Biology subjects

Virji-Babul, N.

Publications and source records attributed to Virji-Babul, N..

6 recordsLinked to original sources

bioRxiv2024

Menstrual cycle phase modulates causal connectivity in the resting-state brain of healthy females.

BackgroundOvarian hormones exert direct and indirect influences on the brain; however, little is known about how these hormones impact causal brain connectivity. Studying the female brain at a single time point may be confounded by distinct hormone phases. Despite this, the menstrual cycle is often overlooked. The primary objective of this pilot study was to evaluate resting-state causal connectivity during the early follicular and mid-luteal menstrual phases corresponding to low vs high estradiol and progesterone, respectively. MethodsFourteen healthy control females (M = 20.36 years, SD =2.02) participated in this study. Participants were scheduled for two resting-state electroencephalography (EEG) scans during their monthly menstrual cycle. A saliva sample was also collected at each EEG session for hormone analyses. Causal connectivity was quantified using information flow rate of EEG source data. Demographic information, emotional empathy, and sleep quality were obtained from self-report questionnaires. ResultsProgesterone levels were significantly higher in the mid-luteal phase compared to the early follicular phase (p = .041). We observed distinct patterns of causal connectivity along the menstrual cycle. Connectivity in the early follicular phase was centralized and shifted posteriorly during the mid-luteal phase. During the early follicular phase, the primary regions driving activity were the right central and left/right parietal regions, with the left central region being the predominant receiver of activity. During the mid-luteal phase, connections were primarily transmitted from the right side and the main receiver region was the left occipital region. Network topology during the mid-luteal phase was found to be significantly more assortative compared to the early follicular phase. ConclusionsThe observed difference in causal connectivity demonstrates how network dynamics reorganize as a function of menstrual phase and level of progesterone. In the mid-luteal phase, there was a strong shift for information flow to be directed at visual spatial processing and visual attention areas, whereas in the follicular phase, there was strong information flow primarily within the sensory-motor regions. The mid-luteal phase was significantly more assortative, suggesting greater network efficiency and resilience. These results contribute to the emerging literature on brain-hormone interactions.

neuroscience↗

bioRxiv2023

Distinct effects of priming the brain using tDCS and observational practice: new evidence from brain effective connectivity.

Complex motor skills can be acquired while observing a model without physical practice. Transcranial direct-current stimulation (tDCS) applied to the primary motor cortex (M1) also facilitates motor learning. However, the effectiveness of observational practice for bimanual coordination skills is debated and there is little research on the effects of tDCS on acquiring bimanual skills and the underlying effective/causal brain connectivity. We compared the effect of primary motor cortex tDCS (M1-tDCS) to action-observation (AO) when acquiring a bimanual, two-ball juggling skill and characterized the brain causal connectivity patterns underlying each condition. Twenty healthy young adults with no juggling experience were randomly assigned to either video observation of a skilled juggler or anodal M1-tDCS. Thirty trials of juggling were performed and scored after the intervention. Resting-state EEG data were collected before and after the intervention. Information flow rate was applied to EEG source data to measure causal connectivity. Juggling scores were significantly higher in the AO group (p =.03). We found the strongest information exchange from (L) parietal to (R) parietal regions, strong bidirectional information exchange between (R) parietal and (R) occipital regions and an extensive network of activity that was (L) lateralized in the AO condition. In contrast, the M1-tDCS condition was characterized by bilateral long-range connections with the strongest information exchange from the (R) occipital region to the (R) temporal and (L) occipital regions. This study provides new results about the distinct network dynamics of priming the brain for skill acquisition using direct stimulation or indirect stimulation via action observation.

neuroscience↗

bioRxiv2020

Recurrent Neural Network-based Acute Concussion Classifier using Raw Resting State EEG Data

Concussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and recovering individuals are more prone to suffer additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications. Currently, concussion management faces two significant challenges: there are no objective, clinically accepted, brain-based approaches for determining (i) whether an athlete has suffered a concussion, and (ii) when the athlete has recovered. Diagnosis is based on clinical testing and self-reporting of symptoms and their severity. Self-reporting is highly subjective and symptoms only indirectly reflect the underlying brain injury. Here, we introduce a deep learning Long Short Term Memory (LSTM)-based recurrent neural network that is able to distinguish between healthy and acute post-concussed adolescent athletes using only a short (i.e. 90 seconds long) sample of resting state EEG data as input. The athletes were neither required to perform a specific task nor subjected to a stimulus during data collection, and the acquired EEG data was neither filtered, cleaned of artefacts, nor subjected to explicit feature extraction. The LSTM network was trained and tested on data from 27 male, adolescent athletes with sports related concussion, bench marked against 35 healthy, adolescent athletes. During rigorous testing, the classifier consistently identified concussions with an accuracy of >90% and its ensemble-median Area Under the Curve (AUC) corresponds to 0.971. This is the first instance of a high-performing classifier that relies only on easy-to-acquire resting state EEG data. It represents a key step towards the development of an easy-to-use, brain-based, automatic classification of concussion at an individual level.

neuroscience↗

bioRxiv2020

Myelin water fraction decrease in mild traumatic brain injury

The increased incidence of reported traumatic brain injury (TBI) and its potentially serious long-term consequences have enormous clinical and societal impacts. The diffuse and continually evolving secondary changes after TBI make it challenging to evaluate the changes in brain-behaviour relationships. In this study we used myelin water imaging to evaluate changes in myelin water fraction (MWF) in individuals with chronic brain injury and evaluated their cognitive status using the NIH Toolbox Cognitive Battery. Twenty-two adults with mild or severe brain injury and twelve age, gender and education matched healthy controls took part in this study. We found a significant decrease in global white matter MWF in individuals with mild TBI compared to the healthy controls. Significantly lower MWF was evident in most white matter ROIs examined including the corpus callosum (separated into genu, body and splenium), minor forceps, right anterior thalamic radiation, left inferior longitudinal fasciculus; and right and left superior longitudinal fasciculus and corticospinal tract. No significant correlations were found between MWF in mild TBI and the cognitive measures. These results show for the first time the loss of myelin in chronic mild TBI.

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bioRxiv2019

Disrupted information flow in resting-state in adolescents with sports related concussion

Children and youth are at a greater risk of concussions than adults, and once injured, take longer to recover. A key feature of concussion is a diffuse increase in functional connectivity; yet it remains unclear how changes in functional connectivity relate to the patterns of information flow within resting state networks following concussion and how these relate to brain function. We applied a data-driven measure of directed effective brain connectivity to compare the patterns of information flow in healthy adolescents and adolescents with subacute concussion during the resting state condition. Data from 32 healthy adolescents (mean age =16 years) and 24 concussed adolescents (mean age = 13.8 years) with subacute concussion (< 3 months post injury) took part in the study. Five minutes of resting state data EEG were collected while participants sat quietly with their eyes closed. We applied the Kleeman-Liang information flow rate to measure the transfer of information between the EEG time series of each individual at different source locations, and therefore between different brain regions. Based on the ensemble means of the magnitude of normalized information flow rate, our analysis shows that information flow in the healthy adolescents is characterized by a predominantly (L) lateralized pattern with bidirectional information flow between frontal regions, between frontal and central/temporal regions and between parietal and occipital regions. In contrast, adolescents with concussion show distinct differences in information flow marked by a more symmetrical pattern with connections evenly distributed across the entire brain, increased information flow in the posterior regions of the brain and the emergence of bidirectional, inter-hemispheric connections between the left and right temporal regions of the brain. We also find that the statistical distribution of the normalized information flow rates in each group (control and concussed) is significantly different. Our results are the first to describe altered patterns of information flow in adolescents with concussion as well as differences in the statistical distribution of information flow rate. We hypothesize that the observed changes in information flow in the concussed group are a consequence of the brain injury and indicate functional reorganization of resting state networks.

neuroscience↗

bioRxiv2019

Resting-state directed brain connectivity patterns in adolescents from source-reconstructed EEG signals based on information flow rate

Quantifying the brains effective connectivity offers a unique window onto the causal architecture coupling the different regions of the brain. Here, we advocate a new, data-driven measure of directed (or effective) brain connectivity based on the recently developed information flow rate coefficient. The concept of the information flow rate is founded in the theory of stochastic dynamical systems and its derivation is based on first principles; unlike various commonly used linear and nonlinear correlations and empirical directional coefficients, the information flow rate can measure causal relations between time series with minimal assumptions. We apply the information flow rate to electroencephalography (EEG) signals in adolescent males to map out the directed, causal, spatial interactions between brain regions during resting-state conditions. To our knowledge, this is the first study of effective connectivity in the adolescent brain. Our analysis reveals that adolescents show a pattern of information flow that is strongly left lateralized, and consists of short and medium ranged bidirectional interactions across the frontal-central-temporal regions. These results suggest an intermediate state of brain maturation in adolescence.

neuroscience↗