scholarly journals Attentional reorientation along the meridians of the visual field: are there different neural mechanisms at play?

2019 ◽  
Author(s):  
Simon R. Steinkamp ◽  
Simone Vossel ◽  
Gereon R. Fink ◽  
Ralph Weidner
Keyword(s):  
Effective Connectivity ◽  
Target Position ◽  
Causal Modeling ◽  
Orientation Discrimination ◽  
Behavioral Measures ◽  
Directional Tuning ◽  
Vertical Meridian ◽  
Attention Networks ◽  
Predictive Approaches ◽  
The Brain

AbstractHemispatial neglect, after unilateral lesions to parietal brain areas, is characterized by an inability to respond to unexpected stimuli in contralesional space. As the visual field’s horizontal meridian is most severely affected, the brain networks controlling visuospatial processes might be tuned explicitly to this axis. We investigated such a potential directional tuning in the dorsal and ventral frontoparietal attention networks, with a particular focus on attentional reorientation. We used an orientation-discrimination task where a spatial pre-cue indicated the target position with 80% validity. Healthy participants (n = 29) performed this task in two runs and were required to (re-)orient attention either only along the horizontal or the vertical meridian, while fMRI and behavioral measures were recorded. By using a General Linear Model for behavioral and fMRI data, Dynamic Causal Modeling for effective connectivity, and other predictive approaches, we found strong statistical evidence for a reorientation effect for horizontal and vertical runs. However, neither neural nor behavioral measures differed between vertical and horizontal reorienting. Moreover, models from one run successfully predicted the cueing condition in the respective other run. Our results suggest that activations in the dorsal and ventral attention networks represent higher-order cognitive processes related to spatial attentional (re-)orientating that are independent of directional tuning.

scholarly journals Quantification of Effective Connectivity in the Brain Using a Measure of Directed Information

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ying Liu ◽  
Selin Aviyente
Keyword(s):  
Granger Causality ◽  
Linear Models ◽  
Effective Connectivity ◽  
Causal Modeling ◽  
Detection Methods ◽  
Information Theoretic ◽  
Model Based ◽  
Model Free ◽  
Directed Information ◽  
The Brain

Effective connectivity refers to the influence one neural system exerts on another and corresponds to the parameter of a model that tries to explain the observed dependencies. In this sense, effective connectivity corresponds to the intuitive notion of coupling or directed causal influence. Traditional measures to quantify the effective connectivity include model-based methods, such as dynamic causal modeling (DCM), Granger causality (GC), and information-theoretic methods. Directed information (DI) has been a recently proposed information-theoretic measure that captures the causality between two time series. Compared to traditional causality detection methods based on linear models, directed information is a model-free measure and can detect both linear and nonlinear causality relationships. However, the effectiveness of using DI for capturing the causality in different models and neurophysiological data has not been thoroughly illustrated to date. In addition, the advantage of DI compared to model-based measures, especially those used to implement Granger causality, has not been fully investigated. In this paper, we address these issues by evaluating the performance of directed information on both simulated data sets and electroencephalogram (EEG) data to illustrate its effectiveness for quantifying the effective connectivity in the brain.

THE EFFECTIVE CONNECTIVITY BETWEEN THE TWO PRIMARY MOTOR AREAS IN THE BRAIN DURING BILATERAL TAPPING OF HAND FINGERS

2012 ◽  
Vol 09 ◽  
pp. 398-405
Author(s):  
A. N. YUSOFF ◽  
K. A. HAMID
Keyword(s):  
Causal Model ◽  
Right Hemisphere ◽  
Effective Connectivity ◽  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Male And Female ◽  
Linear Behavior ◽  
Left And Right ◽  
Dynamic Causal Model ◽  
The Brain

Dynamic causal modeling (DCM) was implemented on datasets obtained from an externally-triggered finger tapping functional MRI experiment performed by 5 male and female subjects. The objective was to model the effective connectivity between two significantly activated primary motor regions (M1). The left and right hemisphere M1s are found to be effectively and bidirectionally connected to each other. Both connections are modulated by the stimulus-free contextual input. These connectivities are however not gated (influenced) by any of the two M1s, ruling out the possibility of the non-linear behavior of connections between both M1s. A dynamic causal model was finally suggested.

scholarly journals Reliability of Dynamic Causal Modeling using the Statistical Parametric Mapping Toolbox

2014 ◽  
Vol 3 (2) ◽  
pp. 1-16
Author(s):  
Pegah T. Hosseini ◽  
Shouyan Wang ◽  
Julie Brinton ◽  
Steven Bell ◽  
David M. Simpson
Keyword(s):  
Operating Systems ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Statistical Parametric Mapping ◽  
Causal Modeling ◽  
Current Work ◽  
Dynamic Causal Modeling ◽  
Somatosensory Stimulation ◽  
Parametric Mapping ◽  
The Brain

Dynamic causal modeling (DCM) is a recently developed approach for effective connectivity measurement in the brain. It has attracted considerable attention in recent years and quite widespread used to investigate brain connectivity in response to different tasks as well as auditory, visual, and somatosensory stimulation. This method uses complex algorithms, and currently the only implementation available is the Statistical Parametric Mapping (SPM8) toolbox with functionality for use on EEG and fMRI. The objective of the current work is to test the robustness of the toolbox when applied to EEG, by comparing results obtained from various versions of the software and operating systems when using identical datasets. Contrary to expectations, it was found that estimated connectivities were not consistent between different operating systems, the version of SPM8, or the version of MATLAB being used. The exact cause of this problem is not clear, but may relate to the high number of parameters in the model. Caution is thus recommended when interpreting the results of DCM estimated with the SPM8 software.

scholarly journals Dynamic Causal Modeling of the Response to Frequency Deviants

2009 ◽  
Vol 101 (5) ◽  
pp. 2620-2631 ◽  
Author(s):  
Marta I. Garrido ◽  
James M. Kilner ◽  
Stefan J. Kiebel ◽  
Karl J. Friston
Keyword(s):  
Mismatch Negativity ◽  
Sensory Input ◽  
Model Comparison ◽  
Memory Trace ◽  
Effective Connectivity ◽  
Predictive Coding ◽  
Causal Modeling ◽  
Evoked Responses ◽  
Dynamic Causal Modeling ◽  
The Brain

This article describes the use of dynamic causal modeling to test hypotheses about the genesis of evoked responses. Specifically, we consider the mismatch negativity (MMN), a well-characterized response to deviant sounds and one of the most widely studied evoked responses. There have been several mechanistic accounts of how the MMN might arise. It has been suggested that the MMN results from a comparison between sensory input and a memory trace of previous input, although others have argued that local adaptation, due to stimulus repetition, is sufficient to explain the MMN. Thus the precise mechanisms underlying the generation of the MMN remain unclear. This study tests some biologically plausible spatiotemporal dipole models that rest on changes in extrinsic top-down connections (that enable comparison) and intrinsic changes (that model adaptation). Dynamic causal modeling suggested that responses to deviants are best explained by changes in effective connectivity both within and between cortical sources in a hierarchical network of distributed sources. Our model comparison suggests that both adaptation and memory comparison operate in concert to produce the early (N1 enhancement) and late (MMN) parts of the response to frequency deviants. We consider these mechanisms in the light of predictive coding and hierarchical inference in the brain.

Behavioral measures and EEG monitoring using the Brain Symmetry Index during the Wada test in children

2012 ◽  
Vol 23 (3) ◽  
pp. 247-253 ◽  
Author(s):  
Jurriaan M. Peters ◽  
Meritxell Tomas-Fernandez ◽  
Michel J.A.M. van Putten ◽  
Tobias Loddenkemper
Keyword(s):  
Eeg Monitoring ◽  
Wada Test ◽  
Behavioral Measures ◽  
Symmetry Index ◽  
The Brain

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nader Moharamzadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Effective Connectivity ◽  
Multivariate Time Series ◽  
Inference System ◽  
Eeg Data ◽  
Adaptive Neurofuzzy Inference System ◽  
Intracranial Electroencephalography ◽  
Fuzzy Network ◽  
The Brain

Abstract The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

scholarly journals Social decision-making in the brain: Input-state-output modelling reveals patterns of effective connectivity underlying reciprocal choices

2018 ◽  
Vol 40 (2) ◽  
pp. 699-712 ◽  
Author(s):  
Daniel Shaw ◽  
Kristína Czekóová ◽  
Martin Gajdoš ◽  
Rostislav Staněk ◽  
Jiří Špalek ◽  
...  
Keyword(s):  
Decision Making ◽  
Effective Connectivity ◽  
Input State ◽  
Social Decision ◽  
Social Decision Making ◽  
The Brain

Direct Causal Networks for the Study of Transcranial Magnetic Stimulation Effects on Focal Epileptiform Discharges

2015 ◽  
Vol 25 (05) ◽  
pp. 1550006 ◽  
Author(s):  
Dimitris Kugiumtzis ◽  
Vasilios K. Kimiskidis
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Network Structure ◽  
Magnetic Stimulation ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Epileptic Focus ◽  
Sham Stimulation ◽  
Focal Seizures ◽  
Epileptiform Discharges ◽  
The Brain

Background: Transcranial magnetic stimulation (TMS) can have inhibitory effects on epileptiform discharges (EDs) of patients with focal seizures. However, the brain connectivity before, during and after EDs, with or without the administration of TMS, has not been extensively explored. Objective: To investigate the brain network of effective connectivity during ED with and without TMS in patients with focal seizures. Methods: For the effective connectivity a direct causality measure is applied termed partial mutual information from mixed embedding (PMIME). TMS-EEG data from two patients with focal seizures were analyzed. Each EEG record contained a number of EDs in the majority of which TMS was administered over the epileptic focus. As a control condition, sham stimulation over the epileptogenic zone or real TMS at a distance from the epileptic focus was also performed. The change in brain connectivity structure was investigated from the causal networks formed at each sliding window. Conclusion: The PMIME could detect distinct changes in the network structure before, within, and after ED. The administration of real TMS over the epileptic focus, in contrast to sham stimulation, terminated the ED prematurely in a node-specific manner and regained the network structure as if it would have terminated spontaneously.

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