Document Type
Abstract
Publication Date
2-2021
Academic Year
2020-2021
Abstract
Traumatic brain injuries (TBI) occur in over one million Americans annually, leading to thousands of deaths, hospitalizations, and individuals with long term disability. The most common causes of TBI which result in hospitalization primarily affect middle-aged to older adults and contribute to the annual economic burden of billions of dollars. The goal of this study is to determine if baseline EEG scans predict responsiveness to supplemental treatment in TBI patients experiencing chronic symptoms, which could improve their care.
Subjects recruited from the greater Philadelphia area were randomly separated into 3 groups—Control, N-acetylcysteine (NAC), or Nutrition—and either received standard treatment for TBI alone, with NAC supplementation, or while maintaining an anti-inflammatory diet. Initial evaluation of the subjects before beginning treatment included fMRI, QEEG, and a concussion symptom questionnaire. At 3 months after the initial evaluation, there was an identical follow-up evaluation. Data analysis involved reviewing the subjects’ initial EEG data and initial and follow-up questionnaire responses, to determine if certain EEG patterns predicted who would respond to each treatment.
Preliminary data indicates Nutrition group responders are predicted by lower delta-wave relative power compared to non-responders, while NAC group responders are predicted by higher delta- and theta-wave coherence. The literature shows chronic TBI patients present with varying EEG patterns, so these results may help providers decide the most effective treatment plan.
The next steps include continuing analysis within treatment groups to evaluate if the responsiveness pattern stays consistent, and comparing the levels of responsiveness between treatment groups.
Recommended Citation
Okafor, Jideofor; Nwasike, Namdi; and Newberg, MD, Andrew, "Nutritional Intervention in Concussion Treatment and Analysis Using MRI and EEG" (2021). Phase 1. Paper 8.
https://jdc.jefferson.edu/si_phr_2023_phase1/8
Language
English