Finger Movement Task Research Articles

Long-term motor learning in focal hand dystonia

ObjectiveBecause focal hand dystonia usually occurs in the over-learned stage, it would be valuable to know long-term motor learning characteristics and underlying pathophysiological features that might predispose to dystonia. MethodsWe conducted a case-control exploratory study of 15 visits over 12 weeks in the non-affected hand of a 4-finger sequence of 8 key presses in eight patients with FHD compared with eight age- and sex-matched, healthy volunteers (HVs). We studied the behavioral data and the physiological changes of the brain, including motor cortical excitability and cortical oscillations. ResultsThere was no significant difference in the time to reach 100 % accuracy between FHD and HV during the 80-day follow-up period. There was a statistically significant difference in the accuracy of sequential finger movement tasks between patients with FHD compared with HVs over 12 weeks, but post-hoc analysis with multiple comparion correction did not show difference. There were no significant differences in recruitment curve changes and task-related power changes of alpha and beta bands. ConclusionOver 12 weeks, FHD have motor learning capacity comparable to HVs and do not show pathophysiological abnormalities. SignificanceFurther studies would be valuable with more patients, more extended periods of practice, and more detailed electrophysiological explorations.

The impact of Playdough games on the development of oral motor skills in preschool children with childhood apraxia of speech

The relevant challenges are the use of innovative approaches that conserve both the speech therapist’s and the child’s resources while addressing multiple goals in speech therapy. The authors of this article have adopted such an approach to develop oral motor skills, which are crucial for speech development in preschoolers, particularly those with childhood apraxia of speech. This study aimed to evaluate the effectiveness of an author-developed therapy using Playdough games for preschool children with childhood apraxia of speech. A controlled design with repeated measures was employed. The primary diagnosis of CAS was established based on clinical assessment. A comparative evaluation of two groups (control and experimental) was conducted using a developed methodology for assessing the level of manual and oral motor skills in preschool children before and after the author-developed therapy using Playdough games. Researchers discovered that the systematic use of their author-developed Playdough games positively impacted the motor skills of children with CAS. Children in the experimental group, who participated in Playdough therapy, demonstrated significant improvements in both manual and oral motor skills, increased precision and focus in movement execution, and enhanced accuracy and speed of articulatory movements. There was a reduction in delays and incorrect movement sequences during finger movement tasks. Children required less additional time and significantly less assistance from the speech therapist, indicating a positive impact of using Playdough in therapy. The following skills improved: transitions between articulatory positions, which helped children avoid coarticulation difficulties; planning of movement sequences; and smooth transitions between sounds and syllables. The practical significance of this study lies in the effectiveness of using Playdough games for significantly improving oral motor skills, movement precision, and concentration in children with childhood apraxia of speech, thereby reducing the need for additional speech therapy support

Selective recruitment of the cerebellum evidenced by task-dependent gating of inputs

Functional magnetic resonance imaging (fMRI) studies have documented cerebellar activity across a wide array of tasks. However, the functional contribution of the cerebellum within these task domains remains unclear because cerebellar activity is often studied in isolation. This is problematic, as cerebellar fMRI activity may simply reflect the transmission of neocortical activity through fixed connections. Here, we present a new approach that addresses this problem. Rather than focus on task-dependent activity changes in the cerebellum alone, we ask if neocortical inputs to the cerebellum are gated in a task-dependent manner. We hypothesize that input is upregulated when the cerebellum functionally contributes to a task. We first validated this approach using a finger movement task, where the integrity of the cerebellum has been shown to be essential for the coordination of rapid alternating movements but not for force generation. While both neocortical and cerebellar activity increased with increasing speed and force, the speed-related changes in the cerebellum were larger than predicted by an optimized cortico-cerebellar connectivity model. We then applied the same approach in a cognitive domain, assessing how the cerebellum supports working memory. Enhanced gating was associated with the encoding of items in working memory, but not with the manipulation or retrieval of the items. Focusing on task-dependent gating of neocortical inputs to the cerebellum offers a promising approach for using fMRI to understand the specific contributions of the cerebellum to cognitive function.

Selective recruitment of the cerebellum evidenced by task-dependent gating of inputs.

Functional magnetic resonance imaging (fMRI) studies have documented cerebellar activity across a wide array of tasks. However, the functional contribution of the cerebellum within these task domains remains unclear because cerebellar activity is often studied in isolation. This is problematic, as cerebellar fMRI activity may simply reflect the transmission of neocortical activity through fixed connections. Here, we present a new approach that addresses this problem. Rather than focus on task-dependent activity changes in the cerebellum alone, we ask if neocortical inputs to the cerebellum are gated in a task-dependent manner. We hypothesize that input is upregulated when the cerebellum functionally contributes to a task. We first validated this approach using a finger movement task, where the integrity of the cerebellum has been shown to be essential for the coordination of rapid alternating movements but not for force generation. While both neocortical and cerebellar activity increased with increasing speed and force, the speed-related changes in the cerebellum were larger than predicted by an optimized cortico-cerebellar connectivity model. We then applied the same approach in a cognitive domain, assessing how the cerebellum supports working memory. Enhanced gating was associated with the encoding of items in working memory, but not with the manipulation or retrieval of the items. Focusing on task-dependent gating of neocortical inputs to the cerebellum offers a promising approach for using fMRI to understand the specific contributions of the cerebellum to cognitive function.

ROBUSTNESS EVALUATION OF MACHINE LEARNING ALGORITHMS FOR NEUROCOMPUTER INTERFACE SOFTWARE USING DISTRIBUTED AND PARALLEL COMPUTING

This article highlights the field of information technology for brain-computer interaction, and the main goal is to use it to determine patterns of human brain activity using electroencephalography (EEG) data. During the execution of the article, machine learning methods were used, namely such classifiers as Random Forest, Multi-Layer Perceptron (MLP), and Logistic Regression. The investigation begins with real-world experiments recording EEG signals during finger movement tasks, providing valuable insight into the complex dynamics of brain operation and interaction. Utilizing 10-fold cross-validation, the performance of each classifier is rigorously evaluated across various metrics, including accuracy, f1_weighted та roc_auc_ovr_weighted. Through this process, the robustness and consistency of classifier performance are assessed, with dispersion values computed to gauge variability across iterations. The findings reveal nuanced differences among the classifiers, with MLP demonstrating the highest robustness, followed by Logistic Regression and Random Forest. The main goal of the article was to find out the importance of such a classifier performance parameter as robustness. Software robustness is a key characteristic, especially in medical applications, where consistent and reliable performance of information technology is paramount. Neural interfaces offer many avenues for solving various limb problems, spinal cord injuries, and neurological diseases in humans. These devices contribute to improving the quality of life by minimizing these problems in people, which leads to increased mobility and functional capabilities of people. The article also emphasizes the potential associated with the transformation of neurointerface technologies in expanding human capabilities and revolutionizing human-machine interaction. In conclusion, the research contributes to advancing the field of brain-computer interaction by leveraging machine learning algorithms to decode neural signals and uncover hidden patterns within EEG data. By identifying the most stable classifier, the study lays the groundwork for the development of robust neurointerface technologies with practical applications in healthcare, rehabilitation, and beyond. Through interdisciplinary collaboration and innovative methodologies, the journey towards unlocking the full potential of brain-computer interaction continues, promising new horizons in human augmentation and technological innovation.

Motor imagery of finger movements: Effects on cortical and muscle activities

PurposeThe purpose of the current study was to explore the immediate effect of motor imagery (MI) involving finger movement of a given limb on cortical response and muscle activity in healthy subjects. MethodsTwenty healthy right-handed adults (7 females and 13 males) with a mean + SD age of 22.05 + 6.08 years participated in the study. The beta-band event-related desynchronization (ERD) at the sensorimotor cortex and muscle activity during finger movement tasks using either the index, middle, or thumb digits on the non-dominant left hand were compared before and after an MI training session. Subjects underwent a pre-MI, MI training, and finally a post-MI session where they either performed or imagined performing a button-pushing action 50 times per session with each of the three digits. ResultsThe ERD power in the beta frequency band was lower in pre-MI compared to post-MI and was significantly different between the pre- and post-MI sessions for both the index and middle fingers, but not the thumb. A significant decrease was seen in the mean muscle activity during post-MI compared to pre-MI for all the digits except the thumb. ConclusionsThe results from the current study suggest that complex MI can result in motor learning and improvement in motor performance, thereby requiring less effort during motion.

A motor task, not working memory, causes the revelation effect.

Performing a cognitive task prior to making a recognition judgment increases the probability of old responses, which is known as the revelation effect. The criterion shift account (Niewiadomski & Hockley, 2001) proposes that occupation of working memory causes the revelation effect. However, we proposed that working memory does not cause it. Two experiments were conducted to disconfirm the relationship between working memory and the revelation effect and to consider an alternative explanation that metacognition causes the effect. In Experiment 1, the revelation effect was caused by a finger movement task, which puts little or no load on working memory. In Experiment 2, a metacognitive instruction that a cognitive task would make subsequent recognition easier induced a conservative criterion shift. The finding that a simple motor task caused the revelation effect in Experiment 1 disconfirms the relationship between working memory and the revelation effect and extends the boundaries of the occurrence of the effect. The findings in Experiment 2 suggest that metacognition may be related to the occurrence of the revelation effect. This study implies a paradoxical aspect of human cognition in that metacognition, which usually makes cognition more effective and rational, may also cause an irrational phenomenon, the revelation effect. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Changes in absolute alpha power during execution, imagination and observation of a finger movement task

Background: Neurons that fire both during execution and observation of a motor act, called mirror neurons, provide strong evidence of its existence in humans. Real, observed and imagined movements seem to share most of the same neural pathways. However, little is known about the neural mechanisms and functional processes of the different areas and the integration of information between the observation and imagined systems. Objective: Compare alpha absolute power levels in sensorimotor areas between real, imagined and observed movement conditions. Methods: The sample consisted of a group of 12 healthy young individuals (4 men and 8 women), right-handed and with mean age of 30 ± 10. The task consisted of 4 blocks of 20 trials of rhythmic flexion-extension movements of the index finger by pressing the left button of a mouse properly fixed on a table. The activation level was analyzed by measuring the absolute power of the alpha band (8-13hz). Results: ANOVA with repeated measures showed that in the real condition, alpha absolute power was higher in the electrodes Cz, P3 and Pz compared to the electrode C3. In the imagined condition, the electrodes P3 and Pz presented higher alpha absolute power compared to the electrode C3. For the observed condition, electrodes P3, Pz and P4 presented higher alpha power compared to the electrode C3. In addition, higher alpha absolute power was observed in the electrode Pz compared to the electrodes Cz and C4. Conclusion: Higher alpha activation was observed on the electrode C3 in all conditions. These findings indicate that the observed movement also shares neural circuits when compared to the real and imagined movements. It was observed that in all conditions there was a significant decrease in alpha power on the C3 electrode, which indicates activation of the motor cortex in a similar way in the conditions.

Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces.

In recent years, motor imagery-based brain-computer interfaces (MI-BCIs) have developed rapidly due to their great potential in neurological rehabilitation. However, the controllable instruction set limits its application in daily life. To extend the instruction set, we proposed a novel movement-intention encoding paradigm based on sequential finger movement. Ten subjects participated in the offline experiment. During the experiment, they were required to press a key sequentially [i.e., Left→Left (LL), Right→Right (RR), Left→Right (LR), and Right→Left (RL)] using the left or right index finger at about 1 s intervals under an auditory prompt of 1 Hz. The movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were used to investigate the electroencephalography (EEG) variation induced by the sequential finger movement tasks. Twelve subjects participated in an online experiment to verify the feasibility of the proposed paradigm. As a result, both the MRCP and ERD features showed the specific temporal-spatial EEG patterns of different sequential finger movement tasks. For the offline experiment, the average classification accuracy of the four tasks was 71.69%, with the highest accuracy of 79.26%. For the online experiment, the average accuracies were 83.33% and 82.71% for LL-versus-RR and LR-versus-RL, respectively. This paper demonstrated the feasibility of the proposed sequential finger movement paradigm through offline and online experiments. This study would be helpful for optimizing the encoding method of motor-related EEG information and providing a promising approach to extending the instruction set of the movement intention-based BCIs.

Improved Selectivity in 7 T Digit Mapping Using VASO-CBV

Functional magnetic resonance imaging (fMRI) at Ultra-high field (UHF, ≥ 7 T) benefits from significant gains in the BOLD contrast-to-noise ratio (CNR) and temporal signal-to-noise ratio (tSNR) compared to conventional field strengths (3 T). Although these improvements enabled researchers to study the human brain to unprecedented spatial resolution, the blood pooling effect reduces the spatial specificity of the widely-used gradient-echo BOLD acquisitions. In this context, vascular space occupancy (VASO-CBV) imaging may be advantageous since it is proposed to have a higher spatial specificity than BOLD. We hypothesized that the assumed higher specificity of VASO-CBV imaging would translate to reduced overlap in fine-scale digit representation maps compared to BOLD-based digit maps. We used sub-millimeter resolution VASO fMRI at 7 T to map VASO-CBV and BOLD responses simultaneously in the motor and somatosensory cortices during individual finger movement tasks. We assessed the cortical overlap in different ways, first by calculating similarity coefficient metrics (DICE and Jaccard) and second by calculating selectivity measures. In addition, we demonstrate a consistent topographical organization of the targeted digit representations (thumb-index-little finger) in the motor areas. We show that the VASO-CBV responses yielded less overlap between the digit clusters than BOLD, and other selectivity measures were higher for VASO-CBV too. In summary, these results were consistent across metrics and participants, confirming the higher spatial specificity of VASO-CBV compared to BOLD.

A Study on Analysis Method for a Real-Time Neurofeedback System Using Non-Invasive Magnetoencephalography

For diseases that affect brain function, such as strokes, post-onset rehabilitation plays a critical role in the wellbeing of patients. MEG is a technique with high temporal and spatial resolution that measures brain functions non-invasively, and it is widely used for clinical applications. Without the ability to concurrently monitor patient brain activity in real-time, the most effective rehabilitation cannot occur. To address this problem, it is necessary to develop a neurofeedback system that can aid rehabilitation in real time; however, doing so requires an analysis method that is quick (less processing time means the patient can better connect the feedback to their mental state), encourages brain-injured patients towards task-necessary neural oscillations, and allows for the spatial location of those oscillation patterns to change over the course of the rehabilitation. As preliminary work to establish such an analysis method, we compared three decomposition methods for their speed and accuracy in detecting event-related synchronization (ERS) and desynchronization (ERD) in a healthy brain during a finger movement task. We investigated FastICA with 10 components, FastICA with 20 components, and spatio-spectral decomposition (SSD). The results showed that FastICA with 10 components was the most suitable for real-time monitoring due to its combination of accuracy and analysis time.

Voluntary Motor Command Release Coincides with Restricted Sensorimotor Beta Rhythm Phases.

Sensory perception and memory are enhanced during restricted phases of ongoing brain rhythms, but whether voluntary movement is constrained by brain rhythm phase is not known. Voluntary movement requires motor commands to be released from motor cortex (M1) and transmitted to spinal motoneurons and effector muscles. Here, we tested the hypothesis that motor commands are preferentially released from M1 during circumscribed phases of ongoing sensorimotor rhythms. Healthy humans of both sexes performed a self-paced finger movement task during electroencephalography (EEG) and electromyography (EMG) recordings. We first estimated the time of motor command release preceding each finger movement by subtracting individually measured corticomuscular transmission latencies from EMG-determined movement onset times. Then, we determined the phase of ipsilateral and contralateral sensorimotor mu (8-12 Hz) and beta (13-35 Hz) rhythms during release of each motor command. We report that motor commands were most often released between 120 and 140° along the contralateral beta cycle but were released uniformly along the contralateral mu cycle. Motor commands were also released uniformly along ipsilateral mu and beta cycles. Results demonstrate that motor command release coincides with restricted phases of the contralateral sensorimotor beta rhythm, suggesting that sensorimotor beta rhythm phase may sculpt the timing of voluntary human movement.SIGNIFICANCE STATEMENT Perceptual and cognitive function is optimal during specific brain rhythm phases. Although brain rhythm phase influences motor cortical neuronal activity and communication between the motor cortex and spinal cord, its role in voluntary movement is poorly understood. Here, we show that the motor commands needed to produce voluntary movements are preferentially released from the motor cortex during contralateral sensorimotor beta rhythm phases. Our findings are consistent with the notion that sensorimotor rhythm phase influences the timing of voluntary human movement.

Effects of Sport Stacking on Neuropsychological, Neurobiological, and Brain Function Performances in Patients With Mild Alzheimer's Disease and Mild Cognitive Impairment: A Randomized Controlled Trial

ObjectiveTo investigate the effects of sport stacking on the overall cognition and brain function in patients with mild Alzheimer's disease (AD) and mild cognitive impairment (MCI).MethodsA single-blind randomized controlled design was performed using sport stacking for 30 min, 5 days/week for 12 weeks. Forty-eight subjects with mild AD or MCI were randomly divided into the sport stacking group (T-mAD = 12, T-MCI = 12) and the active control group (C-mAD = 11, C-MCI = 13). Auditory Verbal Learning Test (AVLT), Alzheimer's Disease Cooperative Study–Activities of Daily Living scale (ADCS-ADL), Geriatric Depression Scale (GDS-30), and Pittsburgh Sleep Quality Index (PSQI) were performed, the level of amyloid β-protein-40 (Aβ-40), Aβ-42, brain-derived neurotrophic factor (BDNF), insulin-like growth factor-1(IGF-1), tumor necrosis factor-alpha (TNF-α), Interleukin-6 (IL-6), and soluble trigger receptor expressed on myeloid cells 2 (sTREM2) in plasma were tested, and brain functional connectivity in resting state and activation under finger movement task were analyzed by functional near-infrared spectroscopy (fNIRS).ResultsThirty-nine patients completed the trial. After 4 weeks, we found a significant increase in AVLT score in T-MCI (6.36 ± 5.08 vs. −1.11 ± 4.23, p = 0.004), and T-mAD group (4.60 ± 4.77 vs. −0.11 ± 2.89, p = 0.039). After 12 weeks, there was a significantly improved in AVLT (9.64 ± 4.90 vs. −0.33 ± 6.10, p = 0.002) and ADCS-ADL (3.36 ± 3.59 vs. −1.89 ± 2.71, p = 0.003) in T-MCI. There was a significant improvement in AVLT (5.30 ± 5.42 vs. 0.44 ± 2.40) in T-mAD (p < 0.05). Plasma levels of BDNF were upregulated in both T-MCI and T-mAD, and IGF-1 increased in T-MCI (P < 0.05) compared to the control groups. The functional connectivity in MCI patients between DLPFC.R and SCA.R, SMA.L, and SCA.R was decreased. In contrast, in mAD patients, the brain regional function connection was increased between DLPFC.R and Broca's.L. The activation of channel 36 located in the left primary somatosensory cortex was significantly increased after 12-week training, which was correlated with the improved AVLT and the increase of BDNF.ConclusionOur findings suggested that sport stacking is effective for patients with MCI and mild AD, possibly through increasing the expression of neuroprotective growth factors and enhancing neural plasticity to improve neurocognitive performance.Clinical Trial Registrationhttps://www.ClinicalTrials.gov, ChiCTR.org.cn, identifier: ChiCTR-2100045980.

The Evaluation of Classifier Performance during Fitting Wrist and Finger Movement Task Based on Forearm HD-sEMG

The transmission of human body movement signals to other devices through wearable smart bracelets has attracted increasing attention in the field of human-machine interfaces. However, owing to the limited data collection range of wearable bracelets, it is necessary to study the relationship between the superposition of the wrist and fingers and their cooperative motions to simplify the data collection system of such devices. Multichannel high-density surface electromyogram (HD-sEMG) signals exhibit high spatial resolutions, and they can help improve the accuracy of the multichannel fitting. In this study, we quantified the HD-sEMG forearm spatial activation features of 256 channels of hand movement and performed a linear fitting of the data obtained for finger and wrist movements in order to verify the linear superposition relationship between the cooperative and independent movements of the wrist and fingers. This study aims to classify and predict the results of the fitting and measured fingers and wrist cooperative actions using four commonly adopted classifiers and evaluate the performance of the classifiers in gesture fitting. The results indicated that linear discriminant analysis affords the highest classification performance, whereas the random forest method achieved the worst performance. This study can serve as a guide for gesture signal simplification in the future.

Finger somatotopy is preserved after tetraplegia but deteriorates over time.

Previous studies showed reorganised and/or altered activity in the primary sensorimotor cortex after a spinal cord injury (SCI), suggested to reflect abnormal processing. However, little is known about whether somatotopically specific representations can be activated despite reduced or absent afferent hand inputs. In this observational study, we used functional MRI and a (attempted) finger movement task in tetraplegic patients to characterise the somatotopic hand layout in primary somatosensory cortex. We further used structural MRI to assess spared spinal tissue bridges. We found that somatotopic hand representations can be activated through attempted finger movements in the absence of sensory and motor hand functioning, and no spared spinal tissue bridges. Such preserved hand somatotopy could be exploited by rehabilitation approaches that aim to establish new hand-brain functional connections after SCI (e.g. neuroprosthetics). However, over years since SCI the hand representation somatotopy deteriorated, suggesting that somatotopic hand representations are more easily targeted within the first years after SCI.

Multilevel Clustering-Evolutionary Random Support Vector Machine Cluster Algorithm-Based Blood Oxygenation Level-Dependent Functional Magnetic Resonance Imaging Images in Analysis of Therapeutic Effects on Cerebral Ischemic Stroke

The study aimed to explore the relationship between cerebral ischemic stroke (CIS) and the patient’s limb movement through the blood oxygenation level-dependent functional magnetic resonance imaging (BOLD-fMRI) based on multilevel clustering-evolutionary random support vector machine cluster (MCRSVMC). Specifically, 20 CIS patients were defined as the experimental group; another 20 healthy volunteers were defined as the control group. All subjects performed finger movement and verb association task. The performance of support vector machine (SVM) and MCRSVMC algorithm was compared and applied to functional magnetic resonance imaging (fMRI) of blood oxygen level in all subjects. The results showed that the average accuracy of MCRSVMC algorithm was significantly higher than that of support vector machine (86.75%, 65.84%; P &lt; 0.05 ). The sensitivity of MCRSVMC algorithm was significantly higher than that of support vector machine (92.52%, 75.41%; P &lt; 0.05 ). In addition, the specificity of MCRSVMC algorithm was significantly higher than that of support vector machine (86.39%, 68.24%; P &lt; 0.05 ). When CIS patients performed finger exercise, the sensory motor areas on both sides were significantly activated, and the activated sensory motor areas on both sides were significantly bigger than the ipsilateral area. The activation rate of the left-sensory motor area (L-SM1) was 87.5%, the activation rate of the right-sensory motor area (R-SM1) was 25%, the activation rate of the left-side auxiliary motor area (L-SMA) was 62.5%, and the activation rate of the right-side auxiliary motor area (R-SMA) was 37.5%. In conclusion, the MCRSVMC algorithm proposed in this study is highly efficient and stable. BOLD-fMRI diagnosis of motor function in CIS patients is mainly related to compensation around the lesion, which occurs on the healthy side after recovery.

Pre-surgical fMRI Localization of the Hand Motor Cortex in Brain Tumors: Comparison Between Finger Tapping Task and a New Visual-Triggered Finger Movement Task.

Introduction: Pre-surgical mapping is clinically essential in the surgical management of brain tumors to preserve functions. A common technique to localize eloquent areas is functional magnetic resonance imaging (fMRI). In tumors involving the peri-rolandic regions, the finger tapping task (FTT) is typically administered to delineate the functional activation of hand-knob area. However, its selectivity may be limited. Thus, here, a novel cue-induced fMRI task was tested, the visual-triggered finger movement task (VFMT), aimed at eliciting a more accurate functional cortical mapping of the hand region as compared with FTT.Method: Twenty patients with glioma in the peri-rolandic regions underwent pre-operative mapping performing both FTT and VFMT. The fMRI data were analyzed for surgical procedures. When the craniotomy allowed to expose the motor cortex, the correspondence with intraoperative direct electrical stimulation (DES) was evaluated through sensitivity and specificity (mean sites = 11) calculated as percentage of true-positive and true-negative rates, respectively.Results: Both at group level and at single-subject level, differences among the tasks emerged in the functional representation of the hand-knob. Compared with FTT, VFMT showed a well-localized activation within the hand motor area and a less widespread activation in associative regions. Intraoperative DES confirmed the greater specificity (97%) and sensitivity (100%) of the VFMT in determining motor eloquent areas.Conclusion: The study provides a novel, external-triggered fMRI task for pre-surgical motor mapping. Compared with the traditional FTT, the new VFMT may have potential implications in clinical fMRI and surgical management due to its focal identification of the hand-knob region and good correspondence to intraoperative DES.

Influence of visual feedback, hand dominance and sex on individuated finger movements

The ability to perform individual finger movements, highly important in daily activities, involves visual monitoring and proprioception. We investigated the influence of vision on the spatial and temporal control of independent finger movements, for the dominant and non-dominant hand and in relation to sex. Twenty-six healthy middle-aged to old adults (M age = 61 years; range 46–79 years; females n = 13) participated. Participants performed cyclic flexion–extension movements at the metacarpophalangeal joint of one finger at a time while keeping the other fingers as still as possible. Movements were recorded using 3D optoelectronic motion technique (120 Hz). The movement trajectory distance; speed peaks (movement smoothness); Individuation Index (II; the degree a finger can move in isolation from the other fingers) and Stationarity Index (SI; how still a finger remains while the other fingers move) were extracted. The main findings were: (1) vision only improved the II and SI marginally; (2) longer trajectories were evident in the no-vision condition for the fingers of the dominant hand in the female group; (3) longer trajectories were specifically evident for the middle and ring fingers within the female group; (4) females had marginally higher II and SI compared with males; and (5) females had fewer speed peaks than males, particularly for the ring finger. Our results suggest that visual monitoring of finger movements marginally improves performance of our non-manipulative finger movement task. A consistent finding was that females showed greater independent finger control compared with males.

Repetitive transcranial magnetic stimulation changes cognitive/motor tasks performance: An absolute alpha and beta power study

The voluntary movement demands integration between cognitive and motor functions. During the initial stages of motor learning until mastery of a new motor task, and during a demanding task that is not automatic, cognitive and motor functions can be perceived as independent from each other. Areas used for actually performing motor tasks are essentially the same used by Motor Imagery (MI). The main objective of this study was to investigate inhibition effects on cognitive functions of motor skills induced by low-frequency (1 Hz) Repetitive Transcranial Magnetic Stimulation (rTMS) at the sensory-motor integration site (Cz). In particular, the goal was to examine absolute alpha and beta power changes on frontal regions during Execution, Action observation, and Motor Imagery of finger movement tasks. Eleven healthy, right-handed volunteers of both sexes (5 males, 6 females; mean age 28 ± 5 years), with no history of psychiatric or neurological disorders, participated in the experiment. The execution task consisted of the subject flexing and extending the index finger. The action observation task involved watching a video of the same movement. The motor imagery task was imagining the flexion and extension of the index finger movement. After performing the tasks randomly, subjects were submitted to 15 min of low-frequency rTMS and performed the tasks again. All tasks were executed simultaneously with EEG signals recording. Our results demonstrated a significant interaction between rTMS and the three tasks in almost all analyzed regions showing that rTMS can affect the frontal region regarding Execution, Action observation, and Motor Imagery tasks.

Multilinear Discriminative Spatial Patterns for Movement-Related Cortical Potential Based on EEG Classification with Tensor Representation.

The discriminative spatial patterns (DSP) algorithm is a classical and effective feature extraction technique for decoding of voluntary finger premovements from electroencephalography (EEG). As a purely data-driven subspace learning algorithm, DSP essentially is a spatial-domain filter and does not make full use of the information in frequency domain. The paper presents multilinear discriminative spatial patterns (MDSP) to derive multiple interrelated lower dimensional discriminative subspaces of low frequency movement-related cortical potential (MRCP). Experimental results on two finger movement tasks' EEG datasets demonstrate the effectiveness of the proposed MDSP method.