NUST Institutions Library Catalogue

The need to develop more efficient neuropsychological biomarkers is paramount in the
identification of neurodegenerative diseases, tracking the efficiency of treatment and in an
effort to avoid the huge financial cost required. While previous research utilizing
neuroimaging techniques has pinpointed changes in functional connectivity (FC) as
promising biomarkers for frontotemporal dementia (FTD), the constraints of cost and
availability of neuroimaging equipment underscore the necessity for accessible
alternatives. Electroencephalography (EEG) has emerged as a viable option due to its
increasing robustness, wider usage, and affordability.
To this end, the research focuses on a resting-state EEG data created from AD, FTD, and
HC groups. Here ground data were obtained from nineteen leads using a clinical EEG
device when the subjects were in a resting state and their eyes were closed. Another
challenge was to follow strict standards for data quality and quality management for data
quality to enhance consistency. It is a cross-sectional study, including data from MiniMental State Examination conducted on each participant, and tapes recorded from 20 AD
patients, 20 FTD patients, and 20 HC. The Neuroimaging Data Structure (BIDS) format
was utilized to present both preprocessed and raw EEG data.
The foremost aim was to determine the Feasibility, Sensitivity, and Specificity of the
preprocessed, feature extracted, time-efficient, and artifact reduced EEG-derived FC
patterns as markers in FTD. Phase-lock values (PLVs) were computed among nineteen
pairs of electrodes across five frequency bands using MATLAB and the Hilbert transform.
Significant variations in brain connectivity were identified through statistical analyses.
The study revealed significant differences in alpha and beta frequency patterns among the
control, Alzheimer's, and FTD groups, particularly in frontal and temporal regions. These
differences suggest alterations in neural activity associated with cognitive processing,
potentially serving as biomarkers for distinguishing between the three groups.
Alterations in beta frequency PLV were noted across various EEG pairs, indicating
disruptions in neural communication and coordination. These alterations suggest
xvi
compensatory mechanisms or hyperactivity in frontal and prefrontal regions, alongside
potential cognitive and motor deficits due to decreased PLV in central and temporal
regions.
While no statistically significant differences were observed in delta and theta frequency
synchronization between groups, trends suggest potential regions of interest for further
research, aligning with existing literature exploring neural oscillations in
neurodegenerative diseases. Similarly, no significant differences were observed in gamma
frequency synchronization between groups, indicating relatively preserved neural
synchronization in this frequency range across control, Alzheimer's, and FTD patients.
In summary, both Alzheimer's and FTD demonstrate significant reductions in alpha and
beta frequency values, particularly in frontal and temporal regions, compared to healthy
controls. These findings underscore the altered functional network topology in AD and
FTD, offering valuable insights into the neural mechanisms underlying these conditions.
The study's results contribute to the development of electrophysiological markers,
potentially enhancing the clinical diagnosis and understanding of AD and FTD. The
specificity and sensitivity of EEG-derived FC patterns highlight their potential as costeffective, accessible biomarkers for neurodegenerative disease.