Functional Neural Networks Stratify Parkinson’s Disease Patients Across the Spectrum of Cognitive Impairment
View/ Open
Access
info:eu-repo/semantics/openAccessDate
2024Author
Hajebrahimi, FarzinBudak, Miray
Saricaoglu, Mevhibe
Temel, Zeynep
Demir, Tugce Kahraman
Hanoglu, Lutfu
Yildirim, Suleyman
Bayraktaroglu, Zubeyir
Metadata
Show full item recordCitation
HAJEBRAHIMI, Farzin, Miray BUDAK, Mevhibe SARICAOĞLU, Zeynep TEMEL, Tuğce Kahraman DEMİR, Lütfü HANOĞLU, Suleyman YILDIRIM & Zübeyir BAYRAKTAROĞLU. "Functional Neural Networks Stratify Parkinson’s Disease Patients Across the Spectrum of Cognitive Impairment". Brain and Behavior, 14.1 (2023): 1-19.Abstract
Introduction: Cognitive impairment (CI) is a significant non-motor symptoms inParkinson’s
disease (PD) that often precedes the emergence of motor symptoms by several
years. Patients with PD hypothetically progress from stages without CI (PD-normal
cognition [NC]) to stageswithMild CI (PD-MCI) and PDdementia (PDD). CI symptoms
in PD are linked to different brain regions and neural pathways, in addition to being the
result of dysfunctional subcortical regions. However, it is still unknown how functional
dysregulation correlates to progression during the CI. Neuroimaging techniques hold
promise in discriminating CI stages of PD and further contribute to the biomarker formation
of CI in PD. In this study, we explore disparities in the clinical assessments and
resting-state functional connectivity (FC) among three CI stages of PD.
Methods: We enrolled 88 patients with PD and 26 healthy controls (HC) for a
cross sectional clinical study and performed intra- and inter-network FC analysis in
conjunction with comprehensive clinical cognitive assessment.
Results: Our findings underscore the significance of several neural networks, namely,
the default mode network (DMN), frontoparietal network (FPN), dorsal attention network,
and visual network (VN) and their inter–intra-network FC in differentiating
between PD-MCI and PDD. Additionally, our results showed the importance of sensory
motor network, VN,DMN, and salience network (SN) in the discriminating PD-NC
from PDD. Finally, in comparison to HC, we found DMN, FPN, VN, and SN as pivotal
networks for further differential diagnosis of CI stages of PD.
Conclusion:We propose that resting-state networks (RSN) can be a discriminating factor
in distinguishing the CI stages of PD and progressing from PD-NC toMCI or PDD.
The integration of clinical and neuroimaging data may enhance the early detection
of PD in clinical settings and potentially prevent the disease from advancing to more
severe stages.