Kinematics Of The Brain Activities: Volume III
Alignment was undertaken independently for data from the two MEG sessions. Data from each movement type were epoched from the 10 task runs and concatenated into a new dataset containing 10 blocks, each containing 5 movement trials.
The fixation cross and movement trials were epoched from the overall block. The movement trials were defined relative to the data glove defined movement onset time movement trial time: 2 s; pre-onset time: 0. The fixation cross period was used as a baseline for the 5 movement trials within each movement block. A high pass filter of 1 Hz and a low pass filter of Hz were applied. MEG analyses were conducted across three frequency bands: alpha Hz , beta Hz and gamma Hz. All of the movements trials for a given movement type were concatenated across the 10 task runs, creating a dataset comprising 50 repeats of a movement.
At this point the data was visually inspected and those trials containing artefacts were removed from further analysis up to a maximum of 10 trials, such that the minimum number of movements trials per movement included in further analysis was In order to reconstruct oscillatory activity at brain locations directly comparable across participants, the individual anatomical MRI was non-linearly warped to the MNI MRI template.
The MNI template was divided into a 10 mm isotropic grid and the inverse of the previously calculated non-linear warp was used to warp the template grid into the anatomical space of each participant. Sensor leadfields were calculated using a semi-realistic volume conduction model based on the individual anatomy The temporal evolution of source activation at each location in the brain was estimated using a linearly constrained minimum variance LCMV beamformer algorithm 73 with the optimal dipole orientation at each voxel estimated using singular value decomposition SVD.
Virtual sensors were then reconstructed from all voxels by multiplying the sensor level data by the corresponding set of optimised weights. At this stage data were subject to multivariate noise normalization 74 , 75 : we calculated the error covariance matrix at sensor level and then used this combined with the filters from the LCMV to create the virtual sensor data. This means that sensors with more noise would be down-weighted compared to those with less noise. At this stage the data was also down-sampled to Hz to reduce computational cost.
The MEG data were split to produce 10 partitions and then averaged within each partition to perform a cross-validated representational similarity analysis to avoid the possibility of over-fitting the data 68 , RSA was performed across time using a sliding time window with a width of 20 ms and a time step of 5 ms creating time windows across 2 seconds of the movement trial 0. After selecting virtual sensors within the left hemisphere motor region of the AAL atlas 76 Precentral L, 31 sources; Figure S9 , the frequency-filtered MEG signal measured during each movement type was compared using a cross-validated leave-one-out approach within each time width.
In each iteration, the signals from one MEG data partition were assigned to fold A, and the signals from the remaining nine partitions were assigned to fold B; squared Euclidean distances were calculated between all possible pairs of the 26 signals across the two folds and averaged The use of multivariate noise normalisation to account for spatial autocorrelation in the MEG signal yielded subject-wise cross-validated Mahalanobis distance RDMs comparing the alpha, beta, or gamma-band signal in the motor ROI across all possible pairings of the 26 movements In light of the interest in contrasting the kinematic and muscle models, these were each assessed in a partial correlation to discount the contribution of the other.
Randomization testing was used for statistical inference 77 , whereby candidate model RDMs were shuffled times and time-resolved correlation coefficients were recomputed in order to estimate an empirical null distribution. P-values were calculated using a cluster thresholding approach across time.
MEG from the period of action observation during the instruction video preceding each movement block were epoched using the same approach as the MEG data recorded during movement. The fixation cross and action observation trials were epoched from the overall block.
The action observation trial was defined relative to the video stimulus onset time pre-onset time: 0. The fixation cross period was used as a baseline for the action observation period. Temporal representational similarity analyses were conducted using the same apporoach as the MEG movement data, as described above. MEG analysis included multivariate noise normalisation to account partially for the effects of motion, where each channels are normalised by an estimate of error covariance across different sensors; this process has been demonstrated to substantially improve multivariate analyses of MEG data Motion parameters for all MEG acquisitions were extracted and analysed to rule out the possibility of excessive head motion as a potential driving force behind any observed patterns of brain activity.
Rotational and translational displacement for each participant and each experimental session are presented in Figure S In addition, the motion parameters during each movement block were extracted and the resulting distribution is presented across the 26 different movement types Figure S The profiles of motion across participants demonstrate a high quality data acquisition.
The area under the electrodes was exfoliated and cleaned with alcohol prior to data acquisition. EMG signals were recorded at Hz. EMG data were initially subject to a bandpass filter Hz and a notch filter 50 Hz. Epoched EMG data were subject to manual artefact rejection. Signals from the three electrodes during each epoch were independently subject to a Hilbert transform and smoothing 5 ms window prior to activity onset segmentation using an adaptive threshold activity duration threshold: ms Hooman Sedghamiz: Matlab File Exchange: Automatic Activity Detection in Noisy Signals using Hilbert Transform.
A conservative estimate of muscle activity onset was derived by taking the earliest signal onset detected across the three EMG channels for each movement trial. Due to constraints of electrode placement alongside the kinematic data glove, measures of activity onset were not robustly measured in all participants. EMG onset data are presented in order to validate the data glove measures of movement onset, which have been used to epoch the MEG data Figures 1 and S Video S1: Compilation of instructional videos used at the beginning of movement blocks in all testing sessions.
Movement labels are provided for reference only; labels were not included during the task VideoS1. Video S2: Visualisation of multidimensional scaling of grand average kinematic model constructed across participants and sessions. View the discussion thread. Supplementary Material. Skip to main content. New Results Action kinematics as an organising principle in the cortical control of human hand movement James Kolasinski , Diana C. Dima , David M. James Kolasinski.
Abstract Hand movements are controlled by neuronal networks in primary motor cortex M1 1 , 2 , 3. Main text Mounting evidence supports the encoding of movements in M1 based on kinematics and synergistic muscle activation, rather than the anatomy of the peripheral musculature 4 , 5. View this table: View inline View popup Download powerpoint. Table 1: Outline of the 26 hand movements used in the motor task. Figure S1: Data-driven kinematic models constructed for each participant and each session type exhibit strong split-half and inter-session consistency.
Muscle model reproducibility data also presented. Figure 1: Spatial and temporal evidence for the encoding of hand kinematics in primary motor cortex prior to movement production. Table S1: Inter-subject consistency of kinematic and muscle models. Table 2: Outline of peak anatomical correspondence between movement models and fMRI calculated using across participant cortical heatmaps.
Figure S2: The average kinematic model across participants for each session type, the muscle model derived from an independent cohort of participants, and the categorical ethological action model.uk.gld.pw/what-is-the-best-cellphone-monitoring-tool-lg-v40.php
Kinematics is only a (good) start | Behavioral and Brain Sciences | Cambridge Core
Figure S3: The average kinematic model constructed from the data glove measures acquired during the A behavioural session, B fMRI sessions, and C MEG sessions; presented alongside the individual subject models for each participant and session. Figure S4: fMRI searchlight analysis conducted using the kinematic model constructed from data glove recordings made during the behavioural testing session. Figure 2: Single participant fMRI representational similarity analysis cortical searchlights using individual kinematic models of hand movement.
Figure S6: Individual participant cortical searchlight results using an muscle model of movement encoding. Figure S7: Individual participant cortical searchlight results using an ethological action model of movement encoding. Figure 3: Comparison of movement models in spatial searchlight analysis reveals a significantly greater model fit for the kinematic model over the muscle model.
Figure S EMG muscle activity onset time. Figure S MEG motion timeseries across participants and sessions. Blue: X-axis, orange: Y-axis, yellow: Z-axis. Figure S MEG motion comparison across movement conditions. Figure S fMRI motion displacement plots.
Figure S EMG recordings from an independent cohort used to generate a muscle model of hand movement. Figure 4: MEG temporal representational similarity analysis searchlight in motor cortex reveals encoding of kinematic information prior to movement onset. Materials and Methods 2. Motor task and kinematic data acquisition During all sessions participants were engaged in a motor task involving the production of a range of 26 hand movements Table 1 with the right hand while wearing a fibre-optic kinematic data glove Data Glove 14 Ultra; Fifth Dimension Technologies: 5DT, Orlando, FL, USA.
Kinematic recording session During the behavioural testing session participants performed five runs of the motor task. Kinematic movement model For each participant kinematic data from the behavioural, MRI, and MEG sessions were each processed in parallel. Muscle model An independent EMG dataset was acquired in order to construct a model of hand movement dissimilarity on the basis of muscle activity in the hand.
Ethological action movement model An alternative ethological action based model was constructed based on more recent evidence of ethological maps in primate M1 13 , and therefore categorises movements on the basis of their specific action, namely prehensile movements, sub-categorised into precision grip and power grip, and non-prehensile movements 17 Figure 1. These values quantify for each frame of an fMRI acquisition the magnitude of signal intensity change in comparison in volume N compared with volume N-1, as per the following formula: Where I i is image intensity at locus on frame i; angle brackets denote the spatial average over the whole brain Data glove movement onset detection: MEG sessions The 14 channels of data glove recordings collected during the MEG sessions were aligned with the MEG acquisitions using the onset of the green bar and were epoched alongside the MEG data.
MEG data analysis 2. MEG temporal representational similarity analysis The MEG data were split to produce 10 partitions and then averaged within each partition to perform a cross-validated representational similarity analysis to avoid the possibility of over-fitting the data 68 , MEG: action observation analysis MEG from the period of action observation during the instruction video preceding each movement block were epoched using the same approach as the MEG data recorded during movement.
MEG motion considerations MEG analysis included multivariate noise normalisation to account partially for the effects of motion, where each channels are normalised by an estimate of error covariance across different sensors; this process has been demonstrated to substantially improve multivariate analyses of MEG data Acknowledgments J. References . Penfield , E. Boldrey , Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation , Brain 60 — OpenUrl CrossRef.
Lemon , The output map of the primate motor cortex , Trends in Neurosciences 11 — Ejaz , M. Hamada , J. Diedrichsen , Hand use predicts the structure of representations in sensorimotor cortex.