Could Ratio-Based Morphometric Analysis of Subcortical Limbic Structures Assist in Alzheimer's Disease Diagnosis?

Abstract

Objectives: Alzheimer’s disease is a neurodegenerative disorder that primarily affects subcortical limbic structures. This study aimed to assess volumetric differences in subcortical limbic structures and to compare the relative volumes of surrounding brain regions - such as the telencephalon, diencephalon, and brainstem subdivisions - between individuals with Alzheimer’s disease and healthy controls.

Methods: This study involved 24 patients with Alzheimer’s disease and 16 healthy controls. Subcortical structures were segmented automatically using MRICloud on 3D T1-weighted magnetic resonance imaging scans. To minimize individual anatomical variability, volume ratios relative to neighboring brain regions were also calculated.

Results: Significant volume reductions were found in the amygdala (left: P=0.004, right: P=0.005, total: P=0.004), hypothalamus (left: P=0.005, right: P>0.05, total: P=0.007), diencephalon (left: P=0.001, right: P=0.012, total: P>0.05), and mammillary bodies (left: P=0.002, right: P=0.003, total: P=0.003) in the Alzheimer’s disease group compared to healthy controls. Although most volume ratios - particularly those involving the amygdala and mammillary bodies - were higher in the Alzheimer’s disease group, they did not reach statistical significance (P>0.05).

Conclusions: This study confirms prominent atrophy in subcortical limbic structures in Alzheimer’s disease. While diencephalon volume was reduced, its ratio to the amygdalae changed minimally, likely reflecting more severe atrophy of the amygdalae. Similarly, the mesencephalon-to-hypothalamus ratio showed no significant difference, suggesting parallel atrophy. These findings support the combined use of abs olute and ratio-based analyses and demonstrate the potential of MRICloud to identify Alzheimer’s disease-related neuroanatomical changes.

Keywords

• Alzheimer’s Disease
• Amygdala
• Hypothalamus
• Mammillary Bodies
• Subcortical Structures
• Magnetic Resonance Imaging

References

1. 1. Livingston G, Huntley J, Liu KY, et al. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. Lancet. 2024;404(10452):572-628. doi: 10.1016/S0140-6736(24)01296-0.
2. 2. Mayo Clinic. Alzheimer’s disease: Symptoms and causes [Internet]. 2024 [cited 2025 Apr 3]. Available from: https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447.
3. 3. 2023 Alzheimer's disease facts and figures. Alzheimers Dement. 2023;19(4):1598-1695. doi: 10.1002/alz.13016.
4. 4. Rao G, Gao H, Wang X, Zhang J, Ye M, Rao L. MRI measurements of brain hippocampus volume in relation to mild cognitive impairment and Alzheimer disease: A systematic review and meta-analysis. Medicine (Baltimore). 2023;102(36):e34997. doi: 10.1097/MD.0000000000034997.
5. 5. Scarmeas N, Hadjigeorgiou GM, Papadimitriou A, et al. Motor signs during the course of Alzheimer disease. Neurology. 2004;63(6):975-982. doi: 10.1212/01.WNL.0000138442.29091.5E.
6. 6. Mega MS, Lee L, Dinov ID, Mishkin F, Toga AW, Cummings JL. Cerebral correlates of psychotic symptoms in Alzheimer's disease. J Neurol Neurosurg Psychiatry. 2000;69(2):167-171. doi: 10.1136/jnnp.69.2.167.
7. 7. Braak H, Braak E. Alzheimer's disease affects limbic nuclei of the thalamus. Acta Neuropathol. 1991;81(3):261-268. doi: 10.1007/BF00305867.
8. 8. Forno G, Lladó A, Hornberger M. Going round in circles-The Papez circuit in Alzheimer's disease. Eur J Neurosci. 2021;54(10):7668-7687. doi: 10.1111/ejn.15494.

9. 9. Gerritsen L, Rijpkema M, van Oostrom I, et al. Amygdala to hippocampal volume ratio is associated with negative memory bias in healthy subjects. Psychol Med. 2012;42(2):335-343. doi: 10.1017/S003329171100122X.
10. 10. Sakamoto R, Marano C, Miller MI, et al. Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening. J Healthc Eng. 2019;2019:9507193. doi: 10.1155/2019/9507193.
11. 11. Hannoun S, Tutunji R, El Homsi M, Saaybi S, Hourani R. Automatic Thalamus Segmentation on Unenhanced 3D T1 Weighted Images: Comparison of Publicly Available Segmentation Methods in a Pediatric Population. Neuroinformatics. 2019;17(3):443-450. doi: 10.1007/s12021-018-9408-7.
12. 12. Xu P, Estrada S, Etteldorf R, et al. Hypothalamic volume is associated with age, sex and cognitive function across lifespan: a comparative analysis of two large population-based cohort studies. EBioMedicine. 2025;111:105513. doi: 10.1016/j.ebiom.2024.105513.
13. 13. Qu H, Ge H, Wang L, Wang W, Hu C. Volume changes of hippocampal and amygdala subfields in patients with mild cognitive impairment and Alzheimer's disease. Acta Neurol Belg. 2023;123(4):1381-1393. doi: 10.1007/s13760-023-02235-9.
14. 14. Copenhaver BR, Rabin LA, Saykin AJ, et al. The fornix and mammillary bodies in older adults with Alzheimer's disease, mild cognitive impairment, and cognitive complaints: a volumetric MRI study. Psychiatry Res. 2006;147(2-3):93-103. doi: 10.1016/j.pscychresns.2006.01.015.
15. 15. Huang WC, Peng Z, Murdock MH, et al. Lateral mammillary body neurons in mouse brain are disproportionately vulnerable in Alzheimer's disease. Sci Transl Med. 2023;15(692):eabq1019. doi: 10.1126/scitranslmed.abq1019.
16. 16. Salman Y, Gérard T, Huyghe L, et al; Alzheimer's Disease Neuroimaging Initiative. Amygdala atrophies in specific subnuclei in preclinical Alzheimer's disease. Alzheimers Dement. 2024;20(10):7205-7219. doi: 10.1002/alz.14235.
17. 17. Raji CA, Meysami S, Merrill DA, Porter VR, Mendez MF. Brain structure in bilingual compared to monolingual individuals with Alzheimer's disease: Proof of concept. J Alzheimers Dis. 2020;76(1):275-280. doi: 10.3233/JAD-200200.
18. 18. Lee JH, Ryan J, Andreescu C, Aizenstein H, Lim HK. Brainstem morphological changes in Alzheimer's disease. Neuroreport. 2015;26(7):411-415. doi: 10.1097/WNR.0000000000000362.