Biology subjects

Yeasin, M.

Publications and source records attributed to Yeasin, M..

10 recordsLinked to original sources

bioRxiv2026

Neural microstates underlying categorical speech perception using Bayesian nonparametrics

Categorical perception (CP) reflects the human auditory systems ability to map continuous acoustic signals onto discrete categories. Understanding the relationship between neural dynamics and perceptual decisions is central to speech-language processing. Here, we implemented a data-driven approach using Bayesian nonparametrics and machine learning to characterize the relationship between auditory cortical responses and speech categorization behaviors. By applying a hierarchical Dirichlet process hidden Markov Model (HDP-HMM) to source-reconstructed event-related potential (ERP) data, we identified temporally distinct neural microstates that capture the evolving stages of speech categorization without imposing predefined temporal windows. Machine learning classifiers, including extreme gradient boosting (XGBoost), support vector machines, and random forests, were applied to decode prototypical (Tk1/5) versus ambiguous (Tk3) speech sound tokens from the microstate data. Using whole-brain activity, the XGBoost classifier achieved the highest decoding accuracy of 94.1% with an area under the curve (AUC) 94.1% within a specific encoding microstate occurring approximately 200-250 ms after stimulus onset. A reduced set of 15 informative brain regions identified via Shapley additive explanations (SHAP) yielded comparable classification performance (90.3% accuracy; AUC 90.0%), with many regions localized to frontal, temporal, and parietal regions in the left hemisphere. Furthermore, neural activity within these regions robustly predicted listeners behavioral identification slopes (R2 = 0.92, p < 0.00001), linking microstate-specific cortical dynamics to individual differences in perceptual gradiency of speech perception. These findings demonstrate that speech categorization emerges within temporally discrete neural microstates during early sensory-perceptual encoding and is supported by a selective, distributed cortical network with clear behavioral relevance.

neuroscience↗

bioRxiv2025

Reduced-order modeling of solute transport within physiologically realistic solid tumor microenvironment

AO_SCPLOWBSTRACTC_SCPLOWSolid tumors are characterized by densely packed extracellular matrices and limited vascularization, creating significant resistance to both diffusive and convective transport. In this study, we developed an integration of numerical computations with a theoretical modeling framework that couples three phase viscous-laminar transient simulations of glycocalyx-patched tumor vessel resolving plasma, red blood cells (RBCs), and white blood cells (WBCs) and tracking their volume fractions to a calibrated reverse advection-diffusion (RAD) model for intratumoral plasma transport. The reduced-order tumor microenvironment model uses histology-informed extracellular matrix (ECM) tumor domain and packing fraction, together with explicit glycocalyx-patch electrohydrodynamics (EHD) at the tumor vessel wall. At the fenestra, EHD increases inlet plasma intensity relative to a non-EHD framework across all models (means: 0.576 non-EHD vs 0.722 EHD; gain 25.34%). Numerical simulations of plasma perfusion in both the tumor ECM domain and a microfluidic benchmark exhibit two-stage kinetics, with an initial advection-dominated regime. The RAD model reproduces this behavior and, after a simple temporal calibration to account for pore-scale hydrodynamic acceleration resolved by computational fluid dynamics (CFD), matches the observed propagation. By using fully resolved, EHD-inclusive multiphase CFD simulations to calibrate a reduced-order RAD model parameterized by measurable geometric features, we bridge the gap between classical Darcy-Starling tissue perfusion models and fully resolved CFD. The resulting framework provides a tractable, mechanism-grounded tool for quantifying plasma progression in dense solid tumors.

physiology↗

bioRxiv2025

OpEnHiMR: Optimization based Ensemble Model for Prediction of Histone Modification in Rice

Histone modifications are central to gene regulation, yet their systematic identification in plants remains limited due to the complexity of epigenomic landscapes. We present OpEnHiMR, an optimization-based ensemble learning framework for multiclass prediction of three key histone modifications, H3K4me3, H3K27me3, and H3K9ac, in rice. The framework integrates Support Vector Machines, Random Forest, and Gradient Boosting models, optimized via Ant Colony Optimization to maximize performance. Biologically meaningful features, including mononucleotide binary encoding, nucleotide chemical properties, GC content, and k-mer frequencies, were used for training after rigorous data curation and redundancy removal. OpEnHiMR achieved a classification accuracy of 77.54%, outperforming individual models and ensuring improved recall, specificity, and Matthews correlation coefficient. Model interpretability was enhanced using SHAP analysis, which highlighted critical sequence features influencing prediction outcomes. To promote community-wide adoption, a user-friendly webserver (https://dipro-sinha.shinyapps.io/OpEnHiMR/) and R package (https://cran.r-project.org/web/packages/OpEnHiMR/index.html) were developed. OpEnHiMR thus provides a scalable, accurate, and interpretable tool for histone modification prediction in plants, advancing epigenomics research and supporting data-driven crop improvement strategies.

bioinformatics↗

bioRxiv2024

Lateral Orbitofrontal Cortex Encodes Presence of Risk and Subjective Risk Preference During Decision-Making

Adaptive decision-making requires consideration of objective risks and rewards associated with each option, as well as subjective preference for risky/safe alternatives. Inaccurate risk/reward estimations can engender excessive risk-taking, a central trait in many psychiatric disorders. The lateral orbitofrontal cortex (lOFC) has been linked to many disorders associated with excessively risky behavior and is ideally situated to mediate risky decision-making. Here, we used single-unit electrophysiology to measure neuronal activity from lOFC of freely moving rats performing in a punishment-based risky decision-making task. Subjects chose between a small, safe reward and a large reward associated with either 0% or 50% risk of concurrent punishment. lOFC activity repeatedly encoded current risk in the environment throughout the decision-making sequence, signaling risk before, during, and after a choice. In addition, lOFC encoded reward magnitude, although this information was only evident during action selection. A Random Forest classifier successfully used neural data accurately to predict the risk of punishment in any given trial, and the ability to predict choice via lOFC activity differentiated between and risk-preferring and risk-averse rats. Finally, risk preferring subjects demonstrated reduced lOFC encoding of risk and increased encoding of reward magnitude. These findings suggest lOFC may serve as a central decision-making hub in which external, environmental information converges with internal, subjective information to guide decision-making in the face of punishment risk.

neuroscience↗

bioRxiv2022

Unraveling Spatial-Spectral Dynamics of Speech Categorization Speed using Convolutional Neural Network

1The process of categorizing sounds into distinct phonetic categories is known as categorical perception (CP). Response times (RTs) provide a measure of perceptual difficulty during labeling decisions (i.e., categorization). The RT is quasi-stochastic in nature due to individuality and variations in perceptual tasks. To identify the source of RT variation in CP, we have built models to decode the brain regions and frequency bands driving fast, medium and slow response decision speeds. In particular, we implemented parameter optimized convolutional neural network (CNN) to classify listeners behavioral RTs from their neural EEG data. We adopted visual interpretation of model response using Guided-GradCAM to identify spatial-spectral correlates of RT. Our framework includes (but is not limited to): (i) a data augmentation technique designed to reduce noise and control the overall variance of EEG dataset; (ii) bandpower topomaps to learn the spatial-spectral representation using CNN; (iii) large-scale Bayesian hyper-parameter optimization to find best performing CNN model; (iv) ANOVA and post-hoc analysis on Guided-GradCAM activation values to measure the effect of neural regions and frequency bands on behavioral responses. Using this framework, we observe that - {beta} (10-20 Hz) activity over left frontal, right prefrontal/frontal, and right cerebellar regions are correlated with RT variation. Our results indicate that attention, template matching, temporal prediction of acoustics, motor control and decision uncertainty are the most probable factors in RT variation.

neuroscience↗

bioRxiv2021

Micro-state-based neural decoding of speech categorization using Bayesian non-parametrics

Understanding the many-to-many mapping between patterns of functional brain connectivity and discrete behavioral responses is critical for speech-language processing. We present a microstate-based analysis of EEG recordings to characterize spatio-temporal dynamics of neural activities that underly rapid speech categorization decisions. We implemented a data driven approach using Bayesian non-parametrics to capture the mapping between EEG and the speed of listeners phoneme identification [i.e., response time (RT)] during speech labeling tasks. Based on our empirical analyses, we show task-relevant events such as resting-state, stimulus coding, auditory-perceptual object (category) formation, and response selection can be explained using patterns of micro-state dwell-time and are decodable as unique time segments during speech perception. State-dependent activities localize to a fronto-temporo-parietal circuit (superior temporal, supramarginal, inferior frontal gyri) exposing a core decision brain network (DN) underlying rapid speech categorization. Furthermore, RTs were inversely proportional to the frequency of state transitions, such that the rate of change between brain microstates was higher for trials with slower compared to faster RTs. Our findings imply that during rapid speech perception, higher uncertainty producing prolonged RTs (slower decision-making) is associated with staying in the DN longer compared lower RTs (faster decisions). We also show that listeners perceptual RTs are highly sensitive to individual differences. Our computational method opens a new avenue in segmentation and dynamic brain connectivity for modeling neuroimaging data and understanding task-related cognitive events.

neuroscience↗

bioRxiv2020

Speech categorization is better described by induced rather than evoked neural activity

Categorical perception (CP) describes how the human brain categorizes speech despite inherent acoustic variability. We examined neural correlates of CP in both evoked and induced EEG activity to evaluate which mode best describes the process of speech categorization. Using source reconstructed EEG, we used band-specific evoked and induced neural activity to build parameter optimized support vector machine (SVMs) model to assess how well listeners speech categorization could be decoded via whole-brain and hemisphere-specific responses. We found whole-brain evoked {beta}-band activity decoded prototypical from ambiguous speech sounds with ~70% accuracy. However, induced {gamma}-band oscillations showed better decoding of speech categories with ~95% accuracy compared to evoked {beta}-band activity (~70% accuracy). Induced high frequency ({gamma}-band) oscillations dominated CP decoding in the left hemisphere, whereas lower frequency ({theta}-band) dominated decoding in the right hemisphere. Moreover, feature selection identified 14 brain regions carrying induced activity and 22 regions of evoked activity that were most salient in describing category-level speech representations. Among the areas and neural regimes explored, we found that induced {gamma}-band modulations were most strongly associated with listeners behavioral CP. Our data suggest that the category-level organization of speech is dominated by relatively high frequency induced brain rhythms.

neuroscience↗

bioRxiv2020

Data-driven machine learning models for decoding speech categorization from evoked brain responses

Categorical perception (CP) of audio is critical to understand how the human brain perceives speech sounds despite widespread variability in acoustic properties. Here, we investigated the spatiotemporal characteristics of auditory neural activity that reflects CP for speech (i.e., differentiates phonetic prototypes from ambiguous speech sounds). We recorded high density EEGs as listeners rapidly classified vowel sounds along an acoustic-phonetic continuum. We used support vector machine (SVM) classifiers and stability selection to determine when and where in the brain CP was best decoded across space and time via source-level analysis of the event related potentials (ERPs). We found that early (120 ms) whole-brain data decoded speech categories (i.e., prototypical vs. ambiguous speech tokens) with 95.16% accuracy [area under the curve (AUC) 95.14%; F1-score 95.00%]. Separate analyses on left hemisphere (LH) and right hemisphere (RH) responses showed that LH decoding was more robust and earlier than RH (89.03% vs. 86.45% accuracy; 140 ms vs. 200 ms). Stability (feature) selection identified 13 regions of interest (ROIs) out of 68 brain regions (including auditory cortex, supramarginal gyrus, and Brocas area) that showed categorical representation during stimulus encoding (0-260 ms). In contrast, 15 ROIs (including fronto-parietal regions, Brocas area, motor cortex) were necessary to describe later decision stages (later 300 ms) of categorization but these areas were highly associated with the strength of listeners categorical hearing (i.e., slope of behavioral identification functions). Our data-driven multivariate models demonstrate that abstract categories emerge surprisingly early ([~]120 ms) in the time course of speech processing and are dominated by engagement of a relatively compact fronto-temporal-parietal brain network.

neuroscience↗

bioRxiv2019

Decoding age-related changes in the spatiotemporal neural processing of speech using machine learning

Speech comprehension in noisy environments depends on complex interactions between sensory and cognitive systems. In older adults, such interactions may be affected, especially in those individuals who have more severe age-related hearing loss. Using a data-driven approach, we assessed the temporal (when in time) and spatial (where in the brain) characteristics of the cortexs speech-evoked response that distinguish older adults with or without mild hearing loss. We used source montage to model scalp-recorded during a phoneme discrimination task conducted under clear and noise-degraded conditions. We applied machine learning analyses (stability selection and control) to choose features of the speech-evoked response that are consistent over a range of model parameters and support vector machine (SVM) classification to investigate the time course and brain regions that segregate groups and speech clarity. Whole-brain data analysis revealed a classification accuracy of 82.03% [area under the curve (AUC)=81.18%; F1-score 82.00%], distinguishing groups within [~]50 ms after speech onset (i.e., as early as the P1 wave).We observed lower accuracy of 78.39% [AUC=78.74%; F1-score=79.00%] and delayed classification performance when the speech token were embedded in noise, with group segregation at 60 ms. Separate analysis using left (LH) and right hemisphere (RH) regions showed that LH speech activity was better at distinguishing hearing groups than activity measured over the RH. Moreover, stability selection analysis identified 13 brain regions (among 1428 total spatiotemporal features from 68 regions) where source activity segregated groups with >80% accuracy (clear speech); whereas 15 regions were critical for noise-degraded speech to achieve a comparable level of group segregation (76% accuracy). Our results identify two core neural networks associated with complex speech perception in older adults and confirm a larger number of neural regions, particularly in RH and frontal lobe, are active when processing degraded speech information.

bioengineering↗

bioRxiv2019

Unsupervised decoding of single-trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions

Categorical perception (CP) is an inherent property of speech perception. The response time (RT) of listeners perceptual speech identification are highly sensitive to individual differences. While the neural correlates of CP have been well studied in terms of the regional contributions of the brain to behavior, functional connectivity patterns that signify individual differences in listeners speed (RT) for speech categorization is less clear. To address these questions, we applied several computational approaches to the EEG including graph mining, machine learning (i.e., support vector machine), and stability selection to investigate the unique brain states (functional neural connectivity) that predict the speed of listeners behavioral decisions. We infer that (i) the listeners perceptual speed is directly related to dynamic variations in their brain connectomics, (ii) global network assortativity and efficiency distinguished fast, medium, and slow RT, (iii) the functional network underlying speeded decisions increases in negative assortativity (i.e., became disassortative) for slower RTs, (iv) slower categorical speech decisions cause excessive use of neural resources and more aberrant information flow within the CP circuitry, (v) slower perceivers tended to utilize functional brain networks excessively (or inappropriately) whereas fast perceivers (with lower global efficiency) utilized the same neural pathways but with more restricted organization. Our results showed that neural classifiers (SVM) coupled with stability selection correctly classify behavioral RTs from functional connectivity alone with over 90% accuracy (AUC=0.9). Our results corroborate previous studies by confirming the engagement of similar temporal (STG), parietal, motor, and prefrontal regions in CP using an entirely data-driven approach.

neuroscience↗