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

Sunil Kumar Khokhar; Rohit Misra; Manoj Kumar; Faheem Arshad; Nithin Thanissery; Sandeep Kumar; Tejaswini Manae; Subasree Ramakrishnan; Mohammad Haris; Sandhya Mangalore; Suvarna Alladi; Tapan Kumar Gandhi; Rose Dawn Bharath

doi:10.53941/ti.2026.100011

Abstract

Background: Differentiating between mild cognitive impairment (MCI), Alzheimer’s disease (AD), and frontotemporal dementia (FTD) subtypes remains a clinical challenge due to overlapping cognitive symptoms, structural atrophy, and metabolic patterns, especially in the early stages. Multimodal classification approaches integrating neuroimaging and cognitive scores may offer early, accurate characterization and, subsequently, improved diagnostic precision. Methods: In this study, we included 100 participants (50 AD, 30 FTD [14 behavioral variant FTD (bvFTD) and 16 primary progressive aphasia (PPA)], and 20 MCI) who underwent simultaneous structural MRI and FDG-PET imaging. Cortical thickness (CTH) from anatomical MRI and standardized uptake values from FDG-PET were extracted using FreeSurfer and PETSurfer pipelines, respectively. CTH and FDG-PET features were combined into a single vector through a logistic weighting function derived from ACE-III scores, capturing the progressive nature of cognitive decline in dementia. A Naive Bayes classifier was then trained to differentiate diagnostic groups based on the merged features. Results: The model achieved classification accuracies of 83% for MCI vs. dementia (AD + FTD), 85% for MCI vs. FTD, 87% for MCI vs. PPA, 71% for MCI vs. bvFTD, 64% for MCI vs. AD, and 69% for AD vs. FTD. The overall classification accuracy was 68%, with the highest discriminative performance observed in separating MCI from FTD subtypes. Conclusions: This study presents a novel, cognition-weighted multimodal approach combining structural and metabolic imaging to enhance the classification of neurodegenerative syndromes. Findings from this study underscore the potential of integrating ACE-III scores with neuroimaging biomarkers to accurately characterize and differentiate early-stage MCI, AD, and FTD variants.

References

1.
Grossman, M.; Seeley, W.W.; Boxer, A.L.; et al. Frontotemporal Lobar Degeneration. Nat. Rev. Dis. Primers 2023, 9, 40. https://doi.org/10.1038/s41572-023-00447-0.
2.
Rascovsky, K.; Hodges, J.R.; Knopman, D.; et al. Sensitivity of Revised Diagnostic Criteria for the Behavioural Variant of Frontotemporal Dementia. Brain 2011, 134, 2456–2477. https://doi.org/10.1093/brain/awr179.
3.
Younes, K.; Miller, B.L. Neuropsychiatric Aspects of Frontotemporal Dementia. Psychiatr. Clin. N. Am. 2020, 43, 345–360. https://doi.org/10.1016/j.psc.2020.02.005.
4.
Gorno-Tempini, M.L.; Hillis, A.E.; Weintraub, S.; et al. Classification of Primary Progressive Aphasia and Its Variants. Neurology 2011, 76, 1006–1014. https://doi.org/10.1212/WNL.0b013e31821103e6.
5.
Russo, M.J.; Bueri, J.; Alba-Ferrara, L.; et al. Recognition Memory in Individuals with Mild Cognitive Impairment and Alzheimer’s Dementia: A Systematic Review. Appl. Neuropsychol. Adult 2026, 1–21. https://doi.org/10.1080/23279095.2025.2610375.
6.
Albert, M.S.; DeKosky, S.T.; Dickson, D.; et al. The Diagnosis of Mild Cognitive Impairment Due to Alzheimer’s Disease: Recommendations from the National Institute on Aging-Alzheimer’s Association Workgroups on Diagnostic Guidelines for Alzheimer’s Disease. Alzheimers Dement. 2011, 7, 270–279. https://doi.org/10.1016/j.jalz.2011.03.008.
7.
Petersen, R.C.; Roberts, R.O.; Knopman, D.S.; et al. Mild Cognitive Impairment: Ten Years Later. Arch. Neurol. 2009, 66, 1447–1455. https://doi.org/10.1001/archneurol.2009.266.
8.
Dubois, B.; von Arnim, C.A.F.; Burnie, N.; et al. Biomarkers in Alzheimer’s Disease: Role in Early and Differential Diagnosis and Recognition of Atypical Variants. Alzheimers Res. Ther. 2023, 15, 175. https://doi.org/10.1186/s13195-023-01314-6.
9.
Finger, E.C. Frontotemporal Dementias. Continuum 2016, 22, 464–489. https://doi.org/10.1212/CON.0000000000000300.
10.
Foster, N.L.; Heidebrink, J.L.; Clark, C.M.; et al. FDG-PET Improves Accuracy in Distinguishing Frontotemporal Dementia and Alzheimer’s Disease. Brain 2007, 130, 2616–2635. https://doi.org/10.1093/brain/awm177.
11.
Herholz, K.; Westwood, S.; Haense, C.; et al. Evaluation of a Calibrated 18F-FDG PET Score as a Biomarker for Progression in Alzheimer Disease and Mild Cognitive Impairment. J. Nucl. Med. 2011, 52, 1218–1226. https://doi.org/10.2967/jnumed.111.090902.
12.
Ou, Y.-N.; Xu, W.; Li, J.-Q.; et al. FDG-PET as an Independent Biomarker for Alzheimer’s Biological Diagnosis: A Longitudinal Study. Alzheimers Res. Ther. 2019, 11, 57. https://doi.org/10.1186/s13195-019-0512-1.
13.
Qiao, Z.; Wang, G.; Zhao, X.; et al. Neuropsychological Performance Is Correlated with Tau Protein Deposition and Glucose Metabolism in Patients with Alzheimer’s Disease. Front. Aging Neurosci. 2022, 14, 841942.
14.
Cuingnet, R.; Gerardin, E.; Tessieras, J.; et al. Automatic Classification of Patients with Alzheimer’s Disease from Structural MRI: A Comparison of Ten Methods Using the ADNI Database. NeuroImage 2011, 56, 766–781. https://doi.org/10.1016/j.neuroimage.2010.06.013.
15.
Ito, K.; Fukuyama, H.; Senda, M.; et al. Prediction of Outcomes in Mild Cognitive Impairment by Using 18F-FDG-PET: A Multicenter Study. J. Alzheimers Dis. 2015, 45, 543–552. https://doi.org/10.3233/JAD-141338.
16.
Illán-Gala, I.; Falgàs, N.; Friedberg, A.; et al. Diagnostic Utility of Measuring Cerebral Atrophy in the Behavioral Variant of Frontotemporal Dementia and Association with Clinical Deterioration. JAMA Netw. Open 2021, 4, e211290. https://doi.org/10.1001/jamanetworkopen.2021.1290.
17.
Pérez-Millan, A.; Lal-Trehan Estrada, U.M.; Falgàs, N.; et al. The Cortical Asymmetry Index for Subtyping Dementia Patients. Eur. Radiol. 2025, 35, 4713–4721. https://doi.org/10.1007/s00330-025-11400-y.
18.
Pérez-Millan, A.; Thirion, B.; Falgàs, N.; et al. Beyond Group Classification: Probabilistic Differential Diagnosis of Frontotemporal Dementia and Alzheimer’s Disease with MRI and CSF Biomarkers. Neurobiol. Aging 2024, 144, 1–11. https://doi.org/10.1016/j.neurobiolaging.2024.08.008.
19.
Kaipainen, A.; Jääskeläinen, O.; Liu, Y.; et al. Cerebrospinal Fluid and MRI Biomarkers in Neurodegenerative Diseases: A Retrospective Memory Clinic-Based Study. J. Alzheimers Dis. 2020, 75, 751–765. https://doi.org/10.3233/JAD-200175.
20.
Poonam, K.; Guha, R.; Chakrabarti, P.P. Artificial Intelligence Based Hierarchical Classification of Frontotemporal Dementia. In Proceedings of the 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 15–19 July 2024; pp. 1–4.
21.
Kim, J.P.; Kim, J.; Park, Y.H.; et al. Machine Learning Based Hierarchical Classification of Frontotemporal Dementia and Alzheimer’s Disease. NeuroImage Clin. 2019, 23, 101811. https://doi.org/10.1016/j.nicl.2019.101811.
22.
Wee, C.-Y.; Yap, P.-T.; Zhang, D.; et al. Identification of MCI Individuals Using Structural and Functional Connectivity Networks. NeuroImage 2012, 59, 2045–2056. https://doi.org/10.1016/j.neuroimage.2011.10.015.
23.
Westman, E.; Muehlboeck, J.-S.; Simmons, A. Combining MRI and CSF Measures for Classification of Alzheimer’s Disease and Prediction of Mild Cognitive Impairment Conversion. NeuroImage 2012, 62, 229–238. https://doi.org/10.1016/j.neuroimage.2012.04.056.
24.
Mekala, S.; Paplikar, A.; Mioshi, E.; et al. Dementia Diagnosis in Seven Languages: The Addenbrooke’s Cognitive Examination-III in India. Arch. Clin. Neuropsychol. 2020, 35, 528–538. https://doi.org/10.1093/arclin/acaa013.
25.
McKhann, G.M.; Knopman, D.S.; Chertkow, H.; et al. The Diagnosis of Dementia Due to Alzheimer’s Disease: Recommendations from the National Institute on Aging-Alzheimer’s Association Workgroups on Diagnostic Guidelines for Alzheimer’s Disease. Alzheimers Dement. 2011, 7, 263–269. https://doi.org/10.1016/j.jalz.2011.03.005.
26.
Petersen, R.C. Mild Cognitive Impairment as a Diagnostic Entity. J. Intern. Med. 2004, 256, 183–194. https://doi.org/10.1111/j.1365-2796.2004.01388.x.
27.
Khokhar, S.K.; Kumar, M.; Kumar, S.; et al. Alzheimer’s Disease Is Associated with Increased Network Assortativity: Evidence from Metabolic Connectivity. Brain Connect. 2023, 13, 610–620. https://doi.org/10.1089/brain.2023.0024.
28.
Khokhar, S.K.; Kumar, M.; Arshad, F.; et al. Multiplex Connectomics Reveal Altered Networks in Frontotemporal Dementia: A Multisite Study. Netw. Neurosci. 2025, 9, 615–630. https://doi.org/10.1162/netn_a_00448.
29.
Fischl, B. FreeSurfer. NeuroImage 2012, 62, 774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021.
30.
Dale, A.M.; Fischl, B.; Sereno, M.I. Cortical Surface-Based Analysis. I. Segmentation and Surface Reconstruction. NeuroImage 1999, 9, 179–194. https://doi.org/10.1006/nimg.1998.0395.
31.
Desikan, R.S.; Ségonne, F.; Fischl, B.; et al. An Automated Labeling System for Subdividing the Human Cerebral Cortex on MRI Scans into Gyral Based Regions of Interest. NeuroImage 2006, 31, 968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021.
32.
Greve, D.N.; Svarer, C.; Fisher, P.M.; et al. Cortical Surface-Based Analysis Reduces Bias and Variance in Kinetic Modeling of Brain PET Data. NeuroImage 2014, 92, 225–236. https://doi.org/10.1016/j.neuroimage.2013.12.021.
33.
Greve, D.N.; Salat, D.H.; Bowen, S.L.; et al. Different Partial Volume Correction Methods Lead to Different Conclusions: An (18)F-FDG-PET Study of Aging. NeuroImage 2016, 132, 334–343. https://doi.org/10.1016/j.neuroimage.2016.02.042.
34.
Pedregosa, F.; Varoquaux, G.; Gramfort, A.; et al. Scikit-Learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825–2830.
35.
Herholz, K.; Ebmeier, K. Clinical Amyloid Imaging in Alzheimer’s Disease. Lancet Neurol. 2011, 10, 667–670. https://doi.org/10.1016/S1474-4422(11)70123-5.
36.
Jack, C.R., Jr.; Bennett, D.A.; Blennow, K.; et al. NIA-AA Research Framework: Toward a Biological Definition of Alzheimer’s Disease. Alzheimers Dement. 2018, 14, 535–562. https://doi.org/10.1016/j.jalz.2018.02.018.
37.
Bi, S.; Chen, Z.; Tao, W.; et al. Multimodal Imaging for Diagnosis of Frontotemporal Dementia Using Integrated PET/MR. J. Nucl. Med. 2024, 65, 241923.
38.
Venugopalan, J.; Tong, L.; Hassanzadeh, H.R.; et al. Multimodal Deep Learning Models for Early Detection of Alzheimer’s Disease Stage. Sci. Rep. 2021, 11, 3254. https://doi.org/10.1038/s41598-020-74399-w.
39.
Lawry Aguila, A.; Lorenzini, L.; Janahi, M.; et al. Deep Normative Modelling Reveals Insights into Early-Stage Alzheimer’s Disease Using Multi-Modal Neuroimaging Data. Alzheimers Res. Ther. 2025, 17, 107. https://doi.org/10.1186/s13195-025-01753-3.
40.
Marinescu, R.V.; Eshaghi, A.; Lorenzi, M.; et al. DIVE: A Spatiotemporal Progression Model of Brain Pathology in Neurodegenerative Disorders. NeuroImage 2019, 192, 166–177. https://doi.org/10.1016/j.neuroimage.2019.02.053.
41.
Wang, Y.; Liu, S.; Spiteri, A.G.; et al. Understanding Machine Learning Applications in Dementia Research and Clinical Practice: A Review for Biomedical Scientists and Clinicians. Alzheimers Res. Ther. 2024, 16, 175. https://doi.org/10.1186/s13195-024-01540-6.
42.
Moradi, E.; Pepe, A.; Gaser, C.; et al. Machine Learning Framework for Early MRI-Based Alzheimer’s Conversion Prediction in MCI Subjects. NeuroImage 2015, 104, 398–412. https://doi.org/10.1016/j.neuroimage.2014.10.002.
43.
Sadiq, M.U.; Kwak, K.; Dayan, E. Model-Based Stratification of Progression along the Alzheimer Disease Continuum Highlights the Centrality of Biomarker Synergies. Alzheimers Res. Ther. 2022, 14, 16. https://doi.org/10.1186/s13195-021-00941-1.
44.
Kubi, N.N.B.A.; Nazir, S. Dementia Prediction with Multimodal Clinical and Imaging Data. Int. J. Inf. Technol. 2025, 17, 5–16. https://doi.org/10.1007/s41870-024-02326-7.
45.
Zhang, D.; Wang, Y.; Zhou, L.; et al. Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment. NeuroImage 2011, 55, 856–867. https://doi.org/10.1016/j.neuroimage.2011.01.008.
46.
Vogel, J.W.; Young, A.L.; Oxtoby, N.P.; et al. Four Distinct Trajectories of Tau Deposition Identified in Alzheimer’s Disease. Nat. Med. 2021, 27, 871–881. https://doi.org/10.1038/s41591-021-01309-6.
47.
Musa, G.; Slachevsky, A.; Muñoz-Neira, C.; et al. Alzheimer’s Disease or Behavioral Variant Frontotemporal Dementia? Review of Key Points Toward an Accurate Clinical and Neuropsychological Diagnosis. J. Alzheimers Dis. JAD 2020, 73, 833–848. https://doi.org/10.3233/JAD-190924.

Scilight Press

Author Information

Abstract

Keywords

References

About Scilight

Journals

Publishing Policies

Contact Us