Task-related brain activity and functional connectivity in upper limb dystonia: a functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS) study

Parkinson's disease patients show delayed hemodynamic changes in primary motor cortex in fine motor tasks and decreased resting-state interhemispheric functional connectivity: a functional near-infrared spectroscopy study

Significance

People with Parkinson's disease (PD) experience changes in fine motor skills, which is viewed as one of the hallmark signs of this disease. Due to its non-invasive nature and portability, functional near-infrared spectroscopy (fNIRS) is a promising tool for assessing changes related to fine motor skills.

Aim

We aim to compare activation patterns in the primary motor cortex using fNIRS, comparing volunteers with PD and sex- and age-matched control participants during a fine motor task and walking. Moreover, inter and intrahemispheric functional connectivity (FC) was investigated during the resting state.

Approach

We used fNIRS to measure the hemodynamic changes in the primary motor cortex elicited by a finger-tapping task in 20 PD patients and 20 controls matched for age, sex, education, and body mass index. In addition, a two-minute walking task was carried out. Resting-state FC was also assessed.

Results

Patients with PD showed delayed hypoactivation in the motor cortex during the fine motor task with the dominant hand and delayed hyperactivation with the non-dominant hand. The findings also revealed significant correlations among various measures of hemodynamic activity in the motor cortex using fNIRS and different cognitive and clinical variables. There were no significant differences between patients with PD and controls during the walking task. However, there were significant differences in interhemispheric connectivity between PD patients and control participants, with a statistically significant decrease in PD patients compared with control participants.

Conclusions

Decreased interhemispheric FC and delayed activity in the primary motor cortex elicited by a fine motor task may one day serve as one of the many potential neuroimaging biomarkers for diagnosing PD.

Task-related brain activity in upper limb dystonia revealed by simultaneous fNIRS and EEG

OBJECTIVE

The aim of this study was to explore differences in brain activity and connectivity using simultaneous electroencephalography and near-infrared spectroscopy in patients with focal dystonia during handwriting and finger-tapping tasks.

METHODS

Patients with idiopathic right upper limb focal dystonia and controls were assessed by simultaneous near-infrared spectroscopy and electroencephalography during the writing and finger-tapping tasks in terms of the mu-alpha, mu-beta, beta and low gamma power and effective connectivity, as well as relative changes in oxyhemoglobin (oxy-Hb) and deoxyhemoglobin using a channel-wise approach with a mixed-effect model.

RESULTS

Patients exhibited higher oxy-Hb levels in the right and left motor cortex and supplementary motor area during writing, but lower oxy-Hb levels in the left sensorimotor and bilateral somatosensory area during finger-tapping compared to controls. During writing, patients showed increased low gamma power in the bilateral sensorimotor cortex and less mu-beta and beta attenuation compared to controls. Additionally, patients had reduced connectivity between the supplementary motor area and the left sensorimotor cortex during writing. No differences were observed in terms of effective connectivity in either task. Finally, patients failed to attenuate the mu-alpha, mu-beta, and beta rhythms during the finger-tapping task.

CONCLUSIONS

Cortical blood flow and EEG spectral power differ between controls and dystonia patients, depending on the task. Writing increased blood flow and altered connectivity in dystonia patients, and it also decreased slow-band attenuation. Finger-tapping decreased blood flow and slow-band attenuation.

SIGNIFICANCE

Simultaneous fNIRS and EEG may show relevant information regarding brain dynamics in movement disorders patients in unconstrained environments.

Correlation and underlying brain mechanisms between rapid eye movement sleep behavior disorder and executive functions in Parkinson's disease: an fNIRS study

Purpose

Rapid eye movement sleep behavior disorder (RBD) affects 30%-40% of patients with Parkinson's disease (PD) and has been linked to a higher risk of cognitive impairment, especially executive dysfunction. The aim of this study was to investigate the brain activation patterns in PD patients with RBD (PD-RBD+) compared to those without RBD (PD-RBD-) and healthy controls (HCs), and to analyze the correlation between changes in cerebral cortex activity and the severity of RBD.

Methods

We recruited 50 PD patients, including 30 PD-RBD+, 20 PD-RBD-, and 20 HCs. We used functional near infrared spectroscopy during a verbal fluency task (VFT-fNIRS) and clinical neuropsychological assessment to explore the correlation between PD-RBD+ and executive function and changes in neural activity.

Results

The VFT-fNIRS analysis revealed a significant reduction in activation among PD-RBD+ patients across multiple channels when compared to both the PD-RBD- and HC groups. Specifically, PD-RBD+ patients exhibited diminished activation in the bilateral dorsolateral prefrontal cortex (DLPFC) and the right ventrolateral prefrontal cortex (VLPFC) relative to their PD-RBD- counterparts. Furthermore, compared to the HC group, PD-RBD+ patients displayed reduced activation specifically in the right DLPFC. Significantly, a noteworthy negative correlation was identified between the average change in oxygenated hemoglobin concentration (ΔHbO2) in the right DLPFC of PD-RBD+ patients and the severity of their RBD.

Conclusion

Our study offers compelling evidence that RBD exacerbates cognitive impairment in PD, manifested as executive dysfunction, primarily attributed to reduced prefrontal activation. These aberrations in brain activation may potentially correlate with the severity of RBD.

The functional anatomy of dystonia: Recent developments

While dystonia has traditionally been viewed as a disorder of the basal ganglia, the involvement of other key brain structures is now accepted. However, just what these structures are remains to be defined. Neuroimaging has been an especially valuable tool in dystonia, yet traditional cross-sectional designs have not been able to separate causal from compensatory brain activity. Therefore, this chapter discusses recent studies using causal brain lesions, and animal models, to converge upon the brain regions responsible for dystonia with increasing precision. This evidence strongly implicates the basal ganglia, thalamus, brainstem, cerebellum, and somatosensory cortex, yet shows that different types of dystonia involve different nodes of this brain network. Nearly all of these nodes fall within the recently identified two-way networks connecting the basal ganglia and cerebellum, suggesting dysfunction of these specific pathways. Localisation of the functional anatomy of dystonia has strong implications for targeted treatment options, such as deep brain stimulation, and non-invasive brain stimulation.

Altered brain connectivity in hyperkinetic movement disorders: A review of resting-state fMRI

BACKGROUND

Hyperkinetic movement disorders (HMD) manifest as abnormal and uncontrollable movements. Despite reported involvement of several neural circuits, exact connectivity profiles remain elusive.

OBJECTIVES

Providing a comprehensive literature review of resting-state brain connectivity alterations using resting-state fMRI (rs-fMRI). We additionally discuss alterations from the perspective of brain networks, as well as correlations between connectivity and clinical measures.

METHODS

A systematic review was performed according to PRISMA guidelines and searching PubMed until October 2022. Rs-fMRI studies addressing ataxia, chorea, dystonia, myoclonus, tics, tremor, and functional movement disorders (FMD) were included. The standardized mean difference was used to summarize findings per region in the Automated Anatomical Labeling atlas for each phenotype. Furthermore, the activation likelihood estimation meta-analytic method was used to analyze convergence of significant between-group differences per phenotype. Finally, we conducted hierarchical cluster analysis to provide additional insights into commonalities and differences across HMD phenotypes.

RESULTS

Most articles concerned tremor (51), followed by dystonia (46), tics (19), chorea (12), myoclonus (11), FMD (11), and ataxia (8). Altered resting-state connectivity was found in several brain regions: in ataxia mainly cerebellar areas; for chorea, the caudate nucleus; for dystonia, sensorimotor and basal ganglia regions; for myoclonus, the thalamus and cingulate cortex; in tics, the basal ganglia, cerebellum, insula, and frontal cortex; for tremor, the cerebello-thalamo-cortical circuit; finally, in FMD, frontal, parietal, and cerebellar regions. Both decreased and increased connectivity were found for all HMD. Significant spatial convergence was found for dystonia, FMD, myoclonus, and tremor. Correlations between clinical measures and resting-state connectivity were frequently described.

CONCLUSION

Key brain regions contributing to functional connectivity changes across HMD often overlap. Possible increases and decreases of functional connections of a specific region emphasize that HMD should be viewed as a network disorder. Despite the complex interplay of physiological and methodological factors, this review serves to gain insight in brain connectivity profiles across HMD phenotypes.

Joint-Channel-Connectivity-Based Feature Selection and Classification on fNIRS for Stress Detection in Decision-Making

Stress is one of the contributing factors affecting decision-making. Therefore, early stress recognition is essential to improve clinicians' decision-making performance. Functional near-infrared spectroscopy (fNIRS) has shown great potential in detecting stress. However, the majority of previous studies only used fNIRS features at the individual level for classification without considering the correlations among channels corresponding to the brain, which may provide distinguishing features. Hence, this study proposes a novel joint-channel-connectivity-based feature selection and classification algorithm for fNIRS to detect stress in decision-making. Specifically, this approach integrates feature selection and classifier modeling into a sparse model, where intra- and inter-channel regularizers are designed to explore potential correlations among channels to obtain discriminating features. In this paper, we simulated the decision-making of medical students under stress through the Trier Social Stress Test and the Balloon Analog Risk Task and recorded their cerebral hemodynamic alterations by fNIRS device. Experimental results illustrated that our method with the accuracy of 0.961 is superior to other machine learning methods. Additionally, the stress correlation and connectivity of brain regions calculated by feature selection have been confirmed in previous studies, which validates the effectiveness of our method and helps optimize the channel settings of fNIRS. This work was the first attempt to utilize a sparse model that simultaneously considers the sparsity of features and the correlation of brain regions for stress detection and obtained an admirable classification performance. Thus, the proposed model might be a useful tool for medical personnel to automatically detect stress in clinical decision-making situations.