the most relevant features from EEG data empirically.<h4>Methods</h4>Schizophrenia (SZ; <i>n</i> = 40) and healthy community (HC ...[more]

PMID: 27375854

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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.

Project description:ImportanceMachine learning algorithms could be used as the basis for clinical decision-making aids to enhance clinical practice.ObjectiveTo assess the ability of machine learning algorithms to predict dementia incidence within 2 years compared with existing models and determine the optimal analytic approach and number of variables required.Design, setting, and participantsThis prognostic study used data from a prospective cohort of 15 307 participants without dementia at baseline to perform a secondary analysis of factors that could be used to predict dementia incidence. Participants attended National Alzheimer Coordinating Center memory clinics across the United States between 2005 and 2015. Analyses were conducted from March to May 2021.Exposures258 variables spanning domains of dementia-related clinical measures and risk factors.Main outcomes and measuresThe main outcome was incident all-cause dementia diagnosed within 2 years of baseline assessment.ResultsIn a sample of 15 307 participants (mean [SD] age, 72.3 [9.8] years; 9129 [60%] women and 6178 [40%] men) without dementia at baseline, 1568 (10%) received a diagnosis of dementia within 2 years of their initial assessment. Compared with 2 existing models for dementia risk prediction (ie, Cardiovascular Risk Factors, Aging, and Incidence of Dementia Risk Score, and the Brief Dementia Screening Indicator), machine learning algorithms were superior in predicting incident all-cause dementia within 2 years. The gradient-boosted trees algorithm had a mean (SD) overall accuracy of 92% (1%), sensitivity of 0.45 (0.05), specificity of 0.97 (0.01), and area under the curve of 0.92 (0.01) using all 258 variables. Analysis of variable importance showed that only 6 variables were required for machine learning algorithms to achieve an accuracy of 91% and area under the curve of at least 0.89. Machine learning algorithms also identified up to 84% of participants who received an initial dementia diagnosis that was subsequently reversed to mild cognitive impairment or cognitively unimpaired, suggesting possible misdiagnosis.Conclusions and relevanceThese findings suggest that machine learning algorithms could accurately predict incident dementia within 2 years in patients receiving care at memory clinics using only 6 variables. These findings could be used to inform the development and validation of decision-making aids in memory clinics.

Project description:Working memory performance can be influenced by motivational factors, which may be associated with specific brain activities, including suppression of alpha oscillations. We investigated whether providing individuals online feedback about their ongoing oscillations (EEG-neurofeedback) can improve working memory under high and low reward expectancies. We combined working memory training with neurofeedback to enhance alpha suppression in a monetary-rewarded delayed match-to-sample task for visual objects. Along with alpha, we considered the neighbouring theta and beta bands. In a double-blind experiment, individuals were trained over 5 days to suppress alpha power by receiving real-time neurofeedback or control neurofeedback (placebo) in reward and no-reward trials. We investigated (i) whether neurofeedback enhances alpha suppression, (ii) whether monetary reward enhances alpha suppression and working memory, and (iii) whether any performance benefits of neurofeedback-training would transfer to unrelated cognitive tasks. With the same experimental design, we conducted two studies with differing instructions given at the maintenance, yielding together 300 EEG recording sessions. In Study I, participants were engaged in a mental calculation task during maintenance. In Study II, they were instructed to visually rehearse the sample image. Results from Study I demonstrated a significant training and reward-anticipation effect on working memory accuracy and reaction times over 5 days. Neurofeedback and reward anticipation showed effects on theta suppression but not on alpha suppression. Moreover, a cognitive training effect was observed on beta suppression. Thus, neurofeedback-training of alpha was unrelated to working memory performance. Study II replicated the training and reward-anticipation effect on working memory but without any effects of neurofeedback-training on oscillations or working memory. Neither study showed transfer effects of either working memory or neurofeedback-training. A linear mixed-effect model analysis of neurofeedback-independent training-related improvement of working memory combining both studies showed that improved working memory performance was related to oscillatory changes over training days in the encoding and maintenance phases. Improvements in accuracy were related to increasing beta amplitude in reward trials over right parietal electrodes. Improvements in reaction times were related to increases in right parietal theta amplitude during encoding and increased right parietal and decreased left parietal beta amplitudes during maintenance. Thus, while our study provided no evidence that neurofeedback targeting alpha improved the efficacy of working memory training or evidence for transfer, it showed a relationship between training-related changes in parietal beta oscillations during encoding and improvements in accuracy. Right parietal beta oscillations could be an intervention target for improving working memory accuracy.

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Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults.

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Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults.

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