Parkinson’s disease (PD) is a progressive multifactorial degenerative disorder that has both motor and nonmotor features. Among the nonmotor features of PD, cognitive impairment; Mild Cognitive Impairment (MCI) and dementia, poses one of the most significant implications for disability. Because of the high possibility of developing Dementia in PD, it is essential to obtain a reliable cognitive screen. Two commonly used cognitive measures include the Mini Mental Status Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). The MoCA is a more sensitive tool for the detection of MCI in compared to the MMSE. Because of the cultural differences, in this study, we evaluated the psychometric properties of the Persian version of the MoCA (MoCA-P) in a group of participants with PD in Iran. Participants were assessed by MMSE, Geriatric Depression Scale (GDS), and Functional Assessment Staging Test (FAST). If they met study eligibility, the MoCA-P scale was administered. Receiver Operating Characteristic (ROC) curve analyses showed that the MoCA-P scale produced significant discrimination of PD with Dementia (PDD) from PD with MCI (PD-MCI). It seems that the MoCA-P has adequate psychometric properties as a screening instrument for the detection of PD-MCI and PDD in Iranian patients. As near to 30% of patients with PD go on to develop dementia, the MoCA-P can be useful in the detection of cognitive impairment in patients with PD and is suitable for executive function assessment.

(1) Background: Alzheimer’s disease (AD) is a neurodegenerative disease with a high prevalence. Despite the cognitive tests to diagnose AD, there are pitfalls in early diagnosis. Brain deposition of pathological markers of AD can affect the direction and intensity of the signaling. The study of effective connectivity allows the evaluation of intensity flow and signaling pathways in functional regions, even in the early stage, known as amnestic mild cognitive impairment (aMCI). (2) Methods: 16 aMCI, 13 AD, and 14 normal subjects were scanned using resting-state fMRI and T1-weighted protocols. After data pre-processing, the signal of the predefined nodes was extracted, and spectral dynamic causal modeling analysis (spDCM) was constructed. Afterward, the mean and standard deviation of the Jacobin matrix of each subject describing effective connectivity was calculated and compared. (3) Results: The maps of effective connectivity in the brain networks of the three groups were different, and the direction and strength of the causal effect with the progression of the disease showed substantial changes. (4) Conclusions: Impaired information flow in the resting-state networks of the aMCI and AD groups was found versus normal groups. Effective connectivity can serve as a potential marker of Alzheimer’s pathophysiology, even in the early stages of the disease.

Background: While gait analysis is well-documented, turn performance—which is a more complex task and involves multiple brain regions—has been less explored. This study aims to assess the diagnostic potential of turn dynamics as a novel tool for detecting cognitive decline.Methods: We recruited 75 participants, including 26 neurotypical (NT) older adults, 25 with amnestic mild cognitive impairment (aMCI), and 24 with mild Alzheimer’s disease (AD). Participants completed a dual-task walk and turn (DTWT) test using a dual Kinect setup while counting backwards by ones. Key measures analyzed included spatial-temporal parameters of gait and turn dynamics. Statistical analyses including analyses of variance and linear regression were performed to identify key features as well as to assess their correlation with cognitive performance.Results: Gait speed and stride time significantly differentiated among groups in DTWT conditions. More notably, turn dynamics, particularly segmental peak speeds and step length, displayed stronger discriminatory power with more significant p-values compared to gait features. Linear regression analysis indicated that turn dynamics had stronger correlations with executive function and working memory, suggesting a more pronounced relationship between cognitive performance and turn features than gait variables.Conclusion: In contrast to straight walk metrics, this study shows that DTWT turn dynamics are more sensitive to detect cognitive impairment. Consequently, incorporating turning movements into gait analysis techniques could enhance diagnostic protocols in clinical settings, offering a valuable tool for monitoring the progression of conditions associated with cognitive aging.