| Literature DB >> 29392456 |
Kazuya Kawabata1,2, Hirohisa Watanabe3,4, Kazuhiro Hara1, Epifanio Bagarinao2, Noritaka Yoneyama5, Aya Ogura1, Kazunori Imai1, Michihito Masuda1, Takamasa Yokoi1, Reiko Ohdake2, Yasuhiro Tanaka1, Takashi Tsuboi1, Tomohiko Nakamura1, Masaaki Hirayama6, Mizuki Ito1, Naoki Atsuta1, Satoshi Maesawa7, Shinji Naganawa8, Masahisa Katsuno1, Gen Sobue9,10.
Abstract
Cognitive deficits in Parkinson's disease (PD) are heterogeneous entities, but a relationship between the heterogeneity of cognitive deficits and resting-state network (RSN) changes remains elusive. In this study, we examined five sub-domain scores according to Addenbrooke's Cognitive Examination-Revised (ACE-R) for the cognitive evaluation and classification of 72 non-demented patients with PD. Twenty-eight patients were classified as PD with normal cognition (PD-NC). The remaining 44 were subdivided into the following 2 groups using a hierarchical cluster analysis: 20 with a predominant decrease in memory (PD with amnestic cognitive deficits: PD-A) and 24 with good memory who exhibited a decrease in other sub-domains (PD with non-amnestic cognitive deficits: PD-NA). We used an independent component analysis of RS-fMRI data to investigate the inter-group differences of RSN. Compared to the controls, the PD-A showed lower FC within the ventral default mode network (vDMN) and the visuospatial network. On the other hand, the PD-NA showed lower FC within the visual networks and the cerebellum-brainstem network. Significant differences in the FC within the vDMN and cerebellum-brainstem network were observed between the PD-A and PD-NA, which provided a good discrimination between PD-A and PD-NA using a support vector machine. Distinct patterns of cognitive deficits correspond to different RSN changes.Entities:
Keywords: Cognitive deficits; Functional connectivity; Independent component analysis; Parkinson’s disease; Resting-state fMRI; Resting-state network
Mesh:
Year: 2018 PMID: 29392456 DOI: 10.1007/s00415-018-8755-5
Source DB: PubMed Journal: J Neurol ISSN: 0340-5354 Impact factor: 4.849