Atrophy Patterns Across Dementia Etiologies Assessed by MRI-Based Clustering of a Memory Clinic Population

Q: What does this research mean for the field?

Unsupervised data-driven clustering of MRI-derived brain volumes can identify distinct atrophy patterns associated with different dementia etiologies and clinical characteristics in a real-world memory clinic population. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Myrthe F. van Haaften; Nathalie J Koorn; Elisabeth J. Vinke; Frans M. Vos; Henri A. Vrooman; Rozemarijn L van Bruchem-Visser; Harro Seelaar; Francesco Mattace‐Raso; Esther van den Berg; Meike W. Vernooij; Esther E. Bron

doi:10.1212/wn9.0000000000000120

August 25, 2026Neurology Open AccessOpen Access

Atrophy Patterns Across Dementia Etiologies Assessed by MRI-Based Clustering of a Memory Clinic Population

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Key Points

To identify distinct patterns of brain atrophy using unsupervised data-driven clustering of MRI-derived brain volumes and examine their associations with clinical characteristics and dementia etiologies in a real-world memory clinic setting.
Analyzed brain MRI scans from a memory clinic population presenting with diverse dementia etiologies.
Applied unsupervised, data-driven clustering algorithms to MRI-derived regional brain volume measurements to delineate distinct structural atrophy subtypes.
Unsupervised clustering revealed distinct spatial patterns of brain volume loss that correspond to different clinical dementia etiologies.
Data-driven imaging subgroups mapped onto variations in patient clinical characteristics, reflecting structural heterogeneity across diverse memory clinic presentations.

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Implication

Observational study identifies brain atrophy patterns across dementia etiologies in a memory clinic cohort, highlighting the diagnostic potential of unsupervised MRI clustering.

Abstract

In clinical practice, it is difficult to accurately diagnose dementia etiologies, although brain MRI has strong diagnostic potential. Unsupervised data-driven methods can provide insights into dementia, but real-world studies involving multiple etiologies are lacking. Using a data-driven clustering of MRI-derived brain volumes, this study aimed to find atrophy patterns and evaluate how these relate to dementia etiologies and other clinical characteristics in a memory clinic population.

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Cite This Study

Haaften et al. (2026) studied this question.

synapsesocial.com/papers/6a9145ead15324a1df3a9245 https://doi.org/10.1212/wn9.0000000000000120

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Atrophy Patterns Across Dementia Etiologies Assessed by MRI-Based Clustering of a Memory Clinic Population

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Atrophy Patterns Across Dementia Etiologies Assessed by MRI-Based Clustering of a Memory Clinic Population

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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