title: Whole-brain analysis reveals increased neuroanatomical asymmetries in dementia for hippocampus and amygdala.
abstract: Structural magnetic resonance imaging data are frequently analysed to reveal morphological changes of the human brain in dementia. Most contemporary imaging biomarkers are scalar values, such as the volume of a structure, and may miss the localized morphological variation of early presymptomatic disease progression. Neuroanatomical shape descriptors, however, can represent complex geometric information of individual anatomical regions and may demonstrate increased sensitivity in association studies. Yet, they remain largely unexplored. In this article, we introduce a novel technique to study shape asymmetries of neuroanatomical structures across subcortical and cortical brain regions. We demonstrate that neurodegeneration of subcortical structures in Alzheimer's disease is not symmetric. The hippocampus shows a significant increase in asymmetry longitudinally and both hippocampus and amygdala show a significantly higher asymmetry cross-sectionally concurrent with disease severity above and beyond an ageing effect. Our results further suggest that the asymmetry in these structures is undirectional and that primarily the anterior hippocampus becomes asymmetric. Based on longitudinal asymmetry measures we subsequently study the progression from mild cognitive impairment to dementia, demonstrating that shape asymmetry in hippocampus, amygdala, caudate and cortex is predictive of disease onset. The same analyses on scalar volume measurements did not produce any significant results, indicating that shape asymmetries, potentially induced by morphometric changes in subnuclei, rather than size asymmetries are associated with disease progression and can yield a powerful imaging biomarker for the early presymptomatic classification and prediction of Alzheimer's disease. Because literature has focused on contralateral volume differences, subcortical disease lateralization may have been overlooked thus far.
authors.affiliation: 1 A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA wachinger@gmail.com mreuter@nmr.mgh.harvard.edu.|2 Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.|3 Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
authors.forename: Christian
authors.initials: C
authors.lastname: Wachinger
authors.affiliation: 1 A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA.|4 Department of Radiology, Harvard Medical School, Boston MA, USA.
authors.forename: David H
authors.initials: DH
authors.lastname: Salat
authors.affiliation: 5 University of California, San Francisco, San Francisco VA Medical Center, San Francisco CA, 94121 USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Weiner
authors.affiliation: 1 A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA wachinger@gmail.com mreuter@nmr.mgh.harvard.edu.|3 Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.|4 Department of Radiology, Harvard Medical School, Boston MA, USA.
authors.forename: Martin
authors.initials: M
authors.lastname: Reuter
mesh_terms: D000544:Alzheimer Disease / Q000000981:diagnostic imaging*; D000679:Amygdala / Q000000981:diagnostic imaging*; D060825:Cognitive Dysfunction / Q000000981:diagnostic imaging*; D016208:Databases, Factual; D018450:Disease Progression; D006624:Hippocampus / Q000000981:diagnostic imaging*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical
keywords: Alzheimer’s disease; brain asymmetry; longitudinal shape analysis; mixed effects models; survival analysis