scholarly journals HyPyP: a Hyperscanning Python Pipeline for inter-brain connectivity analysis

2020 ◽  
Author(s):  
Anaël Ayrolles ◽  
Florence Brun ◽  
Phoebe Chen ◽  
Amir Djalovski ◽  
Yann Beauxis ◽  
...  
Keyword(s):  
Social Interaction ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Simultaneous Recording ◽  
Research Field ◽  
Connectivity Analysis ◽  
Social Stimuli ◽  
Brain Responses ◽  
Social Tasks ◽  
Open Source Software Package

Abstract The bulk of social neuroscience takes a ‘stimulus-brain’ approach, typically comparing brain responses to different types of social stimuli, but most of the time in the absence of direct social interaction. Over the last two decades, a growing number of researchers have adopted a ‘brain-to-brain’ approach, exploring similarities between brain patterns across participants as a novel way to gain insight into the social brain. This methodological shift has facilitated the introduction of naturalistic social stimuli into the study design (e.g. movies) and, crucially, has spurred the development of new tools to directly study social interaction, both in controlled experimental settings and in more ecologically valid environments. Specifically, ‘hyperscanning’ setups, which allow the simultaneous recording of brain activity from two or more individuals during social tasks, has gained popularity in recent years. However, currently, there is no agreed-upon approach to carry out such ‘inter-brain connectivity analysis’, resulting in a scattered landscape of analysis techniques. To accommodate a growing demand to standardize analysis approaches in this fast-growing research field, we have developed Hyperscanning Python Pipeline, a comprehensive and easy open-source software package that allows (social) neuroscientists to carry-out and to interpret inter-brain connectivity analyses.

scholarly journals HyPyP: a Hyperscanning Python Pipeline for inter-brain connectivity analysis

2020 ◽  
Author(s):  
Anaël Ayrolles ◽  
Florence Brun ◽  
Phoebe Chen ◽  
Amir Djalovski ◽  
Yann Beauxis ◽  
...  
Keyword(s):  
Social Interaction ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Simultaneous Recording ◽  
Research Field ◽  
Connectivity Analysis ◽  
Social Stimuli ◽  
Brain Responses ◽  
Social Tasks ◽  
Open Source Software Package

The bulk of social neuroscience takes a ‘stimulus-brain’ approach, typically comparing brain responses to different types of social stimuli, but most of the time in the absence of true social interaction. Over the last two decades, a growing number of researchers have adopted a ‘brain-to-brain’ approach, exploring similarities between brain patterns across participants as a novel way to gain insight into the social brain. This methodological shift has facilitated the introduction of naturalistic social stimuli into the study design (e.g., movies), and, crucially, has spurred the development of new tools to directly study social interaction, both in controlled experimental settings and in more ecologically valid environments. Specifically, hyperscanning setups, which allow the simultaneous recording of brain activity from two or more individuals during social tasks has gained popularity in recent years. However, currently there is no agreed-upon approach to carry out such inter-brain connectivity analysis, resulting in a scattered landscape of analysis techniques. To accommodate a growing demand to standardize analysis approaches in this fast-growing research field, we have developed HyPyP, a comprehensive and easy open-source software package that allows (social) neuroscientists to carry-out and to interpret inter-brain connectivity analyses.

scholarly journals Gesture in hyperscanning during observation. Inter-brain connectivity

2020 ◽  
pp. 59-81
Author(s):  
Michela Balconi ◽  
Giulia Fronda
Keyword(s):  
Joint Action ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Action Observation ◽  
Verbal Communication ◽  
Theta Activity ◽  
Neural Responses ◽  
Brain Responses ◽  
Connectivity Patterns

Non-verbal communication is a joint action defined by the use of different gestures’ types. The present research aimed to investigate the electrophysiological (EEG) correlates during the observation of affective, social and informative gestures in non-verbal communication between encoder and decoder. Moreover, the hyperscanning paradigm allows investigating the individuals’ inter-brain connectivity. Regarding gestures’ type, the study’s results showed a decrease of alpha (increased brain activity), and an increase of delta and theta brain responsiveness and inter-brain connectivity for affective and social gestures in frontal and posterior areas for informative ones. Concerning gestures’ valence, an increase of left frontal theta activity and inter-brain connectivity was observed. Finally, about the inter-agents’ role, the same brain responses and inter-brain connectivity patterns emerged both in encoder and decoder. This study allows discovering neural responses underlying gestures’ type and valence during action observation, highlighting the validity of hyperscanning to investigate inter-brain connectivity mechanisms.

Behavior in the Brain

2010 ◽  
Vol 24 (2) ◽  
pp. 76-82 ◽  
Author(s):  
Martin M. Monti ◽  
Adrian M. Owen
Keyword(s):  
Motor Behavior ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Motor Output ◽  
The Unconscious ◽  
Ethical Implications ◽  
Brain Responses ◽  
Minimally Conscious ◽  
Brain Insults ◽  
Brain Behavior

Recent evidence has suggested that functional neuroimaging may play a crucial role in assessing residual cognition and awareness in brain injury survivors. In particular, brain insults that compromise the patient’s ability to produce motor output may render standard clinical testing ineffective. Indeed, if patients were aware but unable to signal so via motor behavior, they would be impossible to distinguish, at the bedside, from vegetative patients. Considering the alarming rate with which minimally conscious patients are misdiagnosed as vegetative, and the severe medical, legal, and ethical implications of such decisions, novel tools are urgently required to complement current clinical-assessment protocols. Functional neuroimaging may be particularly suited to this aim by providing a window on brain function without requiring patients to produce any motor output. Specifically, the possibility of detecting signs of willful behavior by directly observing brain activity (i.e., “brain behavior”), rather than motoric output, allows this approach to reach beyond what is observable at the bedside with standard clinical assessments. In addition, several neuroimaging studies have already highlighted neuroimaging protocols that can distinguish automatic brain responses from willful brain activity, making it possible to employ willful brain activations as an index of awareness. Certainly, neuroimaging in patient populations faces some theoretical and experimental difficulties, but willful, task-dependent, brain activation may be the only way to discriminate the conscious, but immobile, patient from the unconscious one.

Imaging the “social brain” in schizophrenia: A systematic review of neuroimaging studies of social reward and punishment

2020 ◽  
Author(s):  
Jessica Mow ◽  
Arti Gandhi ◽  
Daniel Fulford
Keyword(s):  
Brain Activity ◽  
Interaction Network ◽  
Social Motivation ◽  
Social Impairment ◽  
Future Research ◽  
Social Reward ◽  
Schizophrenia Spectrum Disorders ◽  
Social Stimuli ◽  
Schizophrenia Spectrum ◽  
Reward And Punishment

Decreased social functioning and high levels of loneliness and social isolation are common in schizophrenia spectrum disorders (SSD), contributing to reduced quality of life. One key contributor to social impairment is low social motivation, which may stem from aberrant neural processing of socially rewarding or punishing stimuli. To summarize research on the neurobiology of social motivation in SSD, we performed a systematic literature review of neuroimaging studies involving the presentation of social stimuli intended to elicit feelings of reward and/or punishment. Across 11 studies meeting criteria, people with SSD demonstrated weaker modulation of brain activity in regions within a proposed social interaction network, including prefrontal, cingulate, and striatal regions, as well as the amygdala and insula. Firm conclusions regarding neural differences in SSD in these regions, as well as connections within networks, are limited due to conceptual and methodological inconsistencies across the available studies. We conclude by making recommendations for the study of social reward and punishment processing in SSD in future research.

scholarly journals Humans with obesity have disordered brain responses to food images during physiological hyperglycemia

2018 ◽  
Vol 314 (5) ◽  
pp. E522-E529 ◽  
Author(s):  
Renata Belfort-DeAguiar ◽  
Dongju Seo ◽  
Cheryl Lacadie ◽  
Sarita Naik ◽  
Christian Schmidt ◽  
...  
Keyword(s):  
Brain Activity ◽  
Blood Oxygen Level Dependent ◽  
Normal Weight ◽  
Self Control ◽  
Food Cues ◽  
Brain Response ◽  
Glucose Levels ◽  
Hyperglycemic Clamp ◽  
Brain Responses ◽  
Food Images

Blood glucose levels influence brain regulation of food intake. This study assessed the effect of mild physiological hyperglycemia on brain response to food cues in individuals with obesity (OB) versus normal weight individuals (NW). Brain responses in 10 OB and 10 NW nondiabetic healthy adults [body mass index: 34 (3) vs. 23 (2) kg/m2, means (SD), P < 0.0001] were measured with functional MRI (blood oxygen level-dependent contrast) in combination with a two-step normoglycemic-hyperglycemic clamp. Participants were shown food and nonfood images during normoglycemia (~95 mg/dl) and hyperglycemia (~130 mg/dl). Plasma glucose levels were comparable in both groups during the two-step clamp ( P = not significant). Insulin and leptin levels were higher in the OB group compared with NW, whereas ghrelin levels were lower (all P < 0.05). During hyperglycemia, insula activity showed a group-by-glucose level effect. When compared with normoglycemia, hyperglycemia resulted in decreased activity in the hypothalamus and putamen in response to food images ( P < 0.001) in the NW group, whereas the OB group exhibited increased activity in insula, putamen, and anterior and dorsolateral prefrontal cortex (aPFC/dlPFC; P < 0.001). These data suggest that OB, compared with NW, appears to have disruption of brain responses to food cues during hyperglycemia, with reduced insula response in NW but increased insula response in OB, an area involved in food perception and interoception. In a post hoc analysis, brain activity in obesity appears to be associated with dysregulated motivation (striatum) and inappropriate self-control (aPFC/dlPFC) to food cues during hyperglycemia. Hyperstimulation for food and insensitivity to internal homeostatic signals may favor food consumption to possibly play a role in the pathogenesis of obesity.

Error-related Persistence of Motor Activity in Resting-state Networks

2018 ◽  
Vol 30 (12) ◽  
pp. 1883-1901 ◽  
Author(s):  
Nicolò F. Bernardi ◽  
Floris T. Van Vugt ◽  
Ricardo Ruy Valle-Mena ◽  
Shahabeddin Vahdat ◽  
David J. Ostry
Keyword(s):  
Motor Learning ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Brain Networks ◽  
Neural Activation ◽  
Resting State Functional Connectivity ◽  
Movement Training ◽  
Movement Error ◽  
Human Participants

The relationship between neural activation during movement training and the plastic changes that survive beyond movement execution is not well understood. Here we ask whether the changes in resting-state functional connectivity observed following motor learning overlap with the brain networks that track movement error during training. Human participants learned to trace an arched trajectory using a computer mouse in an MRI scanner. Motor performance was quantified on each trial as the maximum distance from the prescribed arc. During learning, two brain networks were observed, one showing increased activations for larger movement error, comprising the cerebellum, parietal, visual, somatosensory, and cortical motor areas, and the other being more activated for movements with lower error, comprising the ventral putamen and the OFC. After learning, changes in brain connectivity at rest were found predominantly in areas that had shown increased activation for larger error during task, specifically the cerebellum and its connections with motor, visual, and somatosensory cortex. The findings indicate that, although both errors and accurate movements are important during the active stage of motor learning, the changes in brain activity observed at rest primarily reflect networks that process errors. This suggests that error-related networks are represented in the initial stages of motor memory formation.

scholarly journals Enhancing Task fMRI Preprocessing via Individualized Model-Based Filtering of Intrinsic Activity Dynamics

2020 ◽  
Author(s):  
Matthew F. Singh ◽  
Anxu Wang ◽  
Michael Cole ◽  
ShiNung Ching ◽  
Todd S. Braver
Keyword(s):  
Statistical Power ◽  
Brain Activity ◽  
Intrinsic Activity ◽  
Bold Signal ◽  
Group Level ◽  
Temporal Precision ◽  
Brain Responses ◽  
Evoked Activity ◽  
Activity Dynamics ◽  
Do So

AbstractBrain responses recorded during fMRI are thought to reflect both rapid, stimulus-evoked activity and the propagation of spontaneous activity through brain networks. In the current work we describe a method to improve the estimation of task-evoked brain activity by first “filtering-out” the intrinsic propagation of pre-event activity from the BOLD signal. We do so using Mesoscale Individualized NeuroDynamic (MINDy; [1]) models built from individualized resting-state data (MINDy-based Filtering). After filtering, time-series are analyzed using conventional techniques. Results demonstrate that this simple operation significantly improves the statistical power and temporal precision of estimated group-level effects. Moreover, estimates based upon our technique better generalize between tasks measuring the same construct (cognitive control) and better predict individual differences in behavior. Thus, by subtracting the propagation of previous activity, we obtain better estimates of task-related neural activity.

scholarly journals Rhythmically Enhanced Music as Analgesic for Chronic Pain: A Pilot, Non-Controlled Observational Study

2021 ◽  
Vol 7 (1) ◽  
pp. 2
Author(s):  
Richard Merrill ◽  
Mariam Taher Amin
Keyword(s):  
Chronic Pain ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Mobile App ◽  
Double Blind ◽  
Pain Scores ◽  
Medication Dosage ◽  
Slow Tempo ◽  
Brainwave Entrainment

Chronic pain changes brain connectivity, brainwaves, and volume, often resulting in disability, anxiety, and depression. Opioid pain relievers impair function, with risk of addiction. Music analgesia research suggests that music for long-term analgesia includes slow tempo, pleasantness, and self-choice. Hypothesis: individuals listening to self-chosen music with embedded beats ½ h twice a day, could show brainwave entrainment (BWE) at healthy frequencies of healthy descending pain modulatory system. BWE may change brain activity, restoring organization in DPMS altered by chronic pain. Volunteers with chronic pain >1 year participated in a study of 4 weeks of listening to one half hour of music twice a day, and four weeks of non-listening, reporting pain and analgesic use bi-weekly using visual analog scale (VAS) and 0–10 numerical pain scores (NPS), medication types, and dosage. Volunteers selected from 27 half-hour pieces of music in several genres in a mobile app. Isochronic beats were embedded in the music with tempo, key, and isochronic theta frequencies proportional, to enhance the brain’s perception of rhythmic patterns and harmonics. Mean NPS showed a 26% reduction (p = 0.018). Significantly, mean medication dosage declined by over 60% (p = 0.008). Double-blind studies, larger populations are needed in future.

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

Sign in / Sign up

Export Citation Format

Share Document