Mapping Motor Cortex Stimulation to Muscle Responses: A Deep Neural Network Modeling Approach

M2M-InvNet: Human Motor Cortex Mapping From Multi-Muscle Response Using TMS and Generative 3D Convolutional Network

Transcranial magnetic stimulation (TMS) is often applied to the motor cortex to stimulate a collection of motor evoked potentials (MEPs) in groups of peripheral muscles. The causal interface between TMS and MEP is the selective activation of neurons in the motor cortex; moving around the TMS 'spot' over the motor cortex causes different MEP responses. A question of interest is whether a collection of MEP responses can be used to identify the stimulated locations on the cortex, which could potentially be used to then place the TMS coil to produce chosen sets of MEPs. In this work we leverage our previous report on a 3D convolutional neural network (CNN) architecture that predicted MEPs from the induced electric field, to tackle an inverse imaging task in which we start with the MEPs and estimate the stimulated regions on the motor cortex. We present and evaluate five different inverse imaging CNN architectures, both conventional and generative, in terms of several measures of reconstruction accuracy. We found that one architecture, which we propose as M2M-InvNet, consistently achieved the best performance.

Sling Exercise Can Drive Cortical Representation of the Transversus Abdominis and Multifidus Muscles in Patients With Chronic Low Back Pain

Objective

The transversus abdominis (TrA) and multifidus (MF) muscles are essential in preventing chronic low back pain (CLBP) recurrence by maintaining segmental stabilization and stiffness. Sling exercise is a high-level core stability training to effectively improve the activities of the TrA and MF muscles. However, the neural mechanism for sling exercise-induced neural plasticity change in the primary motor cortex (M1) remains unclear. This study aimed to investigate the role of sling exercise in the reorganization of the motor cortical representation of the TrA and MF muscles.

Methods

Twenty patients with CLBP and 10 healthy individuals were recruited. For map volume, area, the center of gravity (CoG) location (medial-lateral location and anterior-posterior location), and latency, two-way ANOVA was performed to compare the effects of groups (the CLBP-pre, CLBP-post, and healthy groups) and the two muscles (the TrA and MF muscles). The Visual Analog Scale (VAS), the Oswestry Disability Index (ODI), and postural balance stability were assessed at baseline and at the end of 2 weeks of sling exercise. Linear correlations between VAS or ODI and CoG locations were assessed by Pearson's correlation test.

Results

2 weeks of sling exercise induced both the anterior-medial (P < 0.001) and anterior-posterior (P = 0.025) shifts of the MF muscle representation at the left motor cortex in patients with CLBP. Anterior-medial (P = 0.009) shift of the TrA muscle representation at the right motor cortex was observed in patients with CLBP. The motor cortical representation of the two muscles in patients with CLBP after sling exercise (TrA: 2.88 ± 0.27 cm lateral and 1.53 ± 0.47 cm anterior of vertex; MF: 3.02 ± 0.48 cm lateral and 1.62 ± 0.40 cm anterior of vertex) closely resembled that observed in healthy individuals (TrA: 2.83 ± 0.48 cm lateral and 2.00 ± 0.43 cm anterior of vertex; MF: 2.94 ± 0.43 cm lateral and 1.77 ± 0.48 cm anterior of vertex). The VAS and the ODI were reduced following the sling exercise (VAS: P < 0.001; ODI: P < 0.001).

Conclusion

This study provides evidence that sling training can drive plasticity changes in the motor system, which corresponds with the reduction in pain and disability levels in patients with CLBP. This study was registered in the Chinese Clinical Trial Registry (Clinical Trial Registration Number: ChiCTR2100045904, http://www.chictr.org.cn/showproj.aspx?proj=125819).

Clinical Trial Registration

ChiCTR2100045904.

PENet: Continuous-Valued Pulmonary Edema Severity Prediction On Chest X-ray Using Siamese Convolutional Networks

For physicians to make rapid clinical decisions for patients with congestive heart failure, the assessment of pulmonary edema severity in chest radiographs is vital. Although deep learning has shown promise in detecting the presence or absence or discrete grades of severity, of such edema, prediction of continuous-valued severity yet remains a challenge. Here, we propose PENet: Siamese convolutional neural networks to assess the continuous spectrum of severity of lung edema from chest radiographs. We present different modes of implementing this network and demonstrate that our best model outperforms that of earlier work (mean AUC of 0.91 over 0.87), while using only 1/16-th the dimension of input images and 1/69-th the size of training data, thus also saving expensive computation.

Approaches to revealing the neural basis of muscle synergies: a review and a critique

The central nervous system (CNS) may produce coordinated motor outputs via the combination of motor modules representable as muscle synergies. Identification of muscle synergies has hitherto relied on applying factorization algorithms to multimuscle electromyographic data (EMGs) recorded during motor behaviors. Recent studies have attempted to validate the neural basis of the muscle synergies identified by independently retrieving the muscle synergies through CNS manipulations and analytic techniques such as spike-triggered averaging of EMGs. Experimental data have demonstrated the pivotal role of the spinal premotor interneurons in the synergies' organization and the presence of motor cortical loci whose stimulations offer access to the synergies, but whether the motor cortex is also involved in organizing the synergies has remained unsettled. We argue that one difficulty inherent in current approaches to probing the synergies' neural basis is that the EMG generative model based on linear combination of synergies and the decomposition algorithms used for synergy identification are not grounded on enough prior knowledge from neurophysiology. Progress may be facilitated by constraining or updating the model and algorithms with knowledge derived directly from CNS manipulations or recordings. An investigative framework based on evaluating the relevance of neurophysiologically constrained models of muscle synergies to natural motor behaviors will allow a more sophisticated understanding of motor modularity, which will help the community move forward from the current debate on the neural versus nonneural origin of muscle synergies.