TY - JOUR PY - 2022// TI - Identifying mild traumatic brain injury using measures of frequency-speciﬁed networks JO - Journal of neural engineering A1 - Salsabilian, Shiva A1 - Bibineyshvili, Yelena A1 - Margolis, David A1 - Najafizadeh, Laleh SP - ePub EP - ePub VL - ePub IS - ePub N2 - OBJECTIVE. Early diagnosis of mild traumatic brain injury (mTBI) is challenging, yet crucial for providing patients with timely treatments and minimizing the risks of developing injury-related disorders. To tackle this problem, this paper presents a framework based on measures of frequency-specified brain functional networks identifying mTBI.Approach. Cortical activity of 15 control and 15 injury Thy1-GCaMP6s mice are recorded, using widefield calcium imaging, prior to and 20 minutes after inducing injury. Power spectral distribution (PSD) of the recorded cortical activities are examined, and the frequency bands with significant difference in PSD between the injury and control groups are identified. Frequency-specified functional networks are then constructed. Employing graph theoretical analysis, various network measures from the constructed frequency-specified functional networks are extracted and used as features. Several classifiers are utilized to evaluate the performance of the computed network measures, either individually or collectively as features, to classify mTBI from control.Main results. Spectral analysis reveals the presence of two dominant frequency bands (low: <1 Hz) and high: [1-8] Hz) in the cortical activities recorded via calcium imaging. Comparison of the brain networks of control and injury groups shows significant reduction (p<0.05) in global functional connectivity following injury, specially for the high frequency band network. Interestingly, graph measures of the high frequency band network provided higher classification accuracy results, compared to those computed from the low frequency band network, suggesting that mTBI network-based features are frequency dependent. Using all network measures collectively as a multi-measure feature vector and a CNN classifier, a model for identifying mTBI is developed, offering an average classification accuracy of 97.28%.Significance.

RESULTS signifies the importance of considering frequency-specific analysis in functional networks for mTBI identification, and demonstrate the possibility of using network measures for early mTBI diagnosis.

Language: en

LA - en SN - 1741-2560 UR - http://dx.doi.org/10.1088/1741-2552/ac954e ID - ref1 ER -