Time-Frequency Domain Analysis of Quantitative Electroencephalography as a Biomarker for Dementia
Biomarkers currently used to diagnose dementia, including Alzheimer’s disease (AD), primarily detect molecular and structural brain changes associated with the condition’s pathology. Although these markers are pivotal in detecting disease-specific neuropathological hallmarks, their association with...
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MDPI AG
2025-06-01
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| author | Chanda Simfukwe Seong Soo A. An Young Chul Youn |
| author_facet | Chanda Simfukwe Seong Soo A. An Young Chul Youn |
| author_sort | Chanda Simfukwe |
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| description | Biomarkers currently used to diagnose dementia, including Alzheimer’s disease (AD), primarily detect molecular and structural brain changes associated with the condition’s pathology. Although these markers are pivotal in detecting disease-specific neuropathological hallmarks, their association with the clinical manifestations of dementia frequently remains poorly defined and exhibits considerable variability. These biomarkers may show abnormalities in cognitively healthy individuals and frequently fail to accurately represent the severity of cognitive and functional impairments in individuals with dementia. Research indicates that synaptic degeneration and functional impairment occur early in the progression of AD and exhibit the strongest correlation with clinical symptoms. This identifies brain functional impairment measurements as promising early indicators for AD detection. Electroencephalography (EEG), a non-invasive and cost-effective method with high temporal resolution, is used as a biomarker for the early detection and diagnosis of AD through frequency-domain analysis of quantitative EEG (qEEG). Many researchers demonstrate that qEEG measures effectively identify disruptions in neuronal activity, including alterations in activity patterns, topographical distribution, and synchronization. Specific findings along the stages of AD include impaired neuronal synchronization, generalized EEG slowing, and an increase in lower-frequency bands accompanied by a decrease in higher-frequency bands of resting state EEG. Moreover, qEEG helps clinicians effectively correlate indicators of AD neuropathology and distinguish between various forms of dementia, positioning it as a promising, low-cost, non-invasive biomarker for dementia. However, additional clinical investigation is required to clarify the diagnostic and prognostic significance of qEEG measurements as early functional markers for AD. This narrative review examines time-frequency domain qEEG analysis as a potential biomarker across various types of dementia. Through a structured search of PubMed and Scopus, we identified studies assessing spectral and connectivity-based qEEG features. Consistent findings include EEG slowing, reduced functional connectivity, and network desynchronization. The review outlines key methodological challenges, such as lack of standardization and limited longitudinal validation, and recommends integrative, multimodal approaches to enhance diagnostic precision and clinical applicability. |
| format | Article |
| id | doaj-art-acd0aad3a6cf4d30a98cdd4efe6616c4 |
| institution | Matheson Library |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Diagnostics |
| spelling | doaj-art-acd0aad3a6cf4d30a98cdd4efe6616c42025-06-25T13:42:18ZengMDPI AGDiagnostics2075-44182025-06-011512150910.3390/diagnostics15121509Time-Frequency Domain Analysis of Quantitative Electroencephalography as a Biomarker for DementiaChanda Simfukwe0Seong Soo A. An1Young Chul Youn2Department of Bionano Technology, Gachon University, Seongnam-si 1342, Republic of KoreaDepartment of Bionano Technology, Gachon University, Seongnam-si 1342, Republic of KoreaDepartment of Neurology, College of Medicine, Chung-Ang University, Seoul 06974, Republic of KoreaBiomarkers currently used to diagnose dementia, including Alzheimer’s disease (AD), primarily detect molecular and structural brain changes associated with the condition’s pathology. Although these markers are pivotal in detecting disease-specific neuropathological hallmarks, their association with the clinical manifestations of dementia frequently remains poorly defined and exhibits considerable variability. These biomarkers may show abnormalities in cognitively healthy individuals and frequently fail to accurately represent the severity of cognitive and functional impairments in individuals with dementia. Research indicates that synaptic degeneration and functional impairment occur early in the progression of AD and exhibit the strongest correlation with clinical symptoms. This identifies brain functional impairment measurements as promising early indicators for AD detection. Electroencephalography (EEG), a non-invasive and cost-effective method with high temporal resolution, is used as a biomarker for the early detection and diagnosis of AD through frequency-domain analysis of quantitative EEG (qEEG). Many researchers demonstrate that qEEG measures effectively identify disruptions in neuronal activity, including alterations in activity patterns, topographical distribution, and synchronization. Specific findings along the stages of AD include impaired neuronal synchronization, generalized EEG slowing, and an increase in lower-frequency bands accompanied by a decrease in higher-frequency bands of resting state EEG. Moreover, qEEG helps clinicians effectively correlate indicators of AD neuropathology and distinguish between various forms of dementia, positioning it as a promising, low-cost, non-invasive biomarker for dementia. However, additional clinical investigation is required to clarify the diagnostic and prognostic significance of qEEG measurements as early functional markers for AD. This narrative review examines time-frequency domain qEEG analysis as a potential biomarker across various types of dementia. Through a structured search of PubMed and Scopus, we identified studies assessing spectral and connectivity-based qEEG features. Consistent findings include EEG slowing, reduced functional connectivity, and network desynchronization. The review outlines key methodological challenges, such as lack of standardization and limited longitudinal validation, and recommends integrative, multimodal approaches to enhance diagnostic precision and clinical applicability.https://www.mdpi.com/2075-4418/15/12/1509Alzheimer’s diseasebiomarkersquantitative electroencephalographyfrequency domain analysis |
| spellingShingle | Chanda Simfukwe Seong Soo A. An Young Chul Youn Time-Frequency Domain Analysis of Quantitative Electroencephalography as a Biomarker for Dementia Diagnostics Alzheimer’s disease biomarkers quantitative electroencephalography frequency domain analysis |
| title | Time-Frequency Domain Analysis of Quantitative Electroencephalography as a Biomarker for Dementia |
| title_full | Time-Frequency Domain Analysis of Quantitative Electroencephalography as a Biomarker for Dementia |
| title_fullStr | Time-Frequency Domain Analysis of Quantitative Electroencephalography as a Biomarker for Dementia |
| title_full_unstemmed | Time-Frequency Domain Analysis of Quantitative Electroencephalography as a Biomarker for Dementia |
| title_short | Time-Frequency Domain Analysis of Quantitative Electroencephalography as a Biomarker for Dementia |
| title_sort | time frequency domain analysis of quantitative electroencephalography as a biomarker for dementia |
| topic | Alzheimer’s disease biomarkers quantitative electroencephalography frequency domain analysis |
| url | https://www.mdpi.com/2075-4418/15/12/1509 |
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