Radiomic based investigation of a potential link between precuneus and fusiform gyrus with Alzheimer disease
Sharma, S.; Kundal, K.; Chandok, I. K.; Kumar, N.; Kumar, R.
Show abstract
Alzheimers disease (AD) is acknowledged as one of the most common types of dementia. Various brain regions were found to associated with AD pathology. Precuneus and fusiform gyrus are two notable regions whose role has been implicated in cognitive function. However, a thorough investigation was lacking to link these regions with AD pathology. In this study, we conducted a comprehensive radiomic based investigation using magnetic resonance imaging (MRI) scans to link precuneus and fusiform gyrus with AD pathology. We obtained T1 weighted MR scans of AD (n=133), MCI (n=311) and CN (n=195) subjects from ADNI database at three different time points (i.e., 0, 6 and 12 months). Then, we conducted statistical analysis to compare these features among AD, MCI and CN subjects. We found significant decline in gray matter volume (GMV) and cortical thickness of both precuneus and fusiform gyrus in AD as compared to the MCI and CN subjects. Further, we utilized these features to develop machine learning classifiers to classify AD from MCI and CN subjects and achieved accuracy of 97.78% and 94.41% respectively. These results strengthen the connection of precuneus and fusiform gyrus with AD pathology and opens a new avenue of AD research.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- White matter integrity is associated with cognition and amyloid burden in older adult Koreans along the Alzheimer’s disease continuum 96%
- Quantitative transport mapping of multi-delay arterial spin labeling MRI detects early blood perfusion alteration in Alzheimer’s disease 96%
- Medial temporal lobe atrophy patterns in early- versus late-onset amnestic Alzheimer's disease 96%
Similar papers in this journal
- Topographical overlapping of the Aβ and Tau pathologies in the Default mode networks predicts Alzheimer’s Disease with higher specificity 97%
- FMRI complexity correlates with tau-PET in Late-Onset and Autosomal Dominant Alzheimer's Disease 97%
- Disentangling the distal association between β-Amyloid and tau pathology at varying stages of tau deposition 97%
Similar papers in this journal
- Associations between regional blood-brain barrier disruption, aging, and Alzheimers disease biomarkers in cognitively normal older adults 96%
- A Confounder Controlled Machine Learning Approach: Group Analysis and Classification of Schizophrenia and Alzheimer's Disease using Resting-State Functional Network Connectivity 96%
- c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer’s disease 95%
Similar papers in this journal
- Peripheral inflammation is associated with structural brain atrophy and cognitive decline linked to mild cognitive impairment and Alzheimer's disease 95%
- Deep learning-based imaging classification identified cingulate island sign in dementia with Lewy bodies 95%
- Predicting cognitive decline in a low-dimensional representation of brain morphology 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.