scholarly journals Revisiting Nonlinear Functional Brain Co-activations: Directed, Dynamic, and Delayed

2021 ◽  
Vol 15 ◽  
Author(s):  
Ignacio Cifre ◽  
Maria T. Miller Flores ◽  
Lucia Penalba ◽  
Jeremi K. Ochab ◽  
Dante R. Chialvo
Keyword(s):  
Functional Connectivity ◽  
Brain Regions ◽  
Significant Loss ◽  
Subsequent Work ◽  
Activation Patterns ◽  
Nonlinear Functional ◽  
Co Activation ◽  
Neuro Imaging ◽  
Experimental Findings ◽  
Correlation Properties

The center stage of neuro-imaging is currently occupied by studies of functional correlations between brain regions. These correlations define the brain functional networks, which are the most frequently used framework to represent and interpret a variety of experimental findings. In the previous study, we first demonstrated that the relatively stronger blood oxygenated level dependent (BOLD) activations contain most of the information relevant to understand functional connectivity, and subsequent work confirmed that a large compression of the original signals can be obtained without significant loss of information. In this study, we revisit the correlation properties of these epochs to define a measure of nonlinear dynamic directed functional connectivity (nldFC) across regions of interest. We show that the proposed metric provides at once, without extensive numerical complications, directed information of the functional correlations, as well as a measure of temporal lags across regions, overall offering a different and complementary perspective in the analysis of brain co-activation patterns. In this study, we provide further details for the computations of these measures and for a proof of concept based on replicating existing results from an Autistic Syndrome database, and discuss the main features and advantages of the proposed strategy for the study of brain functional correlations.

scholarly journals Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Arian Ashourvan ◽  
Preya Shah ◽  
Adam Pines ◽  
Shi Gu ◽  
Christopher W. Lynn ◽  
...  
Keyword(s):  
White Matter ◽  
Maximum Entropy ◽  
Large Scale ◽  
Structural Connectivity ◽  
Quantitative Relationship ◽  
Brain Regions ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Activation Patterns ◽  
Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

scholarly journals The Relationship between BOLD and Neural Activity Arises from Temporally Sparse Events

2019 ◽  
Author(s):  
Xiaodi Zhang ◽  
Wen-Ju Pan ◽  
Shella Dawn Keilholz
Keyword(s):  
Neural Activity ◽  
Field Potential ◽  
High Amplitude ◽  
Brain Regions ◽  
Functional Networks ◽  
Time Varying ◽  
Bold Signal ◽  
Activation Patterns ◽  
Amplitude Data ◽  
Co Activation

Resting state functional magnetic resonance (rs-fMRI) imaging offers insights into how different brain regions are connected into functional networks. It was recently shown that networks that are almost identical to the ones created from conventional correlation analysis can be obtained from a subset of high-amplitude data, suggesting that the functional networks may be driven by instantaneous co-activations of multiple brain regions rather than ongoing oscillatory processes. The rs-fMRI studies, however, rely on the blood oxygen level dependent (BOLD) signal, which is only indirectly sensitive to neural activity through neurovascular coupling. To provide more direct evidence that the neuronal co-activation events produce the time-varying network patterns seen in rs-fMRI studies, we examined the simultaneous rs-fMRI and local field potential (LFP) recordings in rats performed in our lab over the past several years. We developed complementary analysis methods that focus on either the temporal or spatial domain, and found evidence that the interaction between LFP and BOLD may be driven by instantaneous co-activation events as well. BOLD maps triggered on high-amplitude LFP events resemble co-activation patterns created from rs-fMRI data alone, though the co-activation time points are defined differently in the two cases. Moreover, only LFP events that fall into the highest or lowest thirds of the amplitude distribution result in a BOLD signal that can be distinguished from noise. These findings provide evidence of an electrophysiological basis for the time-varying co-activation patterns observed in previous studies.

ASHA 2007 Zemlin Memorial Award Lecture: The Neural Control of Speech

2008 ◽  
Vol 18 (1) ◽  
pp. 7-14
Author(s):  
Frank H. Guenther
Keyword(s):  
Neural Control ◽  
Brain Activity ◽  
Brain Regions ◽  
Apraxia Of Speech ◽  
Activation Patterns ◽  
Model Account ◽  
Wide Range ◽  
Ataxic Dysarthria ◽  
Memorial Award ◽  
Experimental Findings

Abstract Speech production involves coordinated processing in many regions of the brain. To better understand these processes, our research team has designed, tested, and refined a neural network model whose components correspond to brain regions involved in speech. Babbling and imitation phases are used to train neural mappings between phonological, articulatory, auditory, and somatosensory representations. After learning, the model can produce combinations of the sounds it has learned by commanding movements of an articulatory synthesizer. Computer simulations of the model account for a wide range of experimental findings, including data on acquisition of speaking skills, articulatory kinematics, and brain activity during speech. The model is also being used to investigate speech motor disorders, such as stuttering, apraxia of speech, and ataxic dysarthria. These projects compare the effects of damage to particular regions of the model to the kinematics, acoustics, or brain activation patterns of speakers with similar damage. Finally, insights from the model are being used to guide the design of a brain-computer interface for providing prosthetic speech to profoundly paralyzed individuals.

scholarly journals Beyond Consensus: Embracing Heterogeneity in Curated Neuroimaging Meta-Analysis

2017 ◽  
Author(s):  
Gia H. Ngo ◽  
Simon B. Eickhoff ◽  
Minh Nguyen ◽  
Gunes Sevinc ◽  
Peter T. Fox ◽  
...  
Keyword(s):  
Mental Disorder ◽  
Topic Model ◽  
Meta Analysis ◽  
Brain Regions ◽  
Small Scale ◽  
Functional Domain ◽  
Seed Region ◽  
Activation Patterns ◽  
Co Activation ◽  
Meta Analyses

AbstractCoordinate-based meta-analysis can provide important insights into mind-brain relationships. A popular approach for curated small-scale meta-analysis is activation likelihood estimation (ALE), which identifies brain regions consistently activated across a selected set of experiments, such as within a functional domain or mental disorder. ALE can also be utilized in meta-analytic co-activation modeling (MACM) to identify brain regions consistently co-activated with a seed region. Therefore, ALE aims to find consensus across experiments, treating heterogeneity across experiments as noise. However, heterogeneity within an ALE analysis of a functional domain might indicate the presence of functional sub-domains. Similarly, heterogeneity within a MACM analysis might indicate the involvement of a seed region in multiple co-activation patterns that are dependent on task contexts. Here, we demonstrate the use of the author-topic model to automatically determine if heterogeneities within ALE-type meta-analyses can be robustly explained by a small number of latent patterns. In the first application, the author-topic modeling of experiments involving self-generated thought (N = 179) revealed cognitive components fractionating the default network. In the second application, the author-topic model revealed that the left inferior frontal junction (IFJ) participated in multiple task-dependent co-activation patterns (N = 323). Furthermore, the author-topic model estimates compared favorably with spatial independent component analysis in both simulation and real data. Overall, the results suggest that the author-topic model is a flexible tool for exploring heterogeneity in ALE-type meta-analyses that might arise from functional sub-domains, mental disorder subtypes or task-dependent co-activation patterns. Code for this study is publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/meta-analysis/Ngo2019_AuthorTopic).

scholarly journals Benchmarking functional connectivity by the structure and geometry of the human brain

2021 ◽  
Author(s):  
Zhen-Qi Liu ◽  
Richard F. Betzel ◽  
Bratislav Misic
Keyword(s):  
Functional Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Underlying Structure ◽  
Functional Connections ◽  
Co Activation ◽  
Natural Frame ◽  
Structural Connections ◽  
Electrical Impulses ◽  
The Brain

The brain’s structural connectivity supports the propagation of electrical impulses, manifesting as patterns of co-activation, termed functional connectivity. Functional connectivity emerges from the underlying sparse structural connections, particularly through poly-synaptic communication. As a result, functional connections between brain regions without direct structural links are numerous, but their organization is not completely understood. Here we investigate the organization of functional connections without direct structural links. We develop a simple, data-driven method to benchmark functional connections with respect to their underlying structural and geometric embedding. We then use this method to re-weigh and re-express functional connectivity. We find evidence of unexpectedly strong functional connectivity within the canonical intrinsic networks of the brain. We also find unexpectedly strong functional connectivity at the apex of the unimodal-transmodal hierarchy. Our results suggest that both phenomena – functional modules and functional hierarchies – emerge from functional interactions that transcend the underlying structure and geometry. These findings also potentially explain recent reports that structural and functional connectivity gradually diverge in transmodal cortex. Collectively, we show how structural connectivity and geometry can be used as a natural frame of reference with which to study functional connectivity patterns in the brain.

Engaging regularly with fiction influences connectivity in cortical areas for language and mentalizing

2017 ◽  
Author(s):  
Roel M. Willems ◽  
Franziska Hartung
Keyword(s):  
Functional Connectivity ◽  
Time Course ◽  
Language Skills ◽  
Brain Regions ◽  
Brain Areas ◽  
Daily Lives ◽  
Cortical Areas ◽  
Social Abilities ◽  
Behavioral Evidence ◽  
Amount Of Reading

Behavioral evidence suggests that engaging with fiction is positively correlated with social abilities. The rationale behind this link is that engaging with fictional narratives offers a ‘training modus’ for mentalizing and empathizing. We investigated the influence of the amount of reading that participants report doing in their daily lives, on connections between brain areas while they listened to literary narratives. Participants (N=57) listened to two literary narratives while brain activation was measured with fMRI. We computed time-course correlations between brain regions, and compared the correlation values from listening to narratives to listening to reversed speech. The between-region correlations were then related to the amount of fiction that participants read in their daily lives. Our results show that amount of fiction reading is related to functional connectivity in areas known to be involved in language and mentalizing. This suggests that reading fiction influences social cognition as well as language skills.

scholarly journals How (not) to cross a boundary: Crosslinguistic influence in simultaneous bilingual children's event construal

2021 ◽  
pp. 1-13
Author(s):  
Helen Engemann
Keyword(s):  
Production Process ◽  
Age Groups ◽  
Boundary Crossing ◽  
Bilingual Children ◽  
Activation Patterns ◽  
Motion Event ◽  
Co Activation ◽  
Crosslinguistic Influence ◽  
Event Construal ◽  
Two Age Groups

Abstract Simultaneous bilingual children sometimes display crosslinguistic influence (CLI), widely attested in the domain of morphosyntax. It remains less clear whether CLI affects bilinguals’ event construal, what motivates its occurrence and directionality, and how developmentally persistent it is. The present study tested predictions generated by the structural overlap hypothesis and the co-activation account in the motion event domain. 96 English–French bilingual children of two age groups and 96 age-matched monolingual English and French controls were asked to describe animated videos displaying voluntary motion events. Semantic encoding in main verbs showed bidirectional CLI. Unidirectional CLI affected French path encoding in the verbal periphery and was predicted by the presence of boundary-crossing, despite the absence of structural overlap. Furthermore, CLI increased developmentally in the French data. It is argued that these findings reflect highly dynamic co-activation patterns sensitive to the requirements of the task and to language-specific challenges in the online production process.

scholarly journals The effect of congenital blindness on resting-state functional connectivity revisited

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maria J. S. Guerreiro ◽  
Madita Linke ◽  
Sunitha Lingareddy ◽  
Ramesh Kekunnaya ◽  
Brigitte Röder
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Visual Experience ◽  
Brain Regions ◽  
State Condition ◽  
Resting State Functional Connectivity ◽  
Congenital Blindness ◽  
Eyes Closed ◽  
Congenitally Blind ◽  
Eyes Open

AbstractLower resting-state functional connectivity (RSFC) between ‘visual’ and non-‘visual’ neural circuits has been reported as a hallmark of congenital blindness. In sighted individuals, RSFC between visual and non-visual brain regions has been shown to increase during rest with eyes closed relative to rest with eyes open. To determine the role of visual experience on the modulation of RSFC by resting state condition—as well as to evaluate the effect of resting state condition on group differences in RSFC—, we compared RSFC between visual and somatosensory/auditory regions in congenitally blind individuals (n = 9) and sighted participants (n = 9) during eyes open and eyes closed conditions. In the sighted group, we replicated the increase of RSFC between visual and non-visual areas during rest with eyes closed relative to rest with eyes open. This was not the case in the congenitally blind group, resulting in a lower RSFC between ‘visual’ and non-‘visual’ circuits relative to sighted controls only in the eyes closed condition. These results indicate that visual experience is necessary for the modulation of RSFC by resting state condition and highlight the importance of considering whether sighted controls should be tested with eyes open or closed in studies of functional brain reorganization as a consequence of blindness.

scholarly journals A cross-species neural integration of gravity for motor optimization

2021 ◽  
Vol 7 (15) ◽  
pp. eabf7800
Author(s):  
Jeremie Gaveau ◽  
Sidney Grospretre ◽  
Bastien Berret ◽  
Dora E. Angelaki ◽  
Charalambos Papaxanthis
Keyword(s):  
Optimization Strategy ◽  
Motor Patterns ◽  
Activation Patterns ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Neural Integration ◽  
Gravity Effects ◽  
Neural Signature ◽  
Experimental Findings ◽  
Muscular Activation

Recent kinematic results, combined with model simulations, have provided support for the hypothesis that the human brain shapes motor patterns that use gravity effects to minimize muscle effort. Because many different muscular activation patterns can give rise to the same trajectory, here, we specifically investigate gravity-related movement properties by analyzing muscular activation patterns during single-degree-of-freedom arm movements in various directions. Using a well-known decomposition method of tonic and phasic electromyographic activities, we demonstrate that phasic electromyograms (EMGs) present systematic negative phases. This negativity reveals the optimal motor plan’s neural signature, where the motor system harvests the mechanical effects of gravity to accelerate downward and decelerate upward movements, thereby saving muscle effort. We compare experimental findings in humans to monkeys, generalizing the Effort-optimization strategy across species.

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