Literature DB >> 23668966

Selectively and progressively disrupted structural connectivity of functional brain networks in Alzheimer's disease - revealed by a novel framework to analyze edge distributions of networks detecting disruptions with strong statistical evidence.

Klaus Hahn1, Nicholas Myers2, Sergei Prigarin3, Karsten Rodenacker4, Alexander Kurz5, Hans Förstl5, Claus Zimmer6, Afra M Wohlschläger7, Christian Sorg2.   

Abstract

Alzheimer's disease (AD) disrupts selectively and progressively (increasing with severity) functional connectivity of intrinsic brain networks (IBNs), most prominent in the default mode network. Given that IBNs' functional connectivity depends on structural connectivity, we hypothesize for our study selective and progressive changes of IBN based structural connectivity in AD. To achieve strong statistical evidence, we introduce a novel statistical method based on the edge frequency distributions of structural connectivity networks. Such non-Gaussian distributions are compared in a multiple testing scheme, combining a flexible nonparametric test statistic with permutation based strong control of the family wise error rate. We assessed 26 healthy elderly, 23 patients with AD-dementia, and 28 patients with mild cognitive impairment (MCI) by resting-state functional MRI, diffusion tensor imaging, and clinical-neuropsychological testing including annual follow-up assessment. After 3years, 50% of the patients with MCI converted to AD. Tractography of diffusion tensor data identifies structural connectivity networks between regions of IBNs, which are detected by an independent component analysis of resting state fMRI data. We find that IBNs' structural connectivity is selectively and progressively disrupted with primary changes in the default mode network. Correspondent results are found for IBNs' functional connectivity. In addition, structural connectivity across the nodes of all IBNs separated individual MCI patients converting to AD from non-converters. Conclusively, our study provides a new approach to analyze connectivity networks by their non-Gaussian edge frequency distributions and achieves strong statistical evidence by application of the family wise error rate. Data analysis provides selective and progressive disruptions of IBN's structural connectivity in AD and demonstrates the increased power of our method compared to recent studies.
Copyright © 2013 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Edge frequency distributions; Intrinsic functional brain networks; Multimodal fMRI/DTI analysis of Alzheimer's disease; Nonparametric Brunner statistic; Structural connectivity networks

Mesh:

Year:  2013        PMID: 23668966     DOI: 10.1016/j.neuroimage.2013.05.011

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


  27 in total

Review 1.  A biased competition account of attention and memory in Alzheimer's disease.

Authors:  Kathrin Finke; Nicholas Myers; Peter Bublak; Christian Sorg
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2013-09-09       Impact factor: 6.237

2.  The association of mid-to late-life systemic inflammation with white matter structure in older adults: The Atherosclerosis Risk in Communities Study.

Authors:  Keenan A Walker; B Gwen Windham; Melinda C Power; Ron C Hoogeveen; Aaron R Folsom; Christie M Ballantyne; David S Knopman; Elizabeth Selvin; Clifford R Jack; Rebecca F Gottesman
Journal:  Neurobiol Aging       Date:  2018-04-04       Impact factor: 4.673

3.  Resting State Abnormalities of the Default Mode Network in Mild Cognitive Impairment: A Systematic Review and Meta-Analysis.

Authors:  Lisa T Eyler; Jeremy A Elman; Sean N Hatton; Sarah Gough; Anna K Mischel; Donald J Hagler; Carol E Franz; Anna Docherty; Christine Fennema-Notestine; Nathan Gillespie; Daniel Gustavson; Michael J Lyons; Michael C Neale; Matthew S Panizzon; Anders M Dale; William S Kremen
Journal:  J Alzheimers Dis       Date:  2019       Impact factor: 4.472

4.  Multi-resolution statistical analysis of brain connectivity graphs in preclinical Alzheimer's disease.

Authors:  Won Hwa Kim; Nagesh Adluru; Moo K Chung; Ozioma C Okonkwo; Sterling C Johnson; Barbara B Bendlin; Vikas Singh
Journal:  Neuroimage       Date:  2015-05-27       Impact factor: 6.556

5.  The elusive concept of brain network. Comment on "Understanding brain networks and brain organization" by Luiz Pessoa.

Authors:  Barry Horwitz
Journal:  Phys Life Rev       Date:  2014-06-24       Impact factor: 11.025

6.  Disrupted pathways from frontal-parietal cortex to basal ganglia and cerebellum in patients with unmedicated obsessive compulsive disorder as observed by whole-brain resting-state effective connectivity analysis - a small sample pilot study.

Authors:  Wei Liu; Minghui Hua; Jun Qin; Qiuju Tang; Yunyi Han; Hongjun Tian; Daxiang Lian; Zhengqing Zhang; Wenqiang Wang; Chunxiang Wang; Ce Chen; Deguo Jiang; Gongying Li; Xiaodong Lin; Chuanjun Zhuo
Journal:  Brain Imaging Behav       Date:  2021-06       Impact factor: 3.978

Review 7.  Imaging Neurodegeneration: Steps Toward Brain Network-Based Pathophysiology and Its Potential for Multi-modal Imaging Diagnostics.

Authors:  C Sorg; J Göttler; C Zimmer
Journal:  Clin Neuroradiol       Date:  2015-07-28       Impact factor: 3.649

8.  Widespread white matter degeneration preceding the onset of dementia.

Authors:  Klaus H Maier-Hein; Carl-Fredrik Westin; Martha E Shenton; Michael W Weiner; Ashish Raj; Philipp Thomann; Ron Kikinis; Bram Stieltjes; Ofer Pasternak
Journal:  Alzheimers Dement       Date:  2014-07-14       Impact factor: 21.566

Review 9.  Diffusion tensor imaging in Alzheimer's disease and affective disorders.

Authors:  Stefan J Teipel; Martin Walter; Yuttachai Likitjaroen; Peter Schönknecht; Oliver Gruber
Journal:  Eur Arch Psychiatry Clin Neurosci       Date:  2014-03-05       Impact factor: 5.270

10.  Effective connectivity in the default mode network is distinctively disrupted in Alzheimer's disease-A simultaneous resting-state FDG-PET/fMRI study.

Authors:  Martin Scherr; Lukas Utz; Masoud Tahmasian; Lorenzo Pasquini; Michel J Grothe; Josef P Rauschecker; Timo Grimmer; Alexander Drzezga; Christian Sorg; Valentin Riedl
Journal:  Hum Brain Mapp       Date:  2019-01-30       Impact factor: 5.038

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