Rahul Venugopal

Background/Objectives: Inadequate macronutrient intake may affect neurodevelopment in early infancy. In India, government-supplied take-home rations (THRs) attempt to meet this gap but lack adequate fat and protein. This study evaluated the impact of a 3-month macronutrient modified-THR intervention on infant neurodevelopment using event-related potentials (ERPs). Methods: A two-group cluster randomized controlled trial (village clusters = 12/group; N = 67/group) enrolled healthy breastfed infants (6–9 months) in Karnataka, India. The intervention group received a milk–legume-based THR providing ~200 kcal/day (ML-THR, estimated protein:energy (PE) and fat:energy (FE) ratios: 12% and 45%) with the standard government THR (SG-THR, estimated PE and FE ratios: 7% and 4%), while controls received SG-THRs only. All mothers received education on infant and young child feeding practices, water sanitation and hygiene. Auditory, visual and attention processing ERPs were measured using a short, culturally adapted, infant-friendly paradigm. Linear mixed models evaluated intervention effects over time. Results: The intervention was associated with region-specific visual ERP changes: increased fractional latencies in anterior regions for face, nonface (500–750 ms); decreased fractional latencies in the central region for frequent (10–250 ms) and face (500–750 ms) and in the posterior region for frequent–rare difference (250–500 ms). The intervention group also demonstrated a reduced area under the curve for nonface stimuli (250–500 ms). No significant auditory or visual hemifield effects were observed. None of the observed effects remained significant after false discovery rate (FDR) correction. Conclusions: The findings are exploratory and suggest the need to examine the effects of improved protein quality and fat content in THRs on visual–cortical processing in larger samples.

Aging affects individuals at varying biological rates, prompting the development of the Brain Age Index (BAI) to quantify neurobiological health relative to chronological age and disease risk. While structural MRI has dominated brain age prediction, its high cost, immobility, and low temporal resolution restrict its clinical scalability and responsiveness to transient neurophysiological changes. Electroencephalography (EEG) offers a highly scalable, portable, and temporally precise alternative capable of capturing dynamic brain states. However, the transition of EEG-based models to clinical biomarkers is impeded by methodological limitations, including small or biased datasets, inconsistent preprocessing pipelines, and a distinct lack of interpretable machine learning approaches. To address these persistent challenges, this paper presents a comprehensive, open-source, end-to-end pipeline for large-scale EEG-based brain age modeling. Developed using the Temple University Hospital EEG Corpus (TUEG) the largest publicly available resting-state EEG dataset. The pipeline encompasses rigorous data engineering, reproducible preprocessing, and robust feature extraction. Following quality control and subject-level dataset partitioning to definitively prevent data leakage, exactly 41,181 recordings were successfully retained. Two independent feature sets were extracted: the Catch22 time-series characteristics and a comprehensive set of spectral, aperiodic, and non-linear dynamics from the CCS toolbox. The methodology evaluates seven regression models, optimized via Optuna for hyperparameter tuning, and integrates SHAP (SHapley Additive exPlanations) for transparent feature importance analysis. By making this infrastructure publicly available, this work lowers the barrier to entry for large-cohort studies, fostering reproducible development and clinical validation of dynamic brain age biomarkers.

Our study adopted a novel approach, utilizing four biomarkers, namely EEG potentials, their first-order derivatives, and phase slip rates derived from each, to discern the differences between novice Vipassana (NVP) subjects and non-meditator controls (NMC). Phase slip rates are discontinuities in instantaneous phase that represent cortical phase transitions, indicating a significant change in the overall brain state. We employed 128-channel EEG data from eight NMC and eight NVP subjects, collected during a gamified protocol, to investigate object identification within a visual oddball paradigm. EEG was continuously acquired during the 50 trials for each subject. We retained 44 artifact-free trials per subject (the minimum common across all subjects) and computed within-subject averages. The EEGs were then averaged separately for NMC and NVP subjects. The EEG was filtered in the alpha band, and the phase was extracted using the Hilbert transform, unwrapped, and the phase slip rates were computed. A montage layout of electrodes was used to make the spatiotemporal plots of biomarkers. Our findings revealed that the spatiotemporal profiles of all four biomarkers differed significantly between the two groups of subjects. Furthermore, the NVP subjects demonstrated faster object recognition. These results not only provide a unique method to use phase slip rates to quantify cognitive differences between NMC and NVP subjects but also present a promising set of biomarkers for quantitative task-based EEG analyses.

Distinguishing between self (internal) and environment (external) is fundamental to human experience, with ordinary waking consciousness structured around this duality. However, contemplative traditions describe non-dual states where this distinction dissolves. Despite its significance, the neural basis of non-duality remains underexplored. Using psychological questionnaires for non-duality experience and EEG-based intrinsic neural timescales as measured by the autocorrelation window (ACW), we studied non-duality in advanced meditators, novice meditators, and controls. All subjects underwent breath-watching meditation (internal attention) and a visual oddball cognitive task (external attention); this allowed us to conceptualize non-duality as a lack of distinction between internal and external attention. Our key findings include: (a) advanced meditators report greater experience of non-duality during breath-watching (psychological scales), (b) EEG-based ACW is longer during internal attention (breath watch) than external attention (oddball task) in all subjects taken together, (c) advanced meditators show no such distinction with equal duration of their ACW during both internal and external attention (we replicated this finding in another dataset of expert meditators); (d) the advanced meditators’ internal-external ACW difference correlated with their experience of the degree of non-duality (psychological scales) during internal attention. Together, these findings suggest that the brain’s intrinsic neural timescales during internal and external attention play a key role in mediating the experience of non-duality in advanced meditators.

Introduction: Mind-body practices, such as meditation, enhance mental well-being. Research studies consistently demonstrate improved brain function and psychological well-being in meditation practitioners. A substantial body of neuroscientific evidence highlights changes in alpha and theta frequency bands during meditation among practitioners. Neurophysiological effects of meditation are reported as average power changes from resting to meditative states. However, there is a notable gap in research concerning the time-to-onset and temporal dynamics of these changes during meditation. Method: Our study addresses this gap by recording high-density 128-channel EEG data during breath- watching meditation in three groups: meditation-naïve controls (n = 28), novice meditators (n = 33), and advanced meditators (n = 42). Meditators were trained in the Isha Yoga tradition. Real-time changes in brain power across different frequency bands were analyzed by segmenting the EEG data into 1-minute intervals. Using the first 30 seconds of breath- watching as the baseline, we calculated within-group power differences between this baseline and successive 1-minute segments (non-overlapping, non-sliding windows). For between- group comparisons, we assessed power differences among the three groups at 0.5, 3, 6, and 9 minutes. Results: Our results indicate that time-to-onset of statistically significant increases in alpha, theta, and beta1 power, as well as decreases in delta and gamma1 power, occur around the 2-3 minute mark, with effects starting to peak between 7- and 10-minutes duration across all three groups. Statistically significant differences were observed between groups in the magnitude of these changes: advanced practitioners exhibited higher theta and theta-alpha power at all time points compared to the other groups. Conclusion: Our findings suggest that neurophysiological changes begin around 2-3 minutes after starting meditation and peak around 7-10 minutes across all three groups. However, the magnitude of these effects is greater in the advanced meditator group. As long as meditation retreats are not possible for many individuals, brief meditation practices of 7 minutes or more, delivered through digital platforms, could offer accessible, effective, and scalable solutions to improve mental well-being. This suggests a broader application of meditation practices in daily life, encouraging even those with tight schedules to incorporate such beneficial practices.