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independent component analysis fMRI

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https://www.readbyqxmd.com/read/28334131/pca-leverage-outlier-detection-for-high-dimensional-functional-magnetic-resonance-imaging-data
#1
Amanda F Mejia, Mary Beth Nebel, Ani Eloyan, Brian Caffo, Martin A Lindquist
Outlier detection for high-dimensional (HD) data is a popular topic in modern statistical research. However, one source of HD data that has received relatively little attention is functional magnetic resonance images (fMRI), which consists of hundreds of thousands of measurements sampled at hundreds of time points. At a time when the availability of fMRI data is rapidly growing-primarily through large, publicly available grassroots datasets-automated quality control and outlier detection methods are greatly needed...
February 27, 2017: Biostatistics
https://www.readbyqxmd.com/read/28333636/sequential-dictionary-learning-from-correlated-data-application-to-fmri-data-analysis
#2
Abd-Krim Seghouane, Asif Iqbal
Sequential dictionary learning via the K-SVD algorithm has been revealed as a successful alternative to conventional data driven methods such as independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data analysis. fMRI datasets are however structured data matrices with notions of spatio-temporal correlation and temporal smoothness. This prior information has not been included in the K-SVD algorithm when applied to fMRI data analysis. In this paper we propose three variants of the K-SVD algorithm dedicated to fMRI data analysis by accounting for this prior information...
March 22, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28325028/tracking-intrinsic-connectivity-brain-network-features-during-successive-pseudo-resting-states-and-interoceptive-task-fmri
#3
Behnaz Jarrahi, Dante Mantini
Advanced multivariate analyses of functional magnetic resonance imaging (fMRI) data based on blood oxygen level-dependent (BOLD) contras have revealed that the human brain organizes its activities into multiple intrinsic connectivity networks (ICNs). Several fMRI studies have evaluated the modulations of these networks during different cognitive or emotional tasks using blind source separation techniques particularly the independent component analysis (ICA). In this exploratory study, we applied ICA methodology to examine ICN modulations under different interoceptive conditions...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28325027/identifying-the-effects-of-visceral-interoception-on-human-brain-connectome-a-multivariate-analysis-of-covariance-of-fmri-data
#4
Behnaz Jarrahi, Dante Mantini
Sources of variations in the neural circuitry of the human brain and interrelationship between intrinsic connectivity networks (ICNs) are still a matter of debate and ongoing research. Here, we applied a multivariate analysis of covariance (MANCOVA) based on high-dimensional independent component analysis (ICA) to identify the effects of interoception and related variables on human brain connectome. Fifteen healthy right-handed subjects (all females, age range 21 - 48 years; mean age = 30.3, SD = 8.7 years) underwent a blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) that included continuous intravesical saline infusion and drainage...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28322859/decoding-the-encoding-of-functional-brain-networks-an-fmri-classification-comparison-of-non-negative-matrix-factorization-nmf-independent-component-analysis-ica-and-sparse-coding-algorithms
#5
Jianwen Xie, Pamela K Douglas, Ying Nian Wu, Arthur L Brody, Ariana E Anderson
BACKGROUND: Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative Matrix Factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks...
March 17, 2017: Journal of Neuroscience Methods
https://www.readbyqxmd.com/read/28295280/structural-and-functional-connectivity-in-children-and-adolescents-with-and-without-attention-deficit-hyperactivity-disorder
#6
Dienke J Bos, Bob Oranje, Michelle Achterberg, Chantal Vlaskamp, Sara Ambrosino, Marcel A de Reus, Martijn P van den Heuvel, Serge A R B Rombouts, Sarah Durston
BACKGROUND: Attention deficit/hyperactivity disorder (ADHD) has frequently been associated with changes in resting-state functional connectivity, and decreased white matter (WM) integrity. In the current study, we investigated functional connectivity within Default Mode and frontal control resting-state networks (RSNs) in children with and without ADHD. We hypothesized the RSNs of interest would show a pattern of impaired functional integration and segregation and corresponding changes in WM structure...
March 10, 2017: Journal of Child Psychology and Psychiatry, and Allied Disciplines
https://www.readbyqxmd.com/read/28294459/identifying-dynamic-functional-connectivity-biomarkers-using-gig-ica-application-to-schizophrenia-schizoaffective-disorder-and-psychotic-bipolar-disorder
#7
Yuhui Du, Godfrey D Pearlson, Dongdong Lin, Jing Sui, Jiayu Chen, Mustafa Salman, Carol A Tamminga, Elena I Ivleva, John A Sweeney, Matcheri S Keshavan, Brett A Clementz, Juan Bustillo, Vince D Calhoun
Functional magnetic resonance imaging (fMRI) studies have shown altered brain dynamic functional connectivity (DFC) in mental disorders. Here, we aim to explore DFC across a spectrum of symptomatically-related disorders including bipolar disorder with psychosis (BPP), schizoaffective disorder (SAD), and schizophrenia (SZ). We introduce a group information guided independent component analysis procedure to estimate both group-level and subject-specific connectivity states from DFC. Using resting-state fMRI data of 238 healthy controls (HCs), 140 BPP, 132 SAD, and 113 SZ patients, we identified measures differentiating groups from the whole-brain DFC and traditional static functional connectivity (SFC), separately...
March 10, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28293468/a-method-for-independent-component-graph-analysis-of-resting-state-fmri
#8
Demetrius Ribeiro de Paula, Erik Ziegler, Pubuditha M Abeyasinghe, Tushar K Das, Carlo Cavaliere, Marco Aiello, Lizette Heine, Carol di Perri, Athena Demertzi, Quentin Noirhomme, Vanessa Charland-Verville, Audrey Vanhaudenhuyse, Johan Stender, Francisco Gomez, Jean-Flory L Tshibanda, Steven Laureys, Adrian M Owen, Andrea Soddu
INTRODUCTION: Independent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non-contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data. OBJECTIVE: Here, we detail a graph building technique that allows these ICNs to be analyzed with graph theory...
March 2017: Brain and Behavior
https://www.readbyqxmd.com/read/28290075/audio-visual-speech-perception-in-adult-readers-with-dyslexia-an-fmri-study
#9
Jascha Rüsseler, Zheng Ye, Ivonne Gerth, Gregor R Szycik, Thomas F Münte
Developmental dyslexia is a specific deficit in reading and spelling that often persists into adulthood. In the present study, we used slow event-related fMRI and independent component analysis to identify brain networks involved in perception of audio-visual speech in a group of adult readers with dyslexia (RD) and a group of fluent readers (FR). Participants saw a video of a female speaker saying a disyllabic word. In the congruent condition, audio and video input were identical whereas in the incongruent condition, the two inputs differed...
March 13, 2017: Brain Imaging and Behavior
https://www.readbyqxmd.com/read/28289811/caffeine-impact-on-working-memory-related-network-activation-patterns-in-early-stages-of-cognitive-decline
#10
Sven Haller, Marie-Louise Montandon, Cristelle Rodriguez, Dominik Moser, Simona Toma, Jeremy Hofmeister, Panteleimon Giannakopoulos
PURPOSE: Recent evidence indicates that caffeine may have a beneficial effect on cognitive decline and dementia. The current investigation assessed the effect of acute caffeine administration on working memory during the earliest stage of cognitive decline in elderly participants. METHODS: The study includes consecutive 45 elderly controls and 18 individuals with mild cognitive impairment (MCI, 71.6 ± 4.7 years, 7 females). During neuropsychological follow-up at 18 months, 24 controls remained stable (sCON, 70...
March 13, 2017: Neuroradiology
https://www.readbyqxmd.com/read/28278208/parallel-group-independent-component-analysis-for-massive-fmri-data-sets
#11
Shaojie Chen, Lei Huang, Huitong Qiu, Mary Beth Nebel, Stewart H Mostofsky, James J Pekar, Martin A Lindquist, Ani Eloyan, Brian S Caffo
Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA)...
2017: PloS One
https://www.readbyqxmd.com/read/28249758/gaussian-process-based-independent-analysis-for-temporal-source-separation-in-fmri
#12
Ditte Høvenhoff Hald, Ricardo Henao, Ole Winther
Functional Magnetic Resonance Imaging (fMRI) gives us a unique insight into the processes of the brain, and opens up for analyzing the functional activation patterns of the underlying sources. Task-inferred supervised learning with restrictive assumptions in the regression set-up, restricts the exploratory nature of the analysis. Fully unsupervised independent component analysis (ICA) algorithms, on the other hand, can struggle to detect clear classifiable components on single-subject data. We attribute this shortcoming to inadequate modeling of the fMRI source signals by failing to incorporate its temporal nature...
February 27, 2017: NeuroImage
https://www.readbyqxmd.com/read/28245732/cortical-plasticity-after-brachial-plexus-injury-and-repair-a-resting-state-functional-mri-study
#13
Dhananjaya I Bhat, B Indira Devi, Komal Bharti, Rajanikant Panda
OBJECTIVE The authors aimed to understand the alterations of brain resting-state networks (RSNs) in patients with pan-brachial plexus injury (BPI) before and after surgery, which might provide insight into cortical plasticity after peripheral nerve injury and regeneration. METHODS Thirty-five patients with left pan-BPI before surgery, 30 patients after surgery, and 25 healthy controls underwent resting-state functional MRI (rs-fMRI). The 30 postoperative patients were subdivided into 2 groups: 14 patients with improvement in muscle power and 16 patients with no improvement in muscle power after surgery...
March 2017: Neurosurgical Focus
https://www.readbyqxmd.com/read/28240389/increased-intrinsic-brain-connectivity-between-pons-and-somatosensory-cortex-during-attacks-of-migraine-with-aura
#14
Anders Hougaard, Faisal Mohammad Amin, Henrik B W Larsson, Egill Rostrup, Messoud Ashina
The neurological disturbances of migraine aura are caused by transient cortical dysfunction due to waves of spreading depolarization that disrupt neuronal signaling. The effects of these cortical events on intrinsic brain connectivity during attacks of migraine aura have not previously been investigated. Studies of spontaneous migraine attacks are notoriously challenging due to their unpredictable nature and patient discomfort. We investigated 16 migraine patients with visual aura during attacks and in the attack-free state using resting state fMRI...
February 27, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28231395/functional-connectivity-in-amygdalar-sensory-pre-motor-networks-at-rest-new-evidence-from-the-human-connectome-project
#15
Nicola Toschi, Andrea Duggento, Luca Passamonti
The word "e-motion" derives from the Latin word "ex-moveo" which literally means "moving away from something / somebody". Emotions are thus fundamental to prime action and goal-directed behavior with obvious implications for individual's survival. However, the brain mechanisms underlying the interactions between emotional and motor cortical systems remain poorly understood. A recent diffusion tensor imaging study in humans has reported the existence of direct anatomical connections between the amygdala and sensory/(pre)motor cortices, corroborating an initial observation in animal research...
February 23, 2017: European Journal of Neuroscience
https://www.readbyqxmd.com/read/28227771/tracking-intrinsic-connectivity-brain-network-features-during-successive-pseudo-resting-states-and-interoceptive-task-fmri
#16
Behnaz Jarrahi, Dante Mantini, Behnaz Jarrahi, Dante Mantini, Behnaz Jarrahi, Dante Mantini
Advanced multivariate analyses of functional magnetic resonance imaging (fMRI) data based on blood oxygen level-dependent (BOLD) contras have revealed that the human brain organizes its activities into multiple intrinsic connectivity networks (ICNs). Several fMRI studies have evaluated the modulations of these networks during different cognitive or emotional tasks using blind source separation techniques particularly the independent component analysis (ICA). In this exploratory study, we applied ICA methodology to examine ICN modulations under different interoceptive conditions...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227769/identifying-the-effects-of-visceral-interoception-on-human-brain-connectome-a-multivariate-analysis-of-covariance-of-fmri-data
#17
Behnaz Jarrahi, Dante Mantini, Behnaz Jarrahi, Dante Mantini, Behnaz Jarrahi, Dante Mantini
Sources of variations in the neural circuitry of the human brain and interrelationship between intrinsic connectivity networks (ICNs) are still a matter of debate and ongoing research. Here, we applied a multivariate analysis of covariance (MANCOVA) based on high-dimensional independent component analysis (ICA) to identify the effects of interoception and related variables on human brain connectome. Fifteen healthy right-handed subjects (all females, age range 21 - 48 years; mean age = 30.3, SD = 8.7 years) underwent a blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) that included continuous intravesical saline infusion and drainage...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28220083/risperidone-effects-on-brain-dynamic-connectivity-a-prospective-resting-state-fmri-study-in-schizophrenia
#18
Kristin K Lottman, Nina V Kraguljac, David M White, Charity J Morgan, Vince D Calhoun, Allison Butt, Adrienne C Lahti
Resting-state functional connectivity studies in schizophrenia evaluating average connectivity over the entire experiment have reported aberrant network integration, but findings are variable. Examining time-varying (dynamic) functional connectivity may help explain some inconsistencies. We assessed dynamic network connectivity using resting-state functional MRI in patients with schizophrenia, while unmedicated (n = 34), after 1 week (n = 29) and 6 weeks of treatment with risperidone (n = 24), as well as matched controls at baseline (n = 35) and after 6 weeks (n = 19)...
2017: Frontiers in Psychiatry
https://www.readbyqxmd.com/read/28214528/adaptive-independent-vector-analysis-for-multi-subject-complex-valued-fmri-data
#19
Li-Dan Kuang, Qiu-Hua Lin, Xiao-Feng Gong, Fengyu Cong, Vince D Calhoun
BACKGROUND: Complex-valued fMRI data can provide additional insights beyond magnitude-only data. However, independent vector analysis (IVA), which has exhibited great potential for group analysis of magnitude-only fMRI data, has rarely been applied to complex-valued fMRI data. The main challenges in this application include the extremely noisy nature and large variability of the source component vector (SCV) distribution. NEW METHOD: To address these challenges, we propose an adaptive fixed-point IVA algorithm for analyzing multiple-subject complex-valued fMRI data...
February 16, 2017: Journal of Neuroscience Methods
https://www.readbyqxmd.com/read/28214015/anomalous-network-architecture-of-the-resting-brain-in-children-who-stutter
#20
Soo-Eun Chang, Michael Angstadt, Ho Ming Chow, Andrew C Etchell, Emily O Garnett, Ai Leen Choo, Daniel Kessler, Robert C Welsh, Chandra Sripada
PURPOSE: We combined a large longitudinal neuroimaging dataset that includes children who do and do not stutter and a whole-brain network analysis in order to examine the intra- and inter-network connectivity changes associated with stuttering. Additionally, we asked whether whole brain connectivity patterns observed at the initial year of scanning could predict persistent stuttering in later years. METHODS: A total of 224 high-quality resting state fMRI scans collected from 84 children (42 stuttering, 42 controls) were entered into an independent component analysis (ICA), yielding a number of distinct network connectivity maps ("components") as well as expression scores for each component that quantified the degree to which it is expressed for each child...
January 25, 2017: Journal of Fluency Disorders
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