Association Mining of the Brain Data: An EEG Study

Author(s):  
Pankush Kalgotra ◽  
Ramesh Sharda ◽  
Goutam Chakraborty
2021 ◽  
Author(s):  
Thijs L van der Plas ◽  
Jérôme Tubiana ◽  
Guillaume Le Goc ◽  
Geoffrey Migault ◽  
Michael Kunst ◽  
...  

Patterns of endogenous activity in the brain reflect a stochastic exploration of the neuronal state space that is constrained by the underlying assembly organization of neurons. Yet it remains to be shown that this interplay between neurons and their assembly dynamics indeed suffices to generate whole-brain data statistics. Here we recorded the activity from ~40,000 neurons simultaneously in zebrafish larvae, and show that a data-driven network model of neuron-assembly interactions can accurately reproduce the mean activity and pairwise correlation statistics of their spontaneous activity. This model, the compositional Restricted Boltzmann Machine, unveils ~200 neural assemblies, which compose neurophysiological circuits and whose various combinations form successive brain states. From this, we mathematically derived an interregional functional connectivity matrix, which is conserved across individual animals and correlates well with structural connectivity. This novel, assembly-based generative model of brain-wide neural dynamics enables physiology-bound perturbation experiments in silico.


2020 ◽  
Author(s):  
Antonino Visalli ◽  
Mariagrazia Capizzi ◽  
Ettore Ambrosini ◽  
Bruno Kopp ◽  
Antonino Vallesi

The brain predicts the timing of forthcoming events to optimize responses to them. Temporal predictions have been formalized in terms of the hazard function, which integrates prior beliefs on the likely timing of stimulus occurrence with information conveyed by the passage of time. However, how the human brain updates prior temporal beliefs is still elusive. Here we investigated electroencephalographic (EEG) signatures associated with Bayes-optimal updating of temporal beliefs. Given that updating usually occurs in response to surprising events, we sought to disentangle EEG correlates of updating from those associated with surprise. Twenty-six participants performed a temporal foreperiod task, which comprised a subset of surprising events not eliciting updating. EEG data were analyzed through a regression-based massive approach in the electrode and source space. Distinct late positive, centro-parietally distributed, event-related potentials (ERPs) were associated with surprise and belief updating in the electrode space. While surprise modulated the commonly observed P3b, updating was associated with a later and more sustained P3b-like waveform deflection. Results from source analyses revealed that surprise encoding comprises neural activity in the cingulo-opercular network (CON). These data provide evidence that temporal predictions are computed in a Bayesian manner, and that this is reflected in P3 modulations, akin to other cognitive domains. Overall, our study revealed that analyzing P3 modulations provides an important window into the Bayesian brain. Data and scripts are shared on OSF: https://osf.io/ckqa5/?view_only=f711e6f878784d4ab94f4b14b31eef46


2018 ◽  
Vol 50 ◽  
pp. 70-76 ◽  
Author(s):  
Ingvild E. Bjerke ◽  
Martin Øvsthus ◽  
Eszter A. Papp ◽  
Sharon C. Yates ◽  
Ludovico Silvestri ◽  
...  

AbstractThe Human Brain Project (HBP), an EU Flagship Initiative, is currently building an infrastructure that will allow integration of large amounts of heterogeneous neuroscience data. The ultimate goal of the project is to develop a unified multi-level understanding of the brain and its diseases, and beyond this to emulate the computational capabilities of the brain. Reference atlases of the brain are one of the key components in this infrastructure. Based on a new generation of three-dimensional (3D) reference atlases, new solutions for analyzing and integrating brain data are being developed. HBP will build services for spatial query and analysis of brain data comparable to current online services for geospatial data. The services will provide interactive access to a wide range of data types that have information about anatomical location tied to them. The 3D volumetric nature of the brain, however, introduces a new level of complexity that requires a range of tools for making use of and interacting with the atlases. With such new tools, neuroscience research groups will be able to connect their data to atlas space, share their data through online data systems, and search and find other relevant data through the same systems. This new approach partly replaces earlier attempts to organize research data based only on a set of semantic terminologies describing the brain and its subdivisions.


Author(s):  
Jerome Kagan

Scientists were unable to study the relation of brain to mind until the invention of technologies that measured the brain activity accompanying psychological processes. Yet even with these new tools, conclusions are tentative or simply wrong. This book describes five conditions that place serious constraints on the ability to predict mental or behavioral outcomes based on brain data: the setting in which evidence is gathered, the expectations of the subject, the source of the evidence that supports the conclusion, the absence of studies that examine patterns of causes with patterns of measures, and the habit of borrowing terms from psychology. The book describes the importance of context, and how the experimental setting—including the room, the procedure, and the species, age, and sex of both subject and examiner—can influence the conclusions. It explains how subject expectations affect all brain measures; considers why brain and psychological data often yield different conclusions; argues for relations between patterns of causes and outcomes rather than correlating single variables; and criticizes the borrowing of psychological terms to describe brain evidence. Brain sites cannot be in a state of “fear.” A deeper understanding of the brain's contributions to behavior, the book argues, requires investigators to acknowledge these five constraints in the design or interpretation of an experiment.


2019 ◽  
Vol 14 (7) ◽  
pp. 709-718 ◽  
Author(s):  
Hannah U Nohlen ◽  
Frenk van Harreveld ◽  
William A Cunningham

Abstract In the current study, we used functional magnetic resonance imaging to investigate how the brain facilitates social judgments despite evaluatively conflicting information. Participants learned consistent (positive or negative) and ambivalent (positive and negative) person information and were then asked to provide binary judgments of these targets in situations that either resolved conflict by prioritizing a subset of information or not. Self-report, decision time and brain data confirm that integrating contextual information into our evaluations of objects or people allows for nuanced (social) evaluations. The same mixed trait information elicited or failed to elicit evaluative conflict dependent on the situation. Crucially, we provide data suggesting that negative judgments are easier and may be considered the ‘default’ action when experiencing evaluative conflict: weaker activation in dorsolateral prefrontal cortex during trials of evaluative conflict was related to a greater likelihood of unfavorable judgments, and greater activation was related to more favorable judgments. Since negative outcome consequences are arguably more detrimental and salient, this finding supports the idea that additional regulation and a more active selection process are necessary to override an initial negative response to evaluatively conflicting information.


2020 ◽  
Author(s):  
Bradley C. Love

Linking models and brain measures offers a number of advantages over standard analyses. Models that have been evaluated on previous datasets can provide theoretical constraints and assist in integrating findings across studies. Model-based analyses can be more sensitive and allow for evaluation of hypotheses that would not otherwise be addressable. For example, a cognitive model that is informed from several behavioural studies could be used to examine how multiple cognitive processes unfold across time in the brain. Models can be linked to brain measures in a number of ways. The information flow and constraints can be from model to brain, brain to model, or reciprocal. Likewise, the linkage from model and brain can be univariate or multivariate, as in studies that relate patterns of brain activity with model states. Models have multiple aspects that can be related to different facets of brain activity. This is well illustrated by deep learning models that have multiple layers or representations that can be aligned with different brain regions. Model-based approaches offer a lens on brain data that is complementary to popular multivariate decoding and representational similarity analysis approaches. Indeed, these approaches can realise greater theoretical significance when situated within a model-based approach.


2019 ◽  
Author(s):  
Ana F. Palenciano ◽  
Carlos González-García ◽  
Juan E. Arco ◽  
Luiz Pessoa ◽  
María Ruz

AbstractRecent multivariate analyses of brain data have boosted our understanding of the organizational principles that shape neural coding. However, most of this progress has focused on perceptual visual regions (Connolly et al., 2012), whereas far less is known about the organization of more abstract, action-oriented representations. In this study, we focused on humans’ remarkable ability to turn novel instructions into actions. While previous research shows that instruction encoding is tightly linked to proactive activations in fronto-parietal brain regions, little is known about the structure that orchestrates such anticipatory representation. We collected fMRI data while participants (both males and females) followed novel complex verbal rules that varied across control-related variables (integrating within/across stimuli dimensions, response complexity, target category) and reward expectations. Using Representational Similarity Analysis (Kriegeskorte et al., 2008) we explored where in the brain these variables explained the organization of novel task encoding, and whether motivation modulated these representational spaces. Instruction representations in the lateral prefrontal cortex were structured by the three control-related variables, while intraparietal sulcus encoded response complexity and the fusiform gyrus and precuneus organized its activity according to the relevant stimulus category. Reward exerted a general effect, increasing the representational similarity among different instructions, which was robustly correlated with behavioral improvements. Overall, our results highlight the flexibility of proactive task encoding, governed by distinct representational organizations in specific brain regions. They also stress the variability of motivation-control interactions, which appear to be highly dependent on task attributes such as complexity or novelty.Significance StatementIn comparison with other primates, humans display a remarkable success in novel task contexts thanks to our ability to transform instructions into effective actions. This skill is associated with proactive task-set reconfigurations in fronto-parietal cortices. It remains yet unknown, however, how the brain encodes in anticipation the flexible, rich repertoire of novel tasks that we can achieve. Here we explored cognitive control and motivation-related variables that might orchestrate the representational space for novel instructions. Our results showed that different dimensions become relevant for task prospective encoding depending on the brain region, and that the lateral prefrontal cortex simultaneously organized task representations following different control-related variables. Motivation exerted a general modulation upon this process, diminishing rather than increasing distances among instruction representations.


2019 ◽  
Author(s):  
Nikul H. Ukani ◽  
Chung-Heng Yeh ◽  
Adam Tomkins ◽  
Yiyin Zhou ◽  
Dorian Florescu ◽  
...  

AbstractThe fruit fly is a key model organism for studying the activity of interconnected brain circuits. A large scattered global research community of neurobiologists and neurogeneticists, computational and theoretical neuroscientists, and computer scientists and engineers has been developing a vast trove of experimental and modeling data that has yet to be distilled into new knowledge and understanding of the functional logic of the brain. Developing open shared models, modelling tools and data repositories that can be accessed from anywhere in the world is the necessary engine for accelerating our understanding of how the brain works.To that end we developed the Fruit Fly Brain Observatory (FFBO), the next generation open-source platform to support open, collaborative Drosophila neuroscience research. FFBO provides a (i) hub for storing and integrating fruit fly brain research data from multiple data sources worldwide, (ii) unified repository of tools and methods to build, emulate and compare fruit fly brain models in health and disease, and (iii) an open framework for fruit fly brain data processing and model execution. FFBO provides access to application tools for visualizing, configuring, simulating and analyzing computational models of brain circuits of the (i) cell type map, (ii) connectome, (iii) synaptome, and (iv) activity map using intuitive queries in plain English. Tools are provided to extract the function inherent in these structural maps. All applications can be accessed with any modern browser.


It is a well-known fact that all the Artificial Intelligence (AI)researches happening across multiple verticals such as Neuro Imaging, Computer Vision, Deep learning etc point to one master goal of modelling the human brain function by understanding how each part of the brain works. The Convolution neural network (CNN) is one of best deep architecture suitable to handle variety of inputs. In this paper we explore the different types of input data the CNN deep architecture can process and some of the CNN configuration changes that has proved good Accuracy. We have highlighted those specialized CNN architectures along with different types of data inputs they handle including the Functional Magnetic Resonance (fMRI) Neuro Image brain data input.


Radiology ◽  
1997 ◽  
Vol 202 (1) ◽  
pp. 41-46 ◽  
Author(s):  
N C Yue ◽  
W T Longstreth ◽  
A D Elster ◽  
C A Jungreis ◽  
D H O'Leary ◽  
...  

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