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Manual Control—Force Perception
Published in Alfred T. Lee, Vehicle Simulation, 2017
In some motor control acts, more than one act is performed in sequence. For example, automobile braking typically involves releasing the pressure on the accelerator pedal before applying pressure to the brake pedal. In experienced drivers, this becomes a highly automated sequence of control actions solely with reference to proprioceptive and force cues. Although susceptible to conscious monitoring and alteration, the sequence of perceptual-motor control acts appears to be localized in an area of the brain termed the supplementary motor area (SMA). Signals are then transmitted on to the basal ganglia, to the thalamus (for sensory integration), and then looping back to the SMA. Closed-loop control acts, such as steering wheel and control yoke inputs, are executed at this low level of brain activity.
Is the Motor Cortex Only an Executive Area? Its Role in Motor Cognition
Published in Alexa Riehle, Eilon Vaadia, Motor Cortex in Voluntary Movements, 2004
A mechanism similar to that of mirror neurons operates in humans. Brain activity during different conditions where subjects were self-representing actions (e.g., executing and imagining actions, inspecting tools, or observing actions performed by other people) was compared.19,30-34 The outcome of these studies is twofold. First, there exists a cortical network common to all conditions. As shown in the preceding section, the motor cortex is part of this network, which also includes cortical areas located in the superior and inferior parietal lobules, the ventral premotor cortex, and the supplementary motor area (SMA). Second, motor representations for each individual condition are clearly specified by the activation of cortical zones, which do not overlap between conditions.32,35,36
Motor Network Reorganization Induced in Chronic Stroke Patients with the Use of a Contralesionally-Controlled Brain Computer Interface
Published in Brain-Computer Interfaces, 2022
Joseph B. Humphries, Daniela J. S. Mattos, Jerrel Rutlin, Andy G. S. Daniel, Kathleen Rybczynski, Theresa Notestine, Joshua S. Shimony, Harold Burton, Alexandre Carter, Eric C. Leuthardt
Analysis of preprocessed MRI data utilized MATLAB (MathWorks, Natick, MA) unless otherwise noted. Cortical regions previously implicated in motor control served as a priori regions of interest (ROIs). These included the hand region of bilateral primary dorsal motor cortex (M1), dorsal premotor area (PMA), and supplementary motor area (SMA). We used Neurosynth [47] for all cortical ROI coordinates. Peak Z-scores for each ROI served as centers for 8 mm diameter spheres. Extracted mean BOLD timeseries were from each ROI. Generation of two aggregate cerebellum (CBL) ROIs were from somatomotor regions in anterior CBL lobules. Separately averaged left and right CBL somatomotor regions formed the basis of left and right CBL mean timeseries [48]. Then, labeling these left- and right-side timeseries as contralesional and ipsilesional was relative to the left/right stroke brain location. Cerebellar laterality was in correspondence to motor network membership (i.e. left cerebellum and right primary motor cortex were in the same functional hemisphere). Excluded ROIs overlaid the stroke lesion. Analyses were of functional connectivity, defined as the Pearson correlation of paired mean ROI timeseries and between select ROIs and all other voxels in the brain. Pre- and post-therapy connectivity differences indicated changes in functional connectivity.
A Comparative Study of Existing Machine Learning Approaches for Parkinson's Disease Detection
Published in IETE Journal of Research, 2021
Gunjan Pahuja, T. N. Nagabhushan
The changes in the functional connectivity of motor networks in the resting state in PD, using fMRI and a network model based on graph theory, were demonstrated by Tao et al. [30]. The authors found that functional connectivity in the supplementary motor area, left dorsal lateral prefrontal cortex and left putamen of PD patients at off state had significantly decreased while functional connectivity in the left cerebellum, left primary motor cortex and left parietal cortex had increased as compared to normal subjects in PD. Defeng et al. [31] conducted a real-time case study using deep brain electrode implantation to predict the PD tremor. Similarly, Christian Salvotre et al. [32] used a dataset of MRI scans from 28 controls, 28 PD patients and 28 Progressive Supranuclear Palsy. Supervised machine learning algorithm was used based on PCA as a feature extraction method and SVM as a classification algorithm. The authors have tried to overcome the problem of imbalance dataset by taking the same number of patients of different classes (PD, HC and PSP). Nowadays many classifiers are available for PD detection and their performance is measured with metrics such as accuracy, sensitivity and specificity [15,17,33,34]. In general, the accuracy is a measure of how many cases are correctly identified in total irrespective of positive or negative cases or it measures the overall performance of the method. Table 2 describes some of the studies available in the literature for PD diagnosis and classification using machine learning approaches.
Predicting developmental language disorders using artificial intelligence and a speech data analysis tool
Published in Human–Computer Interaction, 2023
Eleonora Aida Beccaluva, Fabio Catania, Fabrizio Arosio, Franca Garzotto
The relationship between language and music is the subject of extensive literature (Atherton et al., 2018; Besson & Schön, 2001; Patel, 1998). A growing body of evidence has highlighted behavioral connections between the processing of musical rhythm and linguistic syntax, suggesting that these abilities may share common neural resources (Chen et al., 2008). In a recent review of neuroimaging studies, Herad & Lee report evidence of common neural structures engaged while performing a representative set of musical rhythm operations (rhythm, beat, and meter) and linguistic syntactic operations (merge, movement, and reanalysis). The bilateral sensorimotor network of inferior frontal gyri, supplementary motor area, superior temporal gyri/temporoparietal junction, insula, intraparietal lobule, and putamen were engaged in performing rhythm operations, while the left sensorimotor network, including the inferior frontal gyrus, posterior superior temporal gyrus, premotor cortex, and supplementary motor area were engaged in performing syntactic operations. The anatomical overlap of sensorimotor regions recruited for the achievement of musical rhythmic operations and linguistic syntactic operations is mainly in the left inferior frontal gyrus, left supplementary motor area, and bilateral insula – neural substrates, involved in temporal hierarchy processing and predictive coding (Heard & Lee, 2020). The latter is particularly interesting since it suggests that the same predictive mechanisms are used in music (e.g., the anticipation of rhythmic or tonal information) and morphosyntactic processing (e.g., the anticipation of linguistic information) (Fiveash et al., 2021; Gordon et al., 2015).