Investigating the Brain Activity Correlates of Humming Bee Sound during Bhramari Pranayama.

Context: Previous research has shown the vast benefits associated with BhP. However, the dynamics of cortical activity in connection with Bhramari sound have not been investigated yet. Aim: To investigate the cortical activity in connection with Bhramari sound. Settings and Design: Humming sound was analyzed with a custom-made nasal device consisting of MAX4466 sensor time synchronized with the EEG setup. We anticipated that the modulation of cortical activity with the humming sound (either of long or short durations) leaves its effects after the Pranayama, which helps to understand the positive impacts of BhP. Methods and Material: 30 participants were instructed to perform the BhP for a period of 90 seconds. We proposed to investigate the cortical correlates before, during, and after the BhP through EEG. A custom-made nasal device consisting of MAX4466 sensor time synchronized with the EEG setup was used for analyzing the humming sound. Statistical Analysis Used: A paired t-test (P < 0.05) with a Bonferroni correction is carried out to explore the statistically significant difference in power spectral density (PSD) values. Results: Results show that the relative spectral power in theta band for short humming durations (less than or equal to 9 seconds) was similar on the frontal cortex during and after the Pranayama practice (P > 0.05) in most of the subjects. Conclusions: In conclusion, for the immediate positive effects of BhP, the humming duration should be kept less than or equal to 9 seconds. A wearable sound recording system can be developed in the future as a feedback system that provides biofeedback to the user so that a constant humming duration can be maintained.

Chronic striatal cholinergic interneuron excitation induces clinically-relevant dystonic behavior in mice.

Dystonia is common, debilitating, often medically refractory, and difficult to diagnose. The gold standard for both clinical and mouse model dystonia evaluation is subjective assessment, ideally by expert consensus. However, this subjectivity makes translational quantification of clinically-relevant dystonia metrics across species nearly impossible. Many mouse models of genetic dystonias display abnormal striatal cholinergic interneuron excitation, but few display subjectively dystonic features. Therefore, whether striatal cholinergic interneuron pathology causes dystonia remains unknown. To address these critical limitations, we first demonstrate that objectively quantifiable leg adduction variability correlates with leg dystonia severity in people. We then show that chemogenetic excitation of striatal cholinergic interneurons in mice causes comparable leg adduction variability in mice. This clinically-relevant dystonic behavior in mice does not occur with acute excitation, but rather develops after 14 days of ongoing striatal cholinergic interneuron excitation. This requirement for prolonged excitation recapitulates the clinically observed phenomena of a delay between an inciting brain injury and subsequent dystonia manifestation and demonstrates a causative link between chronic striatal cholinergic interneuron excitation and clinically-relevant dystonic behavior in mice. Therefore, these results support targeting striatal ChIs for dystonia drug development and suggests early treatment in the window following injury but prior to dystonia onset. One Sentence Summary: Chronic excitation of dorsal striatal cholinergic interneuron causes clinically-relevant dystonic phenotypes in mice.

Dynamic Musculoskeletal Simulation of a Passive Exoskeleton for Simulating Contracture.

Gait assessment scores are used for quantifying the abnormalities in the gait. Evaluation of the performance of these scores is a must for their clinical acceptance. However, current methods of assessing the performance of the gait assessment scores for clinically relevant gait abnormalities are prone to error. For example, values of intra-observer reliability, inter-observer reliability and sensitivity calculated for a gait assessment score change with the population of patients and observers. Therefore, there is a need for a methodology for replicating musculoskeletal deformations such as contracture in healthy individuals for objectively evaluating the performance of gait assessment scores with variable severity of musculoskeletal deformations. In this study, a series of dynamic musculoskeletal simulations are performed to simulate and verify a mathematical model of a passive exoskeleton for simulating contractures. The proposed model achieved a root mean square error of 1.864° and a correlation of coefficient of 0.984 while testing on five unique combinations of linear and non-linear torques and seven degrees of severity of hamstring contracture. To understand the tolerance of the proposed model to environmental noises, its performance is also tested at various perturbations. The results indicate that a passive exoskeleton attached to an unimpaired musculoskeletal model can accurately simulate the contracture of the targeted muscles. Clinical relevance - The proposed methodology has a utility in evaluating performances of gait assessment scores and understanding the effect of contractures on biomechanics of gait.

A-GAS: A Probabilistic Approach for Generating Automated Gait Assessment Score for Cerebral Palsy Children.

Gait disorders in children with cerebral palsy (CP) affect their mental, physical, economic, and social lives. Gait assessment is one of the essential steps of gait management. It has been widely used for clinical decision making and evaluation of different treatment outcomes. However, most of the present methods of gait assessment are subjective, less sensitive to small pathological changes, time-taking and need a great effort of an expert. This work proposes an automated, comprehensive gait assessment score (A-GAS) for gait disorders in CP. Kinematic data of 356 CP and 41 typically developing subjects is used to validate the performance of A-GAS. For the computation of A-GAS, instance abnormality index (AII) and abnormality index (AI) are calculated. AII quantifies gait abnormality of a gait cycle instance, while AI quantifies gait abnormality of a joint angle profile during walking. AII is calculated for all gait cycle instances by performing probabilistic and statistical analyses. Abnormality index (AI) is a weighted sum of AII, computed for each joint angle profile. A-GAS is a weighted sum of AI, calculated for a lower limb. Moreover, a graphical representation of the gait assessment report, including AII, AI, and A-GAS is generated for providing a better depiction of the assessment score. Furthermore, the work compares A-GAS with a present rating-based gait assessment scores to understand fundamental differences. Finally, A-GAS's performance is verified for a high-cost multi-camera set-up using nine joint angle profiles and a low-cost single camera set-up using three joint angle profiles. Results show no significant differences in performance of A-GAS for both the set-ups. Therefore, A-GAS for both the set-ups can be used interchangeably.

Automated EEG pathology detection based on different convolutional neural network models: Deep learning approach.

The brain electrical activity, recorded and materialized as electroencephalogram (EEG) signals, is known to be very useful in the diagnosis of brain-related pathology. However, manual examination of these EEG signals has various limitations, including time-consuming inspections, the need for highly trained neurologists, and the subjectiveness of the evaluation. Thus, an automated EEG pathology detection system would be helpful to assist neurologists to enhance the treatment procedure by making a quicker diagnosis and reducing error due to the human element. This work proposes the application of a time-frequency spectrum to convert the EEG signals onto the image domain. The spectrum images are then applied to the Convolutional Neural Network (CNN) to learn robust features that can aid the automatic detection of pathology and normal EEG signals. Three popular CNN in the form of the DenseNet, Inception-ResNet v2, and SeizureNet were employed. The extracted deep-learned features from the spectrum images are then passed onto the support vector machine (SVM) classifier. The effectiveness of the proposed approach was assessed using the publicly available Temple University Hospital (TUH) abnormal EEG corpus dataset, which is demographically balanced. The proposed SeizureNet-SVM-based system achieved state-of-the-art performance: accuracy, sensitivity, and specificity of 96.65%, 90.48%, and 100%, respectively. The results show that the proposed framework may serve as a diagnostic tool to assist clinicians in the detection of EEG pathology for early treatment.