Biology subjects

Bragin, A.

Publications and source records attributed to Bragin, A..

3 recordsLinked to original sources

bioRxiv2024

Nonlinear EEG Analysis for Distinguishing Mind Wandering and Focused Attention: A Machine Learning Approach

This study uses nonlinear analysis techniques to distinguish between mind wandering (MW) and focused attention (FA) states using EEG data. EEG recordings from 21 sessions were segmented into intervals of 2, 3, 5, 6, 10, and 15 seconds, and seven nonlinear features were extracted to capture the brains dynamic complexity. Machine learning models, including gradient boosting trees, were applied to classify MW and FA states, with the highest accuracy of 75% achieved using 5-second segments. Frequency-related features, particularly mean frequency and global frequency, were the most important in distinguishing between MW and FA. These findings emphasize the role of nonlinear EEG analysis in understanding the chaotic brain patterns underlying cognitive states. Future work should focus on temporal dynamics and personalized models to improve classification accuracy, with potential applications in cognitive enhancement and mental health.

bioinformatics↗

bioRxiv2020

Scalp EEG interictal high frequency oscillations as an objective EEG biomarker of infantile spasms

ObjectiveTo investigate the diagnostic utility of high frequency oscillations (HFOs) via scalp electroencephalogram (EEG) in infantile spasms. MethodsWe retrospectively analyzed interictal slow-wave sleep EEGs sampled at 2,000 Hz recorded from 30 consecutive patients who were suspected of having infantile spasms. We measured the rate of HFOs (80-500 Hz) and the strength of the cross-frequency coupling between HFOs and slow-wave activity (SWA) at 3-4 Hz and 0.5-1 Hz as quantified with modulation indices (MIs). ResultsTwenty-three patients (77%) exhibited active spasms during the overnight EEG recording. Although the HFOs were detected in all children, increased HFO rate and MIs correlated with the presence of active spasms (p < 0.001 by HFO rate; p < 0.01 by MIs at 3-4 Hz; p = 0.02 by MIs at 0.5-1 Hz). The presence of active spasms was predicted by the logistic regression models incorporating HFO-related metrics (AUC: 0.80-0.98) better than that incorporating hypsarrhythmia (AUC: 0.61). The predictive performance of the best model remained favorable (87.5% accuracy) after a cross-validation procedure. ConclusionsIncreased rate of HFOs and coupling between HFOs and SWA are associated with active epileptic spasms. SignificanceScalp-recorded HFOs may serve as an objective EEG biomarker for active epileptic spasms. HighlightsO_LIObjective analyses of scalp high frequency oscillations and its coupling with slow-wave activity in infantile spasms were feasible. C_LIO_LIIncreased rate of high frequency oscillations and its coupling with slow-wave activity correlated with active epileptic spasms. C_LIO_LIThe scalp high frequency oscillations were also detected in neurologically normal children (although at the low rate). C_LI

neuroscience↗

bioRxiv2019

DOWN state in the anterior cingulate and prelimbic areas in rats during immobility.

BackgroundUP-DOWN state is considering as a dominant electrographic pattern during immobility and slow wave sleep. This study is focused on the analysis of spatial distribution and neuronal correlates of the DOWN state in rats MethodsLocal field potentials and multiunit discharges were recorded bilaterally in the prefrontal cortex (PFC) and hippocampus of non-anesthetized, tethered rats (Sprague Dawley, males, weight 350-400g) with 256 channel, 4 shank silicon probes. We have focused our study on the analysis of the positive wave of slow oscillations (SOs), which is considered as the DOWN (silent) state of the UP-DOWN state in the anterior cingulate (AC), prelimbic (PL) areas of PFC and hippocampus during immobility. ResultsOur experiments showed that SOs occurred intermittently with a mean interval 1.4{+/-}0.8 ({+/-}SD) seconds. The SOs began with the DOWN state, and they were generated locally within AC or PL areas, or simultaneously in AC, PL and hippocampus bilaterally (generalized SOs). The DOWN state of local SOs in the AC was associated with a decreased rate of multiunit discharges. Similar waves in the PL area were associated with increased multiunit discharges. We observed high speed propagation of generalized SOs that occurred with 3-6ms delay within left and right PFC and less than 10ms delay between the PFC and CA1 area of hippocampus. All generalized SOs were associated with decreased multiunit discharges. ConclusionOur data support the hypothesis that neocortical networks are sufficient to generate focal SOs but the participation of external input is needed for occurrence of generalized SOs.

neuroscience↗