Investigating neural markers of Alzheimer's disease in posttraumatic stress disorder using machine learning algorithms and magnetic resonance imaging

被引:1
|
作者
Yakemow, Gabriella [1 ,2 ]
Kolesar, Tiffany A. [1 ,2 ]
Wright, Natalie [1 ,2 ,3 ]
Beheshti, Iman [1 ,2 ]
Choi, Eun Hyung [1 ,2 ]
Ryner, Lawrence [4 ]
Chaulk, Sarah [5 ]
Patel, Ronak [5 ]
Ko, Ji Hyun [1 ,2 ,6 ]
机构
[1] Univ Manitoba, Dept Human Anat & Cell Sci, Winnipeg, MB, Canada
[2] Kleysen Inst Adv Med, PrairieNeuro Brain Res Ctr, Hlth Sci Ctr, Winnipeg, MB, Canada
[3] Univ Manitoba, Rady Fac Hlth Sci, Undergrad Med Educ, Winnipeg, MB, Canada
[4] Univ Manitoba, Rady Fac Hlth Sci, Dept Radiol, Winnipeg, MB, Canada
[5] Univ Manitoba, Dept Clin Hlth Psychol, Winnipeg, MB, Canada
[6] Univ Manitoba, Price Fac Engn, Biomed Engn, Winnipeg, MB, Canada
来源
FRONTIERS IN NEUROLOGY | 2024年 / 15卷
基金
加拿大自然科学与工程研究理事会;
关键词
Alzheimer's disease; posttraumatic stress disorder (PTSD); MRI; arterial spin labeling (ASL); machine learning; CEREBRAL-BLOOD-FLOW; STRUCTURAL BRAIN ABNORMALITIES; HIPPOCAMPAL VOLUME; MATTER VOLUME; T; MRI; PREVALENCE; PTSD; METAANALYSIS; PERFUSION;
D O I
10.3389/fneur.2024.1470727
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Introduction Posttraumatic stress disorder (PTSD) is a mental health disorder caused by experiencing or witnessing traumatic events. Recent studies show that patients with PTSD have an increased risk of developing dementia, including Alzheimer's disease (AD), but there is currently no way to predict which patients will go on to develop AD. The objective of this study was to identify structural and functional neural changes in patients with PTSD that may contribute to the future development of AD. Methods Neuroimaging (pseudo-continuous arterial spin labeling [pCASL] and structural magnetic resonance imaging [MRI]) and behavioral data for the current study (n = 67) were taken from our non-randomized open label clinical trial (ClinicalTrials.gov Identifier: NCT03229915) for treatment-seeking individuals with PTSD (n = 40) and age-matched healthy controls (HC; n = 27). Only the baseline measures were utilized for this study. Mean cerebral blood flow (CBF) and gray matter (GM) volume were compared between groups. Additionally, we utilized two previously established machine learning-based algorithms, one representing AD-like brain activity (Machine learning-based AD Designation [MAD]) and the other focused on AD-like brain structural changes (AD-like Brain Structure [ABS]). MAD scores were calculated from pCASL data and ABS scores were calculated from structural T1-MRI images. Correlations between neuroimaging data (regional CBF, GM volume, MAD scores, ABS scores) and PTSD symptom severity scores measured by the clinician-administered PTSD scale for DSM-5 (CAPS-5) were assessed. Results Decreased CBF was observed in two brain regions (left caudate/striatum and left inferior parietal lobule/middle temporal lobe) in the PTSD group, compared to the HC group. Decreased GM volume was also observed in the PTSD group in the right temporal lobe (parahippocampal gyrus, middle temporal lobe), compared to the HC group. GM volume within the right temporal lobe cluster negatively correlated with CAPS-5 scores and MAD scores in the PTSD group. Conclusion Results suggest that patients with PTSD with reduced GM volume in the right temporal regions (parahippocampal gyrus) experienced greater symptom severity and showed more AD-like brain activity. These results show potential for early identification of those who may be at an increased risk for future development of dementia.
引用
收藏
页数:9
相关论文
共 50 条
  • [21] Magnetic resonance imaging markers of Parkinson's disease nigrostriatal signature
    Peran, Patrice
    Cherubini, Andrea
    Assogna, Francesca
    Piras, Fabrizio
    Quattrocchi, Carlo
    Peppe, Antonella
    Celsis, Pierre
    Rascol, Olivier
    Demonet, Jean-Francois
    Stefani, Alessandro
    Pierantozzi, Mariangela
    Pontieri, Francesco Ernesto
    Caltagirone, Carlo
    Spalletta, Gianfranco
    Sabatini, Umberto
    BRAIN, 2010, 133 : 3423 - 3433
  • [22] Alzheimer's Disease Detection Using Machine Learning and Deep Learning Algorithms
    Sentamilselvan, K.
    Swetha, J.
    Sujitha, M.
    Vigasini, R.
    INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS, IBICA 2021, 2022, 419 : 296 - 306
  • [23] Improved Alzheimer's Disease Detection by MRI Using Multimodal Machine Learning Algorithms
    Battineni, Gopi
    Hossain, Mohmmad Amran
    Chintalapudi, Nalini
    Traini, Enea
    Dhulipalla, Venkata Rao
    Ramasamy, Mariappan
    Amenta, Francesco
    DIAGNOSTICS, 2021, 11 (11)
  • [24] Using CatBoost and Other Supervised Machine Learning Algorithms to Predict Alzheimer's Disease
    An, Jessica
    2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA, 2022, : 1732 - 1739
  • [25] Blood-Based Biomarkers for Alzheimer's Disease in Older Adults with Posttraumatic Stress Disorder
    Cimino, Nicolas
    Kang, Min Suk
    Honig, Lawrence S.
    Rutherford, Bret R.
    JOURNAL OF ALZHEIMERS DISEASE REPORTS, 2022, 6 (01) : 49 - 56
  • [26] Magnetic resonance imaging of Alzheimer's disease
    Xanthakos, S
    Krishnan, KRR
    Kim, DM
    Charles, HC
    PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY, 1996, 20 (04) : 597 - 626
  • [27] Periodontitis and brain magnetic resonance imaging markers of Alzheimer's disease and cognitive aging
    Rubinstein, Tom
    Brickman, Adam M.
    Cheng, Bin
    Burkett, Sandra
    Park, Heekuk
    Annavajhala, Medini K.
    Uhlemann, Anne-Catrin
    Andrews, Howard
    Gutierrez, Jose
    Paster, Bruce J.
    Noble, James M.
    Papapanou, Panos N.
    ALZHEIMERS & DEMENTIA, 2024, 20 (03) : 2191 - 2208
  • [28] Alzheimer disease diagnosis for magnetic resonance brain images using deep learning neural networks
    Ahmed, Heba M.
    Elsharkawy, Zeinab F.
    Elkorany, Ahmed S.
    MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (12) : 17963 - 17977
  • [29] Alzheimer disease diagnosis for magnetic resonance brain images using deep learning neural networks
    Heba M. Ahmed
    Zeinab F. Elsharkawy
    Ahmed S. Elkorany
    Multimedia Tools and Applications, 2023, 82 : 17963 - 17977
  • [30] Magnetic resonance imaging for diagnosis of early Alzheimer's disease
    Colliot, O.
    Hamelin, L.
    Sarazin, M.
    REVUE NEUROLOGIQUE, 2013, 169 (10) : 724 - 728