Cortical sensorimotor activity in the execution and suppression of discrete and rhythmic movements

AbstractAlthough the engagement of sensorimotor cortices in movement is well documented, the functional relevance of brain activity patterns remains ambiguous. Especially, the cortical engagement specific to the pre-, within-, and post-movement periods is poorly understood. The present study addressed this issue by examining sensorimotor EEG activity during the performance as well as STOP-signal cued suppression of movements pertaining to two distinct classes, namely, discrete vs. ongoing rhythmic movements. Our findings indicate that the lateralized readiness potential (LRP), which is classically used as a marker of pre-movement processing, indexes multiple pre- and in- movement-related brain dynamics in a movement-class dependent fashion. In- and post-movement event-related (de)synchronization (ERD/ERS) observed in the Mu (8–13 Hz) and Beta (15–30 Hz) frequency ranges were associated with estimated brain sources in both motor and somatosensory cortical areas. Notwithstanding, Beta ERS occurred earlier following cancelled than actually performed movements. In contrast, Mu power did not vary. Whereas Beta power may reflect the evaluation of the sensory predicted outcome, Mu power might engage in linking perception to action. Additionally, the rhythmic movement forced stop (only) showed a post-movement Mu/Beta rebound, which might reflect an active "clearing-out" of the motor plan and its feedback-based online control. Overall, the present study supports the notion that sensorimotor EEG modulations are key markers to investigate control or executive processes, here initiation and inhibition, which are exerted when performing distinct movement classes.

Download Full-text

Cortical sensorimotor activity in the execution and suppression of discrete and rhythmic movements

10.1101/2021.09.30.462656 ◽

2021 ◽

Author(s):

Mario Hervault ◽

Pier–Giorgio Zanone ◽

Jean–Christophe Buisson ◽

Raoul Huys

Keyword(s):

Brain Activity ◽

Activity Patterns ◽

Brain Dynamics ◽

Executive Processes ◽

Rhythmic Movements ◽

Cortical Areas ◽

Beta Power ◽

Sensorimotor Activity ◽

Functional Relevance ◽

Somatosensory Cortical Areas

AbstractAlthough the engagement of sensorimotor cortices in movement is well documented, the functional relevance of brain activity patterns remains ambiguous. Especially, the cortical engagement specific to the pre-, within-, and post-movement periods is poorly understood. The present study addressed this issue by examining sensorimotor EEG activity during the performance as well as STOP-signal cued suppression of movements pertaining to two distinct classes, namely, discrete vs. ongoing rhythmic movements. Our findings indicate that the lateralized readiness potential (LRP), which is classically used as a marker of pre-movement processing, indexes multiple pre- and in-movement-related brain dynamics in a movement-class dependent fashion. In- and post-movement event-related (de)synchronization (ERD/ERS) observed in the Mu (8-13 Hz) and Beta (15-30 Hz) frequency ranges were associated with estimated brain sources in both motor and somatosensory cortical areas. Notwithstanding, Beta ERS occurred earlier following cancelled than actually performed movements. In contrast, Mu power did not vary. Whereas Beta power may reflect the evaluation of the sensory predicted outcome, Mu power might engage in linking perception to action. Additionally, the rhythmic movement forced stop (only) showed a post-movement Mu/Beta rebound, which might reflect an active “clearing-out” of the motor plan and its feedback-based online control. Overall, the present study supports the notion that sensorimotor EEG modulations are key markers to investigate control or executive processes, here initiation and inhibition, which are exerted when performing distinct movement classes.

Download Full-text

Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

The Neuroscientist ◽

10.1177/1073858417728032 ◽

2017 ◽

Vol 24 (3) ◽

pp. 277-293 ◽

Cited By ~ 29

Author(s):

Selen Atasoy ◽

Gustavo Deco ◽

Morten L. Kringelbach ◽

Joel Pearson

Keyword(s):

Neural Activity ◽

Brain Activity ◽

Activity Patterns ◽

Structural Connectivity ◽

Building Blocks ◽

Mental States ◽

Brain Dynamics ◽

Space And Time ◽

Wide Range ◽

The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

Download Full-text

Harmonic brain modes: a unifying framework for linking space and time in brain dynamics

10.1101/162040 ◽

2017 ◽

Cited By ~ 1

Author(s):

Selen Atasoy ◽

Gustavo Deco ◽

Morten L. Kringelbach ◽

Joel Pearson

Keyword(s):

Neural Activity ◽

Brain Activity ◽

Activity Patterns ◽

Structural Connectivity ◽

Building Blocks ◽

Mental States ◽

Brain Dynamics ◽

Space And Time ◽

Wide Range ◽

The Brain

AbstractA fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at ‘rest’. Here, we introduce the concept of “harmonic brain modes” – fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; i.e. connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal and network-level changes in the brain across different mental states; (wakefulness, sleep, anaesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

Download Full-text

Ipsilateral finger representations are engaged in active movement, but not sensory processing

10.1101/285809 ◽

2018 ◽

Cited By ~ 2

Author(s):

Eva Berlot ◽

George Prichard ◽

Jill O’Reilly ◽

Naveed Ejaz ◽

Jörn Diedrichsen

Keyword(s):

Feedback Control ◽

High Field ◽

Brain Activity ◽

Activity Patterns ◽

Sensory Information ◽

Movement Planning ◽

Active Movement ◽

Contralateral Hemisphere ◽

Ipsilateral Hemisphere ◽

Functional Relevance

AbstractHand and finger movements are mostly controlled through crossed corticospinal projections from the contralateral hemisphere. During unimanual movements, activity in the contralateral hemisphere is increased while the ipsilateral hemisphere is suppressed below resting baseline. Despite this suppression, unimanual movements can be decoded from ipsilateral activity alone. This indicates that ipsilateral activity patterns represent parameters of ongoing movement, but the origin and functional relevance of these representations is unclear. Here, we asked whether human ipsilateral representations are caused by active movement, or whether they are driven by sensory input. Participants alternated between performing single finger presses and having fingers passively stimulated, while we recorded brain activity using high-field (7T) functional imaging. We contrasted active and passive finger representations in sensorimotor areas of ipsilateral and contralateral hemispheres. Finger representations in the contralateral hemisphere were equally strong under passive and active conditions, highlighting the importance of sensory information in feedback control. In contrast, ipsilateral finger representations were stronger during active presses. Furthermore, the spatial distribution of finger representations differed between hemispheres: the contralateral hemisphere showed the strongest finger representations in Brodmann area 3a and 3b, while the ipsilateral hemisphere exhibited stronger representations in premotor and parietal areas. This suggests that finger representations in the two hemispheres have different origins – contralateral representations are driven by both active movement and sensory stimulation, whereas ipsilateral representations are mainly engaged during active movement. This suggests that a possible contribution of the ipsilateral hemisphere lies in movement planning, rather than in the dexterous feedback control of the movement.Significance statementMovements of the human body are mostly controlled by contralateral cortical regions. However, activity in ipsilateral sensorimotor regions is also modulated during active movements. The origin and functional relevance of these ipsilateral representations is unclear. Here we used high-field neuroimaging to investigate how human contralateral and ipsilateral hemispheres represent active finger presses and passive finger stimulation. We report that while the contralateral hemisphere was equally strongly recruited during active and passive conditions, the ipsilateral hemisphere was mostly recruited during active movement. We propose that the ipsilateral hemisphere may play a role in bilateral movement planning.

Download Full-text

Brain Activity Patterns in Anxiety-Prone People Suggest Deficits in Handling Fear

PsycEXTRA Dataset ◽

10.1037/e614992011-001 ◽

2011 ◽

Keyword(s):

Brain Activity ◽

Activity Patterns

Download Full-text

Neural alignment predicts learning outcomes in students taking an introduction to computer science course

Nature Communications ◽

10.1038/s41467-021-22202-3 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Meir Meshulam ◽

Liat Hasenfratz ◽

Hanna Hillman ◽

Yun-Fei Liu ◽

Mai Nguyen ◽

...

Keyword(s):

Computer Science ◽

Learning Outcomes ◽

Brain Activity ◽

Activity Patterns ◽

Real Life ◽

Final Exam ◽

Neural Representations ◽

Real Life Setting ◽

The Right ◽

Mri Study

AbstractDespite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural approach to predicting and assessing learning outcomes in a real-life setting. Our approach hinges on the idea that successful learning involves forming the right set of neural representations, which are captured in canonical activity patterns shared across individuals. Specifically, we hypothesized that learning is mirrored in neural alignment: the degree to which an individual learner’s neural representations match those of experts, as well as those of other learners. We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. We additionally scanned graduate student experts in computer science. We show that alignment among students successfully predicts overall performance in a final exam. Furthermore, within individual students, we find better learning outcomes for concepts that evoke better alignment with experts and with other students, revealing neural patterns associated with specific learned concepts in individuals.

Download Full-text

Using Brain Activity Patterns to Differentiate Real and Virtual Attended Targets during Augmented Reality Scenarios

Information ◽

10.3390/info12060226 ◽

2021 ◽

Vol 12 (6) ◽

pp. 226

Author(s):

Lisa-Marie Vortmann ◽

Leonid Schwenke ◽

Felix Putze

Keyword(s):

Augmented Reality ◽

Eye Tracking ◽

Brain Activity ◽

Activity Patterns ◽

Training Data ◽

Machine Learning Techniques ◽

Neural Net ◽

Dependent Manner ◽

Tracking Data ◽

Average Accuracy

Augmented reality is the fusion of virtual components and our real surroundings. The simultaneous visibility of generated and natural objects often requires users to direct their selective attention to a specific target that is either real or virtual. In this study, we investigated whether this target is real or virtual by using machine learning techniques to classify electroencephalographic (EEG) and eye tracking data collected in augmented reality scenarios. A shallow convolutional neural net classified 3 second EEG data windows from 20 participants in a person-dependent manner with an average accuracy above 70% if the testing data and training data came from different trials. This accuracy could be significantly increased to 77% using a multimodal late fusion approach that included the recorded eye tracking data. Person-independent EEG classification was possible above chance level for 6 out of 20 participants. Thus, the reliability of such a brain–computer interface is high enough for it to be treated as a useful input mechanism for augmented reality applications.

Download Full-text