A Novel Spatial Feature for the Identification of Motor Tasks Using High-Density Electromyography.
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ABSTRACT:
SUBMITTER: Jordanic M
PROVIDER: S-EPMC5539712 | biostudies-other | 2017 Jul
REPOSITORIES: biostudies-other
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Project description:Background:Objective assessments of Parkinson's disease (PD) patients' motor state using motion capture techniques are still rarely used in clinical practice, even though they may improve clinical management. One major obstacle relates to the large dimensionality of motor abnormalities in PD. We aimed to extract global motor performance measures covering different everyday motor tasks, as a function of a clinical intervention, i.e., deep brain stimulation (DBS) of the subthalamic nucleus. Methods:We followed a data-driven, machine-learning approach and propose performance measures that employ Random Forests with probability distributions. We applied this method to 14 PD patients with DBS switched-off or -on, and 26 healthy control subjects performing the Timed Up and Go Test (TUG), the Functional Reach Test (FRT), a hand coordination task, walking 10-m straight, and a 90° curve. Results:For each motor task, a Random Forest identified a specific set of metrics that optimally separated PD off DBS from healthy subjects. We noted the highest accuracy (94.6%) for standing up. This corresponded to a sensitivity of 91.5% to detect a PD patient off DBS, and a specificity of 97.2% representing the rate of correctly identified healthy subjects. We then calculated performance measures based on these sets of metrics and applied those results to characterize symptom severity in different motor tasks. Task-specific symptom severity measures correlated significantly with each other and with the Unified Parkinson's Disease Rating Scale (UPDRS, part III, correlation of r2?=?0.79). Agreement rates between different measures ranged from 79.8 to 89.3%. Conclusion:The close correlation of PD patients' various motor abnormalities quantified by different, task-specific severity measures suggests that these abnormalities are only facets of the underlying one-dimensional severity of motor deficits. The identification and characterization of this underlying motor deficit may help to optimize therapeutic interventions, e.g., to "automatically" adapt DBS settings in PD patients.
| S-EPMC5694559 | biostudies-literature
Project description:Introduction: The role of the spinal muscles in scoliogenesis is not fully substantiated. Do they act scoliogenic (inducing scoliosis) or counteract scoliosis in adolescent idiopathic scoliosis (AIS)? In this study, we will examine this by using selectively placed Transcutaneous Electric Stimulation (TES) combined with a cinematic radiographic technique and by performing electromyographic (EMG) evaluations during various motor tasks. Method: This is a cross-sectional study of subjects with small-curve AIS. Using cinematic radiography, they were evaluated dynamically either under electrical stimulation or when performing motor tasks of left and right lateral bending and rotation whilst measuring the muscle activity by EMG. Results: Forty-five patients with AIS were included as subjects. Five subjects volunteered for TES and six subjects performed the motor tasks with EMG. At the initial visual evaluation, and when stimulated with TES, the frontal plane spatial positions of the vertebral bodies showed discrete changes without an apparent pattern. However, analyzing the spatial positions when calibrated, we found that the spinal muscles exert a compressive 'response' with a minor change in the Cobb angle (CA) in small-curve AIS (CA = 10-20°). In larger curves (CA > 20°), TES induced a 'larger deformity' with a relative four-fold change in the CA compared to small-curve AIS with a ratio of 0.6. When evaluating local amplitude (peak) or cumulative (mean) EMG signals, we were unable to find consistent asymmetries. However, one subject had rapid progression and one regressed to a straight spine. When adding the absolute EMG ratios for all four motor tasks, the subject with progression had almost 10-fold less summed EMG ratios, and the subject with regression had more than 3-fold higher summed EMG ratios. Discussion: Based on these findings, we suggest that the spinal muscles in small-curve AIS have a stabilizing function maintaining a straight spine and keeping it in the midline. When deformities are larger (CA > 20°), the spine muscle curve exerts a scoliogenic 'response'. This suggests that the role of the muscles converts from counteracting AIS and stabilizing the spine to being scoliogenic for a CA of more than 20°. Moreover, we interpret higher EMG ratios as heightened asymmetric spinal muscle activity when the spinal muscles try to balance the spine to maintain or correct the deformity. When progression occurs, this is preceded or accompanied by decreased EMG ratios. These findings must be substantiated by larger studies.
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Project description:Type 1 diabetes mellitus (T1DM) results from immune mediated destruction of pancreatic beta cells. However, clinical and immunologic phenotypes of T1DM are variable. Several auto-antibodies including GADA, IA-2A, and ZnT8A, were identified in T1DM, but the prevalence of these auto-antibodies varied for a broad spectrum of T1DM. Here, we systemically profiled auto-antibodies from serum samples of 16 T1DM, 16 type 2 diabetes (T2DM) patients, and 27 healthy controls with normal glucose tolerance (NGT) using protein microarrays containing 9,480 proteins. Among 9,480 different proteins on the array, we identified novel auto-antibody candidates (EEF1A1-AAb and UBE2L3-AAb) by M-test coupled with PLS-DA. These auto-antibodies were highly present in T1DM than controls and detected in 40% of T1DM without GADA. Furthermore, these auto-antibodies might help to differentiate subtype of T1DM when combined with GADA. These novel auto-antibodies provide new diagnostic information of T1DM, as well as new insights into the pathogenesis of T1DM.
2014-07-30 | GSE50866 | GEO
Project description:Type 1 diabetes mellitus (T1DM) results from immune mediated destruction of pancreatic beta cells. However, clinical and immunologic phenotypes of T1DM are variable. Several auto-antibodies including GADA, IA-2A, and ZnT8A, were identified in T1DM, but the prevalence of these auto-antibodies varied for a broad spectrum of T1DM. Here, we systemically profiled auto-antibodies from serum samples of 16 T1DM, 16 type 2 diabetes (T2DM) patients, and 27 healthy controls with normal glucose tolerance (NGT) using protein microarrays containing 9,480 proteins. Among 9,480 different proteins on the array, we identified novel auto-antibody candidates (EEF1A1-AAb and UBE2L3-AAb) by M-test coupled with PLS-DA. These auto-antibodies were highly present in T1DM than controls and detected in 40% of T1DM without GADA. Furthermore, these auto-antibodies might help to differentiate subtype of T1DM when combined with GADA. These novel auto-antibodies provide new diagnostic information of T1DM, as well as new insights into the pathogenesis of T1DM. Auto-antibodies from serum samples were profiled using a high-density, fluorescence-based protein microarray containing duplicate spots of 9,480 human proteins derived from the Ultimate ORF collection The cohort of patients and controls consisted of 16 T1DM, 16 T2DM patients, and 27 healthy controls with NGT. This cohort was used to screen candidate auto-antibodies using protein microarrays (ProtoArray platform version 5.0, Invitrogen Corp., Carlsbad, CA). Serum samples were drawn from T1DM patients who have 1) fasting C-peptide level <0.3 nmol/L or serum C-peptide <0.6 nmol/L after glucagon loading, 2) initiation of insulin treatment within six months after diagnosis, and 3) duration of diabetes M-bM-^IM-$12 months. Mean age of T1DM in the first cohort was 42 M-BM-1 16 years. The control serum samples were obtained from T2DM patients who were treated only with oral anti-diabetic drug at least 5 years and from NGTs who had no history of diabetes, no first-degree relatives with diabetes, a fasting plasma glucose concentration of <6.1 mmol/l, and a HbA1c value of <5.8%.
2014-07-30 | E-GEOD-50866 | biostudies-arrayexpress
Project description:Identification of microbial pathogens in clinical specimens is still performed by phenotypic methods that are often slow and cumbersome, despite the availability of more comprehensive genotyping technologies. We present an approach based on whole-genome amplification and resequencing microarrays for unbiased pathogen detection. This 10 h process identifies a broad spectrum of bacterial and viral species and predicts antibiotic resistance and pathogenicity and virulence profiles. We successfully identify a variety of bacteria and viruses, both in isolation and in complex mixtures, and the high specificity of the microarray distinguishes between different pathogens that cause diseases with overlapping symptoms. The resequencing approach also allows identification of organisms whose sequences are not tiled on the array, greatly expanding the repertoire of identifiable organisms and their variants. We identify organisms by hybridization of their DNA in as little as 1-4 h. Using this method, we identified Monkeypox virus and drug-resistant Staphylococcus aureus in a skin lesion taken from a child suspected of an orthopoxvirus infection, despite poor transport conditions of the sample, and a vast excess of human DNA. Our results suggest this technology could be applied in a clinical setting to test for numerous pathogens in a rapid, sensitive and unbiased manner.
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Project description:String-pulling and patterned-string tasks are often used to analyse perceptual and cognitive abilities in animals. In addition, the paradigm can be used to test the interrelation between visual-spatial and motor performance. Two Australian parrot species, the galah (Eolophus roseicapilla) and the cockatiel (Nymphicus hollandicus), forage on the ground, but only the galah uses its feet to manipulate food. I used a set of string pulling and patterned-string tasks to test whether usage of the feet during foraging is a prerequisite for solving the vertical string pulling problem. Indeed, the two species used techniques that clearly differed in the extent of beak-foot coordination but did not differ in terms of their success in solving the string pulling task. However, when the visual-spatial skills of the subjects were tested, the galahs outperformed the cockatiels. This supports the hypothesis that the fine motor skills needed for advanced beak-foot coordination may be interrelated with certain visual-spatial abilities needed for solving patterned-string tasks. This pattern was also found within each of the two species on the individual level: higher motor abilities positively correlated with performance in patterned-string tasks. This is the first evidence of an interrelation between visual-spatial and motor abilities in non-mammalian animals.
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Project description:This paper describes a natural language processing system for the task of pneumonia identification. Based on the information extracted from the narrative reports associated with a patient, the task is to identify whether or not the patient is positive for pneumonia.A binary classifier was employed to identify pneumonia from a dataset of multiple types of clinical notes created for 426 patients during their stay in the intensive care unit. For this purpose, three types of features were considered: (1) word n-grams, (2) Unified Medical Language System (UMLS) concepts, and (3) assertion values associated with pneumonia expressions. System performance was greatly increased by a feature selection approach which uses statistical significance testing to rank features based on their association with the two categories of pneumonia identification.Besides testing our system on the entire cohort of 426 patients (unrestricted dataset), we also used a smaller subset of 236 patients (restricted dataset). The performance of the system was compared with the results of a baseline previously proposed for these two datasets. The best results achieved by the system (85.71 and 81.67 F1-measure) are significantly better than the baseline results (50.70 and 49.10 F1-measure) on the restricted and unrestricted datasets, respectively.Using a statistical feature selection approach that allows the feature extractor to consider only the most informative features from the feature space significantly improves the performance over a baseline that uses all the features from the same feature space. Extracting the assertion value for pneumonia expressions further improves the system performance.
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Project description:This paper presents a dataset of high-density surface EMG signals (HD-sEMG) designed to study patterns of sEMG spatial distribution over upper limb muscles during voluntary isometric contractions. Twelve healthy subjects performed four different isometric tasks at different effort levels associated with movements of the forearm. Three 2-D electrode arrays were used for recording the myoelectric activity from five upper limb muscles: biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres. Technical validation comprised a signals quality assessment from outlier detection algorithms based on supervised and non-supervised classification methods. About 6% of the total number of signals were identified as "bad" channels demonstrating the high quality of the recordings. In addition, spatial and intensity features of HD-sEMG maps for identification of effort type and level, have been formulated in the framework of this database, demonstrating better performance than the traditional time-domain features. The presented database can be used for pattern recognition and MUAP identification among other uses.
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Project description:The red nucleus (RN) is required for limb control, specifically fine motor coordination. There is some evidence for a role of the RN in reaching and grasping, mainly from lesion studies, but results so far have been inconsistent. In addition, the role of RN neurons in such learned motor functions at the level of synaptic transmission has been largely neglected. Here, we show that Vglut2-expressing RN neurons undergo plastic events and encode the optimization of fine movements. RN light-ablation severely impairs reaching and grasping functions while sparing general locomotion. We identify a neuronal population co-expressing Vglut2, PV and C1QL2, which specifically undergoes training-dependent plasticity. Selective chemo-genetic inhibition of these neurons perturbs reaching and grasping skills. Our study highlights the role of the Vglut2-positive rubral population in complex fine motor tasks, with its related plasticity representing an important starting point for the investigation of mechanistic substrates of fine motor coordination training.
| S-EPMC6529416 | biostudies-literature
Project description:ObjectiveNumerous devices have been designed to support the back during lifting tasks. To improve the utility of such devices, this research explores the use of preparatory muscle activity to classify muscle loading and initiate appropriate device activation. The goal of this study was to determine the earliest time window that enabled accurate load classification during a dynamic lifting task.MethodsNine subjects performed thirty symmetrical lifts, split evenly across three weight conditions (no-weight, 10-lbs and 24-lbs), while low-back muscle activity data was collected. Seven descriptive statistics features were extracted from 100 ms windows of data. A multinomial logistic regression (MLR) classifier was trained and tested, employing leave-one subject out cross-validation, to classify lifted load values. Dimensionality reduction was achieved through feature cross-correlation analysis and greedy feedforward selection. The time of full load support by the subject was defined as load-onset.ResultsRegions of highest average classification accuracy started at 200 ms before until 200 ms after load-onset with average accuracies ranging from 80% (±10%) to 81% (±7%). The average recall for each class ranged from 69-92%.ConclusionThese inter-subject classification results indicate that preparatory muscle activity can be leveraged to identify the intent to lift a weight up to 100 ms prior to load-onset. The high accuracies shown indicate the potential to utilize intent classification for assistive device applications.SignificanceActive assistive devices, e.g. exoskeletons, could prevent back injury by off-loading low-back muscles. Early intent classification allows more time for actuators to respond and integrate seamlessly with the user.
| S-EPMC5814006 | biostudies-literature