Biology subjects

Prashad, S.

Publications and source records attributed to Prashad, S..

4 recordsLinked to original sources

bioRxiv2025

Higher attention is associated with stronger sensorimotor connectivity during a target pursuit task

Attention is critical in processes related to motor learning and recovery. While neuroimaging studies have highlighted its relevance to frontal-parietal networks, it is unclear how attention affects movement-related activity from the sensorimotor regions of the brain during a motor task. In this study, participants pursued a moving target with a computer mouse, and the level of attention was manipulated via two conditions in which the target moved in either a predictable or unpredictable fashion. We compared event-related desynchronization (ERD) between rest and movement as well as coherence during movement between the predictable and unpredictable trials. We found that alpha- and beta-band ERDs in the contralateral central areas did not change significantly between the two conditions. However, unpredictable trials had larger alpha-band suppression in the frontal and parietal areas, larger beta-band suppression in the ipsilateral parietal area, and larger alpha-band functional connectivity across the central areas. Our study highlights that performing limb movements with higher levels of attention was associated with stronger cortical communication in the sensorimotor areas, rather than strengthening neural activity in these areas. However, higher levels of attention were associated with stronger activation in the frontal and parietal areas which reflects engagement in attention-related networks. This increased activation of attentional networks and stronger sensorimotor communication may facilitate the formation of neural connections that occur in motor skill training and recovery. Impact StatementHow the brains attentional networks interact with motor learning processes is still poorly understood. In our study, we found that performing motor tasks with higher levels of attention was associated with stronger frontal and parietal ERDs, and stronger bilateral communication in the central motor areas. This corroborates with prevailing theories that the prefrontal cortex, which moderates attention, induces communication within the sensorimotor areas to promote the formation of neural connections that occur with motor learning.

neuroscience↗

bioRxiv2025

Cannabis use in humans is associated with impaired implicit motor learning and supranormal baseline cortical activity

Chronic cannabis use is associated with cognitive impairment, but its impact on implicit motor learning is unclear. Implicit learning of movement sequences (i.e., their specific ordinal and temporal structure) is vital for performing complex motor behavior and lays the foundation for performing daily activities and interacting socially. We collected data from 30 individuals who used cannabis regularly and 32 individuals who did not use cannabis. We utilized the serial reaction time task to assess implicit motor sequence learning and the Corsi block-tapping test to assess visuospatial short-term and working memory. We also recorded resting state electroencephalography (EEG). While implicit motor learning was evident at the group level, longer cannabis use was associated with a smaller index of motor learning and increased activity in beta and gamma EEG frequencies during resting state. The cannabis group also had a significantly shorter Corsi span (in both forward and backward conditions). These findings indicate that longer chronic cannabis use is associated with impaired implicit motor learning that may be a function of increased resting state neural oscillatory activity, resulting in increased cortical noise, and reduced visuospatial short-term and working memory. These findings suggest that chronic cannabis use may disrupt corticostriatal pathways that underlie implicit motor sequence learning, indicating a more extensive effect of cannabis on the motor system.

neuroscience↗

bioRxiv2023

Repetitive execution of a reach-and-lift task causes longitudinal attenuation in movement-related EEG features

Brain Machine Interfaces (BMIs) can help restore motor function to individuals with paralysis. These systems allow users to control an assistive device through the detection of movement-related brain activity. Such neural signatures are found through machine learning algorithms and training datasets that are generated from participants performing repetitive motor tasks. We anticipate that the movement-related brain waves of interest can attenuate over time due to neural efficiency, where the brain becomes more efficient with practice in a motor task. To explore this hypothesis, we used three open-access EEG datasets where participants performed a simple reach-and-lift task. From each trial, time windows associated with resting and movement periods were segmented. Alpha- and beta-band spectral power was estimated for each epoch, and event-related desynchronization (ERD) was estimated as the suppression in spectral power from rest to movement. These ERDs were compared between early and late trials in the dataset. We also used linear discriminant analysis to assess a machine learning algorithms accuracy in classifying whether the time windows belonged to rest or movement based on spectral power. In some cases, the ERDs were significantly different between earlier and later trials, and these differences led to changes in predicting the presence of movement from these ERDs. These results call for a reevaluation of BMI performance in datasets with numerous trials and an exploration of strategies that can compensate for longitudinal changes in movement-related brain activity used for BMIs.

neuroscience↗

bioRxiv2019

The Glycolytic Protein Phosphofructokinase Dynamically Relocalizes into Subcellular Compartments with Liquid-like Properties in vivo

While much is known about the biochemical regulation of glycolytic enzymes, less is understood about how they are organized inside cells. Here we built a hybrid microfluidic-hydrogel device for use in Caenorhabditis elegans to systematically examine and quantify the dynamic subcellular localization of the rate-limiting enzyme of glycolysis, phosphofructokinase-1/PFK-1.1. We determine that endogenous PFK-1.1 localizes to distinct, tissue-specific subcellular compartments in vivo. In neurons, PFK-1.1 is diffusely localized in the cytosol, but capable of dynamically forming phase-separated condensates near synapses in response to energy stress from transient hypoxia. Restoring animals to normoxic conditions results in the dispersion of PFK-1.1 in the cytosol, indicating that PFK-1.1 reversibly organizes into biomolecular condensates in response to cues within the cellular environment. PFK-1.1 condensates exhibit liquid-like properties, including spheroid shapes due to surface tension, fluidity due to deformations, and fast internal molecular rearrangements. Prolonged conditions of energy stress during sustained hypoxia alter the biophysical properties of PFK-1.1 in vivo, affecting its viscosity and mobility within phase-separated condensates. PFK-1.1s ability to form tetramers is critical for its capacity to form condensates in vivo, and heterologous self-association domain such as cryptochrome 2 (CRY2) is sufficient to constitutively induce the formation of PFK-1.1 condensates. PFK-1.1 condensates do not correspond to stress granules and might represent novel metabolic subcompartments. Our studies indicate that glycolytic protein PFK-1.1 can dynamically compartmentalize in vivo to specific subcellular compartments in response to acute energy stress via multivalency as phase-separated condensates.

cell biology↗