1. Spatial vs. temporal features in ICA of resting-state fMRI - A quantitative and qualitative investigation in the context of response inhibition
- Author
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Yu-Feng Zang, Stephen M. Smith, Lixia Tian, Gaël Varoquaux, Yazhuo Kong, Juejing Ren, Department of Biomedical Engineering, Beijing Jiaotong University (BJTU), Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford [Oxford], State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University (BNU), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Center for Cognition and Brain Disorders, Hangzhou Normal University, University of Oxford, Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
- Subjects
Male ,Central Nervous System ,Anatomy and Physiology ,lcsh:Medicine ,Bioinformatics ,Standard deviation ,Behavioral Neuroscience ,Engineering ,0302 clinical medicine ,Neural Pathways ,lcsh:Science ,Physics ,Brain Mapping ,0303 health sciences ,Multidisciplinary ,medicine.diagnostic_test ,fMRI ,Magnetic Resonance Imaging ,Smoothing ,Research Article ,Adult ,Neural Networks ,Rest ,Cognitive Neuroscience ,ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES ,Biomedical Engineering ,Neuroimaging ,Bioengineering ,Context (language use) ,Neurological System ,Young Adult ,03 medical and health sciences ,Reaction Time ,medicine ,Humans ,Time series ,Biology ,Spatial analysis ,030304 developmental biology ,Computational Neuroscience ,Resting state fMRI ,business.industry ,[SCCO.NEUR]Cognitive science/Neuroscience ,lcsh:R ,Pattern recognition ,Connectomics ,Independent component analysis ,Neuroanatomy ,Signal Processing ,lcsh:Q ,Artificial intelligence ,Functional magnetic resonance imaging ,business ,030217 neurology & neurosurgery ,Neuroscience - Abstract
International audience; Independent component analysis (ICA) can identify covarying functional networks in the resting brain. Despite its relatively widespread use, the potential of the temporal information (unlike spatial information) obtained by ICA from resting state fMRI (RS-fMRI) data is not always fully utilized. In this study, we systematically investigated which features in ICA of resting-state fMRI relate to behaviour, with stop signal reaction time (SSRT) in a stop-signal task taken as a test case. We did this by correlating SSRT with the following three kinds of measure obtained from RS-fMRI data: (1) the amplitude of each resting state network (RSN) (evaluated by the standard deviation of the RSN timeseries), (2) the temporal correlation between every pair of RSN timeseries, and (3) the spatial map of each RSN. For multiple networks, we found significant correlations not only between SSRT and spatial maps, but also between SSRT and network activity amplitude. Most of these correlations are of functional interpretability. The temporal correlations between RSN pairs were of functional significance, but these correlations did not appear to be very sensitive to finding SSRT correlations. In addition, we also investigated the effects of the decomposition dimension, spatial smoothing and Z-transformation of the spatial maps, as well as the techniques for evaluating the temporal correlation between RSN timeseries. Overall, the temporal information acquired by ICA enabled us to investigate brain function from a complementary perspective to the information provided by spatial maps.
- Published
- 2013