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independent component analysis fMRI

Jiayu Chen, Barnaly Rashid, Qingbao Yu, Jingyu Liu, Dongdong Lin, Yuhui Du, Jing Sui, Vince D Calhoun
Imaging genetics posits a valuable strategy for elucidating genetic influences on brain abnormalities in psychiatric disorders. However, association analysis between 2D genetic data (subject × genetic variable) and 3D first-level functional magnetic resonance imaging (fMRI) data (subject × voxel × time) has been challenging given the asymmetry in data dimension. A summary feature needs to be derived for the imaging modality to compute inter-modality association at subject level. In this work, we propose to use variability in resting state networks (RSNs) and functional network connectivity (FNC) as potential features for purpose of association analysis...
2018: Frontiers in Neuroscience
Martin Lamos, Radek Marecek, Tomáš Slavíček, Michal Mikl, Ivan Rektor, Jiri Jan
Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during EEG data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity.
 Approach. The blind decomposition of EEG spectrogram by Parallel Factor Analysis has been shown to be a useful technique for uncovering patterns of neural activity...
March 14, 2018: Journal of Neural Engineering
Valerio Santangelo
Higher-order cognitive processes were shown to rely on the interplay between large-scale neural networks. However, brain networks involved with the capability to split attentional resource over multiple spatial locations and multiple stimuli or sensory modalities have been largely unexplored to date. Here I re-analyzed data from Santangelo et al. (2010) to explore the causal interactions between large-scale brain networks during divided attention. During fMRI scanning, participants monitored streams of visual and/or auditory stimuli in one or two spatial locations for detection of occasional targets...
2018: Frontiers in Integrative Neuroscience
Stefania Evangelisti, Claudia Testa, Lorenzo Ferri, Laura Ludovica Gramegna, David Neil Manners, Giovanni Rizzo, Daniel Remondini, Gastone Castellani, Ilaria Naldi, Francesca Bisulli, Caterina Tonon, Paolo Tinuper, Raffaele Lodi
Objectives: To evaluate functional connectivity (FC) in patients with sleep-related hypermotor epilepsy (SHE) compared to healthy controls. Methods: Resting state fMRI was performed in 13 patients with a clinical diagnosis of SHE (age = 38.3 ± 11.8 years, 6 M) and 13 matched healthy controls (age = 38.5 ± 10.8 years, 6 M).Data were first analysed using probabilistic independent component analysis (ICA), then a graph theoretical approach was applied to assess topological and organizational properties at the whole brain level...
2018: NeuroImage: Clinical
María Díez-Cirarda, Antonio P Strafella, Jinhee Kim, Javier Peña, Natalia Ojeda, Alberto Cabrera-Zubizarreta, Naroa Ibarretxe-Bilbao
The objective was to assess dynamic functional connectivity (FC) and local/global connectivity in Parkinson's disease (PD) patients with mild cognitive impairment (PD-MCI) and with normal cognition (PD-NC). The sample included 35 PD patients and 26 healthy controls (HC). Cognitive assessment followed an extensive neuropsychological battery. For resting-state functional MRI (rs-fMRI) analysis, independent component analysis (ICA) was performed and components were located in 7 networks: Subcortical (SC), Auditory (AUD), Somatomotor (SM), visual (VI), cognitive-control (CC), default-mode (DMN), and cerebellar (CB)...
2018: NeuroImage: Clinical
Grigori Yourganov, Julius Fridriksson, Brielle Stark, Christopher Rorden
We examined the effect of lesion on the resting-state functional connectivity in chronic post-stroke patients. We found many instances of strong correlations in BOLD signal measured at different locations within the lesion, making it hard to distinguish from the connectivity between intact and strongly connected regions. Regression of the mean cerebro-spinal fluid signal did not alleviate this problem. The connectomes computed by exclusion of lesioned voxels were not good predictors of the behavioral measures...
2018: NeuroImage: Clinical
Shruti G Vij, Jason S Nomi, Dina R Dajani, Lucina Q Uddin
Development and aging are associated with functional changes in the brain across the lifespan. These changes manifest in a variety of spatial and temporal features of resting state functional MRI (rs-fMRI) but have seldom been explored exhaustively. We present a comprehensive study assessing age-related changes in spatial and temporal features of blind-source separated components identified by independent vector analysis (IVA) in a cross-sectional lifespan sample (ages 6-85 years). We show that while large-scale network configurations remain consistent throughout the lifespan, changes persist in both local and global organization of these networks...
March 6, 2018: NeuroImage
Yanzhe Ning, Ruwen Zheng, Kuangshi Li, Yong Zhang, Diyang Lyu, Hongxiao Jia, Yi Ren, Yihuai Zou
Numerous fMRI studies have confirmed functional abnormalities in resting-state brain networks in migraine patients. However, few studies focusing on causal relationships of pain-related brain networks in migraine have been conducted. This study aims to explore the difference of Granger causality connection among pain-related brain networks in migraine without aura (MWoA) patients.Twenty two MWoA patients and 17 matched healthy subjects were recruited to undergo resting-state fMRI scanning. Independent component analysis was used to extract pain-related brain networks, and Granger causality analysis to characterize the difference of Granger causality connection among pain-related brain networks was employed...
March 2018: Medicine (Baltimore)
Akanksha Juneja, Bharti Rana, R K Agrawal
BACKGROUND AND OBJECTIVES: Schizophrenia is a severe brain disorder primarily diagnosed through externally observed behavioural symptoms due to the dearth of established clinical tests. Functional magnetic resonance imaging (fMRI) can capture the distortions caused by schizophrenia in the brain activation. Hence, it can be useful for developing a decision model that performs computer-aided diagnosis of schizophrenia. But, fMRI data is huge in dimension. Therefore dimension reduction is indispensable...
March 2018: Computer Methods and Programs in Biomedicine
Shijie Zhao, Junwei Han, Xi Jiang, Heng Huang, Huan Liu, Jinglei Lv, Lei Guo, Tianming Liu
In recent years, natural stimuli such as audio excerpts or video streams have received increasing attention in neuroimaging studies. Compared with conventional simple, idealized and repeated artificial stimuli, natural stimuli contain more unrepeated, dynamic and complex information that are more close to real-life. However, there is no direct correspondence between the stimuli and any sensory or cognitive functions of the brain, which makes it difficult to apply traditional hypothesis-driven analysis methods (e...
February 27, 2018: Neuroinformatics
Antoine Bernas, Evelien M Barendse, Albert P Aldenkamp, Walter H Backes, Paul A M Hofman, Marc P H Hendriks, Roy P C Kessels, Frans M J Willems, Peter H N de With, Svitlana Zinger, Jacobus F A Jansen
Introduction: Autism spectrum disorder (ASD) is mainly characterized by functional and communication impairments as well as restrictive and repetitive behavior. The leading hypothesis for the neural basis of autism postulates globally abnormal brain connectivity, which can be assessed using functional magnetic resonance imaging (fMRI). Even in the absence of a task, the brain exhibits a high degree of functional connectivity, known as intrinsic, or resting-state, connectivity. Global default connectivity in individuals with autism versus controls is not well characterized, especially for a high-functioning young population...
February 2018: Brain and Behavior
Tobias Egli, David Coynel, Klara Spalek, Matthias Fastenrath, Virginie Freytag, Angela Heck, Eva Loos, Bianca Auschra, Andreas Papassotiropoulos, Dominique J-F de Quervain, Annette Milnik
Working memory (WM) is an important cognitive domain for everyday life functioning and is often disturbed in neuropsychiatric disorders. Functional magnetic resonance imaging (fMRI) studies in humans show that distributed brain areas typically described as fronto-parietal regions are implicated in WM tasks. Based on data from a large sample of healthy young adults ( N = 1369), we applied independent component analysis (ICA) to the WM-fMRI signal and identified two distinct networks that were relevant for differences in individual WM task performance...
January 2018: ENeuro
Xintao Hu, Heng Huang, Bo Peng, Junwei Han, Nian Liu, Jinglei Lv, Lei Guo, Christine Guo, Tianming Liu
Blind source separation (BSS) is commonly used in functional magnetic resonance imaging (fMRI) data analysis. Recently, BSS models based on restricted Boltzmann machine (RBM), one of the building blocks of deep learning models, have been shown to improve brain network identification compared to conventional single matrix factorization models such as independent component analysis (ICA). These models, however, trained RBM on fMRI volumes, and are hence challenged by model complexity and limited training set...
February 18, 2018: Human Brain Mapping
Zikuan Chen, Vince Calhoun
Spatial smoothing is a widely used preprocessing step in functional magnetic resonance imaging (fMRI) data analysis. In this work, we report on the spatial smoothing effect on task-evoked fMRI brain functional mapping and functional connectivity. Initially, we decomposed the task fMRI data into a collection of components or networks by independent component analysis (ICA). The designed task paradigm helps identify task-modulated ICA components (highly correlated with the task stimuli). For the ICA-extracted primary task component, we then measured the task activation volume at the task response foci...
2018: Frontiers in Neuroscience
Farras Abdelnour, Michael Dayan, Orrin Devinsky, Thomas Thesen, Ashish Raj
How structural connectivity (SC) gives rise to functional connectivity (FC) is not fully understood. Here we mathematically derive a simple relationship between SC measured from diffusion tensor imaging, and FC from resting state fMRI. We establish that SC and FC are related via (structural) Laplacian spectra, whereby FC and SC share eigenvectors and their eigenvalues are exponentially related. This gives, for the first time, a simple and analytical relationship between the graph spectra of structural and functional networks...
February 14, 2018: NeuroImage
Minghui Zhang, Haiyan Zhou, Liqing Liu, Lei Feng, Jie Yang, Gang Wang, Ning Zhong
OBJECTIVE: Some studies have shown that the functional electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) networks in those with major depressive disorders (MDDs) have an abnormal random topology. In this study we aimed to further investigate the characteristics of the randomized functional brain networks in MDDs by examining resting-state scalp-EEG data. METHODS: Based on the methods of independent component analysis (ICA) and graph theoretic analysis, the abnormalities in the power spectral density (PSD) functional brain networks were compared between 13 MDDs and 13 matched healthy controls (HCs)...
January 31, 2018: Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology
Anna R Egbert, Bharat Biswal, Keerthana Karunakaran, Suril Gohel, Agnieszka Pluta, Tomasz Wolak, Bogna Szymańska, Ewa Firląg-Burkacka, Marta Sobańska, Natalia Gawron, Przemysław Bieńkowski, Halina Sienkiewicz-Jarosz, Anna Ścińska-Bieńkowska, Robert Bornstein, Stephen Rao, Emilia Łojek
This study examined the effects of age and HIV infection on the resting state (RS) functional connectivity (FC) of the brain and cognitive functioning. The objective was to evaluate the moderating role of age and HIV on the relationship between RS-FC and cognition. To examine RS-FC we implemented the Independent Component Analysis (ICA) and Regional Homogeneity (ReHo). Neurocognition was evaluated with comprehensive battery of standardized neuropsychological tests. Age and HIV were entered as the independent variables...
February 6, 2018: Behavioural Brain Research
Jain Mangalathu-Arumana, Einat Liebenthal, Scott A Beardsley
Joint independent component analysis (jICA) can be applied within subject for fusion of multi-channel event-related potentials (ERP) and functional magnetic resonance imaging (fMRI), to measure brain function at high spatiotemporal resolution (Mangalathu-Arumana et al., 2012). However, the impact of experimental design choices on jICA performance has not been systematically studied. Here, the sensitivity of jICA for recovering neural sources in individual data was evaluated as a function of imaging SNR, number of independent representations of the ERP/fMRI data, relationship between instantiations of the joint ERP/fMRI activity (linear, non-linear, uncoupled), and type of sources (varying parametrically and non-parametrically across representations of the data), using computer simulations...
2018: Frontiers in Neuroscience
Constantin Tuleasca, Jean Régis, Elena Najdenovska, Tatiana Witjas, Nadine Girard, Jérôme Champoudry, Mohamed Faouzi, Jean-Philippe Thiran, Meritxell Bach Cuadra, Marc Levivier, Dimitri Van De Ville
OBJECTIVE: To correlate pretherapeutic resting-state functional MRI (rs-fMRI) measures with pretherapeutic head tremor presence and/or further improvement 1 year after stereotactic radiosurgical thalamotomy (SRS-T) for essential tremor (ET). METHODS: We prospectively collected head tremor scores (range 0-3) and rs-fMRI data for a cohort of 17 consecutive ET patients in pretherapeutic and 1 year after SRS-T states. We additionally acquired rs-fMRI data for a healthy control (HC) group (n=12)...
January 31, 2018: World Neurosurgery
Elizabeth Randell, Rachel McNamara, Leena Subramanian, Kerenza Hood, David Linden
BACKGROUND: A core principle of creating a scientific evidence base is that results can be replicated in independent experiments and in health intervention research. The TIDieR (Template for Intervention Description and Replication) checklist has been developed to aid in summarising key items needed when reporting clinical trials and other well designed evaluations of complex interventions in order that findings can be replicated or built on reliably. Neurofeedback (NF) using functional MRI (fMRI) is a multicomponent intervention that should be considered a complex intervention...
January 21, 2018: European Psychiatry: the Journal of the Association of European Psychiatrists
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