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Sökning: L773:1353 8020 OR L773:1873 5126 > Unraveling Parkinso...

Unraveling Parkinson's disease heterogeneity using subtypes based on multimodal data

Albrecht, F. (författare)
Karolinska Institutet,Karolinska Institute,Karolinska University Hospital
Poulakis, K. (författare)
Karolinska Institutet,Karolinska Institute
Freidle, M. (författare)
Karolinska Institutet,Karolinska Institute
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Johansson, H. (författare)
Karolinska Institutet,Karolinska Institute,Karolinska University Hospital
Ekman, U. (författare)
Karolinska Institutet,Karolinska Institute,Karolinska University Hospital
Volpe, Giovanni, 1979 (författare)
University of Gothenburg,Gothenburg University,Göteborgs universitet,Institutionen för fysik (GU),Department of Physics (GU)
Westman, E. (författare)
Karolinska Institutet,Karolinska Institute,Lund University,King's College London
Pereira, Joana B. (författare)
Karolinska Institutet,Karolinska Institute,Lund University,Lunds universitet,Klinisk minnesforskning,Forskargrupper vid Lunds universitet,Clinical Memory Research,Lund University Research Groups,Aging Research Center
Franzén, E. (författare)
Karolinska Institute,Karolinska University Hospital
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 (creator_code:org_t)
Elsevier BV, 2022
2022
Engelska.
Ingår i: Parkinsonism and Related Disorders. - : Elsevier BV. - 1353-8020 .- 1873-5126. ; 102, s. 19-29
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • Background: Parkinson's disease (PD) is a clinically and neuroanatomically heterogeneous neurodegenerative disease characterized by different subtypes. To this date, no studies have used multimodal data that combines clinical, motor, cognitive and neuroimaging assessments to identify these subtypes, which may provide complementary, clinically relevant information. To address this limitation, we subtyped participants with mild-moderate PD based on a rich, multimodal dataset of clinical, cognitive, motor, and neuroimaging variables. Methods: Cross-sectional data from 95 PD participants from our randomized EXPANd (EXercise in PArkinson's disease and Neuroplasticity) controlled trial were included. Participants were subtyped using clinical, motor, and cognitive assessments as well as structural and resting-state MRI data. Subtyping was done by random forest clustering. We extracted information about the subtypes by inspecting their neuroimaging profiles and descriptive statistics. Results: Our multimodal subtyping analysis yielded three PD subtypes: a motor-cognitive subtype characterized by widespread alterations in brain structure and function as well as impairment in motor and cognitive abilities; a cognitive dominant subtype mainly impaired in cognitive function that showed frontoparietal structural and functional changes; and a motor dominant subtype impaired in motor variables without any brain alterations. Motor variables were most important for the subtyping, followed by gray matter volume in the right medial postcentral gyrus. Conclusions: Three distinct PD subtypes were identified in our multimodal dataset. The most important features to subtype PD participants were motor variables in addition to structural MRI in the sensorimotor region. These findings have the potential to improve our understanding of PD heterogeneity, which in turn can lead to personalized interventions and rehabilitation.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Geriatrik (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Geriatrics (hsv//eng)

Nyckelord

Clustering
Magnetic resonance imaging
Parkinson's disease
Physical activity
Random forest
Subtyping

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