Discrete tactile feature comparison subprocess in human brain during a decision-making process.

From sensory input to motor action, encoded sensory features flow sequentially along cortical networks for decision-making. Despite numerous studies probing the decision-making process, the subprocess that compares encoded sensory features before making a decision has not been fully elucidated in humans. In this study, we investigated sensory feature comparison by presenting two different tasks (a discrimination task, in which participants made decisions by comparing two sequential tactile stimuli; and a detection task, in which participants responded to the second tactile stimulus in two sequential stimuli) to epilepsy patients while recording electrocorticography (ECoG). By comparing tactile-specific gamma band (30-200 Hz) power between the two tasks, the decision-making process was divided into three subprocesses-categorization, comparison, and decision-consistent with a previous study (Heekeren et al., 2004). These subprocesses occurred sequentially in the dorsolateral prefrontal cortex, premotor cortex, secondary somatosensory cortex, and parietal lobe. Gamma power showed two different patterns of correlation with response time. In the inferior parietal lobule (IPL), there was a negative correlation. This means that as gamma power increased, response time decreased. In the secondary somatosensory cortex (S2), there was a positive correlation. Here, as gamma power increased, response time also increased. These results indicate that the IPL and S2 encode tactile feature comparison differently. Our connectivity analysis showed that the S2 transmitted tactile information to the IPL. Our findings suggest that multiple areas in the parietal lobe encode sensory feature comparison differently before making a decision.

Unraveling tactile categorization and decision-making in the subregions of supramarginal gyrus via direct cortical stimulation.

OBJECTIVE: This study aims to investigate the potential of direct cortical stimulation (DCS) to modulate tactile categorization and decision-making, as well as to identify the specific locations where these cognitive functions occur. METHODS: We analyzed behavioral changes in three epilepsy patients with implanted electrodes using electrocorticography (ECoG) and a vibrotactile discrimination task. DCS was applied to investigate its impact on tactile categorization and decision-making processes. We determined the precise location of the electrodes where each cognitive function was modulated. RESULTS: This functional discrimination was related with gamma band activity from ECoG. DCS selectively affected either tactile categorization or decision-making processes. Tactile categorization was modulated by stimulating the rostral part of the supramarginal gyrus, while decision-making was modulated by stimulating the caudal part. CONCLUSIONS: DCS can enhance cognitive processes and map brain regions responsible for tactile categorization and decision-making within the supramarginal gyrus. This study also demonstrates that DCS and the gamma activity of ECoG can concordantly identify the detailed brain mapping in a tactile process compared to other functional neuroimaging. SIGNIFICANCE: The combination of DCS and ECoG gamma activity provides a more nuanced and detailed understanding of brain function than traditional neuroimaging techniques alone.

Cortical maps of somatosensory perception in human.

Tactile and movement-related somatosensory perceptions are crucial for our daily lives and survival. Although the primary somatosensory cortex is thought to be the key structure of somatosensory perception, various cortical downstream areas are also involved in somatosensory perceptual processing. However, little is known about whether cortical networks of these downstream areas can be dissociated depending on each perception, especially in human. We address this issue by combining data from direct cortical stimulation (DCS) for eliciting somatosensation and data from high-gamma band (HG) elicited during tactile stimulation and movement tasks. We found that artificial somatosensory perception is elicited not only from conventional somatosensory-related areas such as the primary and secondary somatosensory cortices but also from a widespread network including superior/inferior parietal lobules and premotor cortex. Interestingly, DCS on the dorsal part of the fronto-parietal area including superior parietal lobule and dorsal premotor cortex often induces movement-related somatosensations, whereas that on the ventral one including inferior parietal lobule and ventral premotor cortex generally elicits tactile sensations. Furthermore, the HG mapping results of the movement and passive tactile stimulation tasks revealed considerable similarity in the spatial distribution between the HG and DCS functional maps. Our findings showed that macroscopic neural processing for tactile and movement-related perceptions could be segregated.

Decoding trajectories of imagined hand movement using electrocorticograms for brain-machine interface.

Objective. Reaching hand movement is an important motor skill actively examined in the brain-computer interface (BCI). Among the various components of movement analyzed is the hand's trajectory, which describes the hand's continuous positions in three-dimensional space. While a large body of studies have investigated the decoding of real movements and the reconstruction of real hand movement trajectories from neural signals, fewer studies have attempted to decode the trajectory of the imagined hand movement. To develop BCI systems for patients with hand motor dysfunctions, the systems essentially have to achieve movement-free control of external devices, which is only possible through successful decoding of purely imagined hand movement.Approach. To achieve this goal, this study used a machine learning technique (i.e. the variational Bayesian least square) to analyze the electrocorticogram (ECoG) of 18 epilepsy patients obtained from when they performed movement execution (ME) and kinesthetic movement imagination (KMI) of the reach-and-grasp hand action.Main results. The variational Bayesian decoding model was able to successfully predict the imagined trajectories of the hand movement significantly above the chance level. The Pearson's correlation coefficient between the imagined and predicted trajectories was 0.3393 and 0.4936 for the KMI (KMI trials only) and MEKMI paradigm (alternating trials of ME and KMI), respectively.Significance. This study demonstrated a high accuracy of prediction for the trajectories of imagined hand movement, and more importantly, a higher decoding accuracy of the imagined trajectories in the MEKMI paradigm compared to the KMI paradigm solely.

Prediction of movement intention using connectivity within motor-related network: An electrocorticography study.

Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user's intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in these movement states. Neuronal communication differs between the states. Therefore, connectivity may change depending on the states. In this study, we investigated the temporal dynamics of connectivity in dorsolateral prefrontal cortex and primary motor cortex to predict movement intention. Movement intention was successfully predicted by connectivity dynamics which may reflect changes in movement states. Furthermore, dorsolateral prefrontal cortex is crucial in predicting movement intention to which primary motor cortex contributes. These results suggest that brain connectivity is an excellent approach in predicting movement intention.

Tactile Frequency-Specific High-Gamma Activities in Human Primary and Secondary Somatosensory Cortices.

Humans can easily detect vibrotactile stimuli up to several hundred hertz, but underlying large-scale neuronal processing mechanisms in the cortex are largely unknown. Here, we investigated the macroscopic neural correlates of various vibrotactile stimuli including artificial and naturalistic ones in human primary and secondary somatosensory cortices (S1 and S2, respectively) using electrocorticography (ECoG). We found that tactile frequency-specific high-gamma (HG, 50-140 Hz) activities are seen in both S1 and S2 with different temporal dynamics during vibration (>100 Hz). Stimulus-evoked S1 HG power, which exhibited short-delayed peaks (50-100 ms), was attenuated more quickly in vibration than in flutter (<50 Hz), and their attenuation patterns were frequency-specific within vibration range. In contrast, S2 HG power, which was activated much later than that of S1 (150-250 ms), strikingly increased with increasing stimulus frequencies in vibration range, and their changes were much greater than those in S1. Furthermore, these S1-S2 HG patterns were preserved in naturalistic stimuli such as coarse/fine textures. Our results provide persuasive evidence that S2 is critically involved in neural processing for high-frequency vibrotaction. Therefore, we propose that S1-S2 neuronal co-operation is crucial for full-range, complex vibrotactile perception in human.

Movement-Related Sensorimotor High-Gamma Activity Mainly Represents Somatosensory Feedback.

Somatosensation plays pivotal roles in the everyday motor control of humans. During active movement, there exists a prominent high-gamma (HG >50 Hz) power increase in the primary somatosensory cortex (S1), and this provides an important feature in relation to the decoding of movement in a brain-machine interface (BMI). However, one concern of BMI researchers is the inflation of the decoding performance due to the activation of somatosensory feedback, which is not elicited in patients who have lost their sensorimotor function. In fact, it is unclear as to how much the HG component activated in S1 contributes to the overall sensorimotor HG power during voluntary movement. With regard to other functional roles of HG in S1, recent findings have reported that these HG power levels increase before the onset of actual movement, which implies neural activation for top-down movement preparation or sensorimotor interaction, i.e., an efference copy. These results are promising for BMI applications but remain inconclusive. Here, we found using electrocorticography (ECoG) from eight patients that HG activation in S1 is stronger and more informative than it is in the primary motor cortex (M1) regardless of the type of movement. We also demonstrate by means of electromyography (EMG) that the onset timing of the HG power in S1 is later (49 ms) than that of the actual movement. Interestingly, we show that the HG power fluctuations in S1 are closely related to subtle muscle contractions, even during the pre-movement period. These results suggest the following: (1) movement-related HG activity in S1 strongly affects the overall sensorimotor HG power, and (2) HG activity in S1 during voluntary movement mainly represents cortical neural processing for somatosensory feedback.

Movement type prediction before its onset using signals from prefrontal area: an electrocorticography study.

Power changes in specific frequency bands are typical brain responses during motor planning or preparation. Many studies have demonstrated that, in addition to the premotor, supplementary motor, and primary sensorimotor areas, the prefrontal area contributes to generating such responses. However, most brain-computer interface (BCI) studies have focused on the primary sensorimotor area and have estimated movements using postonset period brain signals. Our aim was to determine whether the prefrontal area could contribute to the prediction of voluntary movement types before movement onset. In our study, electrocorticography (ECoG) was recorded from six epilepsy patients while performing two self-paced tasks: hand grasping and elbow flexion. The prefrontal area was sufficient to allow classification of different movements through the area's premovement signals (-2.0 s to 0 s) in four subjects. The most pronounced power difference frequency band was the beta band (13-30 Hz). The movement prediction rate during single trial estimation averaged 74% across the six subjects. Our results suggest that premovement signals in the prefrontal area are useful in distinguishing different movement tasks and that the beta band is the most informative for prediction of movement type before movement onset.