Using action understanding to understand the left inferior parietal cortex in the human brain

Published in final edited form as: Brain Res. 2014 September 25; 1582: 64–76. doi:10.1016/j.brainres.2014.07.035.Humans have a sophisticated knowledge of the actions that can be performed with objects. In an fMRI study we tried to establish whether this depends on areas that are homologous with the inferior parietal cortex (area PFG) in macaque monkeys. Cells have been described in area PFG that discharge differentially depending upon whether the observer sees an object being brought to the mouth or put in a container. In our study the observers saw videos in which the use of different objects was demonstrated in pantomime; and after viewing the videos, the subject had to pick the object that was appropriate to the pantomime. We found a cluster of activated voxels in parietal areas PFop and PFt and this cluster was greater in the left hemisphere than in the right. We suggest a mechanism that could account for this asymmetry, relate our results to handedness and suggest that they shed light on the human syndrome of apraxia. Finally, we suggest that during the evolution of the hominids, this same pantomime mechanism could have been used to ‘name’ or request objects.We thank Steve Wise for very detailed comments on a draft of this paper. We thank Rogier Mars for help with identifying the areas that were activated in parietal cortex and for comments on a draft of this paper. Finally, we thank Michael Nahhas for help with the imaging figures. This work was supported in part by the NIH grant RO1NS064100 to LMV. (RO1NS064100 - NIH)Accepted manuscrip

Neural Modeling and Imaging of the Cortical Interactions Underlying Syllable Production

This paper describes a neural model of speech acquisition and production that accounts for a wide range of acoustic, kinematic, and neuroimaging data concerning the control of speech movements. The model is a neural network whose components correspond to regions of the cerebral cortex and cerebellum, including premotor, motor, auditory, and somatosensory cortical areas. Computer simulations of the model verify its ability to account for compensation to lip and jaw perturbations during speech. Specific anatomical locations of the model's components are estimated, and these estimates are used to simulate fMRI experiments of simple syllable production with and without jaw perturbations.National Institute on Deafness and Other Communication Disorders (R01 DC02852, RO1 DC01925

The Role of the Dorsal Premotor and Superior Parietal Cortices in Decoupled Visuomotor Transformations

In order to successfully interact with objects located within our environment, the brain must be capable of combining visual information with the appropriate felt limb position (i.e. proprioception) in order compute an appropriate coordinated muscle plan for accurate motor control. Eye-hand coordination is essential to our independence as a species and relies heavily on the reciprocally-connected regions of the parieto-frontal reach network. The dorsal premotor cortex (PMd) and the superior parietal lobule (SPL) remain prime candidates within this network for controlling the transformations required during visually-guided reaching movements. Our brains are primed to reach directly towards a viewed object, a situation that has been termed a “standard” or coupled reach. Such direct eye-hand coordination is common across species and is crucial for basic survival. Humans, however, have developed the capacity for tool-use and thus have learned to interact indirectly with an object. In such “non-standard” or decoupled situations, the directions of gaze and arm movement have been spatially decoupled and rely on both the implementation of a cognitive rule and on online feedback of the decoupled limb. The studies included within this dissertation were designed to further characterize the role of PMd and SPL during situations in which when a reach requires a spatial transformation between the actions of the eyes and the hand. More specifically, we were interested in examining whether regions within PMd (PMdr, PMdc) and SPL (PEc, MIP) responded differently during coupled versus decoupled visuomotor transformations. To address the relative contribution of these various cortical regions during decoupled reaching movements, we trained two female rhesus macaques on both coupled and decoupled visually-guided reaching tasks. We recorded the neural activity (single units and local field potentials) within each region while the animals performed each condition. We found that two separate networks emerged each contributing in a distinct ways to the performance of coupled versus decoupled eye-hand reaches. While PMdr and PEc showed enhanced activity during decoupled reach conditions, PMdc and MIP were more enhanced during coupled reaches. Taken together, these data presented here provide further evidence for the existence of alternate task-dependent neural pathways for visuomotor integration

Neuronal correlates of continuous manual tracking under varying visual movement feedback in a virtual reality environment

To accurately guide one's actions online, the brain predicts sensory action feedback ahead of time based on internal models, which can be updated by sensory prediction errors. The underlying operations can be experimentally investigated in sensorimotor adaptation tasks, in which moving under perturbed sensory action feedback requires internal model updates. Here we altered healthy participants’ visual hand movement feedback in a virtual reality setup, while assessing brain activity with functional magnetic resonance imaging (fMRI). Participants tracked a continually moving virtual target object with a photorealistic, three-dimensional (3D) virtual hand controlled online via a data glove. During the continuous tracking task, the virtual hand's movements (i.e., visual movement feedback) were repeatedly periodically delayed, which participants had to compensate for to maintain accurate tracking. This realistic task design allowed us to simultaneously investigate processes likely operating at several levels of the brain's motor control hierarchy. FMRI revealed that the length of visual feedback delay was parametrically reflected by activity in the inferior parietal cortex and posterior temporal cortex. Unpredicted changes in visuomotor mapping (at transitions from synchronous to delayed visual feedback periods or vice versa) activated biological motion-sensitive regions in the lateral occipitotemporal cortex (LOTC). Activity in the posterior parietal cortex (PPC), focused on the contralateral anterior intraparietal sulcus (aIPS), correlated with tracking error, whereby this correlation was stronger in participants with higher tracking performance. Our results are in line with recent proposals of a wide- spread cortical motor control hierarchy, where temporoparietal regions seem to evaluate visuomotor congruence and thus possibly ground a self-attribution of movements, the LOTC likely processes early visual prediction errors, and the aIPS computes action goal errors and possibly corresponding motor corrections

Cortical Orchestra Conducted by Purpose and Function

학위논문(박사)--서울대학교 대학원 :자연과학대학 협동과정 뇌과학전공,2020. 2. 정천기.촉각과 자기수용감각은 우리의 생존 및 일상생활에 절대적인 영향을 미치는 중요한 감각 기능이다. 말초신경계에서 이 두 가지 기능들에 필요한 정보를 수집하고 전달하는 기계적 수용기 및 그 구심성 신경들에 대한 신호 전달 메커니즘 및 그 특징들은 상대적으로 잘 알려져 있는 편이다. 그러나, 촉각과 자기수용감각을 형성하기 위한 인간 뇌의 피질에서의 정보 처리 메커니즘에 대하여 우리가 현재 알고 있는 바는 극히 일부분이다. 이 논문에서 제시하는 일련의 연구들은 인간 뇌 피질 단계에서 촉각과 자기수용감각의 지각적 처리과정에 대한 거시적 신경계 정보처리 메커니즘을 다룬다. 첫 번째 연구에서는 뇌피질뇌파를 이용하여 인간 일차 및 이차 체성감각 피질에서 인공적인 자극과 일상생활에서 접할 수 있는 자극을 포함하는 다양한 진동촉감각 및 질감 자극에 대한 거시적 신경계 정보처리 특성을 밝혔다. 이 연구에서는 일차 및 이차 체성감각 피질의 촉감각 주파수 특이적인 하이-감마 영역 신경활동이 자극 주파수에 따라 각각 상이한 시간적 다이나믹스를 가지고 변화하는 것을 확인하였다. 또한, 이러한 하이-감마 활동은 성긴 질감과 미세한 입자감을 가진 자연스러운 질감 자극에 대해서도 진동촉감각의 경우와 유사한 패턴을 보였다. 이러한 결과들은 인간의 진동촉감각이 매우 단순한 형태에 자극일지라도 대뇌 체성감각 시스템에 있어 거시적인 다중 영역에서의 계층적 정보처리를 동반한다는 점을 시사한다. 두 번째 연구에서는 인간의 움직임과 관련된 두정엽 영역에서의 하이-감마 뇌활성이 자기수용감각과 같은 말초신경계로부터의 체성감각 피드백을 주로 반영하는지, 아니면 움직임 준비 및 제어를 위한 피질 간 신경 프로세스에 대한 활동을 반영하는지를 조사하였다. 연구 결과, 자발적 운동 중 대뇌 운동감각령에서의 하이-감마 활동은 일차 체성감각피질이 일차 운동피질보다 더 지배적인 것으로 나타났다. 또한 이 연구에서는, 움직임과 관련된 일차 체성감각피질에서의 하이-감마 뇌활동은 말초신경계로부터의 자기수용감각과 촉각에 대한 신경계 정보처리를 주로 반영하는 것을 밝혔다. 이러한 연구들을 바탕으로, 마지막 연구에서는 인간 대뇌에서의 체성감각 지각 프로세스에 대한 거시적 피질 간 네트워크를 규명하고자 하였다. 이를 위해, 51명의 뇌전증 환자에게서 체성감각을 유발했던 뇌피질전기자극 데이터와 46명의 환자에게서 촉감각 자극 및 운동 수행 중에 측정한 뇌피질뇌파 하이-감마 매핑 데이터를 종합적으로 분석하였다. 그 결과, 체성감각 지각 프로세스는 대뇌에서 넓은 영역에 걸쳐 분포하는 체성감각 관련 네트워크의 신경 활성을 수반한다는 것을 알아냈다. 또한, 뇌피질전기자극을 통한 대뇌 지도와 하이-감마 매핑을 통한 대뇌 지도는 서로 상당한 유사성을 보였다. 흥미롭게도, 뇌피질전기자극과 하이-감마 활동을 종합한 뇌지도들로부터 체성감각 관련 뇌 영역의 공간적 분포가 체성감각 기능에 따라 서로 달랐고, 그에 해당하는 각 영역들은 서로 뚜렷하게 다른 시간적 다이나믹스를 가지고 순차적으로 활성화되었다. 이러한 결과들은 체성감각에 대한 거시적 신경계 프로세스가 그 지각적 기능에 따라 뚜렷이 다른 계층적 네트워크를 가진다는 점을 시사한다. 더 나아가, 본 연구에서의 결과들은 체성감각 시스템의 지각-행동 관련 신경활동 흐름에 관한 이론적인 가설에 대하여 설득력 있는 증거를 제시하고 있다.Tactile and proprioceptive perceptions are crucial for our daily life as well as survival. At the peripheral level, the transduction mechanisms and characteristics of mechanoreceptive afferents containing information required for these functions, have been well identified. However, our knowledge about the cortical processing mechanism for them in human is limited. The present series of studies addressed the macroscopic neural mechanism for perceptual processing of tactile and proprioceptive perception in human cortex. In the first study, I investigated the macroscopic neural characteristics for various vibrotactile and texture stimuli including artificial and naturalistic ones in human primary and secondary somatosensory cortices (S1 and S2, respectively) using electrocorticography (ECoG). I found robust tactile frequency-specific high-gamma (HG, 50–140 Hz) activities in both S1 and S2 with different temporal dynamics depending on the stimulus frequency. Furthermore, similar HG patterns of S1 and S2 were found in naturalistic stimulus conditions such as coarse/fine textures. These results suggest that human vibrotactile sensation involves macroscopic multi-regional hierarchical processing in the somatosensory system, even during the simplified stimulation. In the second study, I tested whether the movement-related HG activities in parietal region mainly represent somatosensory feedback such as proprioception from periphery or primarily indicate cortico-cortical neural processing for movement preparation and control. I found that sensorimotor HG activities are more dominant in S1 than in M1 during voluntary movement. Furthermore, the results showed that movement-related HG activities in S1 mainly represent proprioceptive and tactile feedback from periphery. Given the results of previous two studies, the final study aimed to identify the large-scale cortical networks for perceptual processing in human. To do this, I combined direct cortical stimulation (DCS) data for eliciting somatosensation and ECoG HG band (50 to 150 Hz) mapping data during tactile stimulation and movement tasks, from 51 (for DCS mapping) and 46 patients (for HG mapping) with intractable epilepsy. The results showed that somatosensory perceptual processing involves neural activation of widespread somatosensory-related network in the cortex. In addition, the spatial distributions of DCS and HG functional maps showed considerable similarity in spatial distribution between high-gamma and DCS functional maps. Interestingly, the DCS-HG combined maps showed distinct spatial distributions depending on the somatosensory functions, and each area was sequentially activated with distinct temporal dynamics. These results suggest that macroscopic neural processing for somatosensation has distinct hierarchical networks depending on the perceptual functions. In addition, the results of the present study provide evidence for the perception and action related neural streams of somatosensory system. Throughout this series of studies, I suggest that macroscopic somatosensory network and structures of our brain are intrinsically organized by perceptual function and its purpose, not by somatosensory modality or submodality itself. Just as there is a purpose for human behavior, so is our brain.PART I. INTRODUCTION 1 CHAPTER 1: Somatosensory System 1 1.1. Mechanoreceptors in the Periphery 2 1.2. Somatosensory Afferent Pathways 4 1.3. Cortico-cortical Connections among Somatosensory-related Areas 7 1.4. Somatosensory-related Cortical Regions 8 CHAPTER 2: Electrocorticography 14 2.1. Intracranial Electroencephalography 14 2.2. High-Gamma Band Activity 18 CHAPTER 3: Purpose of This Study 24 PART II. EXPERIMENTAL STUDY 26 CHAPTER 4: Apparatus Design 26 4.1. Piezoelectric Vibrotactile Stimulator 26 4.2. Magnetic Vibrotactile Stimulator 29 4.3. Disc-type Texture Stimulator 33 4.4. Drum-type Texture Stimulator 36 CHAPTER 5: Vibrotactile and Texture Study 41 5.1. Introduction 42 5.2. Materials and Methods 46 5.2.1. Patients 46 5.2.2. Apparatus 47 5.2.3. Experimental Design 49 5.2.4. Data Acquisition and Preprocessing 50 5.2.5. Analysis 51 5.3. Results 54 5.3.1. Frequency-specific S1/S2 HG Activities 54 5.3.2. S1 HG Attenuation during Flutter and Vibration 62 5.3.3. Single-trial Vibration Frequency Classification 64 5.3.4. S1/S2 HG Activities during Texture Stimuli 65 5.4. Discussion 69 5.4.1. Comparison with Previous Findings 69 5.4.2. Tactile Frequency-dependent Neural Adaptation 70 5.4.3. Serial vs. Parallel Processing between S1 and S2 72 5.4.4. Conclusion of Chapter 5 73 CHAPTER 6: Somatosensory Feedback during Movement 74 6.1. Introduction 75 6.2. Materials and Methods 79 6.2.1. Subjects 79 6.2.2. Tasks 80 6.2.3. Data Acquisition and Preprocessing 82 6.2.4. S1-M1 HG Power Difference 85 6.2.5. Classification 86 6.2.6. Timing of S1 HG Activity 86 6.2.7. Correlation between HG and EMG signals 87 6.3. Results 89 6.3.1. HG Activities Are More Dominant in S1 than in M1 89 6.3.2. HG Activities in S1 Mainly Represent Somatosensory Feedback 94 6.4. Discussion 100 6.4.1. S1 HG Activity Mainly Represents Somatosensory Feedback 100 6.4.2. Further Discussion and Future Direction in BMI 102 6.4.3. Conclusion of Chapter 6 103 CHAPTER 7: Cortical Maps of Somatosensory Function 104 7.1. Introduction 106 7.2. Materials and Methods 110 7.2.1. Participants 110 7.2.2. Direct Cortical Stimulation 114 7.2.3. Classification of Verbal Feedbacks 115 7.2.4. Localization of Electrodes 115 7.2.5. Apparatus 116 7.2.6. Tasks 117 7.2.7. Data Recording and Processing 119 7.2.8. Mapping on the Brain 120 7.2.9. ROI-based Analysis 122 7.3. Results 123 7.3.1. DCS Mapping 123 7.3.2. Three and Four-dimensional HG Mapping 131 7.3.3. Neural Characteristics among Somatosensory-related Areas 144 7.4. Discussion 146 7.4.1. DCS on the Non-Primary Areas 146 7.4.2. Two Streams of Somatosensory System 148 7.4.3. Functional Role of ventral PM 151 7.4.4. Limitation and Perspective 152 7.4.5. Conclusion of Chapter 7 155 PART III. CONCLUSION 156 CHAPTER 8: Conclusion and Perspective 156 8.1. Perspective and Future Work 157 References 160 Abstract in Korean 173Docto

A Role for the Somatosensory System in Motor Learning by Observing

An influential idea in neuroscience is that action observation activates an observer’s sensory-motor system. This idea has recently been extended to motor learning; observing another individual undergoing motor learning can promote sensory-motor plasticity as well as behavioural changes in both the motor and somatosensory domains. While previous research has suggested a role for the motor system in motor learning by observing, this thesis presents a series of experiments testing the hypothesis that the somatosensory system is also involved in motor learning by observing. The experiments included in this thesis used force field (FF) adaptation as a model of motor learning, a task in which subjects adapt their reaches to a robot-imposed FF. Subjects observed a video showing another individual adapting his or her reaches to a FF, and motor learning by observing was assessed behaviourally following observation. First, we used functional magnetic resonance imaging (fMRI) to assess changes in resting-state functional connectivity (FC) associated with motor learning by observing. We identified a functional network consisting of visual area V5/MT, cerebellum, primary motor cortex (M1), and primary somatosensory cortex (S1) in which post- observation FC changes were correlated with subsequent behavioural measures of motor learning achieved through observation. We then investigated if pre-observation measures of brain function or structure could predict subsequent motor learning by observing. We found that individual differences in pre-observation resting-state FC predicted observation-related gains in motor learning. Subjects who exhibited greater FC between bilateral S1, M1, dorsal premotor cortex (PMd), and left superior parietal lobule (SPL) prior to observation achieved greater motor learning by observing on the following day. In a subsequent experiment, we tested the involvement of the somatosensory system in motor learning by observing using median nerve stimulation and electroencephalogra- phy (EEG). In experiment 1, we showed that interfering with somatosensory cortical processing throughout observation (by delivering median nerve stimulation) can disrupt motor learning by observing. In a follow-up experiment, we assessed pre- to post- observation changes in S1 excitability by acquiring somatosensory evoked potentials (SEPs) using EEG. We showed that SEP amplitudes increased after observing motor learning. Post-observtion SEP increases were correlated with subsequent behavioural measures of motor learning achieved through observation. In a final experiment, we tested if improving subjects’ somatosensory function would enhance subsequent motor learning by observing. Subjects underwent perceptual training to improve their proprioceptive acuity prior to observation. We found that improving proprioceptive acuity prior to observation enhanced the extent to which subjects benefitted from observing motor learning (compared to subjects who had not undergone perceptual training). We further found that post-training increases in proprioceptive acuity were correlated with subsequent observation-related gains in motor performance. Collectively, these studies suggest that motor learning by observing is supported by a fronto-parieto-occipital network in which the somatosensory system is an active element. We have shown that observing motor learning changes somatosensory activity in a behaviourally-relevant manner. Observing motor learning resulted in S1 plasticity that corresponded to the extent of learning achieved through observation. Moreover, manipulating somatosensory activity influenced motor learning by observing. Interfering with somatosensory processing throughout observation disrupted motor learning by observing whereas improving somatosensory function prior to observation enhanced motor learning by observing. These experiments therefore suggest that the somatosensory system is indeed involved in motor learning by observing

The Effects of Separating Visual and Motor Workspaces on the Generalization of Visuomotor Adaptation across Movement Conditions

Separating visual and proprioceptive information in terms of workspace locations during reaching movement has been shown to disturb transfer of visuomotor adaptation across the arms. Here, we investigated whether separating visual and motor workspaces would also disturb generalization of visuomotor adaptation across movement conditions within the same arm. In our behavioral study, subjects were divided into four experimental groups (plus three control groups). The first two groups adapted to a visual rotation under a dissociation condition in which the targets for reaching movement were presented in midline while their arm performed reaching movement laterally. Following that, they were tested in an association condition in which the visual and motor workspaces were combined in midline or laterally. The other two groups first adapted to the rotation in one association condition (medial or lateral), then were tested in the other association condition. The latter groups demonstrated complete transfer from the training to the generalization session, whereas the former groups demonstrated substantially limited transfer. In our fMRI study, we examined brain activity while subjects learned a visuomotor adaptation task in a condition in which visual and motor workspaces were either dissociated or associated with each other, and subsequently performed the same visuomotor task with the same hand in a condition in which visual and motor workspace were associated. Our main results showed that the neural involvement is similar between the early training and the early generalization phases in the `dissociation-to-association\u27 conditions; while that is similar between the late adaptation and the early generalization phases in the `association-to-association\u27 condition. These findings suggest that a visual-proprioceptive conflict in terms of workspace locations disrupts the development of a neural representation, or an internal model, that is associated with novel visuomotor adaptation, thus resulting in limited generalization of visuomotor adaptation

Role of The Cortex in Visuomotor Control of Arm Stability

Whereas numerous motor control theories describe the control of arm trajectory during reach, the control of stabilization in a constant arm position (i.e., visuomotor control of arm posture) is less clear. Three potential mechanisms have been proposed for visuomotor control of arm posture: 1) increased impedance of the arm through co-contraction of antagonistic muscles, 2) corrective muscle activity via spinal/supraspinal reflex circuits, and/or 3) intermittent voluntary corrections to errors in position. We examined the cortical mechanisms of visuomotor control of arm posture and tested the hypothesis that cortical error networks contribute to arm stabilization. We collected electroencephalography (EEG) data from 10 young healthy participants across four experimental planar movement tasks. We examined brain activity associated with intermittent voluntary corrections of position error and antagonist co-contraction during stabilization. EEG beta-band (13–26 Hz) power fluctuations were used as indicators of brain activity, and coherence between EEG electrodes was used as a measure of functional connectivity between brain regions. Cortical activity in the sensory, motor, and visual areas during arm stabilization was similar to activity during volitional arm movements and was larger than activity during co-contraction of the arm. However, cortical connectivity between the sensorimotor and visual regions was higher during arm stabilization compared with volitional arm movements and co-contraction of the arm. The difference in cortical activity and connectivity between tasks might be attributed to an underlying visuomotor error network used to update motor commands for visuomotor control of arm posture