Internetwork Connectivity Predicts Cognitive Decline in Parkinson’s and Is Altered by Genetic Variants

Wei, Xiangyu; Shen, Qian; Litvan, Irene; Huang, Mingxiong; Lee, Roland R.; Harrington, Deborah L.

doi:10.3389/fnagi.2022.853029

Cited by 5 publications

(5 citation statements)

References 90 publications

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“…Although defined as cognitively normal, a subtle CI is also reported in the PD‐NC population without the diagnosis of PD‐MCI (Chua et al., 2021 ). Compared to HC, PD‐NC has both increased and decreased FC, including decreased FC of FPN with DMN, VN, and DAN (Klobušiaková et al., 2019 ) and increased FC of VN‐LFPN with a reduction in visuospatial processing by demonstrating a crucial decline in working memory (Wei et al., 2022 ). In an important study by Peraza et al.…”

Section: Discussionmentioning

confidence: 99%

Functional neural networks stratify Parkinson's disease patients across the spectrum of cognitive impairment

Hajebrahimi,

Budak,

Saricaoglu

et al. 2024

Brain and Behavior

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IntroductionCognitive impairment (CI) is a significant non‐motor symptoms in Parkinson'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 stages with Mild CI (PD‐MCI) and PD dementia (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.MethodsWe 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.ResultsOur 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.ConclusionWe propose that resting‐state networks (RSN) can be a discriminating factor in distinguishing the CI stages of PD and progressing from PD‐NC to MCI 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.

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Section: Discussionmentioning

confidence: 99%

Functional neural networks stratify Parkinson's disease patients across the spectrum of cognitive impairment

Hajebrahimi,

Budak,

Saricaoglu

et al. 2024

Brain and Behavior

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“…Furthermore, our observed interaction between tau and gradient score supports previous work on greater inter-network connectivity and network specialization blurring in AD 88 – 90 . This increased connectedness between different networks may in-turn drive future protein propagation and cognitive decline 56 , 91 . Taken together, our findings support the contribution of connectome-related pathology distribution on cognitive impairment.…”

Section: Discussionmentioning

confidence: 99%

Tau follows principal axes of functional and structural brain organization in Alzheimer’s disease

Ottoy,

Kang,

Tan

et al. 2024

Nat Commun

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Alzheimer’s disease (AD) is a brain network disorder where pathological proteins accumulate through networks and drive cognitive decline. Yet, the role of network connectivity in facilitating this accumulation remains unclear. Using in-vivo multimodal imaging, we show that the distribution of tau and reactive microglia in humans follows spatial patterns of connectivity variation, the so-called gradients of brain organization. Notably, less distinct connectivity patterns (“gradient contraction”) are associated with cognitive decline in regions with greater tau, suggesting an interaction between reduced network differentiation and tau on cognition. Furthermore, by modeling tau in subject-specific gradient space, we demonstrate that tau accumulation in the frontoparietal and temporo-occipital cortices is associated with greater baseline tau within their functionally and structurally connected hubs, respectively. Our work unveils a role for both functional and structural brain organization in pathology accumulation in AD, and supports subject-specific gradient space as a promising tool to map disease progression.

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“…The use of DBS for the treatment of NMS in patients with PD has gradually become a hotspot in recent studies. NMS includes behavioral and mental changes, cognitive changes, autonomic nervous system failure, sensory disturbances, and sleep disturbances [ 6 , 7 , 8 ]. However, compared with motor symptoms, improvements in NMS, especially in cognitive status, are often overlooked and poorly managed by clinicians [ 9 ].…”

Section: Discussionmentioning

confidence: 99%

Nomogram to Predict Cognitive State Improvement after Deep Brain Stimulation for Parkinson’s Disease

et al. 2022

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Purpose: Parkinson’s disease (PD) is a common neurodegenerative disease, for which cognitive impairment is a non-motor symptom (NMS). Bilateral subthalamic nucleus deep brain stimulation (STN-DBS) is an effective treatment for PD. This study established a nomogram to predict cognitive improvement rate after STN-DBS in PD patients. Methods: We retrospectively analyzed 103 PD patients who underwent STN-DBS. Patients were followed up to measure improvement in MoCA scores one year after surgery. Univariate and multivariate logistic regression analyses were used to identify factors affecting improvement in cognitive status. A nomogram was developed to predict this factor. The discrimination and fitting performance were evaluated by receiver operating characteristics (ROC) analysis, calibration diagram, and decision curve analysis (DCA). Results: Among 103 patients, the mean improvement rate of the MoCA score was 37.3% and the median improvement rate was 27.3%, of which 64% improved cognition, 27% worsened cognition, and 8.7% remained unchanged. Logistic multivariate regression analysis showed that years of education, UPDRSIII drug use, MoCA Preop, and MMSE Preop scores were independent factors affecting the cognitive improvement rate. A nomogram model was subsequently developed. The C-index of the nomogram was 0.98 (95%CI, 0.97–1.00), and the area under the ROC was 0.98 (95%CI 0.97–1.00). The calibration plot and DCA demonstrated the goodness-of-fit between nomogram predictions and actual observations. Conclusion: Our nomogram could effectively predict the possibility of achieving good cognitive improvement one year after STN-DBS in patients with PD. This model has value in judging the expected cognitive improvement of patients with PD undergoing STN-DBS.

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Internetwork Connectivity Predicts Cognitive Decline in Parkinson’s and Is Altered by Genetic Variants

Cited by 5 publications

References 90 publications

Functional neural networks stratify Parkinson's disease patients across the spectrum of cognitive impairment

Functional neural networks stratify Parkinson's disease patients across the spectrum of cognitive impairment

Tau follows principal axes of functional and structural brain organization in Alzheimer’s disease

Nomogram to Predict Cognitive State Improvement after Deep Brain Stimulation for Parkinson’s Disease

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