Altered resting state EEG microstates dynamics in adolescents with concussion

2033 

Abstract Submission 

Sahar Sattari¹, Maryam Mirian¹, Lyndia Wu¹, Naznin Virji-Babul¹

¹University of British Columbia, Vancouver, British Columbia

Sahar Sattari  
University of British Columbia
Vancouver, British Columbia

Maryam Mirian  
University of British Columbia
Vancouver, British Columbia

Lyndia Wu  
University of British Columbia
Vancouver, British Columbia

Naznin Virji-Babul  
University of British Columbia
Vancouver, British Columbia

Concussion or mild traumatic brain injury (mTBI) is an urgent public health concern. Canadian and US data (1) indicate an annual rate of 1100 reported mTBIs per 100,000 people, 75% of whom are children, youths and young adults (2). Children and youths are especially vulnerable: they are disproportionately affected by concussions (3) and take longer than adults to recover (4). Currently, the most significant challenge in concussion management is the lack of objective, clinically-accepted, brain-based approaches for determining whether an athlete has suffered a concussion. Previous research has demonstrated alterations in resting state following acute concussion (5), however microstate dynamics of EEG (6), has not yet been investigated. Microstate analysis of EEG, allows investigations of brain dynamics with millisecond resolution and reflects synchronized activities of large-scale networks. Our goal is to investigate brain dynamics in a cohort of concussed youth to evaluate the potential of EEG microstates as a brain-based biomarker for concussion identification.

We analyzed eyes closed resting state, 64-channel EEG data from 33 healthy male adolescent athletes (age: 16±1.2 years) and 20 male adolescents diagnosed with sports-related concussions within one week of injury (age: 15±2.1 years).Microstate analysis was conducted using the open-source Python package Pycrostates (7). We employed a modified k-means algorithm to identify six microstates, by their topography (labeled A-F) according to existing literature (8), using data across groups. We computed average duration, occurrence rate, fraction of total time duration and transition probabilities at an individual level. These features were then compared between groups. We used a non-parametric permutation test to assess differences in microstate features between the two groups, applying Bonferroni correction to account for multiple comparisons. To calculate the distribution of transition probabilities, random subgroups comprising 10 participants from each group were generated 10,000 times.

We successfully identified six well-established microstates in the data, achieving a global explained variance (GEV) of 79.65% across the entire concatenated dataset. The results revealed significant differences in microstate dynamics between the two groups. Specifically, the average duration of microstates A, B, D, E, and F was significantly lower in the concussed group (p < 0.0001), while the average occurrence rate of microstates A, B, C, E, and F was significantly higher (p < 0.0001). Consequently, the concussed group exhibited more time in microstates A (p = 0.005) and C (p < 0.001) but less in D and E (p < 0.001). Regarding transition probabilities, the control group demonstrated higher stability with greater self-transition probabilities for all microstates (p<0.0001). Intriguingly, when excluding self-transition, the control group showed lower transition probabilities, which underscores the reduced state stability in the concussed group.

Extending our group's previous work using fMRI (9), our current findings highlight that individuals with concussions tend to remain in specific states for extended periods, frequently reverting to states associated with auditory and default mode (DMN) networks (states A and C) with decreased time in networks associated with attention and switching of attention (states D and E). These findings show for the first time, how concussion disrupts the rapid switching among brain networks in resting state. EEG microstate analysis may be an effective, low cost brain based marker for concussion identification as well as providing insights into the underlying neurophysiological mechanisms associated with concussion recovery.

Neurodevelopmental/ Early Life (eg. ADHD, autism)

Early life, Adolescence, Aging

EEG/MEG Modeling and Analysis

Task-Independent and Resting-State Analysis ¹

EEG ²

Data analysis

Development

DISORDERS

Electroencephaolography (EEG)

Trauma