Literature DB >> 15501101

Computational anatomy and neuropsychiatric disease: probabilistic assessment of variation and statistical inference of group difference, hemispheric asymmetry, and time-dependent change.

John G Csernansky1, Lei Wang, Sarang C Joshi, J Tilak Ratnanather, Michael I Miller.   

Abstract

Three components of computational anatomy (CA) are reviewed in this paper: (i) the computation of large-deformation maps, that is, for any given coordinate system representations of two anatomies, computing the diffeomorphic transformation from one to the other; (ii) the computation of empirical probability laws of anatomical variation between anatomies; and (iii) the construction of inferences regarding neuropsychiatric disease states. CA utilizes spatial-temporal vector field information obtained from large-deformation maps to assess anatomical variabilities and facilitate the detection and quantification of abnormalities of brain structure in subjects with neuropsychiatric disorders. Neuroanatomical structures are divided into two types: subcortical structures-gray matter (GM) volumes enclosed by a single surface-and cortical mantle structures-anatomically distinct portions of the cerebral cortical mantle layered between the white matter (WM) and cerebrospinal fluid (CSF). Because of fundamental differences in the geometry of these two types of structures, image-based large-deformation high-dimensional brain mapping (HDBM-LD) and large-deformation diffeomorphic metric matching (LDDMM) were developed for the study of subcortical structures and labeled cortical mantle distance mapping (LCMDM) was developed for the study of cortical mantle structures. Studies of neuropsychiatric disorders using CA usually require the testing of hypothesized group differences with relatively small numbers of subjects per group. Approaches that increase the power for testing such hypotheses include methods to quantify the shapes of individual structures, relationships between the shapes of related structures (e.g., asymmetry), and changes of shapes over time. Promising preliminary studies employing these approaches to studies of subjects with schizophrenia and very mild to mild Alzheimer's disease (AD) are presented.

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Year:  2004        PMID: 15501101     DOI: 10.1016/j.neuroimage.2004.07.025

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  51 in total

1.  3D maps localize caudate nucleus atrophy in 400 Alzheimer's disease, mild cognitive impairment, and healthy elderly subjects.

Authors:  S K Madsen; A J Ho; X Hua; P S Saharan; A W Toga; C R Jack; M W Weiner; P M Thompson
Journal:  Neurobiol Aging       Date:  2010-06-11       Impact factor: 4.673

2.  Morphometric analysis of amygdla and hippocampus shape in impulsively aggressive and healthy control subjects.

Authors:  Emil F Coccaro; Royce Lee; Michael McCloskey; John G Csernansky; Lei Wang
Journal:  J Psychiatr Res       Date:  2015-07-13       Impact factor: 4.791

3.  Abnormalities of cingulate gyrus neuroanatomy in schizophrenia.

Authors:  Lei Wang; Malini Hosakere; Joshua C L Trein; Alex Miller; J Tilak Ratnanather; Deanna M Barch; Paul A Thompson; Anqi Qiu; Mokhtar H Gado; Michael I Miller; John G Csernansky
Journal:  Schizophr Res       Date:  2007-04-11       Impact factor: 4.939

4.  Structural pathology underlying neuroendocrine dysfunction in schizophrenia.

Authors:  Morris B Goldman; Lei Wang; Carly Wachi; Sheeraz Daudi; John Csernansky; Megan Marlow-O'Connor; Sarah Keedy; Ivan Torres
Journal:  Behav Brain Res       Date:  2010-11-17       Impact factor: 3.332

5.  Combining anatomical manifold information via diffeomorphic metric mappings for studying cortical thinning of the cingulate gyrus in schizophrenia.

Authors:  Anqi Qiu; Laurent Younes; Lei Wang; J Tilak Ratnanather; Sarah K Gillepsie; Gillian Kaplan; John Csernansky; Michael I Miller
Journal:  Neuroimage       Date:  2007-05-18       Impact factor: 6.556

6.  Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls.

Authors:  Jonathan H Morra; Zhuowen Tu; Liana G Apostolova; Amity E Green; Christina Avedissian; Sarah K Madsen; Neelroop Parikshak; Arthur W Toga; Clifford R Jack; Norbert Schuff; Michael W Weiner; Paul M Thompson
Journal:  Neuroimage       Date:  2008-11-08       Impact factor: 6.556

7.  Automated 3D mapping of hippocampal atrophy and its clinical correlates in 400 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls.

Authors:  Jonathan H Morra; Zhuowen Tu; Liana G Apostolova; Amity E Green; Christina Avedissian; Sarah K Madsen; Neelroop Parikshak; Xue Hua; Arthur W Toga; Clifford R Jack; Norbert Schuff; Michael W Weiner; Paul M Thompson
Journal:  Hum Brain Mapp       Date:  2009-09       Impact factor: 5.038

8.  Cannabis-related episodic memory deficits and hippocampal morphological differences in healthy individuals and schizophrenia subjects.

Authors:  Matthew J Smith; Derin J Cobia; James L Reilly; Jodi M Gilman; Andrea G Roberts; Kathryn I Alpert; Lei Wang; Hans C Breiter; John G Csernansky
Journal:  Hippocampus       Date:  2015-03-11       Impact factor: 3.899

9.  Progressive deformation of deep brain nuclei and hippocampal-amygdala formation in schizophrenia.

Authors:  Lei Wang; Daniel Mamah; Michael P Harms; Meghana Karnik; Joseph L Price; Mokhtar H Gado; Paul A Thompson; Deanna M Barch; Michael I Miller; John G Csernansky
Journal:  Biol Psychiatry       Date:  2008-09-23       Impact factor: 13.382

10.  In vivo hippocampal subfield shape related to TDP-43, amyloid beta, and tau pathologies.

Authors:  Veronika Hanko; Alexandra C Apple; Kathryn I Alpert; Kristen N Warren; Julie A Schneider; Konstantinos Arfanakis; David A Bennett; Lei Wang
Journal:  Neurobiol Aging       Date:  2018-10-25       Impact factor: 4.673

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