Author ORCID Identifier
0000-0001-6416-0490
Document Type
Dissertation
Date of Award
5-31-2022
Degree Name
Doctor of Philosophy in Biomedical Engineering - (Ph.D.)
Department
Biomedical Engineering
First Advisor
Bharat Biswal
Second Advisor
Xiaobo Li
Third Advisor
Tara L. Alvarez
Fourth Advisor
Xin Di
Fifth Advisor
Hai Sun
Sixth Advisor
William Graves
Seventh Advisor
Sridhar Kannurpatti
Abstract
In the year 2019, life began to change at the advent of a global pandemic caused by the novel coronavirus. Mask mandates and mass vaccinations have mitigated the effects significantly, yet cases keep rising with new variants, especially, in densely populated countries, like India. Recent neuroimaging evidence shows the virus can attack the central nervous system (CNS). However, exactly which brain regions undergo structural and functional changes remain largely unknown. Many patients experience 'loss of/reduced sense of smell' (i.e., hyposmic) and an alarming number of survivors develop persistent symptoms ('long-COVID') for several months after initial infection. Fatigue is the most reported symptom among several others that show signs of cognitive deficits. Therefore, how these brain alterations differ among healthy controls and patient subtypes (non-hyposmic and hyposmic) and how they relate to fatigue need to be investigated.
To address these gaps, 35 healthy controls and 47 COVID-19 survivors, two weeks after hospital discharge, are recruited from a single site located at Delhi, India. T1-weighted structural magnetic resonance imaging (MRI) and resting state functional MRI (RS-fMRI) are used to test our hypothesis that brain structure and function change across healthy, non-hyposmic and hyposmic groups. Furthermore, correlations of structural and functional brain imaging metrics with self-reported fatigue at work are reported. Fatigue levels are higher in the COVID group (hyposmic and non-hyposmic) compared to the healthy group (p < 0.05). For the structural morphometry analysis, ANOVA reveals differences in global gray matter volume (GMV) across groups (F = 3.48, p < 0.05), which is observed to be higher in the hyposmic group from post-hoc tests. After controlling for age, sex and total intracranial volume (TIV), voxel-based morphometry (VBM) reveals four clusters (pFwE< 0.05) from the ANOVA analysis comprising regions from the limbic system, occipitotemporal and cerebellar lobes. Post-hoc analysis on these clusters reveal that hyposmic patients have higher GMV compared to non-hyposmic and healthy control groups. Furthermore, the COVID group demonstrate stronger correlation of fatigue with GMV (p = 0.41, p <0.05) within precuneus, posterior cingulate cortex and superior parietal lobule. From functional data analysis, amplitude of low frequency fluctuations (ALFF) is higher in the hyposmic and non-hyposmic groups compared to the healthy controls, within the limbic system and basal ganglia. Functional connectivity (FC) derived from independent component analysis (ICA) is reduced in the hyposmic group, compared to both non-hyposmic and healthy groups with medial and orbito-frontal regions for the basal ganglia network On the other hand, the hyposmic group show enhanced FC compared to healthy and non-hyposmic groups within the precuneus and somato-sensory networks, respectively. Moreover, the FC of the superior parietal lobule, is negatively correlated with work-related fatigue (p = -0.47, p <0.05) for the precuneus network. The results indicate that COVID survivors demonstrate brain alterations at an early stage of recovery and more strongly correlate with work-related fatigue, which can be an early marker for 'long-COVID'. Altered brain regions observed from this study also match with clinical MRI reports and current fMRI literature, suggesting these findings could have relevance to both clinical diagnosis and research related investigations.
Recommended Citation
Hafiz, Rakibul, "Assessing structural and functional brain alterations and work-related fatigue in non-hyposmic and hyposmic COVID-19 survivors" (2022). Dissertations. 1749.
https://digitalcommons.njit.edu/dissertations/1749
Included in
Bioimaging and Biomedical Optics Commons, Biomedical Commons, Neuroscience and Neurobiology Commons
