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Updated: Apr 30, 2026
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Conceptualizing neuropsychiatric diseases with multimodal data-driven meta-analyses - the case of behavioral variant
Matthias L Schroeter1, Angela R Laird2, Caroline Chwiesko3
1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Clinic of Cognitive Neurology, University of Leipzig, Germany; Leipzig Research Center for Civilization Diseases, University of Leipzig, Germany; FTLD Consortium, Germany.
Introduction:
Uniform coordinate systems in neuroimaging research have enabled comprehensive systematic and quantitative meta-analyses. Such approaches are particularly relevant for neuropsychiatric diseases, the understanding of their symptoms, prediction and treatment. Behavioral variant frontotemporal dementia (bvFTD), a common neurodegenerative syndrome, is characterized by deep alterations in behavior and personality. Investigating this 'nexopathy' elucidates the healthy social and emotional brain.
Methods:
Here, we combine three multimodal meta-analyses approaches - anatomical and activation likelihood estimates and behavioral domain profiles - to identify neural correlates of bvFTD in 417 patients and 406 control subjects and to extract mental functions associated with this disease by meta-analyzing functional activation studies in the comprehensive probabilistic functional brain atlas of the BrainMap database.
Results:
The analyses identify the frontomedian cortex, basal ganglia, anterior insulae and thalamus as most relevant hubs, with a regional dissociation between atrophy and hypometabolism. Neural networks affected by bvFTD were associated with emotion and reward processing, empathy and executive functions (mainly inhibition), suggesting these functions as core domains affected by the disease and finally leading to its clinical symptoms. In contrast, changes in theory of mind or mentalizing abilities seem to be secondary phenomena of executive dysfunctions.
Conclusions:
The study creates a novel conceptual framework to understand neuropsychiatric diseases by powerful data-driven meta-analytic approaches that shall be extended to the whole neuropsychiatric spectrum in the future.
