Alzheimer's Disease and Related Dementia (ADRD)
Importance of key biomarkers in predicting The Alzheimer's Disease Assessment Scale (ADAS) scores across diagnostic groups: Amyloid-β from PET (top-left), average gray matter thickness from MRI (top-right), and mean diffusivity from DTI (bottom). (Hojjati et al., 2024).
Abbreviations: AD: Alzheimer's Disease; CN: Cognitively Normal; MCI: Mild Cognitive Impairment
We are among the first groups showing importance of alteration of the resting-state fMRI brain network in Alzheimer's disease (AD) (Khazaee et al., 2015). We demonstrated alteration of the brain network in early stages of AD and mild cognitive impairment (MCI) (Hojjati et al., 2019). We demonstrated that integrating multimodal neuroimaging data (MRI, fMRI, and PET) with AI can predict neuropsychological scores in AD, suggesting a robust framework for early disease detection (Hojjati et al., 2021).We further identified key biomarkers and brain regions associated with cognitive decline in AD, providing insights into disease progression and potential therapeutic targets (Hojjati et al., 2024).
Average saliency maps generated by the deep learning model, highlighting key brain regions influential in predicting brain age.
A simple 3D CNN-based deep learning model to predict brain age from the MRI scans.
In an ongoing project, we are developing, training, and testing advanced 3D CNN-based deep learning models to predict brain age from MRI scans in a large cohort of cognitively normal (CN) subjects. By comparing the difference between chronological and predicted brain age—referred to as the "age gap"—we aim to demonstrate that this gap is significantly larger in subjects with neurodegenerative diseases such as AD, LBD, and Parkinson's disease (PD). This suggests accelerated brain aging associated with these conditions, enhancing our understanding of age-related neuroanatomical changes in neurodegenerative diseases.
In an ongoing project, we use MEG to improve diagnostic accuracy and develop targeted interventions for Alzheimer's Disease and Related Dementia (ADRD).
Related Publications:
[1] S. H. Hojjati, A. Babajani-Feremi, and I. Alzheimer's Disease Neuroimaging, "Seeing beyond the symptoms: biomarkers and brain regions linked to cognitive decline in Alzheimer's disease," Front Aging Neurosci, vol. 16, p. 1356656, 2024, doi: https://doi.org/10.3389/fnagi.2024.1356656.
[2] S. H. Hojjati, A. Babajani-Feremi, and I. Alzheimer's Disease Neuroimaging, "Prediction and Modeling of Neuropsychological Scores in Alzheimer's Disease Using Multimodal Neuroimaging Data and Artificial Neural Networks," Front Comput Neurosci, vol. 15, p. 769982, 2021, doi: https://doi.org/10.3389/fncom.2021.769982.
[3] S. H. Hojjati, A. Ebrahimzadeh, and A. Babajani-Feremi, "Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI," Front Neurol, vol. 10, p. 904, 2019, doi: https://doi.org/10.3389/fneur.2019.00904.
[4] S. H. Hojjati, A. Ebrahimzadeh, A. Khazaee, A. Babajani-Feremi, and I. Alzheimer's Disease Neuroimaging, "Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI," Comput Biol Med, vol. 102, pp. 30-39, Nov 1 2018, doi: https://doi.org/10.1016/j.compbiomed.2018.09.004.
[5] S. H. Hojjati, A. Ebrahimzadeh, A. Khazaee, A. Babajani-Feremi, and I. Alzheimer's Disease Neuroimaging, "Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM," J Neurosci Methods, vol. 282, pp. 69-80, Apr 15 2017, doi: https://doi.org/10.1016/j.jneumeth.2017.03.006.
[6] A. Khazaee, A. Ebrahimzadeh, and A. Babajani-Feremi, "Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease," Brain Imaging Behav, vol. 10, no. 3, pp. 799-817, Sep 2016, doi: https://doi.org/10.1007/s11682-015-9448-7.
[7] A. Khazaee, A. Ebrahimzadeh, and A. Babajani-Feremi, "Application of Pattern Recognition and Graph Theoretical Approaches to Analysis of Brain Network in Alzheimer's Disease," Journal of Medical Imaging and Health Informatics, vol. 5, no. 6, pp. 1145-1155, 2015.
[8] A. Khazaee, A. Ebrahimzadeh, and A. Babajani-Feremi, "Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory," Clin Neurophysiol, vol. 126, no. 11, pp. 2132-41, Nov 2015, doi: https://doi.org/10.1016/j.clinph.2015.02.060.