Memory, Short-Term
Research Papers
Showing 6 of 13Improving cognitive control: Is theta neurofeedback training associated with proactive rather than reactive control enhancement?
Frontal-midline (FM) theta activity (4-8 Hz) is proposed to reflect a mechanism for cognitive control that is needed for working memory retention, manipulation, and interference resolution. Modulation of FM theta activity via neurofeedback training (NFT) demonstrated transfer to some but not all types of cognitive control. Therefore, the present study investigated whether FM theta NFT enhances performance and modulates underlying EEG characteristics in a delayed match to sample (DMTS) task requiring mainly proactive control and a color Stroop task requiring mainly reactive control. Moreover, temporal characteristics of transfer were explored over two posttests. Across seven 30-min NFT sessions, an FM theta training group exhibited a larger FM theta increase compared to an active control group who upregulated randomly chosen frequency bands. In a posttest performed 13 days after the last training session, the training group showed better retention performance in the DMTS task. Furthermore, manipulation performance was associated with NFT theta increase for the training but not the control group. Contrarily, behavioral group differences and their relation to FM theta change were not significant in the Stroop task, suggesting that NFT is associated with proactive but not reactive control enhancement. Transfer to both tasks at a posttest one day after training was not significant. Behavioral improvements were not accompanied by changes in FM theta activity, indicating no training-induced modulation of EEG characteristics. Together, these findings suggest that NFT supports transfer to cognitive control that manifests late after training but that other training-unspecific factors may also contribute to performance enhancement.
View Full Paper →The Effect of Mindfulness-based Programs on Cognitive Function in Adults: A Systematic Review and Meta-analysis
Mindfulness-based programs (MBPs) are increasingly utilized to improve mental health. Interest in the putative effects of MBPs on cognitive function is also growing. This is the first meta-analysis of objective cognitive outcomes across multiple domains from randomized MBP studies of adults. Seven databases were systematically searched to January 2020. Fifty-six unique studies (n = 2,931) were included, of which 45 (n = 2,238) were synthesized using robust variance estimation meta-analysis. Meta-regression and subgroup analyses evaluated moderators. Pooling data across cognitive domains, the summary effect size for all studies favored MBPs over comparators and was small in magnitude (g = 0.15; [0.05, 0.24]). Across subgroup analyses of individual cognitive domains/subdomains, MBPs outperformed comparators for executive function (g = 0.15; [0.02, 0.27]) and working memory outcomes (g = 0.23; [0.11, 0.36]) only. Subgroup analyses identified significant effects for studies of non-clinical samples, as well as for adults aged over 60. Across all studies, MBPs outperformed inactive, but not active comparators. Limitations include the primarily unclear within-study risk of bias (only a minority of studies were considered low risk), and that statistical constraints rendered some p-values unreliable. Together, results partially corroborate the hypothesized link between mindfulness practices and cognitive performance. This review was registered with PROSPERO [CRD42018100904].
View Full Paper →Electroencephalography-based cortical sources of working memory in the subjects with opioid addiction: A pilot study
BACKGROUND & OBJECTIVES: Working memory impairments in the subjects of opioid addiction may stem from an aberrant cortical activity in the executive areas, and may help in early identification of individuals with addictive tendencies and may also be used as a neurofeedback mechanism in adjunct to the existing therapeutics. METHODS: Electrical neuroimaging via 128-channel electroencephalography (EEG) recording was done in 15 male subjects with opioid addiction (29.45±5.6 yr) during the performance of Sternberg Working Memory Task. EEG data were acquired and analyzed for cortical sources during task as compared to resting (baseline) condition. RESULTS: Working memory deficits were manifested as decrease in accuracy percentage in the subjects with opioid addiction, while no significant difference was seen in reaction time, on comparison with laboratory-acquired matched controls. Standardized low-resolution brain electromagnetic tomography (sLORETA)-based EEG source analysis revealed higher cortical activity in the anterior cingulate cortex, inferior, middle and superior temporal gyri, inferior frontal gyrus, superior parietal lobule, inferior parietal lobule and precuneus, whereas significant lower activity was seen in superior and middle frontal gyri, parietal lobule, cingulate cortex and pre- and postcentral gyri when the task was compared to baseline in the subjects with opioid addiction. Further, a negative correlation was seen between the accuracy of task performance and activation ratio for the significant gyri in the subjects with opioid addiction. INTERPRETATION & CONCLUSIONS: EEG cortical sources revealed the failure of deactivation of default-mode network (DMN) during the task amongst the subjects with opioid addiction. In addition, there was a decrease in the executive function areas in the subjects with opioid addiction. This lack of sufficiently active executive network and persistence of DMN during the task (as compared to baseline) may potentially form the basis of functional impairments in the subjects with opioid addiction.
View Full Paper →A game-based neurofeedback training system to enhance cognitive performance in healthy elderly subjects and in patients with amnestic mild cognitive impairment
Introduction: This study examines the clinical efficacy of a game-based neurofeedback training (NFT) system to enhance cognitive performance in patients with amnestic mild cognitive impairment (aMCI) and healthy elderly subjects. The NFT system includes five games designed to improve attention span and cognitive performance. The system estimates attention levels by investigating the power spectrum of Beta and Alpha bands. Methods: We recruited 65 women with aMCI and 54 healthy elderly women. All participants were treated with care as usual (CAU); 58 were treated with CAU + NFT (20 sessions of 30 minutes each, 2-3 sessions per week), 36 with CAU + exergame-based training, while 25 patients had only CAU. Cognitive functions were assessed using the Cambridge Neuropsychological Test Automated Battery both before and after treatment. Results: NFT significantly improved rapid visual processing and spatial working memory (SWM), including strategy, when compared with exergame training and no active treatment. aMCI was characterized by impairments in SWM (including strategy), pattern recognition memory, and delayed matching to samples. Conclusion: In conclusion, treatment with NFT improves sustained attention and SWM. Nevertheless, NFT had no significant effect on pattern recognition memory and short-term visual memory, which are the other hallmarks of aMCI. The NFT system used here may selectively improve sustained attention, strategy, and executive functions, but not other cognitive impairments, which characterize aMCI in women.
View Full Paper →Interpretable, highly accurate brain decoding of subtly distinct brain states from functional MRI using intrinsic functional networks and long short-term memory recurrent neural networks
Decoding brain functional states underlying cognitive processes from functional MRI (fMRI) data using multivariate pattern analysis (MVPA) techniques has achieved promising performance for characterizing brain activation patterns and providing neurofeedback signals. However, it remains challenging to decode subtly distinct brain states for individual fMRI data points due to varying temporal durations and dependency among different cognitive processes. In this study, we develop a deep learning based framework for brain decoding by leveraging recent advances in intrinsic functional network modeling and sequence modeling using long short-term memory (LSTM) recurrent neural networks (RNNs). Particularly, subject-specific intrinsic functional networks (FNs) are computed from resting-state fMRI data and are used to characterize functional signals of task fMRI data with a compact representation for building brain decoding models, and LSTM RNNs are adopted to learn brain decoding mappings between functional profiles and brain states. Validation results on fMRI data from the HCP dataset have demonstrated that brain decoding models built on training data using the proposed method could learn discriminative latent feature representations and effectively distinguish subtly distinct working memory tasks of different subjects with significantly higher accuracy than conventional decoding models. Informative FNs of the brain decoding models identified as brain activation patterns of working memory tasks were largely consistent with the literature. The method also obtained promising decoding performance on motor and social cognition tasks. Our results suggest that LSTM RNNs in conjunction with FNs could build interpretable, highly accurate brain decoding models.
View Full Paper →Neurofeedback training with a low-priced EEG device leads to faster alpha enhancement but shows no effect on cognitive performance: A single-blind, sham-feedback study
INTRODUCTION: Findings of recent studies indicate that it is possible to enhance cognitive capacities of healthy individuals by means of individual upper alpha neurofeedback training (NFT). Although these results are promising, most of this research was conducted based on high-priced EEG systems developed for clinical and research purposes. This study addresses the question whether such effects can also be shown with an easy to use and comparably low-priced Emotiv Epoc EEG headset available for the average consumer. In addition, critical voices were raised regarding the control group designs of studies addressing the link between neurofeedback training and cognitive performance. Based on an extensive literature review revealing considerable methodological issues in an important part of the existing research, the present study addressed the question whether individual upper alpha neurofeedback has a positive effect on alpha amplitudes (i.e. increases alpha amplitudes) and short-term memory performance focussing on a methodologically sound, single-blinded, sham controlled design. METHOD: Participants (N = 33) took part in four test sessions over four consecutive days of either neurofeedback training (NFT group) or sham feedback (SF group). In the NFT group, five three-minute periods of visual neurofeedback training were administered each day whereas in the SF group (control group), the same amount of sham feedback was presented. Performance on eight digit-span tests as well as participants' affective states were assessed before and after each of the daily training sessions. RESULTS: NFT did not show an effect on individual upper alpha and cognitive performance. While performance increased in both groups over the course of time, this effect could not be explained by changes in individual upper alpha. Additional analyses however revealed that participants in the NFT group showed faster and larger increase in alpha compared to the SF group. Surprisingly, exploratory analyses showed a significant correlation between the initial alpha level and the alpha improvement during the course of the study. This finding suggests that participants with high initial alpha levels benefit more from alpha NFT interventions. In the discussion, the appearance of the alpha enhancement in the SF group and possible reasons for the absence of a connection between NFT and short-term memory are addressed.
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