| 000 -LEADER |
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| fixed length control field |
04245nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Ali, Salwa |
| 245 ## - TITLE STATEMENT |
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| Title |
Identifying Neurophysiological Correlates of Frontotemporal Dementia: Resting State EEG and Phase Synchronization Analysis / |
| Statement of responsibility, etc. |
Salwa Ali |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
Islamabad : |
| Name of producer, publisher, distributor, manufacturer |
SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice |
2024. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
123p. |
| Other physical details |
Soft Copy |
| Dimensions |
30cm |
| 500 ## - GENERAL NOTE |
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| General note |
The need to develop more efficient neuropsychological biomarkers is paramount in the<br/>identification of neurodegenerative diseases, tracking the efficiency of treatment and in an<br/>effort to avoid the huge financial cost required. While previous research utilizing<br/>neuroimaging techniques has pinpointed changes in functional connectivity (FC) as<br/>promising biomarkers for frontotemporal dementia (FTD), the constraints of cost and<br/>availability of neuroimaging equipment underscore the necessity for accessible<br/>alternatives. Electroencephalography (EEG) has emerged as a viable option due to its<br/>increasing robustness, wider usage, and affordability.<br/>To this end, the research focuses on a resting-state EEG data created from AD, FTD, and<br/>HC groups. Here ground data were obtained from nineteen leads using a clinical EEG<br/>device when the subjects were in a resting state and their eyes were closed. Another<br/>challenge was to follow strict standards for data quality and quality management for data<br/>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<br/>patients, 20 FTD patients, and 20 HC. The Neuroimaging Data Structure (BIDS) format<br/>was utilized to present both preprocessed and raw EEG data.<br/>The foremost aim was to determine the Feasibility, Sensitivity, and Specificity of the<br/>preprocessed, feature extracted, time-efficient, and artifact reduced EEG-derived FC<br/>patterns as markers in FTD. Phase-lock values (PLVs) were computed among nineteen<br/>pairs of electrodes across five frequency bands using MATLAB and the Hilbert transform.<br/>Significant variations in brain connectivity were identified through statistical analyses.<br/>The study revealed significant differences in alpha and beta frequency patterns among the<br/>control, Alzheimer's, and FTD groups, particularly in frontal and temporal regions. These<br/>differences suggest alterations in neural activity associated with cognitive processing,<br/>potentially serving as biomarkers for distinguishing between the three groups.<br/>Alterations in beta frequency PLV were noted across various EEG pairs, indicating<br/>disruptions in neural communication and coordination. These alterations suggest<br/>xvi<br/>compensatory mechanisms or hyperactivity in frontal and prefrontal regions, alongside<br/>potential cognitive and motor deficits due to decreased PLV in central and temporal<br/>regions.<br/>While no statistically significant differences were observed in delta and theta frequency<br/>synchronization between groups, trends suggest potential regions of interest for further<br/>research, aligning with existing literature exploring neural oscillations in<br/>neurodegenerative diseases. Similarly, no significant differences were observed in gamma<br/>frequency synchronization between groups, indicating relatively preserved neural<br/>synchronization in this frequency range across control, Alzheimer's, and FTD patients.<br/>In summary, both Alzheimer's and FTD demonstrate significant reductions in alpha and<br/>beta frequency values, particularly in frontal and temporal regions, compared to healthy<br/>controls. These findings underscore the altered functional network topology in AD and<br/>FTD, offering valuable insights into the neural mechanisms underlying these conditions.<br/>The study's results contribute to the development of electrophysiological markers,<br/>potentially enhancing the clinical diagnosis and understanding of AD and FTD. The<br/>specificity and sensitivity of EEG-derived FC patterns highlight their potential as costeffective, accessible biomarkers for neurodegenerative disease. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
MS Biomedical Engineering (BME) |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Supervisor : Dr. Muhammad Nabeel Anwar |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="http://10.250.8.41:8080/xmlui/handle/123456789/44568">http://10.250.8.41:8080/xmlui/handle/123456789/44568</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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| Koha item type |
Thesis |
