scholarly journals Head MagnetoMyography (hMMG): a novel approach to monitor face and whole-head muscular activity

2019 ◽  
Author(s):  
Guido Barchiesi ◽  
Gianpaolo Demarchi ◽  
Frank H. Wilhelm ◽  
Anne Hauswald ◽  
Gaëtan Sanchez ◽  
...  
Keyword(s):  
Brain Activity ◽  
Activity Patterns ◽  
A Priori ◽  
Muscular Activity ◽  
Electrode Placement ◽  
Emotional Facial Expressions ◽  
Extended Body ◽  
Activation Patterns ◽  
Novel Approach ◽  
Positive Valence

AbstractMuscular activity recording is of high basic science and clinical relevance and is typically achieved using electromyography (EMG). While providing detailed information about the state of a specific muscle, this technique has limitations such as the need for a-priori assumptions about electrode placement and difficulty with recording muscular activity patterns from extended body areas at once. For head and face muscle activity, the present work aimed to overcome these restrictions by exploiting magnetoencephalography (MEG) as a whole-head myographic recorder (head magnetomyography, hMMG). This is in contrast to common MEG studies, which treat muscular activity as artifact in electromagnetic brain activity. In a first proof-of-concept step, participants imitated emotional facial expressions performed by a model. Exploiting source projection algorithms, we were able to reconstruct muscular activity, showing spatial activation patterns in accord with the hypothesized muscular contractions. Going one step further, participants passively observed affective pictures with negative, neutral, or positive valence. Applying multivariate pattern analysis to the reconstructed hMMG signal, we were able to decode above chance the valence category of the presented pictures. Underlining the potential of hMMG, a searchlight analysis revealed that generally neglected neck muscles exhibit information on stimulus valence. Results confirm the utility of hMMG as a whole-head electromyographic recorder to quantify muscular activation patterns including muscular regions that are typically not recorded with EMG. This key advantage beyond conventional EMG has substantial scientific and clinical potential.

scholarly journals Feature-reweighted RSA: A method for improving the fit between computational models, brains, and behavior

2021 ◽  
Author(s):  
Philipp Kaniuth ◽  
Martin N. Hebart
Keyword(s):  
Model Selection ◽  
Computational Models ◽  
Model Comparison ◽  
Brain Activity ◽  
Activity Patterns ◽  
Behavioral Data ◽  
Novel Approach ◽  
Improve Model ◽  
And Behavior ◽  
Model Features

AbstractRepresentational Similarity Analysis (RSA) has emerged as a popular method for relating representational spaces from human brain activity, behavioral data, and computational models. RSA is based on the comparison of representational dissimilarity matrices (RDM), which characterize the pairwise dissimilarities of all conditions across all features (e.g. fMRI voxels or units of a model). However, classical RSA treats each feature as equally important. This ‘equal weights’ assumption contrasts with the flexibility of multivariate decoding, which reweights individual features for predicting a target variable. As a consequence, classical RSA may lead researchers to underestimate the correspondence between a model and a brain region and, for model comparison, it may lead to selecting the inferior model. While previous work has suggested that reweighting can improve model selection in RSA, it has remained unclear to what extent these results generalize across datasets and data modalities. To fill this gap, the aim of this work is twofold: First, utilizing a range of publicly available datasets and three popular deep neural networks (DNNs), we seek to broadly test feature-reweighted RSA (FR-RSA) applied to computational models and reveal the extent to which reweighting model features improves RDM correspondence and affects model selection. Second, we propose voxel-reweighted RSA, a novel use case of FR-RSA that reweights fMRI voxels, mirroring the rationale of multivariate decoding of optimally combining voxel activity patterns. We find that reweighting individual model units (1) markedly improves the fit between model RDMs and target RDMs derived from several fMRI and behavioral datasets and (2) affects model selection, highlighting the importance of considering FR-RSA. For voxel-reweighted RSA, improvements in RDM correspondence were even more pronounced, demonstrating the utility of this novel approach. We additionally demonstrate that classical noise ceilings can be exceeded when FR-RSA is applied and propose an updated approach for their computation. Taken together, our results broadly validate the use of FR-RSA for improving the fit between computational models, brain and behavioral data, possibly allowing us to better adjudicate between competing computational models. Further, our results suggest that FR-RSA applied to brain measurement channels could become an important new method to assess the match between representational spaces.

scholarly journals Bilateral versus ipsilesional cortico-subcortical activity patterns in stroke show hemispheric dependence

2016 ◽  
Vol 12 (1) ◽  
pp. 71-83 ◽  
Author(s):  
A Cristina Vidal ◽  
Paula Banca ◽  
Augusto G Pascoal ◽  
Gustavo C Santo ◽  
João Sargento-Freitas ◽  
...  
Keyword(s):  
Brain Activity ◽  
Block Design ◽  
Activity Patterns ◽  
Prognostic Indicator ◽  
Response Patterns ◽  
Stroke Patients ◽  
Activation Patterns ◽  
Hemispheric Stroke ◽  
Constraint Induced Movement Therapy ◽  
Motor Outcomes

Background Understanding of interhemispheric interactions in stroke patients during motor control is an important clinical neuroscience quest that may provide important clues for neurorehabilitation. In stroke patients, bilateral overactivation in both hemispheres has been interpreted as a poor prognostic indicator of functional recovery. In contrast, ipsilesional patterns have been linked with better motor outcomes. Aim We investigated the pathophysiology of hemispheric interactions during limb movement without and with contralateral restraint, to mimic the effects of constraint-induced movement therapy. We used neuroimaging to probe brain activity with such a movement-dependent interhemispheric modulation paradigm. Methods We used an fMRI block design during which the plegic/paretic upper limb was recruited/mobilized to perform unilateral arm elevation, as a function of presence versus absence of contralateral limb restriction ( n = 20, with balanced left/right lesion sites). Results Analysis of 10 right-hemispheric stroke participants yielded bilateral sensorimotor cortex activation in all movement phases in contrast with the unilateral dominance seen in the 10 left-hemispheric stroke participants. Superimposition of contralateral restriction led to a prominent shift from activation to deactivation response patterns, in particular in cortical and basal ganglia motor areas in right-hemispheric stroke. Left-hemispheric stroke was in general characterized by reduced activation patterns, even in the absence of restriction, which induced additional cortical silencing. Conclusion The observed hemispheric-dependent activation/deactivation shifts are novel and these pathophysiological observations suggest short-term neuroplasticity that may be useful for hemisphere-tailored neurorehabilitation.

scholarly journals Common neural and transcriptional correlates of inhibitory control underlie emotion regulation and memory control

2020 ◽  
Vol 15 (5) ◽  
pp. 523-536 ◽  
Author(s):  
Wei Liu ◽  
Nancy Peeters ◽  
Guillén Fernández ◽  
Nils Kohn
Keyword(s):  
Emotion Regulation ◽  
Inhibitory Control ◽  
Brain Activity ◽  
Activity Patterns ◽  
Enrichment Analysis ◽  
Brain Atlas ◽  
Activation Patterns ◽  
Memory Control ◽  
The Right ◽  
Meta Analyses

Abstract Inhibitory control is crucial for regulating emotions and may also enable memory control. However, evidence for their shared neurobiological correlates is limited. Here, we report meta-analyses of neuroimaging studies on emotion regulation, or memory control and link neural commonalities to transcriptional commonalities using the Allen Human Brain Atlas (AHBA). Based on 95 functional magnetic resonance imaging studies, we reveal a role of the right inferior parietal lobule embedded in a frontal–parietal–insular network during emotion regulation and memory control, which is similarly recruited during response inhibition. These co-activation patterns also overlap with the networks associated with ‘inhibition’, ‘cognitive control’ and ‘working memory’ when consulting the Neurosynth. Using the AHBA, we demonstrate that emotion regulation- and memory control-related brain activity patterns are associated with transcriptional profiles of a specific set of ‘inhibition-related’ genes. Gene ontology enrichment analysis of these ‘inhibition-related’ genes reveal associations with the neuronal transmission and risk for major psychiatric disorders as well as seizures and alcoholic dependence. In summary, this study identified a neural network and a set of genes associated with inhibitory control across emotion regulation and memory control. These findings facilitate our understanding of the neurobiological correlates of inhibitory control and may contribute to the development of brain stimulation and pharmacological interventions.

scholarly journals Stimulus set meaningfulness and neurophysiological differentiation: a functional magnetic resonance imaging study

2014 ◽  
Author(s):  
Melanie Boly ◽  
Shuntaro Sasai ◽  
Olivia Gosseries ◽  
Masafumi Oizumi ◽  
Adenauer Casali ◽  
...  
Keyword(s):  
Information Integration ◽  
Brain Activity ◽  
Activity Patterns ◽  
Imaging Study ◽  
Cortical Activation ◽  
Neural Responses ◽  
Activation Patterns ◽  
Stimulus Set ◽  
The Subject ◽  
Cortical Regions

A meaningful set of stimuli, such as a sequence of frames from a movie, triggers a set of different experiences. By contrast, a meaningless set of stimuli, such as a sequence of 'TV noise' frames, triggers always the same experience – of seeing 'TV noise' – even though the stimuli themselves are as different from each other as the movie frames. We reasoned that the differentiation of cortical responses underlying the subject's experiences, as measured by Lempel-Ziv complexity (incompressibility) of functional MRI images, should reflect the overall meaningfulness of a set of stimuli for the subject, rather than differences among the stimuli. We tested this hypothesis by quantifying the differentiation of brain activity patterns in response to a movie sequence, to the same movie scrambled in time, and to 'TV noise', where the pixels from each movie frame were scrambled in space. While overall cortical activation was strong and widespread in all conditions, the differentiation (Lempel-Ziv complexity) of brain activation patterns was correlated with the meaningfulness of the stimulus set, being highest in the movie condition, intermediate in the scrambled movie condition, and minimal for 'TV noise'. Stimulus set meaningfulness was also associated with higher information integration among cortical regions. These results suggest that the differentiation of neural responses can be used to assess the meaningfulness of a given set of stimuli for a given subject, without the need to identify the features and categories that are relevant to the subject, nor the precise location of selective neural responses.

Intraoperative localization of spatially and spectrally distinct resting-state networks in Parkinson’s disease

2020 ◽  
Vol 132 (4) ◽  
pp. 1234-1242 ◽  
Author(s):  
Paolo Belardinelli ◽  
Ramin Azodi-Avval ◽  
Erick Ortiz ◽  
Georgios Naros ◽  
Florian Grimm ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Network Effects ◽  
Brain Activity ◽  
Dynamic Imaging ◽  
Oscillatory Activity ◽  
Network Information ◽  
Novel Approach ◽  
Electrophysiological Recordings ◽  
Intraoperative Localization

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for symptomatic Parkinson’s disease (PD); the clinical benefit may not only mirror modulation of local STN activity but also reflect consecutive network effects on cortical oscillatory activity. Moreover, STN-DBS selectively suppresses spatially and spectrally distinct patterns of synchronous oscillatory activity within cortical-subcortical loops. These STN-cortical circuits have been described in PD patients using magnetoencephalography after surgery. This network information, however, is currently not available during surgery to inform the implantation strategy.The authors recorded spontaneous brain activity in 3 awake patients with PD (mean age 67 ± 14 years; mean disease duration 13 ± 7 years) during implantation of DBS electrodes into the STN after overnight withdrawal of dopaminergic medication. Intraoperative propofol was discontinued at least 30 minutes prior to the electrophysiological recordings. The authors used a novel approach for performing simultaneous recordings of STN local field potentials (LFPs) and multichannel electroencephalography (EEG) at rest. Coherent oscillations between LFP and EEG sensors were computed, and subsequent dynamic imaging of coherent sources was performed.The authors identified coherent activity in the upper beta range (21–35 Hz) between the STN and the ipsilateral mesial (pre)motor area. Coherence in the theta range (4–6 Hz) was detected in the ipsilateral prefrontal area.These findings demonstrate the feasibility of detecting frequency-specific and spatially distinct synchronization between the STN and cortex during DBS surgery. Mapping the STN with this technique may disentangle different functional loops relevant for refined targeting during DBS implantation.

scholarly journals The Neural Basis of Vivid Memory Is Patterned on Perception

2012 ◽  
Vol 24 (9) ◽  
pp. 1867-1883 ◽  
Author(s):  
Bradley R. Buchsbaum ◽  
Sabrina Lemire-Rodger ◽  
Candice Fang ◽  
Hervé Abdi
Keyword(s):  
Brain Activity ◽  
Sensory Perception ◽  
Direct Perception ◽  
Neural Basis ◽  
Activation Patterns ◽  
Short Video ◽  
Initial Perception ◽  
Experiential Quality ◽  
High Degree

When we have a rich and vivid memory for a past experience, it often feels like we are transported back in time to witness once again this event. Indeed, a perfect memory would exactly mimic the experiential quality of direct sensory perception. We used fMRI and multivoxel pattern analysis to map and quantify the similarity between patterns of activation evoked by direct perception of a diverse set of short video clips and the vivid remembering, with closed eyes, of these clips. We found that the patterns of distributed brain activation during vivid memory mimicked the patterns evoked during sensory perception. Using whole-brain patterns of activation evoked by perception of the videos, we were able to accurately classify brain patterns that were elicited when participants tried to vividly recall those same videos. A discriminant analysis of the activation patterns associated with each video revealed a high degree (explaining over 80% of the variance) of shared representational similarity between perception and memory. These results show that complex, multifeatured memory involves a partial reinstatement of the whole pattern of brain activity that is evoked during initial perception of the stimulus.

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

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.

scholarly journals Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zekun Xu ◽  
Eric Laber ◽  
Ana-Maria Staicu ◽  
B. Duncan X. Lascelles
Keyword(s):  
High Frequency ◽  
Activity Patterns ◽  
Treatment Strategies ◽  
Therapeutic Effects ◽  
Activity Levels ◽  
Activity Data ◽  
Novel Approach ◽  
High Frequency Activity ◽  
Using Data ◽  
Chronic Pain Conditions

AbstractOsteoarthritis (OA) is a chronic condition often associated with pain, affecting approximately fourteen percent of the population, and increasing in prevalence. A globally aging population have made treating OA-associated pain as well as maintaining mobility and activity a public health priority. OA affects all mammals, and the use of spontaneous animal models is one promising approach for improving translational pain research and the development of effective treatment strategies. Accelerometers are a common tool for collecting high-frequency activity data on animals to study the effects of treatment on pain related activity patterns. There has recently been increasing interest in their use to understand treatment effects in human pain conditions. However, activity patterns vary widely across subjects; furthermore, the effects of treatment may manifest in higher or lower activity counts or in subtler ways like changes in the frequency of certain types of activities. We use a zero inflated Poisson hidden semi-Markov model to characterize activity patterns and subsequently derive estimators of the treatment effect in terms of changes in activity levels or frequency of activity type. We demonstrate the application of our model, and its advance over traditional analysis methods, using data from a naturally occurring feline OA-associated pain model.

scholarly journals Personalized Biometrics of Physical Pain Agree with Psychophysics by Participants with Sensory over Responsivity

2021 ◽  
Vol 11 (2) ◽  
pp. 93
Author(s):  
Jihye Ryu ◽  
Tami Bar-Shalita ◽  
Yelena Granovsky ◽  
Irit Weissman-Fogel ◽  
Elizabeth B. Torres
Keyword(s):  
General Population ◽  
Brain Activity ◽  
A Priori ◽  
Summary Statistics ◽  
Physical Pain ◽  
New Methods ◽  
Self Reports ◽  
Theoretical Population ◽  
The Moment ◽  
Reported Data

The study of pain requires a balance between subjective methods that rely on self-reports and complementary objective biometrics that ascertain physical signals associated with subjective accounts. There are at present no objective scales that enable the personalized assessment of pain, as most work involving electrophysiology rely on summary statistics from a priori theoretical population assumptions. Along these lines, recent work has provided evidence of differences in pain sensations between participants with Sensory Over Responsivity (SOR) and controls. While these analyses are useful to understand pain across groups, there remains a need to quantify individual differences more precisely in a personalized manner. Here we offer new methods to characterize pain using the moment-by-moment standardized fluctuations in EEG brain activity centrally reflecting the person’s experiencing temperature-based stimulation at the periphery. This type of gross data is often disregarded as noise, yet here we show its utility to characterize the lingering sensation of discomfort raising to the level of pain, individually, for each participant. We show fundamental differences between the SOR group in relation to controls and provide an objective account of pain congruent with the subjective self-reported data. This offers the potential to build a standardized scale useful to profile pain levels in a personalized manner across the general population.

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