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July 12, 2026Translational InsightsOpen Access

Cognition-Weighted Multimodal MRI and FDG-PET Features for Classification of Mild Cognitive Impairment, Alzheimer’s Disease, and Frontotemporal Dementia

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Authors

RMRohit MisraSKSunil Kumar KhokharMKManoj Kumar

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Overview

Randomized trial evaluates multimodal imaging for accurate classification of cognitive disorders, suggesting improved diagnostic capability.

Key Points

  • The study aims to differentiate between mild cognitive impairment, Alzheimer's disease, and frontotemporal dementia using multimodal imaging.
  • Included 100 participants (50 AD, 30 FTD, 20 MCI) undergoing simultaneous structural MRI and FDG-PET imaging.
  • Extracted cortical thickness and standardized uptake values using FreeSurfer and PETSurfer tools.
  • Combined imaging features into a vector using a logistic weighting function from ACE-III scores for classification with a Naive Bayes classifier.
  • Model achieved classification accuracy of 83% for MCI vs. dementia, 85% for MCI vs. FTD, and 87% for MCI vs. PPA.
  • Overall classification accuracy was 68%, with the highest performance in separating MCI from FTD subtypes.
  • Findings suggest integration of ACE-III scores and neuroimaging can significantly enhance early-stage cognitive disorder classification.

Cite This Study

Misra et al. (2026) studied this question.

synapsesocial.com/papers/6a5333754f7abc118adee0bchttps://doi.org/10.53941/ti.2026.100011
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Multimodal Imaging and Logistic Weighted Cognitive Scores for Classification of MCI, AD, and FTD Subtypes2025
  2. 2Detecting Alzheimer’s Disease Stages and Frontotemporal Dementia in Time Courses of Resting-State fMRI Data Using a Machine Learning Approach2024 · 7 citations
  3. 3Early Detection of Alzheimer’s Disease: Leveraging Biomarker from FDG-PET Using Weighted SVM Clustering2024
  4. 4Classification and prediction of Alzheimer’s disease stages and conversion from mild cognitive impairment based on multimodal data fusion2026
  5. 5A Two-Stage Framework for Early Detection and Subtype Identification of Alzheimer’s Disease Through Multimodal Biomarker Extraction and Improved GCN2026 · 1 citations