Functional Neural Networks Stratify Parkinson’s Disease Patients Across the Spectrum of Cognitive Impairment

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

Rehabilitation of Cognitive Disorder After Temporal Lobe Epilepsy Surgery: Proposal for a Protocol

Objectives: Surgical intervention is a crucial and effective treatment option for patients with temporal lobe epilepsy whose seizures are not under con-trol. However, there is a possibility that surgical intervention may have a negative effect on cognitive functions. Cognitive rehabilitation is a treatment option that has been recently investigated for various neurocognitive problems. This study proposes a protocol for the rehabilitation of the cognitive dysfunctions after temporal lobe epilepsy surgery

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

Abstract Introduction Cognitive 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. 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 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