scholarly journals An Approach to Automatically Label & Order Brain Activity/Component Maps

2020 ◽  
Author(s):  
Mustafa S. Salman ◽  
Tor D. Wager ◽  
Eswar Damaraju ◽  
Vince D. Calhoun
Keyword(s):  
Brain Imaging ◽  
Brain Activity ◽  
Region Of Interest ◽  
Network Connectivity ◽  
Data Driven ◽  
Reproducible Research ◽  
Validation Dataset ◽  
Imaging Data ◽  
Intrinsic Connectivity Networks ◽  
Using Data

AbstractFunctional magnetic resonance imaging (fMRI) is a brain imaging technique which provides detailed in-sights into brain function and its disruption in various brain disorders. fMRI data can be analyzed using data-driven or region-of-interest based methods. The data-driven analysis of brain activity maps involves several steps, the first of which is identifying whether the maps capture what might be interpreted as intrinsic connectivity networks (ICNs) or artifacts. This is followed by linking the ICNs to known anatomical and/or functional parcellations. Optionally, as in the study of functional network connectivity (FNC), rearranging the connectivity graph is also necessary for systematic interpretation. Here we present a toolbox that automates all these processes under minimal or no supervision with high accuracy. We provide a pretrained cross-validated elastic-net regularized general linear model for the noisecloud toolbox to separate the ICNs from artifacts. We include several well-known anatomical and functional parcellations from which researchers can choose to label the activity maps. Finally, we integrate a method for maximizing the within-domain modularity to generate a more systematically structured FNC matrix. We improve upon and integrate existing techniques and new methods to design this toolbox which can take care of all the above needs. Specifically, we show that our pretrained model achieves 89% accuracy and 100% precision at classifying ICNs from artifacts in a validation dataset. Researchers are generating brain imaging data and analyzing brain activity at an ever-increasing rate. The Autolabeller toolbox can help automate such analyses for faster and reproducible research.

scholarly journals Making the brain-activity-to-information leap using a novel framework: Stimulus Information Representation (SIR)

2019 ◽  
Author(s):  
Philippe G. Schyns ◽  
Robin A.A. Ince
Keyword(s):  
Brain Imaging ◽  
Brain Activity ◽  
Behavioral Responses ◽  
Data Driven ◽  
Information Representation ◽  
Stimulus Information ◽  
Fundamental Challenge ◽  
Per Se ◽  
The Brain ◽  
Different Levels

AbstractA fundamental challenge in neuroscience is to understand how the brain processes information. Neuroscientists have approached this question partly by measuring brain activity in space, time and at different levels of granularity. However, our aim is not to discover brain activity per se, but to understand the processing of information that this activity reflects. To make this brain-activity-to-information leap, we believe that we should reconsider brain imaging from the methodological foundations of psychology. With this goal in mind, we have developed a new data-driven framework, called Stimulus Information Representation (SIR), that enables us to better understand how the brain processes information from measures of brain activity and behavioral responses. In this article, we explain this approach, its strengths and limitations, and how it can be applied to understand how the brain processes information to perform behavior in a task.“It is no good poking around in the brain without some idea of what one is looking for. That would be like trying to find a needle in a haystack without having any idea what needles look like. The theorist is the [person] who might reasonably be asked for [their] opinion about the appearance of needles.” HC Longuet-Higgins, 1969.

scholarly journals No Alteration Between Intrinsic Connectivity Networks by a Pilot Study on Localized Exposure to the Fourth-Generation Wireless Communication Signals

2022 ◽  
Vol 9 ◽  
Author(s):  
Lei Yang ◽  
Qingmeng Liu ◽  
Yu Zhou ◽  
Xing Wang ◽  
Tongning Wu ◽  
...  
Keyword(s):  
Clinical Symptoms ◽  
Network Connectivity ◽  
Fourth Generation ◽  
Imaging Data ◽  
Communication Signals ◽  
Exposure Effect ◽  
Brain Functions ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks ◽  
The Impact

Neurophysiological effect of human exposure to radiofrequency signals has attracted considerable attention, which was claimed to have an association with a series of clinical symptoms. A few investigations have been conducted on alteration of brain functions, yet no known research focused on intrinsic connectivity networks, an attribute that may relate to some behavioral functions. To investigate the exposure effect on functional connectivity between intrinsic connectivity networks, we conducted experiments with seventeen participants experiencing localized head exposure to real and sham time-division long-term evolution signal for 30 min. The resting-state functional magnetic resonance imaging data were collected before and after exposure, respectively. Group-level independent component analysis was used to decompose networks of interest. Three states were clustered, which can reflect different cognitive conditions. Dynamic connectivity as well as conventional connectivity between networks per state were computed and followed by paired sample t-tests. Results showed that there was no statistical difference in static or dynamic functional network connectivity in both real and sham exposure conditions, and pointed out that the impact of short-term electromagnetic exposure was undetected at the ICNs level. The specific brain parcellations and metrics used in the study may lead to different results on brain modulation.

scholarly journals Estimation and Validation of Individualized Dynamic Brain Models with Resting State fMRI

2019 ◽  
Author(s):  
Matthew F. Singh ◽  
Todd S. Braver ◽  
Michael W. Cole ◽  
ShiNung Ching
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Nonlinear Dynamical Systems ◽  
Brain Activity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Data Driven ◽  
Imaging Data ◽  
Individual Level

AbstractA key challenge for neuroscience is to develop generative, causal models of the human nervous system in an individualized, data-driven manner. Previous initiatives have either constructed biologically-plausible models that are not constrained by individual-level human brain activity or used data-driven statistical characterizations of individuals that are not mechanistic. We aim to bridge this gap through the development of a new modeling approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling, wherein we fit nonlinear dynamical systems models directly to human brain imaging data. The MINDy framework is able to produce these data-driven network models for hundreds to thousands of interacting brain regions in just 1-3 minutes per subject. We demonstrate that the models are valid, reliable, and robust. We show that MINDy models are predictive of individualized patterns of resting-state brain dynamical activity. Furthermore, MINDy is better able to uncover the mechanisms underlying individual differences in resting state activity than functional connectivity methods.

scholarly journals Decoding individual differences in STEM learning from functional MRI data

2018 ◽  
Author(s):  
Joshua S. Cetron ◽  
Andrew C. Connolly ◽  
Solomon G. Diamond ◽  
Vicki V. May ◽  
James V. Haxby ◽  
...  
Keyword(s):  
Individual Differences ◽  
Conceptual Understanding ◽  
Brain Activity ◽  
Data Driven ◽  
Stem Learning ◽  
Concept Knowledge ◽  
Wide Range ◽  
Concept Inventories ◽  
Using Data ◽  
The Brain

Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural 'score' to complement traditional scores of an individual’s conceptual understanding. Using a novel data-driven multivariate neuroimaging approach—informational network analysis—we successfully derived a neural score from patterns of activity across the brain that predicted individual differences in multiple concept knowledge tasks in the physics and engineering domain. These tasks include an fMRI paradigm, as well as two other previously validated concept inventories. The informational network score outperformed alternative neural scores computed using data-driven neuroimaging methods, including multivariate representational similarity analysis. This technique could be applied to quantify concept knowledge in a wide range of domains, including classroom-based education research, machine learning, and other areas of cognitive science.

scholarly journals Idiosynchrony: From shared responses to individual differences during naturalistic neuroimaging

2019 ◽  
Author(s):  
Emily S. Finn ◽  
Enrico Glerean ◽  
Arman Y. Khojandi ◽  
Dylan Nielson ◽  
Peter J. Molfese ◽  
...  
Keyword(s):  
Individual Differences ◽  
Brain Activity ◽  
Future Research ◽  
Imaging Data ◽  
Stimulus Choice ◽  
Human Connectome Project ◽  
Single Subjects ◽  
Using Data ◽  
The Individual ◽  
Empirical Demonstration

Two ongoing movements in human cognitive neuroscience have researchers shifting focus from group-level inferences to characterizing single subjects, and complementing tightly controlled tasks with rich, dynamic paradigms such as movies and stories. Yet relatively little work combines these two, perhaps because traditional analysis approaches for naturalistic imaging data are geared toward detecting shared responses rather than between-subject variability. Here, we review recent work using naturalistic stimuli to study individual differences, and advance a framework for detecting structure in idiosyncratic patterns of brain activity, or “idiosynchrony”. Specifically, we outline the emerging technique of inter-subject representational similarity analysis (IS-RSA), including its theoretical motivation and an empirical demonstration of how it recovers brain-behavior relationships during movie watching using data from the Human Connectome Project. We also consider how stimulus choice may affect the individual signal and discuss areas for future research. We argue that naturalistic neuroimaging paradigms have the potential to reveal meaningful individual differences above and beyond those observed during traditional tasks or at rest.

scholarly journals MEG-BIDS: an extension to the Brain Imaging Data Structure for magnetoencephalography

2017 ◽  
Author(s):  
Guiomar Niso ◽  
Krzysztof J. Gorgolewski ◽  
Elizabeth Bock ◽  
Teon L. Brooks ◽  
Guillaume Flandin ◽  
...  
Keyword(s):  
Data Structure ◽  
Brain Imaging ◽  
Brain Activity ◽  
Large Data ◽  
Imaging Data ◽  
Source Imaging ◽  
Standard Data ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Imaging Data ◽  
The Brain

AbstractWe present a significant extension of the Brain Imaging Data Structure (BIDS) to support the specific aspects of magnetoencephalography (MEG) data. MEG provides direct measurement of brain activity with millisecond temporal resolution and unique source imaging capabilities. So far, BIDS has provided a solution to structure the organization of magnetic resonance imaging (MRI) data, which nature and acquisition parameters are different. Despite the lack of standard data format for MEG, MEG-BIDS is a principled solution to store, organize and share the typically-large data volumes produced. It builds on BIDS for MRI, and therefore readily yields a multimodal data organization by construction. This is particularly valuable for the anatomical and functional registration of MEG source imaging with MRI. With MEG-BIDS and a growing range of software adopting the standard, the MEG community has a solution to minimize curation overheads, reduce data handling errors and optimize usage of computational resources for analytics. The standard also includes well-defined metadata, to facilitate future data harmonization and sharing efforts.

scholarly journals Resting-state functional connectivity relates to interindividual variations in positive memory

2021 ◽  
Author(s):  
Ayako Isato ◽  
Tetsuya Suhara ◽  
Makiko Yamada
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Memory Performance ◽  
Region Of Interest ◽  
Network Connectivity ◽  
Resting State Functional Connectivity ◽  
Interindividual Variations ◽  
Frontal Operculum

Individual differences in positive memory recollection are of interest in mental health, as positive memories can help protect people against stress and depression. However, it is unclear how individual differences in positive memory recollection are reflected in brain activity in the resting state. Here, we investigate the resting-state functional connectivity (FC) associated with interindividual variations in positive memory by employing cluster-level inferences based on randomization/permutation region of interest (ROI)-to-ROI analyses. We identified a cluster of FCs that was positively associated with positive memory performance, including the frontal operculum, central operculum, parietal operculum, Heschl's gyrus, and planum temporale. The current results suggest that positive memory is innervated by frontotemporal network connectivity, which may have implications for future investigations of vulnerability to stress and depression.

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