Sequential patterning of dynamic brain states distinguish Parkinson’s disease patients with mild cognitive impairments
Parkinson’s disease (PD) is a neurodegenerative disease which presents clinically with progressive impairments in motoric and cognitive functioning. Pathophysiologic mechanisms underlying these impairments are believed to be attributable to a breakdown in the spatiotemporal coordination of functiona...
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Elsevier
2025-01-01
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| Series: | NeuroImage: Clinical |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S221315822500049X |
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| author | Aaron S. Kemp A. Journey Eubank Yahya Younus James E. Galvin Fred W. Prior Linda J. Larson-Prior |
| author_facet | Aaron S. Kemp A. Journey Eubank Yahya Younus James E. Galvin Fred W. Prior Linda J. Larson-Prior |
| author_sort | Aaron S. Kemp |
| collection | DOAJ |
| description | Parkinson’s disease (PD) is a neurodegenerative disease which presents clinically with progressive impairments in motoric and cognitive functioning. Pathophysiologic mechanisms underlying these impairments are believed to be attributable to a breakdown in the spatiotemporal coordination of functional neural networks across multiple cortical and subcortical regions. The current investigation used resting state, functional magnetic resonance imaging (rs-fMRI) to determine whether the temporal characteristics or sequential patterning of dynamic functional network connectivity (dFNC) states could accurately distinguish among people with PD who had normal cognition (PD-NC, n = 18), those with PD who had mild cognitive impairment (PD-MCI, n = 15), and older-aged healthy control (HC, n = 22) individuals. Results indicated that the proportion of time during the rs-fMRI scan that was spent in each of three identified dFNC states (dwell time) differed among these three groups. Individuals in the PD-MCI group spent significantly more time in a dFNC state characterized by low functional network connectivity, relative to participants in both the PD-NC (p = 0.0226) and HC (p = 0.0027) cohorts and tend to spend less time in a state characterized by anti-correlated thalamo-cortical connectivity, relative to both the PD-NC (p = 0.016) and HC (p = 0.0562) groups. A machine-learning method using sequential pattern mining was also found to distinguish among the groups with moderate accuracies ranging from 0.53 to 0.80, revealing distinct sequential patterns in the temporal ordering of dFNC states. These findings underscore the potential of dFNC and sequential pattern mining as relevant methods for further exploration of the pathophysiologic underpinnings of cognitive impairment among people living with PD. |
| format | Article |
| id | doaj-art-0b8b4cb40a7c4baabec778e3d671b59f |
| institution | Kabale University |
| issn | 2213-1582 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj-art-0b8b4cb40a7c4baabec778e3d671b59f2025-08-20T03:26:35ZengElsevierNeuroImage: Clinical2213-15822025-01-014610377910.1016/j.nicl.2025.103779Sequential patterning of dynamic brain states distinguish Parkinson’s disease patients with mild cognitive impairmentsAaron S. Kemp0A. Journey Eubank1Yahya Younus2James E. Galvin3Fred W. Prior4Linda J. Larson-Prior5Department of Biomedical Informatics, 4301 W. Markham St., Little Rock, AR 72205, United States; Arkansas Children’s Research Institute, 13 Children’s Way, Little Rock, AR 72202, United States; Corresponding author at: Department of Biomedical Informatics, 4301 W. Markham St., Little Rock, AR 72205, United States.Department of Biomedical Informatics, 4301 W. Markham St., Little Rock, AR 72205, United StatesLittle Rock Central High School, 1500 S. Little Rock Nine Way, Little Rock, AR 72202, United StatesDepartment of Neurology, University of Miami, Miller School of Medicine, Comprehensive Center for Brain Health, 7700 W Camino Real, Suite 200, Boca Raton, FL 33433, United StatesDepartment of Biomedical Informatics, 4301 W. Markham St., Little Rock, AR 72205, United States; Arkansas Children’s Research Institute, 13 Children’s Way, Little Rock, AR 72202, United StatesDepartment of Biomedical Informatics, 4301 W. Markham St., Little Rock, AR 72205, United States; Department of Neurology, 4301 W. Markham St., Little Rock, AR 72205, United States; Department of Neurobiology & Developmental Sciences, 4301 W. Markham St., Little Rock, AR 72205, United States; Department of Pediatrics, at the University of Arkansas for Medical Sciences (UAMS), 4301 W. Markham St., Little Rock, AR 72205, United States; Arkansas Children’s Research Institute, 13 Children’s Way, Little Rock, AR 72202, United StatesParkinson’s disease (PD) is a neurodegenerative disease which presents clinically with progressive impairments in motoric and cognitive functioning. Pathophysiologic mechanisms underlying these impairments are believed to be attributable to a breakdown in the spatiotemporal coordination of functional neural networks across multiple cortical and subcortical regions. The current investigation used resting state, functional magnetic resonance imaging (rs-fMRI) to determine whether the temporal characteristics or sequential patterning of dynamic functional network connectivity (dFNC) states could accurately distinguish among people with PD who had normal cognition (PD-NC, n = 18), those with PD who had mild cognitive impairment (PD-MCI, n = 15), and older-aged healthy control (HC, n = 22) individuals. Results indicated that the proportion of time during the rs-fMRI scan that was spent in each of three identified dFNC states (dwell time) differed among these three groups. Individuals in the PD-MCI group spent significantly more time in a dFNC state characterized by low functional network connectivity, relative to participants in both the PD-NC (p = 0.0226) and HC (p = 0.0027) cohorts and tend to spend less time in a state characterized by anti-correlated thalamo-cortical connectivity, relative to both the PD-NC (p = 0.016) and HC (p = 0.0562) groups. A machine-learning method using sequential pattern mining was also found to distinguish among the groups with moderate accuracies ranging from 0.53 to 0.80, revealing distinct sequential patterns in the temporal ordering of dFNC states. These findings underscore the potential of dFNC and sequential pattern mining as relevant methods for further exploration of the pathophysiologic underpinnings of cognitive impairment among people living with PD.http://www.sciencedirect.com/science/article/pii/S221315822500049XBrain statesDynamic functional network connectivity (dFNC)Functional magnetic resonance imaging (fMRI)Machine learningMild cognitive impairment (MCI)Parkinson’s disease |
| spellingShingle | Aaron S. Kemp A. Journey Eubank Yahya Younus James E. Galvin Fred W. Prior Linda J. Larson-Prior Sequential patterning of dynamic brain states distinguish Parkinson’s disease patients with mild cognitive impairments NeuroImage: Clinical Brain states Dynamic functional network connectivity (dFNC) Functional magnetic resonance imaging (fMRI) Machine learning Mild cognitive impairment (MCI) Parkinson’s disease |
| title | Sequential patterning of dynamic brain states distinguish Parkinson’s disease patients with mild cognitive impairments |
| title_full | Sequential patterning of dynamic brain states distinguish Parkinson’s disease patients with mild cognitive impairments |
| title_fullStr | Sequential patterning of dynamic brain states distinguish Parkinson’s disease patients with mild cognitive impairments |
| title_full_unstemmed | Sequential patterning of dynamic brain states distinguish Parkinson’s disease patients with mild cognitive impairments |
| title_short | Sequential patterning of dynamic brain states distinguish Parkinson’s disease patients with mild cognitive impairments |
| title_sort | sequential patterning of dynamic brain states distinguish parkinson s disease patients with mild cognitive impairments |
| topic | Brain states Dynamic functional network connectivity (dFNC) Functional magnetic resonance imaging (fMRI) Machine learning Mild cognitive impairment (MCI) Parkinson’s disease |
| url | http://www.sciencedirect.com/science/article/pii/S221315822500049X |
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