scholarly journals On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification

2017 ◽  
Vol 38 (12) ◽  
pp. 5845-5858 ◽  
Author(s):  
Rachel N. Pläschke ◽  
Edna C. Cieslik ◽  
Veronika I. Müller ◽  
Felix Hoffstaedter ◽  
Anna Plachti ◽  
...  
2016 ◽  
Vol 31 (11) ◽  
pp. 1676-1684 ◽  
Author(s):  
Brian D. Berman ◽  
Jason Smucny ◽  
Korey P. Wylie ◽  
Erika Shelton ◽  
Eugene Kronberg ◽  
...  

2001 ◽  
Vol 8 (2) ◽  
pp. 91-94 ◽  
Author(s):  
Masafumi Fukuda ◽  
Christine Edwards ◽  
David Eidelberg

2014 ◽  
Vol 35 (9) ◽  
pp. 4620-4634 ◽  
Author(s):  
Hugo-Cesar Baggio ◽  
Roser Sala-Llonch ◽  
Bàrbara Segura ◽  
Maria-José Marti ◽  
Francesc Valldeoriola ◽  
...  

2015 ◽  
Vol 21 (10) ◽  
pp. 793-801 ◽  
Author(s):  
Hugo C. Baggio ◽  
Bàrbara Segura ◽  
Carme Junque

2019 ◽  
Vol 35 (3) ◽  
pp. 499-503 ◽  
Author(s):  
Robert L. White ◽  
Meghan C. Campbell ◽  
Dake Yang ◽  
William Shannon ◽  
Abraham Z. Snyder ◽  
...  

2021 ◽  
Author(s):  
Joan Duprez ◽  
Judie Tabbal ◽  
Mahmoud Hassan ◽  
Julien Modolo ◽  
Aya Kabbara ◽  
...  

Among the cognitive symptoms that are associated with Parkinson's disease (PD), alterations in cognitive action control (CAC) are commonly reported in patients. CAC enables the suppression of an automatic action, in favor of a goal-directed one. The implementation of CAC is time-resolved and arguably associated with dynamic changes in functional brain networks. However, the electrophysiological functional networks involved, their dynamic changes, and how these changes are affected by PD, still remain unknown. In this study, to address this gap of knowledge, 21 PD patients and 10 healthy controls (HC) underwent a Simon task while high-density electroencephalography (HD-EEG) was recorded. Source-level dynamic connectivity matrices were estimated using the phase-locking value in the beta (12-25 Hz) and gamma (30-45 Hz) frequency bands. Temporal independent component analyses were used as a dimension reduction tool to isolate the group-specific brain network states that were dominant during the task. Typical microstate metrics were quantified to investigate the presence of these states at the subject-level. Our results first confirmed that PD patients experienced difficulties in inhibiting automatic responses during the task. At the group-level, HC displayed a significant functional network state that involved typical CAC-related prefrontal and cingulate nodes (e.g., inferior frontal cortex). Both group- and subject-level analyses showed that this network was less present in PD to the benefit of other networks involving lateralized temporal and insular components. The presence of this prefrontal network was associated with decreased reaction time. In the gamma band, two networks (fronto-cingulate and fronto-temporal) followed one another in HC, while 3 partially overlapping networks that included fronto-temporal, fronto-occipital and cross-hemispheric temporal connections were found in PD. At the subject-level, differences between PD and HC were less marked. Altogether, this study showed that the functional brain networks observed during CAC and their temporal changes were different in PD patients as compared to HC, and that these differences partially relate to behavioral changes. This study also highlights that task-based dynamic functional connectivity is a promising approach in understanding the cognitive dysfunctions observed in PD and beyond.


Author(s):  
Xueling Suo ◽  
Du Lei ◽  
Nannan Li ◽  
Wenbin Li ◽  
Graham J. Kemp ◽  
...  

AbstractWhile previous structural-covariance studies have an advanced understanding of brain alterations in Parkinson's disease (PD), brain–behavior relationships have not been examined at the individual level. This study investigated the topological organization of grey matter (GM) networks, their relation to disease severity, and their potential imaging diagnostic value in PD. Fifty-four early-stage PD patients and 54 healthy controls (HC) underwent structural T1-weighted magnetic resonance imaging. GM networks were constructed by estimating interregional similarity in the distributions of regional GM volume using the Kullback–Leibler divergence measure. Results were analyzed using graph theory and network-based statistics (NBS), and the relationship to disease severity was assessed. Exploratory support vector machine analyses were conducted to discriminate PD patients from HC and different motor subtypes. Compared with HC, GM networks in PD showed a higher clustering coefficient (P = 0.014) and local efficiency (P = 0.014). Locally, nodal centralities in PD were lower in postcentral gyrus and temporal-occipital regions, and higher in right superior frontal gyrus and left putamen. NBS analysis revealed decreased morphological connections in the sensorimotor and default mode networks and increased connections in the salience and frontoparietal networks in PD. Connection matrices and graph-based metrics allowed single-subject classification of PD and HC with significant accuracy of 73.1 and 72.7%, respectively, while graph-based metrics allowed single-subject classification of tremor-dominant and akinetic–rigid motor subtypes with significant accuracy of 67.0%. The topological organization of GM networks was disrupted in early-stage PD in a way that suggests greater segregation of information processing. There is potential for application to early imaging diagnosis.


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