Biology subjects

Sasidharan, A.

Publications and source records attributed to Sasidharan, A..

14 recordsLinked to original sources

bioRxiv2026

An Open-Source End-to-End Pipeline for Large-Scale EEG-Based Brain Age Modelling

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.

neuroscience↗

bioRxiv2026

Behavioral and ERP Markers of Emotion Recognition: Comparing Embodied, Facial, and Emoji Stimuli

While extensive research has been conducted on emotion recognition from facial stimuli, it remains unclear how embodied emotional cues conveyed through body postures, gestures and actions (e.g., stick figures) compare with facial (human faces) and face-like symbolic representations (emoji faces) in shaping behavioral and neural responses. We employed a multimodal approach, combining behavioral measures (accuracy and reaction time) with electroencephalography (EEG) to examine the electrophysiological correlates of emotion recognition. Overall, sixty-six (equal number of males and females; 18-30 year old) participants identified positive, negative, and neutral emotions depicted in the three formats. Behavioral results showed significant effects of both emotion and format on accuracy and reaction time. Emoji faces were recognized with the highest accuracy and fastest reaction times, followed by stick figures and then human faces (p < .001, some comparisons p < .05). Thirty-four participants (17 males and 17 females) underwent EEG, which revealed distinct patterns of event-related potentials (ERPs). The Early Posterior Negativity (EPN) amplitude showed a significant overall effect of format. Post-hoc comparisons indicated that stick figures elicited greater (more negative) EPN amplitudes than human faces during positive emotion recognition (p = .003), and greater amplitudes than both emoji faces (p = .027) and human faces (p = .007) during negative emotion recognition. No significant differences were observed between emoji and human faces. N170 and Late Positive Potential (LPP) amplitudes did not reveal significant differences (all p > .05). Correlation analyses revealed no significant associations between ERP and behavioral measures. Thus, abstract, minimalistic representations like stick figures elicit enhanced early emotion-related processing despite similar early and later processing across formats. HighlightsO_LIEmotion recognition across realistic (human faces), symbolic (emoji faces) and embodied (stick figures) modalities were compared. C_LIO_LIEmotions were recognized fastest and most accurately in emoji faces format. C_LIO_LIStick figures format elicited significantly higher EPN compared to other two formats across positive and negative emotions. C_LIO_LINo significant differences were observed in terms of N170 and LPP. C_LIO_LIEmbodied, abstract emotional cues modulate automatic emotional appraisal, rather than initial sensory encoding and sustained cognitive evaluation. C_LI

neuroscience↗

bioRxiv2026

The Neurodynamic Core of Meditation: Dissociating Meditation from Rest and Task in a Reliability-based EEG study

BackgroundElectroencephalographic (EEG) studies attempting to characterise the neural signature of meditation typically rely on contrasts with passive rest or comparisons among practitioners based on experience. However, these approaches rarely include active control states and seldom establish the reliability and robustness of identified quantitative EEG features. Consequently, the validity of proposed neurophysiological markers of meditative state remains uncertain. The present study addressed these limitations by using a reliability-informed, multi-session within-subject design to characterise distinct state-dependent EEG dynamics in experienced meditators from the Brahmakumaris Rajayoga tradition. MethodsThirty long-term meditators underwent repeated EEG recordings over two days, comprising two meditation sessions per day. Each meditation block was flanked by rest periods, with a cognitive task between sessions to reduce carryover effects. We quantified broadband spectral power, aperiodic slope and intercept, and nonlinear dynamical measures, including detrended fluctuation analysis (DFA), Higuchi fractal dimension, and permutation entropy (PE), across meditation, rest, and task conditions. ResultsCompared with both rest and task states, meditation was associated with increased theta-alpha power, an elevated aperiodic intercept, and systematic modulation of nonlinear indices (DFA, Higuchi, PE). Further meditative core features demonstrated high inter-session test-retest reliability, strong inter-individual consistency, stability across guided and silent meditation states, and were not moderated by years of meditative experience. ConclusionThe present framework identifies a reproducible neurodynamic core of meditation, distinct from passive and active control states, spanning spectral, aperiodic, and nonlinear EEG domains in long-term meditators. These findings enhance the construct validity and measurement reliability of meditation-specific neural markers.

neuroscience↗

bioRxiv2025

EEG Phase Slips Reveal Detailed Brain Activity Patterns of Novice Vipassana Meditators During Decision Making Tasks

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.

neuroscience↗

bioRxiv2025

Similar States, Different Paths: Neurodynamics of diverse meditation techniques

Meditation encompasses diverse practices that train attention inward, in contrast to externally oriented task states. However, the neurodynamic features distinguishing meditative states from non-meditative states across traditions remain unclear. We analyzed high-density EEG data (N=170; 121 advanced meditators, 49 controls) across four traditions: Vipassana, Brahma Kumaris Raja Yoga, Heartfulness, and Isha Yoga. EEG features spanned oscillatory, aperiodic, nonlinear, and timescale components. Using random forest classifiers, we distinguished meditative from non-meditative states with robust classification performance (91%). Nonlinear features contributed the most, suggesting a core neurodynamic profile. Classification performance was higher in advanced meditators (92%) than in controls (85%), with distinct feature importance: nonlinear and aperiodic features dominated in meditators, and oscillatory and timescale features in controls. Each tradition showed distinct neurodynamic profiles, indicating technique-specific constellations. Our findings revealed shared yet distinct neurodynamic signatures across meditation techniques, suggesting that multiple neurodynamic pathways lead to meditative states.

neuroscience↗

bioRxiv2025

Non-duality in brain and experience of advanced meditators - Key role for Intrinsic Neural Timescales

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 brains intrinsic neural timescales during internal and external attention play a key role in mediating the experience of non-duality in advanced meditators.

neuroscience↗

bioRxiv2025

Time-to-onset and temporal dynamics of EEG during breath-watching meditation

IntroductionMind-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. MethodOur study addresses this gap by recording high-density 128-channel EEG data during breath- watching meditation in three groups: meditation-naive 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. ResultsOur 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. ConclusionOur 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.

neuroscience↗

bioRxiv2024

The Balanced Mind and its Intrinsic Neural Timescales in Advanced Meditators

A balanced mind, or equanimity, cultivated through meditation and other spiritual practices, is considered one of the highest mental states. Its core features include deidentification and non-duality. Despite its significance, its neural correlates remain unknown. To address this, we acquired 128-channel EEG data (n = 103) from advanced and novice meditators (from the Isha Yoga tradition) and controls during an internal attention (breath-watching) and an external attention task (visual-oddball paradigm). We calculated the auto-correlation window (ACW), a measure of brains intrinsic neural timescales (INTs) and assessed equanimity through self-report questionnaires. Advanced meditators showed higher levels of equanimity and shorter duration of INTs (shorter ACW) during breath-watching, indicating deidentification with mental contents. Furthermore, they demonstrated no significant differences in INTs between tasks, indicating non-dual awareness. Finally, shorter duration of INTs correlated with the participants subjective perceptions of equanimity. In conclusion, we show that the shorter duration of brains INT may serve as a neural marker of equanimity.

neuroscience↗

bioRxiv2024

The B-box protein BBX13/COL15 suppresses photoperiodic flowering by attenuating the action of CONSTANS in Arabidopsis

The optimal timing of transition from vegetative to floral reproductive phase is critical for plant productivity and agricultural yields. Light plays a decisive role in regulating this transition. The B-box (BBX) family of transcription factors regulates several light-mediated developmental processes in plants, including flowering. Here, we identify a previously uncharacterized group II member of the BBX family, BBX13/COL15, as a negative regulator of flowering under long-day conditions. BBX13 is primarily expressed in the leaf vasculature, buds, and flowers. Its spatial expression pattern is similar to the major flowering time regulators CO and FT. bbx13 mutants flower early, while BBX13-overexpressors exhibit delayed flowering under long days. Genetic analyses showed that BBX13 acts upstream to CO and FT in the flowering pathway and negatively regulates their expression. We found that BBX13 physically interacts with CO and inhibits the CO-mediated transcriptional activation of FT. In addition, BBX13 directly binds to the CORE2 motif on the FT promoter, a site where CO also binds. Furthermore, our ChIP data indicates that BBX13 reduces the in vivo binding of CO on the FT promoter. Through luciferase assay, we found that BBX13 inhibits the CO-mediated transcriptional activation of FT. All these data together suggest that BBX13/COL15 represses flowering in Arabidopsis by attenuating CO activity. Summary statementBBX13, a previously uncharacterized protein in the B-Box family of transcription factors, negatively regulates flowering time in Arabidopsis thaliana. BBX13 physically and genetically interacts with CO and inhibits the CO-mediated transcriptional activation of FT.

plant biology↗

bioRxiv2022

Electroencephalographic power spectrum and intersubject correlation on acoustic stimulation with modes of Indian music: a randomized controlled trial

BackgroundMusic not just entertains an individual but causes changes in the frequency spectrum of the brain waves and cognition that are recognizable using signals obtained through electroencephalography (EEG). EEG studies on the effect of passive listening to music have predominantly used multi-instrumental western classical music as an acoustic stimulus with very few analyzing solo instrumental Indian music, and thus in the current study Indian modes (Hindustani ragas) were used. The study aimed to investigate overall power spectral changes on EEG and specifically, those changes that show high inter-subject correlation (ISC) on passive listening to three different Indian modes as acoustic intervention, in comparison to control stimuli, heard for 10 minutes. Material & MethodsA randomized control triple-blind trial with 4 groups (three music intervention groups and a control group; n=35 each) was conducted while undergoing EEG recording. The music intervention groups listened to 10-minute audio of one of the three different modes (namely raga Miyan ki Todi, raga Malkauns, and raga Puriya), while the control group received predominant silence with few natural sounds interspersed. EEG data before, during, and after acoustic interventions were first evaluated for electrode-level power changes in standard spectral bands (delta, theta, alpha, beta1, beta2, and gamma). To understand spectral power changes more specific to music listening, a novel component-level analysis was also done, where the raw spectral data were grouped into the three most prominent components (C1, C2 & C3) based on spatiospectral consistency across subjects (correlated component analysis or CorrCA) and their ISC scores were also computed. For statistical analysis, we applied a hierarchical general linear model with cluster statistics to the electrode-level data and robust ANOVA with post hoc tests to the component-level data. ResultsIn electrode level analysis, the group listening to raga Malkauns showed a significant increase in gamma power in the left frontal regions during the intervention. While the group listening to raga Puriya showed a right frontoparietal decrease in delta power, raga Miyan ki Todi showed a frontal increase in beta1 power after the intervention. In component-level analysis, C1 was globally distributed low-frequency activity, C2 was posteriorly dominant alpha-beta1 activity, and C3 was peripherally dominant broad-band activity, consistent between groups. Besides agreement with electrode-level findings, the most prominent component-level finding was a decrease in C1 power and an increase in C2 power shown by raga Malkauns (strong both during and after intervention) and raga Miyan ki Todi (strong during and weak after intervention), whereas raga Puriya showed only a weak decrease in C1 (after intervention), compared to control group. ISC scores were comparable between groups, except for raga Puriya, which showed a marginal drop for C3 after the intervention. ConclusionsReduction in globally distributed low-frequency activity and increase in posterior dominant alpha-beta1 activity may be characteristic of passive listening to relaxing Indian modes, which may persist even after the listening period. Among the modes, raga Malkauns showed this effect most prominently, followed by raga Miyan ki Todi and least by raga Puriya. As the increase in posterior alpha and low beta power is associated with default mode network (DMN) activity and a decrease in delta power with positive emotional memory, the spectral pattern we observed may indicate observing positive autobiographical memory while listening to musical scales and thereby contributing to a relaxing experience. Further studies that also include phenomenological reports are highly recommended to be taken up to support these findings, and thus build a scientific foundation for the use of Indian music in medicine. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/519709v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1ef8b2eorg.highwire.dtl.DTLVardef@19a9091org.highwire.dtl.DTLVardef@894557org.highwire.dtl.DTLVardef@2774e1_HPS_FORMAT_FIGEXP M_FIG C_FIG Impact StatementIn this manuscript on EEG power spectral changes, correlated component analysis, and Intersubject correlation, we found a reduction in globally distributed low-frequency activity and an increase in posterior dominant alpha-beta1 activity during listening to Indian modes of music, in comparison to the control group. Mode-specific changes in correlated components, indicating default mode network activity and probable activation of autobiographical memories were observed.

neuroscience↗

bioRxiv2022

Simple Neurofeedback via Machine Learning: Challenges in real time multivariate assessment of meditation state

Attaining proficiency in meditation is difficult, especially without feedback since the mind may be easily distracted with thoughts and only long term efforts see any impact. Self-regulation would be much more effective if provided real time assessment and this can be achieved through EEG neurofeedback. Therefore, this work proposes a scheme for assessing meditation-like state in real time from short EEG segments, using low computational settings. Signal processing techniques are used to extract features from long term meditation practitioners multichannel EEG data. An autoencoder model is then trained on these features such that the model can be run in real time. Its reconstruction errors or its latent variables are used to provide non typical feedback parameters which are used to establish an objective measure of meditation ability. Our approach is optimised to have lightweight architectures handling small blocks of data and can be conveniently used on low density EEG acquisition systems as it requires only a few channels. However, our experimental results suggest that the meditation state has substantial overlap even in terms of multivariate EEG features and show prominent temporal dynamics, both of which are not captured using simple one class algorithms. Being an extremely flexible one-class model, we have described multiple improvements to the proposed autoencoder model to address the above issues and develop simple yet high precision neurofeedback protocols.

neuroscience↗

bioRxiv2019

Simultaneous assessment of P50, MMN, ERN and P300 event-related potentials among patients with Schizophrenia - an exploratory study

PurposeP50 suppression (sensory gating or inhibition), MMN (mismatch negativity; bottom-up detection of change), ERN (error related negativity; conflict monitoring) and P300 (attention allocation and memory updating for salient events) are event related potentials (ERPs) widely reported to show abnormal cognitive functioning among patients with schizophrenia. In real-life scenarios the brain processing underlying these ERPs occur simultaneously, and yet prior ERP studies have evaluated them in isolation. The current study uses a novel paradigm that can examine these multiple ERPs simultaneously, and explore if the reported ERP deficits would hold true during a more realistic setting.\n\nMethodData from 21 patients with schizophrenia and 25 age- and gender-matched healthy controls were used, who underwent ERP recordings during the Assessing Neurocognition via Gamified Experimental Logic (ANGEL) paradigm. This is a gamified visual odd-ball paradigm that generates P300, the error responses generate ERN, and paired-tone audio distractors generate P50 and MMN. Peak-peak amplitude, mean amplitude and area-under-curve measures of ERP were measured at electrodes reflecting best morphology.\n\nResultsThough patients showed apparent ERP morphology differences relative to the controls, the standard ERP measures were comparable between groups, except for reduced ERN among patients. Interestingly, significant group differences were seen in N1-P2 complex suppression, despite comparable P50 suppression.\n\nContribution of the researchThe current study is the first to report multiple ERP component measures simultaneously evoked among patients with schizophrenia, and shows greater signs for impaired prediction mechanism. The findings of the study would provide a more ecologically valid evaluation of ERP-based cognitive functioning, and need to be replicated in a larger sample as well as other mental disorders.

neuroscience↗

bioRxiv2019

Schizophrenia patients show aberrant brain dynamics associated with gestalt-perception and corollary discharge: Reflections from ERP and fMRI findings

BackgroundSchizophrenia is a disorder of higher mental attributes, and is characterized by psychotic symptoms that are believed to involve a basic inability to make valid predictions about expected sensations and experiences. These have been reported separately while monitoring either externally generated environmental patterns (e.g. gestalt-perception) or self-generated sensory experiences (e.g. corollary-discharge). As the pathophysiology behind predictive dysfunction is better viewed as an aberration in brains functional synchrony, a whole brain assessment using electroencephalographic (EEG) event related potential (ERP) and functional magnetic resonance imaging (fMRI) techniques, would offer a wider perspective to brain network abnormalities in schizophrenia.\n\nMethodWe used our lab-developed game-based task which presents degraded two-tone images to assess gestalt-perception, and simultaneously alters the congruency between participants button-press response and its auditory feedback to assess corollary-discharge. In both patients with schizophrenia and age-matched healthy controls, we explored event-related changes in an EEG-ERP study (n=21 each) and whole brain functional connectivity changes in a fMRI study (patients,n=12; controls,n=16), using the same task.\n\nResultsPatients showed reduced event-related EEG dynamics during both the error-prediction conditions (gestalt-perception and corollary-discharge), which include reduction in average waveforms (around N170 and N1-P2 complex, respectively) and altered theta dynamics (power and phase). Source-level EEG measures were clustered around the cingulo-insular network. fMRI functional connectivity analysis also found the abnormality in these brain regions, forming significantly weak connections with right insular/opercular cortex.\n\nConclusionsThis is the first study to explore thalamo-cortical dysfunction hypothesis in schizophrenia by integrating prediction-error-coding during perception ( gestalt-perception) and action ( corollary-discharge) using two neuroimaging modalities (EEG-ERP and fMRI). Besides adding to the knowledgebase of schizophrenia research, our novel task design and findings on theta-oscillation could benefit in the development of effective neuromodulatory therapeutic tools for patients with schizophrenia such as neurofeedback and transcranial brain stimulation.

neuroscience↗

bioRxiv2019

One-week exposure to South Indian Classical music clip having incremental variation in tempo and octave promotes better anxiety reduction among medical students - an EEG based study

1.Several scientific studies using Western classical music and some using Indian classical music have reported benefits of listening to musical pieces of specific genre or Raga, in terms of stress reduction and mental well-being. Within the realm of a Raga, presentation of musical pieces varies in terms of low-level musical components (like tempo, octave, timbre, etc.), and yet there is hardly any research on their effect. A commonly preferred musical pattern in Carnatic classical music is to have incremental modulations in tempo and octave ( Ragam-Tanam-Pallavi), and we wanted to examine whether this could have better anxiolytic effect than music without such modulations.\n\nAccordingly, in the current study, we exposed 21 male undergraduate medical students to a custom recorded South Indian classical music clip for 1 week (8 minutes clip; Raaga Kaapi; only two instruments - Violin and Mridangam; listened thrice daily for 6 days). One set of the participants (Varying Music; n=11) listened to a version that had the incremental variations, whereas the other set (Stable Music; n=10) listened to a version without such variations. On all 6 days, one of the music listening sessions was conducted in the lab while collecting electroencephalography (EEG; 32 channels) and electrocardiography (ECG; 1 channel) data. Psychological assessment for anxiety (State-Trait Anxiety Inventory - STAI and Beck Anxiety Inventory - BAI) was conducted before (day 1) and after (day 6) the intervention. Physiological parameters studied included power spectrum across the scalp in delta, alpha, beta, theta and gamma bands from EEG and heart rate variability (HRV) from ECG, during the baseline recordings of day 1 and day 6 of intervention.\n\nOur results show that participants when exposed to varying music showed significant reduction in anxiety, in contrast to stable music or silence intervention. A global examination of power spectral changes showed a stark contrast between stable and varying music intervention in comparison to silence - former showing greater increase in higher frequencies whereas latter showing prominent decrease especially in lower frequencies, both in bilateral temporo-parieto-occipital regions. A more detailed spectral analysis in frontal region revealed that both music intervention showed greater left-dominant alpha/beta asymmetry (i.e., greater right brain activation) and decrease in overall midline power (i.e., lower default mode network or DMN activity), when compared to silence intervention. Interestingly, stable music resulted in more left asymmetry, whereas, varying music showed more midline power reduction. Both music intervention also didnt show the reduction in HRV parameters that was associated with silence intervention.\n\nWe speculate that, the enhancement in mind calming effect of Kaapi raaga when presented with incremental variations, could be brought about by a balanced switching between a heightened mind wandering state with attention to self during the lower-slower portions and a reduced mind wandering state with attention to music during the higher-faster portions of the music. Such a dynamic mind wandering exercise would allow training ones creative thinking as well as sustained attention, during the respective high and low mind wandering states - both helping prevent ruminating thoughts. Therefore, musical properties such as tempo and octave have non-trivial influence on the various neurological and psychological mechanisms underlying stress management. Considering the impact of this finding in selection of music clips for music therapy, further studies with larger sample size is warranted.

neuroscience↗