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Dementia is a term for the impaired ability to remember, think, or make decisions that interferes with doing everyday activities. Alzheimer's disease is the most common type of dementia. Though dementia mostly affects older adults, it is not a part of normal aging. This study aims to assess the role of volumetric MRI in evaluation of different types of dementia.
Neurocognitive disorders (dementia) are prevalent clinical problems. With the increase in average life expectancy, its prevalence is rising at an alarming rate. In 2005, an estimated 24 million people around the world suffered from dementia, and that number is expected to double every 20 years. By 2040, it is predicted that over 81 million people worldwide will suffer from dementia. Most experts agree that treatment will be most beneficial if applied early, before significant, potentially irreversible neurodegeneration and functional impairment has occurred.
Clinical symptoms of most subtypes of dementia arise from progressive neuron and synapse loss, with the resulting tissue atrophy. Brain magnetic resonance imaging (MRI) is regularly used in diagnosing dementia as it visualises the structural changes caused by neurodegeneration. It is key in defining subtle differences between healthy and pathological cerebral volume loss and between dementia subtypes.
Visual assessment of brain atrophy patterns is commonly supported through the use of visual rating scales. However, these scales have a subjective element and their application relies heavily on the prior experience of the radiologist using them. And they have poor sensitivity to subtle or prodromal changes and have ceiling and/or floor effects.
Application of quantitative volumetric reporting tools, which automatically quantify an individual patient's regional brain volumes and compare them to healthy populations, can potentially help in interpretation of the severity and distribution of brain atrophy and contextualize their findings by referencing normative brain volumes in healthy populations. This will improve the accuracy of radiological diagnosis of different subtypes of dementia
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Magnetic Resonance Imaging (MRI) | Diagnostic Test | A standardized multi-parametric MRI protocol will be implemented for all patients. All sequences will be acquired on a 1.5 Tesla scanner. After completion of the MRI study, the digital imaging and communication data will be sent to a diagnostic workstation for post-processing and analysis. Patients of the study will be subjected to detailed structural MRI for detection of signs of neurodegenerative changes. Volumetric high-resolution 3D scans will be used for quantitative study. |
| Measure | Description | Time Frame |
|---|---|---|
| MRI Accuracy in dementia | Assess the accuracy of magnetic resonance imaging in differentiate between normal aging and cases of dementia | 1 week |
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Inclusion Criteria:
Exclusion Criteria:
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The study will be conducted over the period of 2 years during which all available cases with dementia presented to the Neurology Centre at Assiut university well be investigated.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Ahmed Abdel-Razzak, McS | Contact | 01147160733 | +20 | Ahmedabdelrazzak90@gmail.com |
| Mohamed Abdeltawab, MD | Contact | +201009319309 | dr.m.tawab@gmail.com |
| Name | Affiliation | Role |
|---|---|---|
| Hosam A Youssif, MD | Assiut University | Study Director |
| Ahmed Abdel-Razzak, McS | Assiut University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Assiut University | Asyut | 71515 | Egypt |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 16360788 | Result | Ferri CP, Prince M, Brayne C, Brodaty H, Fratiglioni L, Ganguli M, Hall K, Hasegawa K, Hendrie H, Huang Y, Jorm A, Mathers C, Menezes PR, Rimmer E, Scazufca M; Alzheimer's Disease International. Global prevalence of dementia: a Delphi consensus study. Lancet. 2005 Dec 17;366(9503):2112-7. doi: 10.1016/S0140-6736(05)67889-0. | |
| 19277975 |
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| ID | Term |
|---|---|
| D003704 | Dementia |
| D000544 | Alzheimer Disease |
| ID | Term |
|---|---|
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
| D019965 | Neurocognitive Disorders |
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| ID | Term |
|---|---|
| D008279 | Magnetic Resonance Imaging |
| ID | Term |
|---|---|
| D014054 | Tomography |
| D003952 | Diagnostic Imaging |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
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| Fennema-Notestine C, Hagler DJ Jr, McEvoy LK, Fleisher AS, Wu EH, Karow DS, Dale AM; Alzheimer's Disease Neuroimaging Initiative. Structural MRI biomarkers for preclinical and mild Alzheimer's disease. Hum Brain Mapp. 2009 Oct;30(10):3238-53. doi: 10.1002/hbm.20744. |
| 18390347 | Result | Duchesne S, Caroli A, Geroldi C, Barillot C, Frisoni GB, Collins DL. MRI-based automated computer classification of probable AD versus normal controls. IEEE Trans Med Imaging. 2008 Apr;27(4):509-20. doi: 10.1109/TMI.2007.908685. |
| 28317647 | Result | Ten Kate M, Barkhof F, Boccardi M, Visser PJ, Jack CR Jr, Lovblad KO, Frisoni GB, Scheltens P; Geneva Task Force for the Roadmap of Alzheimer's Biomarkers. Clinical validity of medial temporal atrophy as a biomarker for Alzheimer's disease in the context of a structured 5-phase development framework. Neurobiol Aging. 2017 Apr;52:167-182.e1. doi: 10.1016/j.neurobiolaging.2016.05.024. |
| D001523 | Mental Disorders |
| D024801 | Tauopathies |
| D019636 | Neurodegenerative Diseases |