ObjectiveAlthough the cerebello‐thalamo‐cortical network has often been suggested to be of importance in the pathogenesis of essential tremor (ET), the origins of tremorgenic activity in this disease are not fully understood. We used a combination of cortical thickness imaging and neurophysiological studies to analyze whether the severity of tremor was associated with anatomical changes in the brain in ET patients. MethodsMagnetic resonance imaging (MRI) and a neurophysiological assessment were performed in 13 nondemented ET patients. High field structural brain MRI images acquired in a 3T scanner and analyses of cortical thickness and surface were carried out. Cortical reconstruction and volumetric segmentation was performed with the FreeSurfer image analysis software. We used high‐density surface electromyography (hdEMG) and inertial measurement units (IMUs) to quantify the tremor severity in upper extrimities of patients. In particular, advanced computer tool was used to reliably identify discharge patterns of individual motor units from surface hdEMG and quantify motor unit synchronization.ResultsWe found significant association between increased motor unit synchronization (i.e., more severe tremor) and cortical changes (i.e., atrophy) in widespread cerebral cortical areas, including the left medial orbitofrontal cortex, left isthmus of the cingulate gyrus, right paracentral lobule, right lingual gyrus, as well as reduced left supramarginal gyrus (inferior parietal cortex), right isthmus of the cingulate gyrus, left thalamus, and left amygdala volumes.InterpretationGiven that most of these brain areas are involved in controlling movement sequencing, ET tremor could be the result of an involuntary activation of a program of motor behavior used in the genesis of voluntary repetitive movements.

Despite the progress in understanding of neural codes, the studies of the cortico-muscular coupling still largely rely on interferential electromyographic (EMG) signal or its rectification for the assessment of motor neuron pool behavior. This assessment is non-trivial and should be used with precaution. Direct analysis of neural codes by decomposing the EMG, also known as neural decoding, is an alternative to EMG amplitude estimation. In this study, we propose a fully-deterministic hybrid surface EMG (sEMG) decomposition approach that combines the advantages of both template-based and Blind Source Separation (BSS) decomposition approaches, a.k.a. guided source separation (GSS), to identify motor unit (MU) firing patterns. We use the single-pass density-based clustering algorithm to identify possible cluster representatives in different sEMG channels. These cluster representatives are then used as initial points of modified gradient Convolution Kernel Compensation (gCKC) algorithm. Afterwards, we use the Kalman filter to reduce the noise impact and increase convergence rate of MU filter identification by gCKC. Moreover, we designed an adaptive soft-thresholding method to identify MU firing times out of estimated MU spike trains. We tested the proposed algorithm on a set of synthetic sEMG signals with known MU firing patterns. A grid of 9 × 10 monopolar surface electrodes with 5-mm inter-electrode distances in both directions was simulated. Muscle excitation was set to 10, 30, and 50%. Colored Gaussian zero-mean noise with the signal-to-noise ratio (SNR) of 10, 20, and 30 dB, respectively, was added to 16 s long sEMG signals that were sampled at 4,096 Hz. Overall, 45 simulated signals were analyzed. Our decomposition approach was compared with gCKC algorithm. Overall, in our algorithm, the average numbers of identified MUs and Rate-of-Agreement (RoA) were 16.41 ± 4.18 MUs and 84.00 ± 0.06%, respectively, whereas the gCKC identified 12.10 ± 2.32 MUs with the average RoA of 90.78 ± 0.08%. Therefore, the proposed GSS method identified more MUs than the gCKC, with comparable performance. Its performance was dependent on the signal quality but not the signal complexity at different force levels. The proposed algorithm is a promising new offline tool in clinical neurophysiology.

We compared non-negative matrix factorization (NMF) and convolution kernel compensation techniques for high-density electromyogram decomposition. The experimental data were recorded from nine healthy persons during controlled single degree of freedom (DOF) wrist flexion-extension, supination-pronation, and ulnar-radial deviation movements. We assembled the identified motor units and NMF components into three groups. Those active mostly during the first and the second movement direction per DOF were placed in the G1 and G3 groups, respectively. The remaining components were nonspecific for movement direction and were placed in the G2 group. In ulnar and radial deviation, the relative energies of identified cumulative motor unit spike trains (CSTs) and NMF components were similarly distributed among the groups. In other two movement types, the energy of NMF components in the G2 group was significantly larger than the energy of CSTs. We further performed a coherence analysis between CSTs and sums of NMF components in each group. Both decompositions demonstrated a solid match, but only at frequencies <3 Hz. At higher frequencies, the coherence hardly exceeded the value of 0.5. Potential reasons for these discrepancies include the negative impact of motor unit action potential shapes and noise on NMF decomposition.

We evaluated different muscle excitation estimation techniques, and their sensitivity to Motor Unit (MU) distribution in muscle tissue. For this purpose, the Convolution Kernel Compensation (CKC) method was used to identify the MU spike trains from High-Density Elec-troMyoGrams (HDEMG). Afterwards, Cumulative MU Spike Train (CST) was calculated by summing up the identified MU spike trains. Muscle excitation estimation from CST was compared to the recently introduced Cumulative Motor Unit Activity Index (CAI) and classically used Root-Mean-Square (RMS) amplitude envelop of EMG. To emphasize their dependence on the MU distribution further, all three muscle excitation estimates were used to calculate the agonist-antagonist co-activation index. We showed on synthetic HDEMG that RMS envelopes are the most sensitive to MU distribution (10 % dispersion around the real value), followed by the CST (7 % dispersion) and CAI (5 % dispersion). In experimental HDEMG from wrist extensors and flexors of post-stroke subjects, RMS envelopes yielded significantly smaller excitations of antagonistic muscles than CST and CAI. As a result, RMS-based co-activation estimates differed significantly from the ones produced by CST and CAI, illuminating the problem of large diversity of muscle excitation estimates when multiple muscles are studied in pathological conditions. Similar results were also observed in experimental HDEMG of six intact young males.

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