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Neuroimage Clin. 2019;22:101812. doi: 10.1016/j.nicl.2019.101812. Epub 2019 Apr 3.

Dynamic functional connectivity changes in dementia with Lewy bodies and Alzheimer's disease.

Author information

1
Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, United Kingdom. Electronic address: j.a.schumacher2@newcastle.ac.uk.
2
Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, United Kingdom; Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, United Kingdom.
3
Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, United Kingdom.
4
Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, United Kingdom; Institute of Neuroscience, Newcastle University, The Henry Wellcome Building, Newcastle upon Tyne NE2 4HH, United Kingdom.
5
Institute of Neuroscience, Newcastle University, The Henry Wellcome Building, Newcastle upon Tyne NE2 4HH, United Kingdom.
6
Department of Psychiatry, University of Cambridge School of Medicine, Cambridge CB2 0SP, United Kingdom.
7
Institute of Cellular Medicine & Newcastle Magnetic Resonance Centre, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, United Kingdom.

Abstract

We studied the dynamic functional connectivity profile of dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) compared to controls, how it differs between the two dementia subtypes, and a possible relation between dynamic connectivity alterations and temporally transient clinical symptoms in DLB. Resting state fMRI data from 31 DLB, 29 AD, and 31 healthy control participants were analyzed using dual regression to determine between-network functional connectivity. Subsequently, we used a sliding window approach followed by k-means clustering and dynamic network analyses to study dynamic functional connectivity. Dynamic connectivity measures that showed significant group differences were tested for correlations with clinical symptom severity. Our results show that AD and DLB patients spent more time than controls in sparse connectivity configurations with absence of strong positive and negative connections and a relative isolation of motor networks from other networks. Additionally, DLB patients spent less time in a more strongly connected state and the variability of global brain network efficiency was reduced in DLB compared to controls. There were no significant correlations between dynamic connectivity measures and clinical symptom severity. An inability to switch out of states of low inter-network connectivity into more highly and specifically connected network configurations might be related to the presence of dementia in general as it was observed in both AD and DLB. In contrast, the loss of global efficiency variability in DLB might indicate the presence of an abnormally rigid brain network and the lack of economical dynamics, factors which could contribute to cognitive slowing and an inability to respond appropriately to situational demands.

KEYWORDS:

Cognitive fluctuations; Dual regression; Dynamic network analysis; Neurodegeneration; Resting state fMRI; Sliding window

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