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Resting state motion correction

Falk Eippert, Yazhuo Kong, Mark Jenkinson, Irene Tracey, Jonathan C W Brooks
Functional magnetic resonance imaging (fMRI) of the human spinal cord is a difficult endeavour due to the cord's small cross-sectional diameter, signal drop-out as well as image distortion due to magnetic field inhomogeneity, and the confounding influence of physiological noise from cardiac and respiratory sources. Nevertheless, there is great interest in spinal fMRI due to the spinal cord's role as the principal sensorimotor interface between the brain and the body and its involvement in a variety of sensory and motor pathologies...
September 28, 2016: NeuroImage
Kees Hermans, Jan Casper de Munck, Rudolf Verdaasdonk, Paul Boon, Gunther Krausz, Robert Prueckl, Pauly Ossenblok
OBJECTIVE: Subtle motion of an epileptic patient examined with co-registered EEG and functional MRI (EEG-fMRI) may often lead to spurious fMRI activation patterns when true epileptic spikes are contaminated with motion artefacts. In recent years, methods relying on reference signals for correcting these subtle movements in the EEG have emerged. In this study, the performance of two reference-based devices are compared to the template-based method with regard to their ability to remove movement-related artifacts in EEG measured during scanning...
December 2016: IEEE Transactions on Bio-medical Engineering
Sharmishtaa Seshamani, Anna I Blazejewska, Susan Mckown, Jason Caucutt, Manjiri Dighe, Christopher Gatenby, Colin Studholme
Recently, there has been considerable interest, especially for in utero imaging, in the detection of functional connectivity in subjects whose motion cannot be controlled while in the MRI scanner. These cases require two advances over current studies: (1) multiecho acquisitions and (2) post processing and reconstruction that can deal with significant between slice motion during multislice protocols to allow for the ability to detect temporal correlations introduced by spatial scattering of slices into account...
November 2016: Human Brain Mapping
Jeffrey W Barker, Andrea L Rosso, Patrick J Sparto, Theodore J Huppert
Functional near-infrared spectroscopy (fNIRS) is a relatively low-cost, portable, noninvasive neuroimaging technique for measuring task-evoked hemodynamic changes in the brain. Because fNIRS can be applied to a wide range of populations, such as children or infants, and under a variety of study conditions, including those involving physical movement, gait, or balance, fNIRS data are often confounded by motion artifacts. Furthermore, the high sampling rate of fNIRS leads to high temporal autocorrelation due to systemic physiology...
July 2016: Neurophotonics
Carol Di Perri, Mohamed Ali Bahri, Enrico Amico, Aurore Thibaut, Lizette Heine, Georgios Antonopoulos, Vanessa Charland-Verville, Sarah Wannez, Francisco Gomez, Roland Hustinx, Luaba Tshibanda, Athena Demertzi, Andrea Soddu, Steven Laureys
BACKGROUND: Between pathologically impaired consciousness and normal consciousness exists a scarcely researched transition zone, referred to as emergence from minimally conscious state, in which patients regain the capacity for functional communication, object use, or both. We investigated neural correlates of consciousness in these patients compared with patients with disorders of consciousness and healthy controls, by multimodal imaging. METHODS: In this cross-sectional, multimodal imaging study, patients with unresponsive wakefulness syndrome, patients in a minimally conscious state, and patients who had emerged from a minimally conscious state, diagnosed with the Coma Recovery Scale-Revised, were recruited from the neurology department of the Centre Hospitalier Universitaire de Liège, Belgium...
July 2016: Lancet Neurology
Victor M Vergara, Eswar Damaraju, Andrew B Mayer, Robyn Miller, Mustafa S Cetin, Vince Calhoun
Traumatic brain injury (TBI) can adversely affect a person's thinking, memory, personality and behavior. For this reason new and better biomarkers are being investigated. Resting state functional network connectivity (rsFNC) derived from functional magnetic resonance (fMRI) imaging is emerging as a possible biomarker. One of the main concerns with this technique is the appropriateness of methods used to correct for subject movement. In this work we used 50 mild TBI patients and matched healthy controls to explore the outcomes obtained from different fMRI data preprocessing...
2015: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Scott Marek, Kai Hwang, William Foran, Michael N Hallquist, Beatriz Luna
Cognitive control, which continues to mature throughout adolescence, is supported by the ability for well-defined organized brain networks to flexibly integrate information. However, the development of intrinsic brain network organization and its relationship to observed improvements in cognitive control are not well understood. In the present study, we used resting state functional magnetic resonance imaging (RS-fMRI), graph theory, the antisaccade task, and rigorous head motion control to characterize and relate developmental changes in network organization, connectivity strength, and integration to inhibitory control development...
December 2015: PLoS Biology
Kazuo Maeda
AIMS: To enable scientific studies on fetal movements and its relation to fetal heart rate directly detecting fetal chest motion with ultrasonic Doppler method. METHODS: A prototype of an ultrasonic Doppler fetal actocardiograph (ACG) was designed and handmade by the author. A 2 MHz ultrasound fetal heart rate (FHR) monitor was remodeled to detect fetal heartbeat and chest movement Doppler signals with a single ultrasound probe. The fetal movement Doppler signal was 20-50 Hz using 2 MHz ultrasound, separated from the fetal heartbeat Doppler signal, which was 100 or more Hz and sent to the autocorrelation FHR meter to record FHR curve, while fetal movement Doppler signals were detected through 20-80 Hz band-pass filter, and changed to spikes recorded on a cardiotocography chart...
January 2016: Journal of Obstetrics and Gynaecology Research
Masami Goto, Osamu Abe, Tosiaki Miyati, Hidenori Yamasue, Tsutomu Gomi, Tohoru Takeda
Resting-state functional magnetic resonance imaging (RS-fMRI) is used to investigate brain functional connectivity at rest. However, noise from human physiological motion is an unresolved problem associated with this technique. Following the unexpected previous result that group differences in head motion between control and patient groups caused group differences in the resting-state network with RS-fMRI, we reviewed the effects of human physiological noise caused by subject motion, especially motion of the head, on functional connectivity at rest detected with RS-fMRI...
2016: Magnetic Resonance in Medical Sciences: MRMS
Zsuzsika Sjoerds, Steven M Stufflebeam, Dick J Veltman, Wim Van den Brink, Brenda W J H Penninx, Linda Douw
Alcohol dependence (AD) is characterized by corticostriatal impairments in individual brain areas such as the striatum. As yet however, complex brain network topology in AD and its association with disease progression are unknown. We applied graph theory to resting-state functional magnetic resonance imaging (RS-fMRI) to examine weighted global efficiency and local (clustering coefficient, degree and eigenvector centrality) network topology and the functional role of the striatum in 24 AD patients compared with 20 matched healthy controls (HCs), and their association with dependence characteristics...
December 22, 2015: Addiction Biology
Shuo Chen, Jian Kang, Guoqing Wang
Functional connectivity analysis using resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a powerful technique for investigating functional brain networks. The functional connectivity is often quantified by statistical metrics (e.g., Pearson correlation coefficient), which may be affected by many image acquisition and preprocessing steps such as the head motion correction and the global signal regression. The appropriate quantification of the connectivity metrics is essential for meaningful and reproducible scientific findings...
2015: Frontiers in Neuroscience
Tim M Tierney, Louise J Weiss-Croft, Maria Centeno, Elhum A Shamshiri, Suejen Perani, Torsten Baldeweg, Christopher A Clark, David W Carmichael
Different noise sources in fMRI acquisition can lead to spurious false positives and reduced sensitivity. We have developed a biophysically-based model (named FIACH: Functional Image Artefact Correction Heuristic) which extends current retrospective noise control methods in fMRI. FIACH can be applied to both General Linear Model (GLM) and resting state functional connectivity MRI (rs-fcMRI) studies. FIACH is a two-step procedure involving the identification and correction of non-physiological large amplitude temporal signal changes and spatial regions of high temporal instability...
January 1, 2016: NeuroImage
Bernadet L Klaassens, Helene C van Gorsel, Najmeh Khalili-Mahani, Jeroen van der Grond, Bradley T Wyman, Brandon Whitcher, Serge A R B Rombouts, Joop M A van Gerven
The serotonergic system is widely distributed throughout the central nervous system. It is well known as a mood regulating system, although it also contributes to many other functions. With resting state functional magnetic resonance imaging (RS-fMRI) it is possible to investigate whole brain functional connectivity. We used this non-invasive neuroimaging technique to measure acute pharmacological effects of the selective serotonin reuptake inhibitor sertraline (75 mg) in 12 healthy volunteers. In this randomized, double blind, placebo-controlled, crossover study, RS-fMRI scans were repeatedly acquired during both visits (at baseline and 3, 5, 7 and 9h after administering sertraline or placebo)...
November 15, 2015: NeuroImage
Leonardo Cerliani, Maarten Mennes, Rajat M Thomas, Adriana Di Martino, Marc Thioux, Christian Keysers
IMPORTANCE: Individuals with autism spectrum disorder (ASD) exhibit severe difficulties in social interaction, motor coordination, behavioral flexibility, and atypical sensory processing, with considerable interindividual variability. This heterogeneous set of symptoms recently led to investigating the presence of abnormalities in the interaction across large-scale brain networks. To date, studies have focused either on constrained sets of brain regions or whole-brain analysis, rather than focusing on the interaction between brain networks...
August 2015: JAMA Psychiatry
Molly G Bright, Kevin Murphy
Noise correction is a critical step towards accurate mapping of resting state BOLD fMRI connectivity. Noise sources related to head motion or physiology are typically modelled by nuisance regressors, and a generalised linear model is applied to regress out the associated signal variance. In this study, we use independent component analysis (ICA) to characterise the data variance typically discarded in this pre-processing stage in a cohort of 12 healthy volunteers. The signal variance removed by 24, 12, 6, or only 3 head motion parameters demonstrated network structure typically associated with functional connectivity, and certain networks were discernable in the variance extracted by as few as 2 physiologic regressors...
July 1, 2015: NeuroImage
Raimon H R Pruim, Maarten Mennes, Jan K Buitelaar, Christian F Beckmann
We proposed ICA-AROMA as a strategy for the removal of motion-related artifacts from fMRI data (Pruim et al., 2015). ICA-AROMA automatically identifies and subsequently removes data-driven derived components that represent motion-related artifacts. Here we present an extensive evaluation of ICA-AROMA by comparing our strategy to a range of alternative strategies for motion-related artifact removal: (i) no secondary motion correction, (ii) extensive nuisance regression utilizing 6 or (iii) 24 realignment parameters, (iv) spike regression (Satterthwaite et al...
May 15, 2015: NeuroImage
Antonio M Chiarelli, Edward L Maclin, Monica Fabiani, Gabriele Gratton
Movements are a major source of artifacts in functional Near-Infrared Spectroscopy (fNIRS). Several algorithms have been developed for motion artifact correction of fNIRS data, including Principal Component Analysis (PCA), targeted Principal Component Analysis (tPCA), Spline Interpolation (SI), and Wavelet Filtering (WF). WF is based on removing wavelets with coefficients deemed to be outliers based on their standardized scores, and it has proven to be effective on both synthetized and real data. However, when the SNR is high, it can lead to a reduction of signal amplitude...
May 15, 2015: NeuroImage
Jonathan D Power, Bradley L Schlaggar, Steven E Petersen
The purpose of this review is to communicate and synthesize recent findings related to motion artifact in resting state fMRI. In 2011, three groups reported that small head movements produced spurious but structured noise in brain scans, causing distance-dependent changes in signal correlations. This finding has prompted both methods development and the re-examination of prior findings with more stringent motion correction. Since 2011, over a dozen papers have been published specifically on motion artifact in resting state fMRI...
January 15, 2015: NeuroImage
András Jakab, Ernst Schwartz, Gregor Kasprian, Gerlinde M Gruber, Daniela Prayer, Veronika Schöpf, Georg Langs
The functional connectivity architecture of the adult human brain enables complex cognitive processes, and exhibits a remarkably complex structure shared across individuals. We are only beginning to understand its heterogeneous structure, ranging from a strongly hierarchical organization in sensorimotor areas to widely distributed networks in areas such as the parieto-frontal cortex. Our study relied on the functional magnetic resonance imaging (fMRI) data of 32 fetuses with no detectable morphological abnormalities...
2014: Frontiers in Human Neuroscience
Yisheng Xu, Yunxia Tong, Siyuan Liu, Ho Ming Chow, Nuria Y AbdulSabur, Govind S Mattay, Allen R Braun
A comprehensive set of methods based on spatial independent component analysis (sICA) is presented as a robust technique for artifact removal, applicable to a broad range of functional magnetic resonance imaging (fMRI) experiments that have been plagued by motion-related artifacts. Although the applications of sICA for fMRI denoising have been studied previously, three fundamental elements of this approach have not been established as follows: 1) a mechanistically-based ground truth for component classification; 2) a general framework for evaluating the performance and generalizability of automated classifiers; and 3) a reliable method for validating the effectiveness of denoising...
December 2014: NeuroImage
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