Biology subjects

Machizawa, M. G.

Publications and source records attributed to Machizawa, M. G..

4 recordsLinked to original sources

bioRxiv2025

Does wearable neurofeedback training improve memory aptitudes in healthy adults?--A systematic review and meta-analysis

Neurofeedback training (NFT) provides individuals with real-time feedback on their brain activity, enabling voluntary modulation. Interest in NFT for memory enhancement has surpassed the cumulative evidence in healthy adults, especially for modalities feasible outside of laboratory settings. To address this gap, this systematic review and meta-analysis focused on electroencephalography (EEG) and functional near- infrared spectroscopy (fNIRS), both of which are portable and widely accessible. Randomized controlled trials published between January 1990 and September 2022 were retrieved from PubMed, Scopus, Web of Science, and APA PsycInfo. Of the 44 eligible studies, 24 reported sufficient data for inclusion in the meta-analysis. Using a random-effects model, we found that NFT produced a small-to-moderate improvement in memory performance (standardized mean difference = 0.28; 95% confidence interval = 0.065-0.48; t(11.9) = 2.86; p = 0.014), with moderate heterogeneity. No publication bias was detected. Subgroup meta-analyses revealed significant effects in younger adults, for EEG-based protocols, and in tasks assessing verbal and short-term memory tasks. Meta-regression identified a positive association between daily session duration and effect size, with sessions longer than 30 minutes more likely to produce positive effects. As effects also varied by targeted brain regions and EEG frequency bands used for neurofeedback, protocol differences likely contributed to the observed variability in outcomes. Long- term follow-up is warranted to determine the persistence of NFT effects.

neuroscience↗

bioRxiv2022

The forward variable selection based optimized decoding toolbox for neuroimaging data

Neuroimaging data is complex and high-dimensional that poses challenges for machine learning (ML) applications. Of varieties of reasons contributing on accuracy decoding, variable feature selection is one of crucial steps for determining target feature in data analysis, especially in the context of neuroimaging studies where the number of features is often much larger than the number of observations. Therefore, optimization of feature selection from such high-dimensional neuroimaging data has been challenging using conventional ML algorithms. Here, we introduce an efficient ML package incorporating a forward variable selection (FVS) algorithm that optimizes the identification of features for both classification and regression models. In our framework, the best ML model and feature pairs that explain the inputs can be automatically determined. Moreover, the toolbox can be executed in a parallel environment for efficient computation. The parallelized FVS algorithm iteratively selects the best feature pair compared against the previous steps to maximize the predictive performance. The FVS algorithm evaluates the goodness-of-fit across different models using the k-fold cross validation and identifies the best subset of features based on a pre-defined criterion for each model. Furthermore, the hyperparameters of each ML model are optimized at each forward iteration. A final outcome highlights an optimized number of selected features (brain regions of interest) with decoding accuracies. Using our pipeline, we examined the effectiveness of our toolbox on an existing neuroimaging (structural MRI) dataset. Compared ML models with and without FVS approach, we demonstrate that the FVS significantly improved the accuracy of the ML algorithm over the counterpart model without FVS. Furthermore, we confirmed the use of parallel computation considerably reduced the computational burden for the high-dimensional MRI data. This oFVSD toolbox efficiently and effectively improves the performance of both classification and regression models on neuroimaging data and should be applicable to many other neuroimaging data and more. This Python package is open-source and freely available, making it a useful toolbox for neuroimaging communities seeking improvement of decoding accuracy for their datasets.

neuroscience↗

bioRxiv2021

Modulation of certainty and valence in cardiac autonomic regulation mediated by high depressive risk

Recent studies have highlighted the essential role of interoception in healthy emotional processing and the pathology of major depressive disorder. However, it is unclear how individual differences in healthy people with high depression risk (HDR; i.e., individual differences in depression risk) are related to the neurophysiological underpinnings of interoception and emotional reactions under different degrees of certainty. We examined whether an individuals depression risk mediates the relationships of heartbeat-evoked potentials (HEPs), a neurophysiological marker of cardiac interoception, with heart rate (HR) and heart rate variability (HRV), indices for cardiac reactivity. In a concurrent electroencephalogram-electrocardiogram (EEG-ECG) experiment, 26 healthy participants completed an emotion-evoking picture-evaluation task. Each trial began with a differential auditory cue that was associated with the certainty of subsequently seeing a pleasant or unpleasant picture. The results showed the following: after participants saw a cue of uncertainty, HDR was associated with 1) reduced neural activity when anticipating upcoming pictures and 2) enhanced physiological reactions to unexpected, unpleasant pictures. These results suggest that weaker predictions and augmented prediction errors for negative emotional stimuli reflect depression risk. Moreover, depression risk significantly mediated the relationships between HEPs and HR and between HEPs and HRV for unexpected, unpleasant stimuli. This study provides evidence that interoception and autonomic cardiac regulation may be altered by depression risk. This highlights the insights provided by specific indices of brain-heart interactions, such as HEPs, into the underlying activity of the autonomic nervous system and unique interoceptive disturbances associated with depression risk.

neuroscience↗

bioRxiv2019

Quantification of anticipation of excitement with three-axial model of emotion with EEG

ObjectivesMultiple facets of human emotions underlie diverse and sparse neural mechanisms. Amongst many models of emotions, the circumplex model of emotion is one of a significant theory. The use of the circumplex model allows us to model variable aspects of emotion; however, such momentary expression of ones internal mental state still lacks to consider another, the third dimension of time. Here, we report an exploratory attempt to build a three-axial model of human emotion to model our sense of anticipatory excitement, "Waku-Waku (in Japanese)," when people are predictively coding upcoming emotional events. ApproachElectroencephalography (EEG) was recorded from 28 young adult participants while they mentalized upcoming emotional pictures. Three auditory tones were used as indicative cues, predicting the likelihood of valence of an upcoming picture, either positive, negative, or unknown. While seeing an image, participants judged its emotional valence during the task, and subsequently rated their subjective experiences on valence, arousal, expectation, and Waku-Waku immediately after the experiment. The collected EEG data were then analyzed to identify contributory neural signatures for each of the three axes. Main ResultsA three axial model was built to quantify Waku-Waku. As was expected, this model revealed considerable contribution of the third dimension over the classical two-dimension model. Distinctive EEG components were identified. Furthermore, a novel brain-emotion interface is proposed and validated within the scope of limitations. SignificanceThe proposed notion may shed new light on the theories of emotion and supports multiplex dimensions of emotion. With an introduction of the cognitive domain for a brain-computer-interface, we propose a novel brain-emotion-interface. Limitations and potential applications are discussed.

neuroscience↗