Objective: (1) To determine the brain connectivity pattern associated with clinical rigidity scores in Parkinson’s disease (PD) and (2) to determine the relation between clinically assessed rigidity and quantitative metrics of motor performance.Background: Rigidity, the resistance to passive movement, is exacerbated in PD by asking the subject to move the contralateral limb, implying that rigidity involves a distributed brain network. Rigidity mainly affects subjects when they attempt to move; yet the relation between clinical rigidity scores and quantitative aspects of motor performance are unknown.Methods: Ten clinically diagnosed PD patients (off-medication) and 10 controls were recruited to perform an fMRI squeeze-bulb tracking task that included both visually guided and internally guided features. The direct functional connectivity between anatomically defined regions of interest was assessed with Dynamic Bayesian Networks (DBNs). Tracking performance was assessed by fitting Linear Dynamical System (LDS) models to the motor performance, and was compared to the clinical rigidity scores. A cross-validated Least Absolute Shrinkage and Selection Operator (LASSO) regression method was used to determine the brain connectivity network that best predicted clinical rigidity scores.Results: The damping ratio of the LDS models significantly correlated with clinical rigidity scores (p = 0.014). An fMRI connectivity network in subcortical and primary and premotor cortical regions accurately predicted clinical rigidity scores (p < 10−5).Conclusion: A widely distributed cortical/subcortical network is associated with rigidity observed in PD patients, which reinforces the importance of altered functional connectivity in the pathophysiology of PD. PD subjects with higher rigidity scores tend to have less overshoot in their tracking performance, and damping ratio may represent a robust, quantitative marker of the motoric effects of increasing rigidity.
In Parkinson’s disease (PD), concurrent declines in cognitive and motor domain function can severely limit an individual’s ability to conduct daily tasks. Current diagnostic methods, however, lack precision in differentiating domain-specific contributions of cognitive or motor impairments based on a patients’ clinical manifestation. Fear of falling (FOF) is a common clinical manifestation among the elderly, in which both cognitive and motor impairments can lead to significant barriers to a patients’ physical and social activities. The present study evaluated whether a set of analytical and machine-learning approaches could be used to help delineate boundary conditions and separate cognitive and motor contributions to a patient’s own perception of self-efficacy and FOF. Cognitive and motor clinical scores, in conjunction with FOF, were collected from 57 Parkinson’s patients during a multi-center rehabilitation intervention trial. Statistical methodology was used to extract a subset of uncorrelated cognitive and motor components associated with cognitive and motor predictors, which were then used to independently identify and visualize cognitive and motor dimensions associated with FOF. We found that a central cognitive process, extracted from tests of executive, attentional, and visuoperceptive function, was a unique and significant independent cognitive predictor of FOF in PD. In addition, we provide evidence that the approaches described here may be used to computationally discern specific types of FOF based on separable cognitive or motor models. Our results are consistent with a contemporary model that the deterioration of a central cognitive mechanism that modulates self-efficacy also plays a critical role in FOF in PD.
BackgroundPrevious studies have established a strong association between depression and suicidal behaviors, yet the relationship between anxiety and suicidal behaviors remains unclear. This study examines whether anxiety and depression are independent risk factors for suicidal behaviors in medical college students, and further, whether anxiety may increase the greater risk of suicidal behaviors (SB) in participants with depression.MethodsThis cross-sectional study was conducted among 4,882 medical students. Demographic information, anxiety, and depression data were collected using online questionnaires or through a widely used social media app named WeChat.ResultsAnxiety and depression were independent risk factors for suicidal behaviors, and levels of risk correlated positively with the severity of both anxiety and depressive symptoms. A dose–response relationship was identified between the severity of anxiety and the risk of SB, as well as the severity of depression and SB. Furthermore, anxiety increased the risk of suicidal behaviors in participants with depression, with a dose–response relationship between the severity of anxiety symptoms and the risk of SB.ConclusionThe findings highlight the importance of screening for anxiety and depressive symptoms in medical college students, as well as reducing anxiety in addition to depressive symptoms in treatment. This study provides valuable data as a reference for clinicians for suicide risk assessments.
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