Repetitive transcranial magnetic stimulation improves Parkinson's freezing of gait via normalizing brain connectivity

Robust, effective treatments for Parkinson's freezing of gait remain elusive. Our previous study revealed beneficial effects of high-frequency rTMS over the supplementary motor area. The present study aims to explore the neural mechanisms of rTMS treatments utilizing novel exploratory multivariate approaches. We first conducted a resting-state functional MRI study with a group of 40 Parkinson's disease patients with freezing of gait, 31 without freezing of gait, and 30 normal controls. A subset of 30 patients with freezing of gait (verum group: N = 20; sham group: N = 10) who participated the aforementioned rTMS study underwent another scan after the treatments. Using the baseline scans, the imaging biomarkers for freezing of gait and Parkinson's disease were developed by contrasting the connectivity profiles of patients with freezing of gait to those without freezing of gait and normal controls, respectively. These two biomarkers were then interrogated to assess the rTMS effects on connectivity patterns. Results showed that the freezing of gait biomarker was negatively correlated with Freezing of Gait Questionnaire score (r = -0.6723, p < 0.0001); while the Parkinson's disease biomarker was negatively correlated with MDS-UPDRS motor score (r = -0.7281, p < 0.0001). After the rTMS treatment, both the freezing of gait biomarker (0.326 ± 0.125 vs. 0.486 ± 0.193, p = 0.0071) and Parkinson's disease biomarker (0.313 ± 0.126 vs. 0.379 ± 0.155, p = 0.0378) were significantly improved in the verum group; whereas no significant biomarker changes were found in the sham group. Our findings indicate that high-frequency rTMS over the supplementary motor area confers the beneficial effect jointly through normalizing abnormal brain functional connectivity patterns specifically associated with freezing of gait, in addition to normalizing overall disrupted connectivity patterns seen in Parkinson's disease.

A genetically informed, group FMRI connectivity modeling approach: application to schizophrenia

While neuroimaging data can provide valuable phenotypic information to inform genetic studies, the opposite is also true: known genotypes can be used to inform brain connectivity patterns from fMRI data. Here, we propose a framework for genetically informed group brain connectivity modeling. Subjects are first stratified according to their genotypes, and then a group regularized regression model is employed for brain connectivity modeling utilizing the time courses from a priori specified regions of interest (ROIs). With such an approach, each ROI time course is in turn predicted from all other ROI time courses at zero lag using a group regression framework which also incorporates a penalty based on genotypic similarity. Simulations supported such an approach when, as previously studies have indicated to be the case, genetic influences impart connectivity differences across subjects. The proposed method was applied to resting state fMRI data from Schizophrenia and normal control subjects. Genotypes were based on D-amino acid oxidase activator (DAOA) single-nucleotide polymorphisms (SNPs) information. With DAOA SNPs information integrated, the proposed approach was able to more accurately model the diversity in connectivity patterns. Specifically, connectivity with the left putamen, right posterior cingulate, and left middle frontal gyri were found to be jointly modulated by DAOA genotypes and the presence of Schizophrenia. We conclude that the proposed framework represents a multimodal analysis approach for incorporating genotypic variability into brain connectivity analysis directly.

Bihemispheric transcranial direct current stimulation enhances effector-independent representations of motor synergy and sequence learning

Complex manual tasks-everything from buttoning up a shirt to playing the piano-fundamentally involve two components: (1) generating specific patterns of muscle activity (here, termed "synergies"); and (2) stringing these into purposeful sequences. Although transcranial direct current stimulation (tDCS) of the primary motor cortex (M1) has been found to increase the learning of motor sequences, it is unknown whether it can similarly facilitate motor synergy learning. Here, we determined the effects of tDCS on the learning of motor synergies using a novel hand configuration task that required the production of difficult muscular activation patterns. Bihemispheric tDCS was applied to M1 of healthy, right-handed human participants during 4 d of repetitive left-hand configuration training in a double-blind design. tDCS augmented synergy learning, leading subsequently to faster and more synchronized execution. This effect persisted for at least 4 weeks after training. Qualitatively similar tDCS-associated improvements occurred during training of finger sequences in a separate subject cohort. We additionally determined whether tDCS only improved the acquisition of motor memories for specific synergies/sequences or whether it also facilitated more general parts of the motor representations, which could be transferred to novel movements. Critically, we observed that tDCS effects generalized to untrained hand configurations and untrained finger sequences (i.e., were nonspecific), as well as to the untrained hand (i.e., were effector-independent). Hence, bihemispheric tDCS may be a promising adjunct to neurorehabilitative training regimes, in which broad transfer to everyday tasks is highly desirable.

A computationally efficient, exploratory approach to brain connectivity incorporating false discovery rate control, a priori knowledge, and group inference

Graphical models appear well suited for inferring brain connectivity from fMRI data, as they can distinguish between direct and indirect brain connectivity. Nevertheless, biological interpretation requires not only that the multivariate time series are adequately modeled, but also that there is accurate error-control of the inferred edges. The PC_fdr algorithm, which was developed by Li and Wang, was to provide a computationally efficient means to control the false discovery rate (FDR) of computed edges asymptotically. The original PC_fdr algorithm was unable to accommodate a priori information about connectivity and was designed to infer connectivity from a single subject rather than a group of subjects. Here we extend the original PC_fdr algorithm and propose a multisubject, error-rate-controlled brain connectivity modeling approach that allows incorporation of prior knowledge of connectivity. In simulations, we show that the two proposed extensions can still control the FDR around or below a specified threshold. When the proposed approach is applied to fMRI data in a Parkinson's disease study, we find robust group evidence of the disease-related changes, the compensatory changes, and the normalizing effect of L-dopa medication. The proposed method provides a robust, accurate, and practical method for the assessment of brain connectivity patterns from functional neuroimaging data.

Subject-specific myoelectric pattern classification of functional hand movements for stroke survivors

In this study, we developed a robust subject-specific electromyography (EMG) pattern classification technique to discriminate intended manual tasks from muscle activation patterns of stroke survivors. These classifications will enable volitional control of assistive devices, thereby improving their functionality. Twenty subjects with chronic hemiparesis participated in the study. Subjects were instructed to perform six functional tasks while their muscle activation patterns were recorded by ten surface electrodes placed on the forearm and hand of the impaired limb. In order to identify intended functional tasks, a pattern classifier using linear discriminant analysis was applied to the EMG feature vectors. The classification accuracy was mainly affected by the impairment level of the subject. Mean classification accuracy was 71.3% for moderately impaired subjects (Chedoke Stage of Hand 4 and 5), and 37.9% for severely impaired subjects (Chedoke Stage of Hand 2 and 3). Most misclassification occurred between grip tasks of similar nature, for example, among pinch, key, and three-fingered grips, or between cylindrical and spherical grips. EMG signals from the intrinsic hand muscles significantly contributed to the inter-task variability of the feature vectors, as assessed by the inter-task squared Euclidean distance, thereby indicating the importance of intrinsic hand muscles in functional manual tasks. This study demonstrated the feasibility of the EMG pattern classification technique to discern the intent of stroke survivors. Future work should concentrate on the construction of a subject-specific EMG classification paradigm that carefully considers both functional and physiological impairment characteristics of each subject in the target task selection and electrode placement procedures.

Network modeling and analysis of lumbar muscle surface EMG signals during flexion-extension in individuals with and without low back pain

In this paper, we propose modeling the activity coordination network between lumbar muscles using surface electromyography (sEMG) signals and performing the network analysis to compare the lumbar muscle coordination patterns between patients with low back pain (LBP) and healthy control subjects. Ten healthy subjects and eleven LBP patients were asked to perform flexion-extension task, and the sEMG signals were recorded. Both the subject-level and the group-level PC(fdr) algorithms are applied to learn the sEMG coordination networks with the error-rate being controlled. The network features are further characterized in terms of network symmetry, global efficiency, clustering coefficient and graph modules. The results indicate that the networks representing the normal group are much closer to the order networks and clearly exhibit globally symmetric patterns between the left and right sEMG channels. While the coordination activities between sEMG channels for the patient group are more likely to cluster locally and the group network shows the loss of global symmetric patterns. As a complementary tool to the physical and anatomical analysis, the proposed network analysis approach allows the visualization of the muscle coordination activities and the extraction of more informative features from the sEMG data for low back pain studies.

Impulses of activation but not motor modules are preserved in the locomotion of subacute stroke patients

It has been hypothesized that the coordinated activation of muscles is controlled by the central nervous system by means of a small alphabet of control signals (also referred to as activation signals) and motor modules (synergies). We analyzed the locomotion of 10 patients recently affected by stroke (maximum of 20 wk) and compared it with that of healthy controls. The aim was to assess whether the walking of subacute stroke patients is based on the same motor modules and/or activation signals as healthy subjects. The activity of muscles of the lower and upper limb and the trunk was measured and used for extracting motor modules. Four modules were sufficient to explain the majority of variance in muscle activation in both controls and patients. Modules from the affected side of stroke patients were different from those of healthy controls and from the unaffected side of stroke patients. However, the activation signals were similar between groups and between the affected and unaffected side of stroke patients, and were characterized by impulses at specific time instants within the gait cycle, underlying an impulsive controller of gait. In conclusion, motor modules observed in healthy subjects during locomotion are different from those used by subacute stroke patients, despite similar impulsive activation signals. We suggest that this pattern is consistent with a neuronal network in which the timing of activity generated by central pattern generators is directed to the motoneurons via a premotor network that distributes the activity in a task-dependent manner determined by sensory and descending control information.

Synergy matrices to estimate fluid wrist movements by surface electromyography

Although many efforts have been undertaken to develop an interface using surface electromyography (sEMG) to connect the gap between a human and a wrist prosthesis, most of these efforts have offered only static positioning (ON/OFF) of the prosthesis. This study introduced synergy matrices to extract fluid wrist movement intents by sEMG to allow individuals with wrist amputations to use wrist prostheses. A non-negative muscle synergy matrix was used to map muscle activities in the forearm into four predefined wrist movement intents (flexion/extension and radial/ulnar deviation). The directions of the predefined intents were constrained to two perpendicular axes, so each movement spanned only a one-dimensional space. A joint synergy matrix was used to span the whole two-dimensional space by combining the four wrist movement intents. Ten healthy subjects volunteered for a validation experiment, which was built as a virtual environment in which people with wrist amputation could receive myoelectric control training. The results showed that proportional two-degree-of-freedom (DOF) movements could be estimated by sEMG. This work could be useful not only for wrist prostheses but also for alternative computer interfaces and studies to examine motor adaptation by sEMG.