Author
Khaled Restom
Bio: Khaled Restom is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Cerebral blood flow & Communication noise. The author has an hindex of 9, co-authored 9 publications receiving 3372 citations.
Papers
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TL;DR: A component based method for the reduction of noise in both blood oxygenation level-dependent (BOLD) and perfusion-based functional magnetic resonance imaging (fMRI) data is presented and the temporal standard deviation of resting-state perfusion and BOLD data in gray matter regions was significantly reduced.
3,370 citations
TL;DR: It is found that simply closing the eyes creates a large physiological deactivation in the visual cortex, and provides a robust paradigm for studying baseline effects in fMRI, which accounts for the changes in OEF seen following baseline changes.
243 citations
TL;DR: The blood oxygenation level-dependent responses to visual stimuli, using both a 1-s long single trial stimulus and a 20-slong block stimulus, were measured in a 4-T magnetic field both before and immediately after a 200-mg caffeine dose.
121 citations
TL;DR: Three methods for the reduction of physiological noise in arterial spin labeling (ASL) functional magnetic resonance imaging (fMRI) are presented and compared and should be particularly useful for ASL studies of cognitive processes where the intrinsic signal to noise ratio is typically lower than for studies of primary sensory regions.
108 citations
TL;DR: The application of ASL fMRI is demonstrated to obtain measures of the CBF and BOLD responses to the encoding of natural scenes in healthy young and elderly adults, consistent with an age-related increase in the cerebral metabolic rate of oxygen metabolism response to memory encoding.
103 citations
Cited by
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TL;DR: The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fc MRI measures.
Abstract: Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn (www.nitrc.org/projects/conn) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method all...
3,388 citations
TL;DR: A component based method for the reduction of noise in both blood oxygenation level-dependent (BOLD) and perfusion-based functional magnetic resonance imaging (fMRI) data is presented and the temporal standard deviation of resting-state perfusion and BOLD data in gray matter regions was significantly reduced.
3,370 citations
University of Western Ontario1, York University2, University of Bergen3, The Mind Research Network4, National Institutes of Health5, University of New Mexico6, Washington University in St. Louis7, University of Chieti-Pescara8, Stanford University9, Georgia Institute of Technology10, Oulu University Hospital11, Indiana University12, Leibniz Institute for Neurobiology13, Otto-von-Guericke University Magdeburg14
TL;DR: Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain.
2,332 citations
TL;DR: The newly developed toolbox, DPABI, which was evolved from REST and DPARSF is introduced, designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies.
Abstract: Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.
2,179 citations
New York University1, Nathan Kline Institute for Psychiatric Research2, MIND Institute3, Katholieke Universiteit Leuven4, University of Utah5, Yale University6, University of California, Los Angeles7, Massachusetts Institute of Technology8, Trinity College, Dublin9, Carnegie Mellon University10, Ben-Gurion University of the Negev11, Ludwig Maximilian University of Munich12, Oregon Health & Science University13, Indiana University14, California Institute of Technology15, San Diego State University16, Netherlands Institute for Neuroscience17, University of Groningen18, University of Wisconsin-Madison19, Cornell University20, University of Pittsburgh21, Stanford University22, University of Michigan23, Kennedy Krieger Institute24, Johns Hopkins University25
TL;DR: W Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity.
Abstract: Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)-a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7-64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.
1,939 citations