scholarly journals A Novel Spatial-Spectra Dynamics-Based Ranking Model for Sorting Time-Varying Functional Networks from Single Subject FMRI Data

2018 ◽  
pp. 431-441
Author(s):  
Nizhuan Wang ◽  
Hongjie Yan ◽  
Yang Yang ◽  
Ruiyang Ge
Keyword(s):  
Fmri Data ◽  
Functional Networks ◽  
Single Subject ◽  
Time Varying ◽  
Sorting Time ◽  
Ranking Model ◽  
Spatial Spectra

Functional Anatomy of Intensity Aspects of Attention

2003 ◽  
Vol 14 (3) ◽  
pp. 181-190 ◽  
Author(s):  
Walter Sturm
Keyword(s):  
Right Hemisphere ◽  
Functional Anatomy ◽  
Fmri Data ◽  
Functional Networks ◽  
Top Down ◽  
Stimulus Modality ◽  
Spatial Orienting ◽  
Phasic Alertness ◽  
Co Activation ◽  
Attention System

Abstract: Behavioral and PET/fMRI-data are presented to delineate the functional networks subserving alertness, sustained attention, and vigilance as different aspects of attention intensity. The data suggest that a mostly right-hemisphere frontal, parietal, thalamic, and brainstem network plays an important role in the regulation of attention intensity, irrespective of stimulus modality. Under conditions of phasic alertness there is less right frontal activation reflecting a diminished need for top-down regulation with phasic extrinsic stimulation. Furthermore, a high overlap between the functional networks for alerting and spatial orienting of attention is demonstrated. These findings support the hypothesis of a co-activation of the posterior attention system involved in spatial orienting by the anterior alerting network. Possible implications of these findings for the therapy of neglect are proposed.

scholarly journals Estimating dynamic brain functional networks using multi-subject fMRI data

NeuroImage ◽  
2018 ◽  
Vol 183 ◽  
pp. 635-649 ◽  
Author(s):  
Suprateek Kundu ◽  
Jin Ming ◽  
Jordan Pierce ◽  
Jennifer McDowell ◽  
Ying Guo
Keyword(s):  
Fmri Data ◽  
Functional Networks ◽  
Brain Functional Networks

Redeployed functions versus spreading activation: A potential confound

2010 ◽  
Vol 33 (4) ◽  
pp. 280-281 ◽  
Author(s):  
Colin Klein
Keyword(s):  
Information Processing ◽  
Meta Analysis ◽  
Fmri Data ◽  
Functional Networks ◽  
Spreading Activation ◽  
Adaptation Studies ◽  
Per Se ◽  
Fmri Adaptation ◽  
Potential Confound

AbstractAnderson's meta-analysis of fMRI data is subject to a potential confound. Areas identified as active may make no functional contribution to the task being studied, or may indicate regions involved in the coordination of functional networks rather than information processing per se. I suggest a way in which fMRI adaptation studies might provide a useful test between these alternatives.

scholarly journals Select and Cluster: A Method for Finding Functional Networks of Clustered Voxels in fMRI

2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Danilo DonGiovanni ◽  
Lucia Maria Vaina
Keyword(s):  
Functional Connectivity ◽  
A Priori ◽  
Data Driven ◽  
Fmri Data ◽  
Support Vector ◽  
Functional Networks ◽  
Compact Groups ◽  
Mutual Correlation ◽  
Pairwise Correlation ◽  
Vector Clustering

Extracting functional connectivity patterns among cortical regions in fMRI datasets is a challenge stimulating the development of effective data-driven or model based techniques. Here, we present a novel data-driven method for the extraction of significantly connected functional ROIs directly from the preprocessed fMRI data without relying on a priori knowledge of the expected activations. This method finds spatially compact groups of voxels which show a homogeneous pattern of significant connectivity with other regions in the brain. The method, called Select and Cluster (S&C), consists of two steps: first, a dimensionality reduction step based on a blind multiresolution pairwise correlation by which the subset of all cortical voxels with significant mutual correlation is selected and the second step in which the selected voxels are grouped into spatially compact and functionally homogeneous ROIs by means of a Support Vector Clustering (SVC) algorithm. The S&C method is described in detail. Its performance assessed on simulated and experimental fMRI data is compared to other methods commonly used in functional connectivity analyses, such as Independent Component Analysis (ICA) or clustering. S&C method simplifies the extraction of functional networks in fMRI by identifying automatically spatially compact groups of voxels (ROIs) involved in whole brain scale activation networks.

scholarly journals Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks

2018 ◽  
Vol 28 (04) ◽  
pp. 1750051 ◽  
Author(s):  
Christoph Schmidt ◽  
Diana Piper ◽  
Britta Pester ◽  
Andreas Mierau ◽  
Herbert Witte
Keyword(s):  
Time Windows ◽  
Quality Criteria ◽  
Brain Regions ◽  
Functional Networks ◽  
Edge Weight ◽  
Module Structure ◽  
Time Varying ◽  
Interaction Patterns ◽  
Brain Functional Networks ◽  
Network Modules

Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework’s potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.

scholarly journals ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data

2021 ◽  
Author(s):  
Lea Waller ◽  
Susanne Erk ◽  
Elena Pozzi ◽  
Yara J. Toenders ◽  
Courtney C. Haswell ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Resting State ◽  
Low Frequency ◽  
Fmri Data ◽  
Single Subject ◽  
Major Advance ◽  
Group Level ◽  
Imaging Data ◽  
Post Processing ◽  
Individual Subject

The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe (Harmonized AnaLysis of Functional MRI pipeline), an open-source, containerized, user-friendly tool that facilitates reproducible analysis of task-based and resting-state fMRI data through uniform application of preprocessing, quality assessment, single-subject feature extraction, and group-level statistics. It provides state-of-the-art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to assess the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post-processing functions at the individual subject level, including calculation of task-based activation, seed-based connectivity, network-template (or dual) regression, atlas-based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed-effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post-processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at https://github.com/HALFpipe/HALFpipe.

scholarly journals The identification of temporal communities through trajectory clustering correlates with single-trial behavioural fluctuations in neuroimaging data

2019 ◽  
Author(s):  
William Hedley Thompson ◽  
Jessey Wright ◽  
James M. Shine ◽  
Russell A. Poldrack
Keyword(s):  
Time Series Data ◽  
Dynamic Network ◽  
Fmri Data ◽  
Series Data ◽  
Time Varying ◽  
Trajectory Clustering ◽  
Single Trial ◽  
Neuroimaging Data ◽  
New Perspective ◽  
Static Connectivity

AbstractInteracting sets of nodes and fluctuations in their interaction are important properties of a dynamic network system. In some cases the edges reflecting these interactions are directly quantifiable from the data collected. However, in many cases (such as functional magnetic resonance imaging (fMRI) data), the edges must be inferred from statistical relations between the nodes. Here we present a new method, Temporal Communities through Trajectory Clustering (TCTC), that derives time-varying communities directly from time-series data collected from the nodes in a network. First, we verify TCTC on resting and task fMRI data by showing that time-averaged results correspond with expected static connectivity results. We then show that the time-varying communities correlate and predict single-trial behaviour. This new perspective on temporal community detection of node-collected data identifies robust communities revealing ongoing spatiotemporal community configurations during task performance.

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