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. 2025 Jul 10;25(1):286.
doi: 10.1186/s12883-025-04293-3.

Validation of dementia-free status in the iranian brain imaging database using ADNI data: a transition point from middle age to older adulthood

Sadegh Ghaderi  1   2 , Sana Mohammadi  3 , Farzad Fatehi  3 , Minoo Sisakhti  4   5 , Seyed Amir Hossein Batouli  6   5 ; Alzheimer’s Disease Neuroimaging Initiative
Affiliations

Validation of dementia-free status in the iranian brain imaging database using ADNI data: a transition point from middle age to older adulthood

Sadegh Ghaderi et al. BMC Neurol. .

Abstract

Objective: To validate the Iranian Brain Imaging Database (IBID) for dementia-free status in middle-aged to older adults (over 55 years old) by comparing dementia probability metrics with Alzheimer's disease (AD) patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Methods: This cross-sectional study included 41 cognitively normal participants from IBID (age > 55 years; median (IQR) = 64 (62, 68) years; females = 51.2%) and 41 age/sex-matched AD patients from ADNI (age > 55 years; median (IQR) = 69 (62, 71) years; females = 53.7%). High-resolution 3D T1-weighted MPRAGE MRI data were harmonized across Siemens 3.0 Tesla scanners, preprocessed via the AssemblyNet-AD pipeline (volBrain), and analyzed for grading scores and hippocampal-amygdalo-ventricular atrophy scores (HAVAs), along with hippocampus, amygdala, and inferior lateral ventricle atrophy scores. Non-parametric Mann-Whitney U tests were used to compare group differences, and quantile regression (25th, 50th, and 75th quantiles) model atrophy severity-dependent associations.

Results: IBID participants exhibited minimal dementia probability (median grading score = 0.20% vs. ADNI = 99.93%; U = 84.0, p < 0.0001) and markedly lower atrophy across all regions (HAVAs: 14.76 vs. 86.41; U = 75.0, p < 0.0001), with non-overlapping interquartile ranges. Quantile regression revealed stable hippocampal contributions (q₀.₅ coefficient = 0.311, p < 0.001) and increasing group disparities at higher atrophy severities (q₀.₇₅: IBID vs. ADNI coefficient = - 11.82, p < 0.001).

Conclusion: We provided validated dementia-free IBID neuroimaging data to distinguish normal aging from dementia-AD status.

Keywords: Aging; Alzheimer's disease; Dementia-free classification; MRI; Neuroimaging; Structural atrophy.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The Ethics Committee of the National Institute for Medical Research Development (NIMAD) approved this study (Ethical Code: IR.NIMAD.REC.1396.319) in accordance with the Declaration of Helsinki. All individuals and/or their legal representatives gave written informed consent before entering the study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Summary of the AssemblyNet-AD pipeline results (Hippocampal-Amygdalo-Ventricular Atrophy Scores [HAVAs]) featuring a 69-year-old male, along with grading score reports for all participants
Fig. 2
Fig. 2
The AssemblyNet-AD pipeline for Alzheimer's disease (AD) prediction was implemented using the cloud-based volBrain platform and applied to a sample dataset from ADNI, featuring a 69-year-old male (https://volbrain.net/)
Fig. 3
Fig. 3
Hippocampal-Amygdalo-Ventricular Atrophy Scores (HAVAs) alongside hippocampal, amygdalar, and inferior lateral ventricular atrophy scores for the IBID cohort (a) and ADNI cohort (b)
See this image and copyright information in PMC

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References

    1. Nichols E, Steinmetz JD, Vollset SE, Fukutaki K, Chalek J, Abd-Allah F, et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. The Lancet Public Health. 2022;7:e105–25. - PMC - PubMed
    1. Rittman T. Neurological update: neuroimaging in dementia. J Neurol. 2020;267:3429–35. - PMC - PubMed
    1. Coupé P, Manjón JV, Mansencal B, Tourdias T, Catheline G, Planche V. Hippocampal-amygdalo-ventricular atrophy score: Alzheimer disease detection using normative and pathological lifespan models. Hum Brain Mapp. 2022;43:3270–82. - PMC - PubMed
    1. Korthauer LE, Zhan L, Ajilore O, Leow A, Driscoll I. Disrupted Topology of the Resting State Structural Connectome in Middle-Aged APOE ε4 Carriers. Neuroimage. 2018;178:295–305. - PMC - PubMed
    1. Hennessee JP, Lung T-C, Park DC, Kennedy KM. Age differences in BOLD modulation to task difficulty as a function of amyloid burden. Cereb Cortex. 2024;34:bhae357. - PMC - PubMed

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