101
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Mayeli A, Al Zoubi O, Misaki M, Stewart JL, Zotev V, Luo Q, Phillips R, Fischer S, Götz M, Paulus MP, Refai H, Bodurka J. Integration of Simultaneous Resting-State Electroencephalography, Functional Magnetic Resonance Imaging, and Eye-Tracker Methods to Determine and Verify Electroencephalography Vigilance Measure. Brain Connect 2020; 10:535-546. [PMID: 33112650 DOI: 10.1089/brain.2019.0731] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background/Introduction: Concurrent electroencephalography and resting-state functional magnetic resonance imaging (rsfMRI) have been widely used for studying the (presumably) awake and alert human brain with high temporal/spatial resolution. Although rsfMRI scans are typically collected while individuals are instructed to focus their eyes on a fixated cross, objective and verified experimental measures to quantify degree of vigilance are not readily available. Electroencephalography (EEG) is the modality extensively used for estimating vigilance, especially during eyes-closed resting state. However, pupil size measured using an eye-tracker device could provide an indirect index of vigilance. Methods: Three 12-min resting scans (eyes open, fixating on the cross) were collected from 10 healthy control participants. We simultaneously collected EEG, fMRI, physiological, and eye-tracker data and investigated the correlation between EEG features, pupil size, and heart rate. Furthermore, we used pupil size and EEG features as regressors to find their correlations with blood-oxygen-level-dependent fMRI measures. Results: EEG frontal and occipital beta power (FOBP) correlates with pupil size changes, an indirect index for locus coeruleus activity implicated in vigilance regulation (r = 0.306, p < 0.001). Moreover, FOBP also correlated with heart rate (r = 0.255, p < 0.001), as well as several brain regions in the anticorrelated network, including the bilateral insula and inferior parietal lobule. Discussion: In this study, we investigated whether simultaneous EEG-fMRI combined with eye-tracker measurements can be used to determine EEG signal feature associated with vigilance measures during eyes-open rsfMRI. Our results support the conclusion that FOBP is an objective measure of vigilance in healthy human subjects. Impact statement We revealed an association between electroencephalography frontal and occipital beta power (FOBP) and pupil size changes during an eyes-open resting state, which supports the conclusion that FOBP could serve as an objective measure of vigilance in healthy human subjects. The results were validated by using simultaneously recorded heart rate and functional magnetic resonance imaging (fMRI). Interestingly, independently verified heart rate changes can also provide an easy-to-determine measure of vigilance during resting-state fMRI. These findings have important implications for an analysis and interpretation of dynamic resting-state fMRI connectivity studies in health and disease.
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Affiliation(s)
- Ahmad Mayeli
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.,School of Electrical and Computer Engineering, University of Oklahoma, Tulsa, Oklahoma, USA
| | - Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.,School of Electrical and Computer Engineering, University of Oklahoma, Tulsa, Oklahoma, USA
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | | | - Vadim Zotev
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Qingfei Luo
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | | | | | | | | | - Hazem Refai
- School of Electrical and Computer Engineering, University of Oklahoma, Tulsa, Oklahoma, USA
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.,Stephenson School of Biomedical Engineering, University of Oklahoma, Tulsa, Oklahoma, USA
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102
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Turner KL, Gheres KW, Proctor EA, Drew PJ. Neurovascular coupling and bilateral connectivity during NREM and REM sleep. eLife 2020; 9:62071. [PMID: 33118932 PMCID: PMC7758068 DOI: 10.7554/elife.62071] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 10/28/2020] [Indexed: 12/11/2022] Open
Abstract
To understand how arousal state impacts cerebral hemodynamics and neurovascular coupling, we monitored neural activity, behavior, and hemodynamic signals in un-anesthetized, head-fixed mice. Mice frequently fell asleep during imaging, and these sleep events were interspersed with periods of wake. During both NREM and REM sleep, mice showed large increases in cerebral blood volume ([HbT]) and arteriole diameter relative to the awake state, two to five times larger than those evoked by sensory stimulation. During NREM, the amplitude of bilateral low-frequency oscillations in [HbT] increased markedly, and coherency between neural activity and hemodynamic signals was higher than the awake resting and REM states. Bilateral correlations in neural activity and [HbT] were highest during NREM, and lowest in the awake state. Hemodynamic signals in the cortex are strongly modulated by arousal state, and changes during sleep are substantially larger than sensory-evoked responses.
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Affiliation(s)
- Kevin L Turner
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, United States.,Center for Neural Engineering, The Pennsylvania State University, University Park, United States
| | - Kyle W Gheres
- Center for Neural Engineering, The Pennsylvania State University, University Park, United States.,Graduate Program in Molecular, Cellular, and Integrative Biosciences, The Pennsylvania State University, University Park, United States
| | - Elizabeth A Proctor
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, United States.,Center for Neural Engineering, The Pennsylvania State University, University Park, United States.,Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, United States.,Department of Neurosurgery, Penn State College of Medicine, Hershey, United States.,Department of Pharmacology, Penn State College of Medicine, Hershey, United States
| | - Patrick J Drew
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, United States.,Center for Neural Engineering, The Pennsylvania State University, University Park, United States.,Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, United States.,Department of Neurosurgery, Penn State College of Medicine, Hershey, United States
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103
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Dynamic functional connectivity impairments in idiopathic rapid eye movement sleep behavior disorder. Parkinsonism Relat Disord 2020; 79:11-17. [DOI: 10.1016/j.parkreldis.2020.08.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 07/26/2020] [Accepted: 08/03/2020] [Indexed: 11/22/2022]
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104
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Sobczak F, He Y, Sejnowski TJ, Yu X. Predicting the fMRI Signal Fluctuation with Recurrent Neural Networks Trained on Vascular Network Dynamics. Cereb Cortex 2020; 31:826-844. [PMID: 32940658 PMCID: PMC7906791 DOI: 10.1093/cercor/bhaa260] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 07/19/2020] [Accepted: 08/12/2020] [Indexed: 02/06/2023] Open
Abstract
Resting-state functional MRI (rs-fMRI) studies have revealed specific low-frequency hemodynamic signal fluctuations (<0.1 Hz) in the brain, which could be related to neuronal oscillations through the neurovascular coupling mechanism. Given the vascular origin of the fMRI signal, it remains challenging to separate the neural correlates of global rs-fMRI signal fluctuations from other confounding sources. However, the slow-oscillation detected from individual vessels by single-vessel fMRI presents strong correlation to neural oscillations. Here, we use recurrent neural networks (RNNs) to predict the future temporal evolution of the rs-fMRI slow oscillation from both rodent and human brains. The RNNs trained with vessel-specific rs-fMRI signals encode the unique brain oscillatory dynamic feature, presenting more effective prediction than the conventional autoregressive model. This RNN-based predictive modeling of rs-fMRI datasets from the Human Connectome Project (HCP) reveals brain state-specific characteristics, demonstrating an inverse relationship between the global rs-fMRI signal fluctuation with the internal default-mode network (DMN) correlation. The RNN prediction method presents a unique data-driven encoding scheme to specify potential brain state differences based on the global fMRI signal fluctuation, but not solely dependent on the global variance.
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Affiliation(s)
- Filip Sobczak
- Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany.,Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, 72074 Tuebingen, Germany
| | - Yi He
- Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany.,Danish Research Centre for Magnetic Resonance, 2650, Hvidovre, Denmark
| | - Terrence J Sejnowski
- Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA.,Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Xin Yu
- Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
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105
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Altered Global Brain Signal during Physiologic, Pharmacologic, and Pathologic States of Unconsciousness in Humans and Rats. Anesthesiology 2020; 132:1392-1406. [PMID: 32205548 DOI: 10.1097/aln.0000000000003197] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Consciousness is supported by integrated brain activity across widespread functionally segregated networks. The functional magnetic resonance imaging-derived global brain signal is a candidate marker for a conscious state, and thus the authors hypothesized that unconsciousness would be accompanied by a loss of global temporal coordination, with specific patterns of decoupling between local regions and global activity differentiating among various unconscious states. METHODS Functional magnetic resonance imaging global signals were studied in physiologic, pharmacologic, and pathologic states of unconsciousness in human natural sleep (n = 9), propofol anesthesia (humans, n = 14; male rats, n = 12), and neuropathological patients (n = 21). The global signal amplitude as well as the correlation between global signal and signals of local voxels were quantified. The former reflects the net strength of global temporal coordination, and the latter yields global signal topography. RESULTS A profound reduction of global signal amplitude was seen consistently across the various unconscious states: wakefulness (median [1st, 3rd quartile], 0.46 [0.21, 0.50]) versus non-rapid eye movement stage 3 of sleep (0.30 [0.24, 0.32]; P = 0.035), wakefulness (0.36 [0.31, 0.42]) versus general anesthesia (0.25 [0.21, 0.28]; P = 0.001), healthy controls (0.30 [0.27, 0.37]) versus unresponsive wakefulness syndrome (0.22 [0.15, 0.24]; P < 0.001), and low dose (0.07 [0.06, 0.08]) versus high dose of propofol (0.04 [0.03, 0.05]; P = 0.028) in rats. Furthermore, non-rapid eye movement stage 3 of sleep was characterized by a decoupling of sensory and attention networks from the global network. General anesthesia and unresponsive wakefulness syndrome were characterized by a dissociation of the majority of functional networks from the global network. This decoupling, however, was dominated by distinct neuroanatomic foci (e.g., precuneus and anterior cingulate cortices). CONCLUSIONS The global temporal coordination of various modules across the brain may distinguish the coarse-grained state of consciousness versus unconsciousness, while the relationship between the global and local signals may define the particular qualities of a particular unconscious state.
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106
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Alderson TH, Bokde ALW, Kelso JAS, Maguire L, Coyle D. Metastable neural dynamics underlies cognitive performance across multiple behavioural paradigms. Hum Brain Mapp 2020; 41:3212-3234. [PMID: 32301561 PMCID: PMC7375112 DOI: 10.1002/hbm.25009] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 01/20/2020] [Accepted: 03/31/2020] [Indexed: 12/24/2022] Open
Abstract
Despite resting state networks being associated with a variety of cognitive abilities, it remains unclear how these local areas act in concert to express particular cognitive operations. Theoretical and empirical accounts indicate that large-scale resting state networks reconcile dual tendencies towards integration and segregation by operating in a metastable regime of their coordination dynamics. Metastability may confer important behavioural qualities by binding distributed local areas into large-scale neurocognitive networks. We tested this hypothesis by analysing fMRI data in a large cohort of healthy individuals (N = 566) and comparing the metastability of the brain's large-scale resting network architecture at rest and during the performance of several tasks. Metastability was estimated using a well-defined collective variable capturing the level of 'phase-locking' between large-scale networks over time. Task-based reasoning was principally characterised by high metastability in cognitive control networks and low metastability in sensory processing areas. Although metastability between resting state networks increased during task performance, cognitive ability was more closely linked to spontaneous activity. High metastability in the intrinsic connectivity of cognitive control networks was linked to novel problem solving or fluid intelligence, but was less important in tasks relying on previous experience or crystallised intelligence. Crucially, subjects with resting architectures similar or 'pre-configured' to a task-general arrangement demonstrated superior cognitive performance. Taken together, our findings support a key linkage between the spontaneous metastability of large-scale networks in the cerebral cortex and cognition.
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Affiliation(s)
- Thomas H. Alderson
- Intelligent Systems Research CentreUlster UniversityAntrimUnited Kingdom
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana‐ChampaignUrbanaIllinoisUnited States
| | - Arun L. W. Bokde
- Trinity College Institute of Neuroscience and Cognitive Systems Group, Discipline of Psychiatry, School of MedicineTrinity College DublinDublinIreland
| | - J. A. Scott Kelso
- Intelligent Systems Research CentreUlster UniversityAntrimUnited Kingdom
- Center for Complex Systems and Brain SciencesFlorida Atlantic UniversityBoca RatonFloridaUnited States
| | - Liam Maguire
- Intelligent Systems Research CentreUlster UniversityAntrimUnited Kingdom
| | - Damien Coyle
- Intelligent Systems Research CentreUlster UniversityAntrimUnited Kingdom
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107
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Simony E, Chang C. Analysis of stimulus-induced brain dynamics during naturalistic paradigms. Neuroimage 2020; 216:116461. [PMID: 31843711 PMCID: PMC7418522 DOI: 10.1016/j.neuroimage.2019.116461] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 10/19/2019] [Accepted: 12/10/2019] [Indexed: 10/28/2022] Open
Abstract
Naturalistic stimuli offer promising avenues for investigating brain function across the rich, realistic spectrum of human experiences. Functional magnetic resonance imaging (fMRI) studies of brain activity during naturalistic paradigms have provided new information about dynamic neural processing in ecologically valid contexts. Yet, the complex, uncontrolled nature of such stimuli -- and the resulting mixture of neuronal and physiological responses embedded within the fMRI signals -- present challenges with respect to data analysis and interpretation. In this brief commentary, we discuss methods and open challenges in naturalistic fMRI investigations, with a focus on extracting and interpreting stimulus-induced fMRI signals.
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Affiliation(s)
- Erez Simony
- Faculty of Electrical Engineering, Holon Institute of Technology, Holon, Israel.
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.
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108
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Zhang J, Huang Z, Tumati S, Northoff G. Rest-task modulation of fMRI-derived global signal topography is mediated by transient coactivation patterns. PLoS Biol 2020; 18:e3000733. [PMID: 32649707 PMCID: PMC7375654 DOI: 10.1371/journal.pbio.3000733] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 07/22/2020] [Accepted: 06/23/2020] [Indexed: 12/26/2022] Open
Abstract
Recent resting-state functional MRI (fMRI) studies have revealed that the global signal (GS) exhibits a nonuniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of GS topography by analyzing static GS correlation and dynamic coactivation patterns in a large sample of an fMRI dataset (n = 837) from the Human Connectome Project. The GS topography in the resting state and in seven different tasks was first measured by correlating the GS with the local time series (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, whereas low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient coactivation patterns at the peak period of GS (GS-peak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of GS in the resting state, whereas both differed during the task states; because of such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of GS topography by showing its rest-task modulation, the underlying dynamic coactivation patterns, and its partial dissociation from respiration effects during task states. Recent resting-state fMRI studies have shown that the global signal exhibits a nonuniform spatial distribution across gray matter, but is this informative? This neuroimaging study reveals novel insights into the informative nature of global signal by rest-task modulation of the global signal topography.
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Affiliation(s)
- Jianfeng Zhang
- Mental Health Center, Zhejiang University School of Medicine, Hangzhou, China
- College of Biomedical Engineering and Instrument Sciences, Zhejiang University, Hangzhou, China
| | - Zirui Huang
- Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Shankar Tumati
- Institute of Mental Health Research, University of Ottawa, Ottawa, Canada
| | - Georg Northoff
- Mental Health Center, Zhejiang University School of Medicine, Hangzhou, China
- Institute of Mental Health Research, University of Ottawa, Ottawa, Canada
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China
- Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan
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109
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Aedo-Jury F, Schwalm M, Hamzehpour L, Stroh A. Brain states govern the spatio-temporal dynamics of resting-state functional connectivity. eLife 2020; 9:53186. [PMID: 32568067 PMCID: PMC7329332 DOI: 10.7554/elife.53186] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 06/18/2020] [Indexed: 01/08/2023] Open
Abstract
Previously, using simultaneous resting-state functional magnetic resonance imaging (fMRI) and photometry-based neuronal calcium recordings in the anesthetized rat, we identified blood oxygenation level-dependent (BOLD) responses directly related to slow calcium waves, revealing a cortex-wide and spatially organized correlate of locally recorded neuronal activity (Schwalm et al., 2017). Here, using the same techniques, we investigate two distinct cortical activity states: persistent activity, in which compartmentalized network dynamics were observed; and slow wave activity, dominated by a cortex-wide BOLD component, suggesting a strong functional coupling of inter-cortical activity. During slow wave activity, we find a correlation between the occurring slow wave events and the strength of functional connectivity between different cortical areas. These findings suggest that down-up transitions of neuronal excitability can drive cortex-wide functional connectivity. This study provides further evidence that changes in functional connectivity are dependent on the brain's current state, directly linked to the generation of slow waves.
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Affiliation(s)
- Felipe Aedo-Jury
- Institute of Pathophysiology, University Medical Center Mainz, Mainz, Germany.,Leibniz Institute for Resilience Research, Mainz, Germany
| | - Miriam Schwalm
- Institute of Pathophysiology, University Medical Center Mainz, Mainz, Germany.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, United States
| | - Lara Hamzehpour
- Institute of Pathophysiology, University Medical Center Mainz, Mainz, Germany
| | - Albrecht Stroh
- Institute of Pathophysiology, University Medical Center Mainz, Mainz, Germany.,Leibniz Institute for Resilience Research, Mainz, Germany
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110
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Damaraju E, Tagliazucchi E, Laufs H, Calhoun VD. Connectivity dynamics from wakefulness to sleep. Neuroimage 2020; 220:117047. [PMID: 32562782 DOI: 10.1016/j.neuroimage.2020.117047] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 06/09/2020] [Indexed: 11/16/2022] Open
Abstract
Interest in time-resolved connectivity in fMRI has grown rapidly in recent years. The most widely used technique for studying connectivity changes over time utilizes a sliding windows approach. There has been some debate about the utility of shorter versus longer windows, the use of fixed versus adaptive windows, as well as whether observed resting state dynamics during wakefulness may be predominantly due to changes in sleep state and subject head motion. In this work we use an independent component analysis (ICA)-based pipeline applied to concurrent EEG/fMRI data collected during wakefulness and various sleep stages and show: 1) connectivity states obtained from clustering sliding windowed correlations of resting state functional network time courses well classify the sleep states obtained from EEG data, 2) using shorter sliding windows instead of longer non-overlapping windows improves the ability to capture transition dynamics even at windows as short as 30 s, 3) motion appears to be mostly associated with one of the states rather than spread across all of them 4) a fixed tapered sliding window approach outperforms an adaptive dynamic conditional correlation approach, and 5) consistent with prior EEG/fMRI work, we identify evidence of multiple states within the wakeful condition which are able to be classified with high accuracy. Classification of wakeful only states suggest the presence of time-varying changes in connectivity in fMRI data beyond sleep state or motion. Results also inform about advantageous technical choices, and the identification of different clusters within wakefulness that are separable suggest further studies in this direction.
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Affiliation(s)
- Eswar Damaraju
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA.
| | - Enzo Tagliazucchi
- Departamento de Física, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Helmut Laufs
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt am Main, Germany; Department of Neurology, Christian Albrechts University, Kiel, Germany
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA
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111
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Dynamic representations in networked neural systems. Nat Neurosci 2020; 23:908-917. [PMID: 32541963 DOI: 10.1038/s41593-020-0653-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 05/12/2020] [Indexed: 11/08/2022]
Abstract
A group of neurons can generate patterns of activity that represent information about stimuli; subsequently, the group can transform and transmit activity patterns across synapses to spatially distributed areas. Recent studies in neuroscience have begun to independently address the two components of information processing: the representation of stimuli in neural activity and the transmission of information in networks that model neural interactions. Yet only recently are studies seeking to link these two types of approaches. Here we briefly review the two separate bodies of literature; we then review the recent strides made to address this gap. We continue with a discussion of how patterns of activity evolve from one representation to another, forming dynamic representations that unfold on the underlying network. Our goal is to offer a holistic framework for understanding and describing neural information representation and transmission while revealing exciting frontiers for future research.
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112
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Betzel RF, Byrge L, Esfahlani FZ, Kennedy DP. Temporal fluctuations in the brain's modular architecture during movie-watching. Neuroimage 2020; 213:116687. [PMID: 32126299 PMCID: PMC7165071 DOI: 10.1016/j.neuroimage.2020.116687] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 02/07/2020] [Accepted: 02/24/2020] [Indexed: 11/26/2022] Open
Abstract
Brain networks are flexible and reconfigure over time to support ongoing cognitive processes. However, tracking statistically meaningful reconfigurations across time has proven difficult. This has to do largely with issues related to sampling variability, making instantaneous estimation of network organization difficult, along with increased reliance on task-free (cognitively unconstrained) experimental paradigms, limiting the ability to interpret the origin of changes in network structure over time. Here, we address these challenges using time-varying network analysis in conjunction with a naturalistic viewing paradigm. Specifically, we developed a measure of inter-subject network similarity and used this measure as a coincidence filter to identify synchronous fluctuations in network organization across individuals. Applied to movie-watching data, we found that periods of high inter-subject similarity coincided with reductions in network modularity and increased connectivity between cognitive systems. In contrast, low inter-subject similarity was associated with increased system segregation and more rest-like architectures. We then used a data-driven approach to uncover clusters of functional connections that follow similar trajectories over time and are more strongly correlated during movie-watching than at rest. Finally, we show that synchronous fluctuations in network architecture over time can be linked to a subset of features in the movie. Our findings link dynamic fluctuations in network integration and segregation to patterns of inter-subject similarity, and suggest that moment-to-moment fluctuations in functional connectivity reflect shared cognitive processing across individuals.
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Affiliation(s)
- Richard F Betzel
- Department of Psychological and Brain Sciences, USA; Cognitive Science Program, USA; Program in Neuroscience, USA; Network Science Institute, Indiana University, Bloomington, IN, 47405, USA.
| | - Lisa Byrge
- Department of Psychological and Brain Sciences, USA
| | | | - Daniel P Kennedy
- Department of Psychological and Brain Sciences, USA; Cognitive Science Program, USA; Program in Neuroscience, USA
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113
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Gu Y, Han F, Sainburg LE, Liu X. Transient Arousal Modulations Contribute to Resting-State Functional Connectivity Changes Associated with Head Motion Parameters. Cereb Cortex 2020; 30:5242-5256. [PMID: 32406488 DOI: 10.1093/cercor/bhaa096] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 03/23/2020] [Accepted: 03/23/2020] [Indexed: 12/25/2022] Open
Abstract
Correlations of resting-state functional magnetic resonance imaging (rsfMRI) signals are being widely used for assessing the functional brain connectivity in health and disease. However, an association was recently observed between rsfMRI connectivity modulations and the head motion parameters and regarded as a causal relationship, which has raised serious concerns about the validity of many rsfMRI findings. Here, we studied the origin of this rsfMRI-motion association and its relationship to arousal modulations. By using a template-matching method to locate arousal-related fMRI changes, we showed that the effects of high motion time points on rsfMRI connectivity are largely due to their significant overlap with arousal-affected time points. The finding suggests that the association between rsfMRI connectivity and the head motion parameters arises from their comodulations at transient arousal modulations, and this information is critical not only for proper interpretation of motion-associated rsfMRI connectivity changes, but also for controlling the potential confounding effects of arousal modulation on rsfMRI metrics.
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Affiliation(s)
- Yameng Gu
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Feng Han
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Lucas E Sainburg
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Xiao Liu
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA.,Institute for Computational and Data Sciences, The Pennsylvania State University, University Park, PA 16802, USA
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114
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Liu TT, Falahpour M. Vigilance Effects in Resting-State fMRI. Front Neurosci 2020; 14:321. [PMID: 32390792 PMCID: PMC7190789 DOI: 10.3389/fnins.2020.00321] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 03/18/2020] [Indexed: 12/02/2022] Open
Abstract
Measures of resting-state functional magnetic resonance imaging (rsfMRI) activity have been shown to be sensitive to cognitive function and disease state. However, there is growing evidence that variations in vigilance can lead to pronounced and spatially widespread differences in resting-state brain activity. Unless properly accounted for, differences in vigilance can give rise to changes in resting-state activity that can be misinterpreted as primary cognitive or disease-related effects. In this paper, we examine in detail the link between vigilance and rsfMRI measures, such as signal variance and functional connectivity. We consider how state changes due to factors such as caffeine and sleep deprivation affect both vigilance and rsfMRI measures and review emerging approaches and methodological challenges for the estimation and interpretation of vigilance effects.
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Affiliation(s)
- Thomas T. Liu
- Center for Functional MRI, University of California, San Diego, La Jolla, CA, United States
- Departments of Radiology, Psychiatry, and Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Maryam Falahpour
- Center for Functional MRI, University of California, San Diego, La Jolla, CA, United States
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115
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Jog M, Jann K, Yan L, Huang Y, Parra L, Narr K, Bikson M, Wang DJJ. Concurrent Imaging of Markers of Current Flow and Neurophysiological Changes During tDCS. Front Neurosci 2020; 14:374. [PMID: 32372913 PMCID: PMC7186453 DOI: 10.3389/fnins.2020.00374] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 03/26/2020] [Indexed: 11/13/2022] Open
Abstract
Despite being a popular neuromodulation technique, clinical translation of transcranial direct current stimulation (tDCS) is hampered by variable responses observed within treatment cohorts. Addressing this challenge has been difficult due to the lack of an effective means of mapping the neuromodulatory electromagnetic fields together with the brain's response. In this study, we present a novel imaging technique that provides the capability of concurrently mapping markers of tDCS currents, as well as the brain's response to tDCS. A dual-echo echo-planar imaging (DE-EPI) sequence is used, wherein the phase of the acquired MRI-signal encodes the tDCS current induced magnetic field, while the magnitude encodes the blood oxygenation level dependent (BOLD) contrast. The proposed technique was first validated in a custom designed phantom. Subsequent test-retest experiments in human participants showed that tDCS-induced magnetic fields can be detected reliably in vivo. The concurrently acquired BOLD data revealed large-scale networks characteristic of a brain in resting-state as well as a 'cathodal' and an 'anodal' resting-state component under each electrode. Moreover, 'cathodal's BOLD-signal was observed to significantly decrease with the applied current at the group level in all datasets. With its ability to image markers of electromagnetic cause as well as neurophysiological changes, the proposed technique may provide an effective means to visualize neural engagement in tDCS at the group level. Our technique also contributes to addressing confounding factors in applying BOLD fMRI concurrently with tDCS.
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Affiliation(s)
- Mayank Jog
- Laboratory of FMRI Technology, Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States.,Department of Neurology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Kay Jann
- Laboratory of FMRI Technology, Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Lirong Yan
- Laboratory of FMRI Technology, Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Yu Huang
- Department of Biomedical Engineering, the City College of The City University of New York, New York, NY, United States
| | - Lucas Parra
- Department of Biomedical Engineering, the City College of The City University of New York, New York, NY, United States
| | - Katherine Narr
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Marom Bikson
- Department of Biomedical Engineering, the City College of The City University of New York, New York, NY, United States
| | - Danny J J Wang
- Laboratory of FMRI Technology, Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
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116
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Pais-Roldán P, Takahashi K, Sobczak F, Chen Y, Zhao X, Zeng H, Jiang Y, Yu X. Indexing brain state-dependent pupil dynamics with simultaneous fMRI and optical fiber calcium recording. Proc Natl Acad Sci U S A 2020; 117:6875-6882. [PMID: 32139609 PMCID: PMC7104268 DOI: 10.1073/pnas.1909937117] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Pupillometry, a noninvasive measure of arousal, complements human functional MRI (fMRI) to detect periods of variable cognitive processing and identify networks that relate to particular attentional states. Even under anesthesia, pupil dynamics correlate with brain-state fluctuations, and extended dilations mark the transition to more arousable states. However, cross-scale neuronal activation patterns are seldom linked to brain state-dependent pupil dynamics. Here, we complemented resting-state fMRI in rats with cortical calcium recording (GCaMP-mediated) and pupillometry to tackle the linkage between brain-state changes and neural dynamics across different scales. This multimodal platform allowed us to identify a global brain network that covaried with pupil size, which served to generate an index indicative of the brain-state fluctuation during anesthesia. Besides, a specific correlation pattern was detected in the brainstem, at a location consistent with noradrenergic cell group 5 (A5), which appeared to be dependent on the coupling between different frequencies of cortical activity, possibly further indicating particular brain-state dynamics. The multimodal fMRI combining concurrent calcium recordings and pupillometry enables tracking brain state-dependent pupil dynamics and identifying unique cross-scale neuronal dynamic patterns under anesthesia.
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Affiliation(s)
- Patricia Pais-Roldán
- High-Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, 72074 Tuebingen, Germany
- Medical Imaging Physics, Institute of Neuroscience and Medicine, Forschungszentrum Juelich, 52425 Juelich, Germany
| | - Kengo Takahashi
- High-Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, 72074 Tuebingen, Germany
| | - Filip Sobczak
- High-Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, 72074 Tuebingen, Germany
| | - Yi Chen
- High-Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, 72074 Tuebingen, Germany
| | - Xiaoning Zhao
- High-Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany
| | - Hang Zeng
- High-Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, 72074 Tuebingen, Germany
| | - Yuanyuan Jiang
- High-Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129
| | - Xin Yu
- High-Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany;
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129
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117
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Huang Z, Zhang J, Wu J, Mashour GA, Hudetz AG. Temporal circuit of macroscale dynamic brain activity supports human consciousness. SCIENCE ADVANCES 2020; 6:eaaz0087. [PMID: 32195349 PMCID: PMC7065875 DOI: 10.1126/sciadv.aaz0087] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 12/16/2019] [Indexed: 05/29/2023]
Abstract
The ongoing stream of human consciousness relies on two distinct cortical systems, the default mode network and the dorsal attention network, which alternate their activity in an anticorrelated manner. We examined how the two systems are regulated in the conscious brain and how they are disrupted when consciousness is diminished. We provide evidence for a "temporal circuit" characterized by a set of trajectories along which dynamic brain activity occurs. We demonstrate that the transitions between default mode and dorsal attention networks are embedded in this temporal circuit, in which a balanced reciprocal accessibility of brain states is characteristic of consciousness. Conversely, isolation of the default mode and dorsal attention networks from the temporal circuit is associated with unresponsiveness of diverse etiologies. These findings advance the foundational understanding of the functional role of anticorrelated systems in consciousness.
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Affiliation(s)
- Zirui Huang
- Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Jun Zhang
- Department of Anesthesiology, Huashan Hospital, Fudan University, Shanghai 200040, PR China
| | - Jinsong Wu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, PR China
| | - George A. Mashour
- Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Anthony G. Hudetz
- Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
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118
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Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem. Neuroimage 2020; 212:116614. [PMID: 32084564 DOI: 10.1016/j.neuroimage.2020.116614] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 02/05/2020] [Accepted: 02/05/2020] [Indexed: 12/13/2022] Open
Abstract
One of the most controversial procedures in the analysis of resting-state functional magnetic resonance imaging (rsfMRI) data is global signal regression (GSR): the removal, via linear regression, of the mean signal averaged over the entire brain. On one hand, the global mean signal contains variance associated with respiratory, scanner-, and motion-related artifacts, and its removal via GSR improves various quality-control metrics, enhances the anatomical specificity of functional-connectivity patterns, and can increase the behavioral variance explained by such patterns. On the other hand, GSR alters the distribution of regional signal correlations in the brain, can induce artifactual anticorrelations, may remove real neural signal, and can distort case-control comparisons of functional-connectivity measures. Global signal fluctuations can be identified visually from a matrix of colour-coded signal intensities, called a carpet plot, in which rows represent voxels and columns represent time. Prior to GSR, large, periodic bands of coherent signal changes that affect most of the brain are often apparent; after GSR, these apparently global changes are greatly diminished. Here, using three independent datasets, we show that reordering carpet plots to emphasize cluster structure in the data reveals a greater diversity of spatially widespread signal deflections (WSDs) than previously thought. Their precise form varies across time and participants, and GSR is only effective in removing specific kinds of WSDs. We present an alternative, iterative correction method called Diffuse Cluster Estimation and Regression (DiCER), that identifies representative signals associated with large clusters of coherent voxels. DiCER is more effective than GSR at removing diverse WSDs as visualized in carpet plots, reduces correlations between functional connectivity and head-motion estimates, reduces inter-individual variability in global correlation structure, and results in comparable or improved identification of canonical functional-connectivity networks. Using task fMRI data across 47 contrasts from 7 tasks in the Human Connectome Project, we also present evidence that DiCER is more successful than GSR in preserving the spatial structure of expected task-related activation patterns. Our findings indicate that care must be exercised when examining WSDs (and their possible removal) in rsfMRI data, and that DiCER is a viable alternative to GSR for removing anatomically widespread and temporally coherent signals. All code for implementing DiCER and replicating our results is available at https://github.com/BMHLab/DiCER.
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119
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Orban C, Kong R, Li J, Chee MWL, Yeo BTT. Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity. PLoS Biol 2020; 18:e3000602. [PMID: 32069275 PMCID: PMC7028250 DOI: 10.1371/journal.pbio.3000602] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 01/15/2020] [Indexed: 12/13/2022] Open
Abstract
The brain exhibits substantial diurnal variation in physiology and function, but neuroscience studies rarely report or consider the effects of time of day. Here, we examined variation in resting-state functional MRI (fMRI) in around 900 individuals scanned between 8 AM and 10 PM on two different days. Multiple studies across animals and humans have demonstrated that the brain’s global signal (GS) amplitude (henceforth referred to as “fluctuation”) increases with decreased arousal. Thus, in accord with known circadian variation in arousal, we hypothesised that GS fluctuation would be lowest in the morning, increase in the midafternoon, and dip in the early evening. Instead, we observed a cumulative decrease in GS fluctuation as the day progressed. Although respiratory variation also decreased with time of day, control analyses suggested that this did not account for the reduction in GS fluctuation. Finally, time of day was associated with marked decreases in resting-state functional connectivity across the whole brain. The magnitude of decrease was significantly stronger than associations between functional connectivity and behaviour (e.g., fluid intelligence). These findings reveal time of day effects on global brain activity that are not easily explained by expected arousal state or physiological artefacts. We conclude by discussing potential mechanisms for the observed diurnal variation in resting brain activity and the importance of accounting for time of day in future studies. The brain exhibits substantial diurnal variation in physiology and function. A large-scale fMRI study reveals that the brain’s global signal amplitude, typically elevated during drowsy states, unexpectedly reduces steadily as the day progresses.
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Affiliation(s)
- Csaba Orban
- Department of Electrical and Computer Engineering, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Clinical Imaging and Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Neuropsychopharmacology Unit, Centre for Psychiatry, Imperial College London, London, United Kingdom
- * E-mail: (CO); (BTTY)
| | - Ru Kong
- Department of Electrical and Computer Engineering, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Clinical Imaging and Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Jingwei Li
- Department of Electrical and Computer Engineering, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Clinical Imaging and Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Michael W. L. Chee
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Clinical Imaging and Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Clinical Imaging and Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States of America
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
- * E-mail: (CO); (BTTY)
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120
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Lurie DJ, Kessler D, Bassett DS, Betzel RF, Breakspear M, Kheilholz S, Kucyi A, Liégeois R, Lindquist MA, McIntosh AR, Poldrack RA, Shine JM, Thompson WH, Bielczyk NZ, Douw L, Kraft D, Miller RL, Muthuraman M, Pasquini L, Razi A, Vidaurre D, Xie H, Calhoun VD. Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw Neurosci 2020; 4:30-69. [PMID: 32043043 PMCID: PMC7006871 DOI: 10.1162/netn_a_00116] [Citation(s) in RCA: 279] [Impact Index Per Article: 69.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 11/22/2019] [Indexed: 12/12/2022] Open
Abstract
The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as "dynamic" or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.
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Affiliation(s)
- Daniel J. Lurie
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Daniel Kessler
- Departments of Statistics and Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Danielle S. Bassett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Richard F. Betzel
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Breakspear
- University of Newcastle, Callaghan, NSW, 2308, Australia
- QIMR Berghofer, Brisbane, Australia
| | - Shella Kheilholz
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Aaron Kucyi
- Department of Neurology and Neurological Sciences, Stanford University, Stanford CA, USA
| | - Raphaël Liégeois
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | | | - Anthony Randal McIntosh
- Rotman Research Institute - Baycrest Centre, Toronto, Canada
- Department of Psychology, University of Toronto, Toronto, Canada
| | | | - James M. Shine
- Brain and Mind Centre, University of Sydney, NSW, Australia
| | - William Hedley Thompson
- Department of Psychology, Stanford University, Stanford, CA, USA
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | | | - Linda Douw
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, The Netherlands
| | - Dominik Kraft
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | | | - Muthuraman Muthuraman
- Biomedical Statistics and Multimodal Signal Processing Unit, Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience, Johannes-Gutenberg-University Hospital, Mainz, Germany
| | - Lorenzo Pasquini
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Adeel Razi
- Monash Institute of Cognitive and Clinical Neurosciences and Monash Biomedical Imaging, Monash University, Clayton, Australia
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
- Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
| | - Diego Vidaurre
- Wellcome Trust Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, United Kingdom
| | - Hua Xie
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, Georgia, USA
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121
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Gotts SJ, Gilmore AW, Martin A. Brain networks, dimensionality, and global signal averaging in resting-state fMRI: Hierarchical network structure results in low-dimensional spatiotemporal dynamics. Neuroimage 2020; 205:116289. [PMID: 31629827 PMCID: PMC6919311 DOI: 10.1016/j.neuroimage.2019.116289] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 10/12/2019] [Accepted: 10/16/2019] [Indexed: 12/11/2022] Open
Abstract
One of the most controversial practices in resting-state fMRI functional connectivity studies is whether or not to regress out the global average brain signal (GS) during artifact removal. Some groups have argued that it is absolutely essential to regress out the GS in order to fully remove head motion, respiration, and other global imaging artifacts. Others have argued that removing the GS distorts the resulting correlation matrices and inappropriately alters the results of group comparisons and relationships to behavior. At the core of this argument is the assessment of dimensionality in terms of the number of brain networks with uncorrelated time series. If the dimensionality is high, then the distortions due to GS removal could be effectively negligible. In the current paper, we examine the dimensionality of resting-state fMRI data using principal component analyses (PCA) and network clustering analyses. In two independent datasets (Set 1: N = 62, Set 2: N = 32), scree plots of the eigenvalues level off at or prior to 10 principal components, with prominent elbows at 3 and 7 components. While network clustering analyses have previously demonstrated that numerous networks can be distinguished with high thresholding of the voxel-wise correlation matrices, lower thresholding reveals a lower-dimensional hierarchical structure, with the first prominent branch at 2 networks (corresponding to the previously described "task-positive"/"task-negative" distinction) and further stable subdivisions at 4, 7 and 17. Since inter-correlated time series within these larger branches do not cancel to zero when averaged, the hierarchical nature of the correlation structure results in low effective dimensionality. Consistent with this, partial correlation analyses revealed that network-specific variance remains present in the GS at each level of the hierarchy, accounting for at least 14-18% of the overall GS variance in each dataset. These results demonstrate that GS regression is expected to remove substantial portions of network-specific brain signals along with artifacts, not simply whole-brain signals corresponding to arousal levels. We highlight alternative means of controlling for residual global artifacts when not removing the GS.
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Affiliation(s)
- Stephen J Gotts
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Adrian W Gilmore
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Alex Martin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
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122
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Englot DJ, Morgan VL, Chang C. Impaired vigilance networks in temporal lobe epilepsy: Mechanisms and clinical implications. Epilepsia 2020; 61:189-202. [PMID: 31901182 DOI: 10.1111/epi.16423] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 12/17/2019] [Accepted: 12/17/2019] [Indexed: 12/19/2022]
Abstract
Mesial temporal lobe epilepsy (mTLE) is a neurological disorder in which patients suffer from frequent consciousness-impairing seizures, broad neurocognitive deficits, and diminished quality of life. Although seizures in mTLE originate focally in the hippocampus or amygdala, mTLE patients demonstrate cognitive deficits that extend beyond temporal lobe function-such as decline in executive function, cognitive processing speed, and attention-as well as diffuse decreases in neocortical metabolism and functional connectivity. Given prior observations that mTLE patients exhibit impairments in vigilance, and that seizures may disrupt the activity and long-range connectivity of subcortical brain structures involved in vigilance regulation, we propose that subcortical activating networks underlying vigilance play a critical role in mediating the widespread neural and cognitive effects of focal mTLE. Here, we review evidence for impaired vigilance in mTLE, examine clinical implications and potential network underpinnings, and suggest neuroimaging strategies for determining the relationship between vigilance, brain connectivity, and neurocognition in patients and healthy controls.
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Affiliation(s)
- Dario J Englot
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
| | - Victoria L Morgan
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
| | - Catie Chang
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
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123
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Zuo N, Salami A, Liu H, Yang Z, Jiang T. Functional maintenance in the multiple demand network characterizes superior fluid intelligence in aging. Neurobiol Aging 2020; 85:145-153. [DOI: 10.1016/j.neurobiolaging.2019.09.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 08/20/2019] [Accepted: 09/14/2019] [Indexed: 12/13/2022]
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124
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Duyn JH, Ozbay PS, Chang C, Picchioni D. Physiological changes in sleep that affect fMRI inference. Curr Opin Behav Sci 2019; 33:42-50. [PMID: 32613032 DOI: 10.1016/j.cobeha.2019.12.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
fMRI relies on a localized cerebral blood flow (CBF) response to changes in cortical neuronal activity. An underappreciated aspect however is its sensitivity to contributions from autonomic physiology that may affect CBF through changes in vascular resistance and blood pressure. As is reviewed here, this is crucial to consider in fMRI studies of sleep, given the close linkage between the regulation of arousal state and autonomic physiology. Typical methods for separating these effects are based on the use of reference signals that may include physiological parameters such as heart rate and respiration; however, the use of time-invariant models may not be adequate due to the possibly changing relationship between reference and fMRI signals with arousal state. In addition, recent research indicates that additional physiological reference signals may be needed to accurately describe changes in systemic physiology, including sympathetic indicators such as finger skin vascular tone and blood pressure.
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Affiliation(s)
- Jeff H Duyn
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke
| | - Pinar S Ozbay
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University
| | - Dante Picchioni
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke
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125
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Forsyth A, McMillan R, Campbell D, Malpas G, Maxwell E, Sleigh J, Dukart J, Hipp J, Muthukumaraswamy SD. Modulation of simultaneously collected hemodynamic and electrophysiological functional connectivity by ketamine and midazolam. Hum Brain Mapp 2019; 41:1472-1494. [PMID: 31808268 PMCID: PMC7267972 DOI: 10.1002/hbm.24889] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 11/06/2019] [Accepted: 11/26/2019] [Indexed: 12/21/2022] Open
Abstract
The pharmacological modulation of functional connectivity in the brain may underlie therapeutic efficacy for several neurological and psychiatric disorders. Functional magnetic resonance imaging (fMRI) provides a noninvasive method of assessing this modulation, however, the indirect nature of the blood‐oxygen level dependent signal restricts the discrimination of neural from physiological contributions. Here we followed two approaches to assess the validity of fMRI functional connectivity in developing drug biomarkers, using simultaneous electroencephalography (EEG)/fMRI in a placebo‐controlled, three‐way crossover design with ketamine and midazolam. First, we compared seven different preprocessing pipelines to determine their impact on the connectivity of common resting‐state networks. Independent components analysis (ICA)‐denoising resulted in stronger reductions in connectivity after ketamine, and weaker increases after midazolam, than pipelines employing physiological noise modelling or averaged signals from cerebrospinal fluid or white matter. This suggests that pipeline decisions should reflect a drug's unique noise structure, and if this is unknown then accepting possible signal loss when choosing extensive ICA denoising pipelines could engender more confidence in the remaining results. We then compared the temporal correlation structure of fMRI to that derived from two connectivity metrics of EEG, which provides a direct measure of neural activity. While electrophysiological estimates based on the power envelope were more closely aligned to BOLD signal connectivity than those based on phase consistency, no significant relationship between the change in electrophysiological and hemodynamic correlation structures was found, implying caution should be used when making cross‐modal comparisons of pharmacologically‐modulated functional connectivity.
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Affiliation(s)
- Anna Forsyth
- School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Rebecca McMillan
- School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Doug Campbell
- Department of Anaesthesiology, Auckland District Health Board, Auckland, New Zealand
| | - Gemma Malpas
- Department of Anaesthesiology, Auckland District Health Board, Auckland, New Zealand
| | - Elizabeth Maxwell
- Department of Anaesthesiology, Auckland District Health Board, Auckland, New Zealand
| | - Jamie Sleigh
- Department of Anaesthesiology Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jörg Hipp
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Suresh D Muthukumaraswamy
- School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
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126
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Abstract
Although often used as a nuisance in resting-state functional magnetic resonance imaging (rsfMRI), the global brain signal in humans and anesthetized animals has important neural basis. However, our knowledge of the global signal in awake rodents is sparse. To bridge this gap, we systematically analyzed rsfMRI data acquired with a conventional single-echo (SE) echo planar imaging (EPI) sequence in awake rats. The spatial pattern of rsfMRI frames during peaks of the global signal exhibited prominent co-activations in the thalamo-cortical and hippocampo-cortical networks, as well as in the basal forebrain, hinting that these neural networks might contribute to the global brain signal in awake rodents. To validate this concept, we acquired rsfMRI data using a multi-echo (ME) EPI sequence and removed non-neural components in the rsfMRI signal. Consistent co-activation patterns were obtained in extensively de-noised ME-rsfMRI data, corroborating the finding from SE-rsfMRI data. Furthermore, during rsfMRI experiments, we simultaneously recorded neural spiking activities in the hippocampus using GCaMP-based fiber photometry. The hippocampal calcium activity exhibited significant correspondence with the global rsfMRI signal. These data collectively suggest that the global rsfMRI signal contains significant neural components that involve coordinated activities in the thalamo-cortical and hippocampo-cortical networks. These results provide important insight into the neural substrate of the global brain signal in awake rodents.
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127
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Grossman S, Yeagle EM, Harel M, Espinal E, Harpaz R, Noy N, Mégevand P, Groppe DM, Mehta AD, Malach R. The Noisy Brain: Power of Resting-State Fluctuations Predicts Individual Recognition Performance. Cell Rep 2019; 29:3775-3784.e4. [DOI: 10.1016/j.celrep.2019.11.081] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 10/08/2019] [Accepted: 11/20/2019] [Indexed: 12/17/2022] Open
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128
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Marek S, Tervo-Clemmens B, Nielsen AN, Wheelock MD, Miller RL, Laumann TO, Earl E, Foran WW, Cordova M, Doyle O, Perrone A, Miranda-Dominguez O, Feczko E, Sturgeon D, Graham A, Hermosillo R, Snider K, Galassi A, Nagel BJ, Ewing SWF, Eggebrecht AT, Garavan H, Dale AM, Greene DJ, Barch DM, Fair DA, Luna B, Dosenbach NUF. Identifying reproducible individual differences in childhood functional brain networks: An ABCD study. Dev Cogn Neurosci 2019; 40:100706. [PMID: 31614255 PMCID: PMC6927479 DOI: 10.1016/j.dcn.2019.100706] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 08/01/2019] [Accepted: 08/21/2019] [Indexed: 02/08/2023] Open
Abstract
The 21-site Adolescent Brain Cognitive Development (ABCD) study provides an unparalleled opportunity to characterize functional brain development via resting-state functional connectivity (RSFC) and to quantify relationships between RSFC and behavior. This multi-site data set includes potentially confounding sources of variance, such as differences between data collection sites and/or scanner manufacturers, in addition to those inherent to RSFC (e.g., head motion). The ABCD project provides a framework for characterizing and reproducing RSFC and RSFC-behavior associations, while quantifying the extent to which sources of variability bias RSFC estimates. We quantified RSFC and functional network architecture in 2,188 9-10-year old children from the ABCD study, segregated into demographically-matched discovery (N = 1,166) and replication datasets (N = 1,022). We found RSFC and network architecture to be highly reproducible across children. We did not observe strong effects of site; however, scanner manufacturer effects were large, reproducible, and followed a "short-to-long" association with distance between regions. Accounting for potential confounding variables, we replicated that RSFC between several higher-order networks was related to general cognition. In sum, we provide a framework for how to characterize RSFC-behavior relationships in a rigorous and reproducible manner using the ABCD dataset and other large multi-site projects.
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Affiliation(s)
- Scott Marek
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, 63110, USA.
| | | | - Ashley N Nielsen
- Department of Medical Social Sciences, Northwestern University, Chicago, IL 60611, USA
| | - Muriah D Wheelock
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - Ryland L Miller
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - Eric Earl
- Departments of Psychiatry & Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97213, USA
| | - William W Foran
- Department of Psychiatry, University of Pittsburgh, Pittsburgh PA, 15213, USA
| | - Michaela Cordova
- Departments of Psychiatry & Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97213, USA
| | - Olivia Doyle
- Departments of Psychiatry & Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97213, USA
| | - Anders Perrone
- Departments of Psychiatry & Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97213, USA
| | - Oscar Miranda-Dominguez
- Departments of Psychiatry & Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97213, USA
| | - Eric Feczko
- Departments of Psychiatry & Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97213, USA; Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, 97213, USA
| | - Darrick Sturgeon
- Departments of Psychiatry & Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97213, USA
| | - Alice Graham
- Departments of Psychiatry & Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97213, USA
| | - Robert Hermosillo
- Departments of Psychiatry & Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97213, USA
| | - Kathy Snider
- Departments of Psychiatry & Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97213, USA
| | - Anthony Galassi
- Departments of Psychiatry & Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97213, USA
| | - Bonnie J Nagel
- Departments of Psychiatry & Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97213, USA
| | - Sarah W Feldstein Ewing
- Departments of Psychiatry & Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97213, USA
| | - Adam T Eggebrecht
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont, Burlington VT 05401, USA
| | - Anders M Dale
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Deanna J Greene
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, 63110, USA; Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, 63110, USA; Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, 63110, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Damien A Fair
- Departments of Psychiatry & Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97213, USA
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, Pittsburgh PA, 15213, USA
| | - Nico U F Dosenbach
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, 63110, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, 63110, USA; Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, 63110, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, 63110, USA
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129
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Chen X, Sobczak F, Chen Y, Jiang Y, Qian C, Lu Z, Ayata C, Logothetis NK, Yu X. Mapping optogenetically-driven single-vessel fMRI with concurrent neuronal calcium recordings in the rat hippocampus. Nat Commun 2019; 10:5239. [PMID: 31748553 PMCID: PMC6868210 DOI: 10.1038/s41467-019-12850-x] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 10/01/2019] [Indexed: 01/07/2023] Open
Abstract
Extensive in vivo imaging studies investigate the hippocampal neural network function, mainly focusing on the dorsal CA1 region given its optical accessibility. Multi-modality fMRI with simultaneous hippocampal electrophysiological recording reveal broad cortical correlation patterns, but the detailed spatial hippocampal functional map remains lacking given the limited fMRI resolution. In particular, hemodynamic responses linked to specific neural activity are unclear at the single-vessel level across hippocampal vasculature, which hinders the deciphering of the hippocampal malfunction in animal models and the translation to critical neurovascular coupling (NVC) patterns for human fMRI. We simultaneously acquired optogenetically-driven neuronal Ca2+ signals with single-vessel blood-oxygen-level-dependent (BOLD) and cerebral-blood-volume (CBV)-fMRI from individual venules and arterioles. Distinct spatiotemporal patterns of hippocampal hemodynamic responses were correlated to optogenetically evoked and spreading depression-like calcium events. The calcium event-related single-vessel hemodynamic modeling revealed significantly reduced NVC efficiency upon spreading depression-like (SDL) events, providing a direct measure of the NVC function at various hippocampal states.
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Affiliation(s)
- Xuming Chen
- Research Group of Translational Neuroimaging and Neural Control, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, 72076, Tuebingen, Germany
- University of Tuebingen, 72074, Tuebingen, Germany
- Department of Neurology, Wuhan University, Renmin Hospital, Wuhan, 430060, China
| | - Filip Sobczak
- Research Group of Translational Neuroimaging and Neural Control, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, 72076, Tuebingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, 72074, Tuebingen, Germany
| | - Yi Chen
- Research Group of Translational Neuroimaging and Neural Control, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, 72076, Tuebingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, 72074, Tuebingen, Germany
| | - Yuanyuan Jiang
- Research Group of Translational Neuroimaging and Neural Control, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, 72076, Tuebingen, Germany
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, 02129, MA, USA
| | - Chunqi Qian
- Department of Radiology, Michigan State University, East Lansing, 48824, MI, USA
| | - Zuneng Lu
- Department of Neurology, Wuhan University, Renmin Hospital, Wuhan, 430060, China
| | - Cenk Ayata
- Neurovascular Research Laboratory, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, 02129, MA, USA
- Stroke Service and Neuroscience Intensive Care Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 02129, Boston, USA
| | - Nikos K Logothetis
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tuebingen, 72076, Germany
- Department of Imaging Science and Biomedical Engineering, University of Manchester, Manchester, M13 9PT, UK
| | - Xin Yu
- Research Group of Translational Neuroimaging and Neural Control, High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, 72076, Tuebingen, Germany.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, 02129, MA, USA.
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130
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Kumral D, Şansal F, Cesnaite E, Mahjoory K, Al E, Gaebler M, Nikulin VV, Villringer A. BOLD and EEG signal variability at rest differently relate to aging in the human brain. Neuroimage 2019; 207:116373. [PMID: 31759114 DOI: 10.1016/j.neuroimage.2019.116373] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 10/17/2019] [Accepted: 11/17/2019] [Indexed: 01/22/2023] Open
Abstract
Variability of neural activity is regarded as a crucial feature of healthy brain function, and several neuroimaging approaches have been employed to assess it noninvasively. Studies on the variability of both evoked brain response and spontaneous brain signals have shown remarkable changes with aging but it is unclear if the different measures of brain signal variability - identified with either hemodynamic or electrophysiological methods - reflect the same underlying physiology. In this study, we aimed to explore age differences of spontaneous brain signal variability with two different imaging modalities (EEG, fMRI) in healthy younger (25 ± 3 years, N = 135) and older (67 ± 4 years, N = 54) adults. Consistent with the previous studies, we found lower blood oxygenation level dependent (BOLD) variability in the older subjects as well as less signal variability in the amplitude of low-frequency oscillations (1-12 Hz), measured in source space. These age-related reductions were mostly observed in the areas that overlap with the default mode network. Moreover, age-related increases of variability in the amplitude of beta-band frequency EEG oscillations (15-25 Hz) were seen predominantly in temporal brain regions. There were significant sex differences in EEG signal variability in various brain regions while no significant sex differences were observed in BOLD signal variability. Bivariate and multivariate correlation analyses revealed no significant associations between EEG- and fMRI-based variability measures. In summary, we show that both BOLD and EEG signal variability reflect aging-related processes but are likely to be dominated by different physiological origins, which relate differentially to age and sex.
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Affiliation(s)
- D Kumral
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; MindBrainBody Institute at the Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany.
| | - F Şansal
- International Graduate Program Medical Neurosciences, Charité-Universitätsmedizin, Berlin, Germany; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - E Cesnaite
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - K Mahjoory
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute for Biomagnetism and Biosignalanalysis, University of Muenster, Muenster, Germany
| | - E Al
- MindBrainBody Institute at the Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - M Gaebler
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; MindBrainBody Institute at the Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - V V Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité Universitätsmedizin Berlin, Berlin, Germany; Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | - A Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; MindBrainBody Institute at the Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany; Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany; Department of Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany
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131
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Özbay PS, Chang C, Picchioni D, Mandelkow H, Chappel-Farley MG, van Gelderen P, de Zwart JA, Duyn J. Sympathetic activity contributes to the fMRI signal. Commun Biol 2019; 2:421. [PMID: 31754651 PMCID: PMC6861267 DOI: 10.1038/s42003-019-0659-0] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 10/21/2019] [Indexed: 12/15/2022] Open
Abstract
The interpretation of functional magnetic resonance imaging (fMRI) studies of brain activity is often hampered by the presence of brain-wide signal variations that may arise from a variety of neuronal and non-neuronal sources. Recent work suggests a contribution from the sympathetic vascular innervation, which may affect the fMRI signal through its putative and poorly understood role in cerebral blood flow (CBF) regulation. By analyzing fMRI and (electro-) physiological signals concurrently acquired during sleep, we found that widespread fMRI signal changes often co-occur with electroencephalography (EEG) K-complexes, signatures of sub-cortical arousal, and episodic drops in finger skin vascular tone; phenomena that have been associated with intermittent sympathetic activity. These findings support the notion that the extrinsic sympathetic innervation of the cerebral vasculature contributes to CBF regulation and the fMRI signal. Accounting for this mechanism could help separate systemic from local signal contributions and improve interpretation of fMRI studies.
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Affiliation(s)
- Pinar Senay Özbay
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD USA
| | | | - Dante Picchioni
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD USA
| | - Hendrik Mandelkow
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD USA
| | | | - Peter van Gelderen
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD USA
| | | | - Jeff Duyn
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD USA
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132
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Gu Y, Han F, Liu X. Arousal Contributions to Resting-State fMRI Connectivity and Dynamics. Front Neurosci 2019; 13:1190. [PMID: 31749680 PMCID: PMC6848024 DOI: 10.3389/fnins.2019.01190] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 10/21/2019] [Indexed: 11/20/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rsfMRI) is being widely used for charting brain connectivity and dynamics in healthy and diseased brains. However, the resting state paradigm allows an unconstrained fluctuation of brain arousal, which may have profound effects on resting-state fMRI signals and associated connectivity/dynamic metrics. Here, we review current understandings of the relationship between resting-state fMRI and brain arousal, in particular the effect of a recently discovered event of arousal modulation on resting-state fMRI. We further discuss potential implications of arousal-related fMRI modulation with a focus on its potential role in mediating spurious correlations between resting-state connectivity/dynamics with physiology and behavior. Multiple hypotheses are formulated based on existing evidence and remain to be tested by future studies.
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Affiliation(s)
- Yameng Gu
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, United States
| | - Feng Han
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, United States
| | - Xiao Liu
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, United States.,Institute for CyberScience, The Pennsylvania State University, University Park, PA, United States
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133
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Thalamic low frequency activity facilitates resting-state cortical interhemispheric MRI functional connectivity. Neuroimage 2019; 201:115985. [DOI: 10.1016/j.neuroimage.2019.06.063] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 06/18/2019] [Accepted: 06/26/2019] [Indexed: 12/20/2022] Open
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134
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Vigilance declines following sleep deprivation are associated with two previously identified dynamic connectivity states. Neuroimage 2019; 200:382-390. [DOI: 10.1016/j.neuroimage.2019.07.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 06/26/2019] [Accepted: 07/01/2019] [Indexed: 11/20/2022] Open
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135
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Podvalny E, Flounders MW, King LE, Holroyd T, He BJ. A dual role of prestimulus spontaneous neural activity in visual object recognition. Nat Commun 2019; 10:3910. [PMID: 31477706 PMCID: PMC6718405 DOI: 10.1038/s41467-019-11877-4] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 08/08/2019] [Indexed: 12/17/2022] Open
Abstract
Vision relies on both specific knowledge of visual attributes, such as object categories, and general brain states, such as those reflecting arousal. We hypothesized that these phenomena independently influence recognition of forthcoming stimuli through distinct processes reflected in spontaneous neural activity. Here, we recorded magnetoencephalographic (MEG) activity in participants (N = 24) who viewed images of objects presented at recognition threshold. Using multivariate analysis applied to sensor-level activity patterns recorded before stimulus presentation, we identified two neural processes influencing subsequent subjective recognition: a general process, which disregards stimulus category and correlates with pupil size, and a specific process, which facilitates category-specific recognition. The two processes are doubly-dissociable: the general process correlates with changes in criterion but not in sensitivity, whereas the specific process correlates with changes in sensitivity but not in criterion. Our findings reveal distinct mechanisms of how spontaneous neural activity influences perception and provide a framework to integrate previous findings. The effect of spontaneous variations in prestimulus neural activity on subsequent perception is incompletely understood. Here, using MEG, the authors identify two distinct neural processes that can influence object recognition in different ways.
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Affiliation(s)
- Ella Podvalny
- Neuroscience Institute, New York University School of Medicine, New York, NY, 10016, USA.
| | - Matthew W Flounders
- Neuroscience Institute, New York University School of Medicine, New York, NY, 10016, USA
| | - Leana E King
- Neuroscience Institute, New York University School of Medicine, New York, NY, 10016, USA
| | - Tom Holroyd
- Magnetoencephalography Core Facility, National Institute of Mental Health, Bethesda, MD, 20892, USA
| | - Biyu J He
- Neuroscience Institute, New York University School of Medicine, New York, NY, 10016, USA. .,Departments of Neurology, Neuroscience & Physiology, and Radiology, New York University School of Medicine, New York, NY, 10016, USA.
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136
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Kassinopoulos M, Mitsis GD. Identification of physiological response functions to correct for fluctuations in resting-state fMRI related to heart rate and respiration. Neuroimage 2019; 202:116150. [PMID: 31487547 DOI: 10.1016/j.neuroimage.2019.116150] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 07/30/2019] [Accepted: 08/30/2019] [Indexed: 12/31/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) is widely viewed as the gold standard for studying brain function due to its high spatial resolution and non-invasive nature. However, it is well established that changes in breathing patterns and heart rate strongly influence the blood oxygen-level dependent (BOLD) fMRI signal and this, in turn, can have considerable effects on fMRI studies, particularly resting-state studies. The dynamic effects of physiological processes are often quantified by using convolution models along with simultaneously recorded physiological data. In this context, physiological response function (PRF) curves (cardiac and respiratory response functions), which are convolved with the corresponding physiological fluctuations, are commonly employed. While it has often been suggested that the PRF curves may be region- or subject-specific, it is still an open question whether this is the case. In the present study, we propose a novel framework for the robust estimation of PRF curves and use this framework to rigorously examine the implications of using population-, subject-, session- and scan-specific PRF curves. The proposed framework was tested on resting-state fMRI and physiological data from the Human Connectome Project. Our results suggest that PRF curves vary significantly across subjects and, to a lesser extent, across sessions from the same subject. These differences can be partly attributed to physiological variables such as the mean and variance of the heart rate during the scan. The proposed methodological framework can be used to obtain robust scan-specific PRF curves from data records with duration longer than 5 min, exhibiting significantly improved performance compared to previously defined canonical cardiac and respiration response functions. Besides removing physiological confounds from the BOLD signal, accurate modeling of subject- (or session-/scan-) specific PRF curves is of importance in studies that involve populations with altered vascular responses, such as aging subjects.
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Affiliation(s)
- Michalis Kassinopoulos
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Georgios D Mitsis
- Department of Bioengineering, McGill University, Montreal, QC, Canada.
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137
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Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest. Neuroimage 2019; 202:116129. [PMID: 31461679 DOI: 10.1016/j.neuroimage.2019.116129] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 08/16/2019] [Accepted: 08/23/2019] [Indexed: 01/26/2023] Open
Abstract
Brain functional connectivity (FC) changes have been measured across seconds using fMRI. This is true for both rest and task scenarios. Moreover, it is well accepted that task engagement alters FC, and that dynamic estimates of FC during and before task events can help predict their nature and performance. Yet, when it comes to dynamic FC (dFC) during rest, there is no consensus about its origin or significance. Some argue that rest dFC reflects fluctuations in on-going cognition, or is a manifestation of intrinsic brain maintenance mechanisms, which could have predictive clinical value. Conversely, others have concluded that rest dFC is mostly the result of sampling variability, head motion or fluctuating sleep states. Here, we present novel analyses suggesting that rest dFC is influenced by short periods of spontaneous cognitive-task-like processes, and that the cognitive nature of such mental processes can be inferred blindly from the data. As such, several different behaviorally relevant whole-brain FC configurations may occur during a single rest scan even when subjects were continuously awake and displayed minimal motion. In addition, using low dimensional embeddings as visualization aids, we show how FC states-commonly used to summarize and interpret resting dFC-can accurately and robustly reveal periods of externally imposed tasks; however, they may be less effective in capturing periods of distinct cognition during rest.
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138
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Moradi N, Dousty M, Sotero RC. Spatiotemporal Empirical Mode Decomposition of Resting-State fMRI Signals: Application to Global Signal Regression. Front Neurosci 2019; 13:736. [PMID: 31396032 PMCID: PMC6664052 DOI: 10.3389/fnins.2019.00736] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 07/02/2019] [Indexed: 12/15/2022] Open
Abstract
Resting-state functional connectivity MRI (rs-fcMRI) is a common method for mapping functional brain networks. However, estimation of these networks is affected by the presence of a common global systemic noise, or global signal (GS). Previous studies have shown that the common preprocessing steps of removing the GS may create spurious correlations between brain regions. In this paper, we decompose fMRI signals into 5 spatial and 3 temporal intrinsic mode functions (SIMF and TIMF, respectively) by means of the empirical mode decomposition (EMD), which is an adaptive data-driven method widely used to analyze non-linear and non-stationary phenomena. For each SIMF, functional connectivity matrices were computed by means of Pearson correlation between TIMFs of different brain areas. Thus, instead of a single connectivity matrix, we obtained 5 × 3 = 15 functional connectivity matrices. Given the high correlation and global efficiency values of the connectivity matrices related to the low spatial maps (SIMF3, SIMF4, and SIMF5), our results suggest that these maps can be considered as spatial global signal masks. Thus, by summing up the first two SIMFs extracted from the fMRI signals, we have automatically excluded the GS which is now voxel-specific. We compared the performance of our method with the conventional GS regression and to the results when the GS was not removed. While the correlation pattern identified by the other methods suffers from a low level of precision in identifying the correct brain network connectivity, our approach demonstrated expected connectivity patterns for the default mode network and task-positive network.
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Affiliation(s)
- Narges Moradi
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Computational Neurophysics Lab, Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Mehdy Dousty
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada.,KITE, Toronto Rehab, University Health Network, Toronto, ON, Canada
| | - Roberto C Sotero
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Computational Neurophysics Lab, Department of Radiology, University of Calgary, Calgary, AB, Canada
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139
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Han F, Gu Y, Liu X. A Neurophysiological Event of Arousal Modulation May Underlie fMRI-EEG Correlations. Front Neurosci 2019; 13:823. [PMID: 31447638 PMCID: PMC6692480 DOI: 10.3389/fnins.2019.00823] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 07/23/2019] [Indexed: 12/11/2022] Open
Affiliation(s)
- Feng Han
- Department of Biomedical Engineering, The Pennsylvania State University, State College, PA, United States
| | - Yameng Gu
- Department of Biomedical Engineering, The Pennsylvania State University, State College, PA, United States
| | - Xiao Liu
- Department of Biomedical Engineering, The Pennsylvania State University, State College, PA, United States.,Institute for CyberScience, The Pennsylvania State University, State College, PA, United States
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140
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Enhanced high-frequency precuneus-cortical effective connectivity is associated with decreased sensory gating following total sleep deprivation. Neuroimage 2019; 197:255-263. [DOI: 10.1016/j.neuroimage.2019.04.057] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 04/13/2019] [Accepted: 04/20/2019] [Indexed: 12/31/2022] Open
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141
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Drew PJ, Winder AT, Zhang Q. Twitches, Blinks, and Fidgets: Important Generators of Ongoing Neural Activity. Neuroscientist 2019; 25:298-313. [PMID: 30311838 PMCID: PMC6800083 DOI: 10.1177/1073858418805427] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Animals and humans continuously engage in small, spontaneous motor actions, such as blinking, whisking, and postural adjustments ("fidgeting"). These movements are accompanied by changes in neural activity in sensory and motor regions of the brain. The frequency of these motions varies in time, is affected by sensory stimuli, arousal levels, and pathology. These fidgeting behaviors can be entrained by sensory stimuli. Fidgeting behaviors will cause distributed, bilateral functional activation in the 0.01 to 0.1 Hz frequency range that will show up in functional magnetic resonance imaging and wide-field calcium neuroimaging studies, and will contribute to the observed functional connectivity among brain regions. However, despite the large potential of these behaviors to drive brain-wide activity, these fidget-like behaviors are rarely monitored. We argue that studies of spontaneous and evoked brain dynamics in awake animals and humans should closely monitor these fidgeting behaviors. Differences in these fidgeting behaviors due to arousal or pathology will "contaminate" ongoing neural activity, and lead to apparent differences in functional connectivity. Monitoring and accounting for the brain-wide activations by these behaviors is essential during experiments to differentiate fidget-driven activity from internally driven neural dynamics.
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Affiliation(s)
- Patrick J Drew
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA
- Department of Neurosurgery and Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Aaron T Winder
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA
| | - Qingguang Zhang
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA
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142
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Abstract
A major challenge in systems-level neuroscience is to understand the dynamic formation and succession of brain states. A new study has extracted reproducible brain states from mouse resting-state fMRI data, revealing interactions between occurrences of these states and the phase of global signal fluctuations and alterations of the states in a mouse model of autism.
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Affiliation(s)
- Dimitri Van De Ville
- Ecole Polytechnique Fédérale de Lausanne, Institute of Bioengineering/Center for Neuroprosthetics, School of Engineering, Switzerland; University of Geneva, Department of Radiology and Medical Informatics, Faculty of Medicine, Switzerland.
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143
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Drew PJ. Vascular and neural basis of the BOLD signal. Curr Opin Neurobiol 2019; 58:61-69. [PMID: 31336326 DOI: 10.1016/j.conb.2019.06.004] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 06/22/2019] [Indexed: 12/26/2022]
Abstract
Neural activity in the brain is usually coupled to increases in local cerebral blood flow, leading to the increase in oxygenation that generates the BOLD fMRI signal. Recent work has begun to elucidate the vascular and neural mechanisms underlying the BOLD signal. The dilatory response is distributed throughout the vascular network. Arteries actively dilate within a second following neural activity increases, while venous distensions are passive and have a time course that last tens of seconds. Vasodilation, and thus local blood flow, is controlled by the activity of both neurons and astrocytes via multiple different pathways. The relationship between sensory-driven neural activity and the vascular dynamics in sensory areas are well-captured with a linear convolution model. However, depending on the behavioral state or brain region, the coupling between neural activity and hemodynamic signals can be weak or even inverted.
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Affiliation(s)
- Patrick J Drew
- Departments of Engineering Science and Mechanics, Biomedical Engineering and Neurosurgery, Pennsylvania State University, University Park, PA 16802, United States.
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144
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Smith DM, Zhao Y, Keilholz SD, Schumacher EH. Investigating the Intersession Reliability of Dynamic Brain-State Properties. Brain Connect 2019; 8:255-267. [PMID: 29924644 DOI: 10.1089/brain.2017.0571] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Dynamic functional connectivity metrics have much to offer to the neuroscience of individual differences of cognition. Yet, despite the recent expansion in dynamic connectivity research, limited resources have been devoted to the study of the reliability of these connectivity measures. To address this, resting-state functional magnetic resonance imaging data from 100 Human Connectome Project subjects were compared across 2 scan days. Brain states (i.e., patterns of coactivity across regions) were identified by classifying each time frame using k means clustering. This was done with and without global signal regression (GSR). Multiple gauges of reliability indicated consistency in the brain-state properties across days and GSR attenuated the reliability of the brain states. Changes in the brain-state properties across the course of the scan were investigated as well. The results demonstrate that summary metrics describing the clustering of individual time frames have adequate test/retest reliability, and thus, these patterns of brain activation may hold promise for individual-difference research.
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Affiliation(s)
- Derek M Smith
- 1 School of Psychology, Georgia Institute of Technology , Atlanta, Georgia
| | - Yrian Zhao
- 2 Biomedical Engineering, Georgia Institute of Technology and Emory University , Atlanta, Georgia
| | - Shella D Keilholz
- 2 Biomedical Engineering, Georgia Institute of Technology and Emory University , Atlanta, Georgia
| | - Eric H Schumacher
- 1 School of Psychology, Georgia Institute of Technology , Atlanta, Georgia
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145
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Tong Y, Yao JF, Chen JJ, Frederick BD. The resting-state fMRI arterial signal predicts differential blood transit time through the brain. J Cereb Blood Flow Metab 2019; 39:1148-1160. [PMID: 29333912 PMCID: PMC6547182 DOI: 10.1177/0271678x17753329] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Previous studies have found that aperiodic, systemic low-frequency oscillations (sLFOs) are present in blood-oxygen-level-dependent (BOLD) data. These signals are in the same low frequency band as the "resting state" signal; however, they are distinct signals which represent non-neuronal, physiological oscillations. The same sLFOs are found in the periphery (i.e. finger tips) as changes in oxy/deoxy-hemoglobin concentration using concurrent near-infrared spectroscopy. Together, this evidence points toward an extra-cerebral origin of these sLFOs. If this is the case, it is expected that these sLFO signals would be found in the carotid arteries with time delays that precede the signals found in the brain. To test this hypothesis, we employed the publicly available MyConnectome dataset (a two-year longitudinal study of a single subject) to extract the sLFOs in the internal carotid arteries (ICAs) with the help of the T1/T2-weighted images. Significant, but negative, correlations were found between the LFO BOLD signals from the ICAs and (1) the global signal (GS), (2) the superior sagittal sinus, and (3) the jugulars. We found the consistent time delays between the sLFO signals from ICAs, GS and veins which coincide with the blood transit time through the cerebral vascular tree.
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Affiliation(s)
- Yunjie Tong
- 1 Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Jinxia Fiona Yao
- 2 Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, USA
| | - J Jean Chen
- 3 Rotman Research Institute, Baycrest Centre, Canada; Department of Medical Biophysics, University of Toronto, Canada
| | - Blaise deB Frederick
- 4 Brain Imaging Center, McLean Hospital, Belmont, MA, USA.,5 Department of Psychiatry, Harvard University Medical School, Boston, MA, USA
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146
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Shine JM. Neuromodulatory Influences on Integration and Segregation in the Brain. Trends Cogn Sci 2019; 23:572-583. [PMID: 31076192 DOI: 10.1016/j.tics.2019.04.002] [Citation(s) in RCA: 123] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Revised: 04/01/2019] [Accepted: 04/04/2019] [Indexed: 12/20/2022]
Abstract
Cognitive function relies on the dynamic cooperation of specialized regions of the brain; however, the elements of the system responsible for coordinating this interaction remain poorly understood. In this Opinion article I argue that this capacity is mediated in part by competitive and cooperative dynamic interactions between two prominent metabotropic neuromodulatory systems - the cholinergic basal forebrain and the noradrenergic locus coeruleus (LC). I assert that activity in these projection nuclei regulates the amount of segregation and integration within the whole brain network by modulating the activity of a diverse set of specialized regions of the brain on a timescale relevant for cognition and attention.
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Affiliation(s)
- James M Shine
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia.
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147
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Cardoso MMB, Lima B, Sirotin YB, Das A. Task-related hemodynamic responses are modulated by reward and task engagement. PLoS Biol 2019; 17:e3000080. [PMID: 31002659 PMCID: PMC6493772 DOI: 10.1371/journal.pbio.3000080] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 05/01/2019] [Accepted: 03/29/2019] [Indexed: 01/18/2023] Open
Abstract
Hemodynamic recordings from visual cortex contain powerful endogenous task-related responses that may reflect task-related arousal, or "task engagement" distinct from attention. We tested this hypothesis with hemodynamic measurements (intrinsic-signal optical imaging) from monkey primary visual cortex (V1) while the animals' engagement in a periodic fixation task over several hours was varied through reward size and as animals took breaks. With higher rewards, animals appeared more task-engaged; task-related responses were more temporally precise at the task period (approximately 10-20 seconds) and modestly stronger. The 2-5 minute blocks of high-reward trials led to ramp-like decreases in mean local blood volume; these reversed with ramp-like increases during low reward. The blood volume increased even more sharply when the animal shut his eyes and disengaged completely from the task (5-10 minutes). We propose a mechanism that controls vascular tone, likely along with local neural responses in a manner that reflects task engagement over the full range of timescales tested.
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Affiliation(s)
- Mariana M. B. Cardoso
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Center for Neural Science, New York University, New York, New York, United States of America
| | - Bruss Lima
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Yevgeniy B. Sirotin
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Identity and Data Science Laboratory of Science Applications International Corporation, Annapolis Junction, Maryland, United States of America
| | - Aniruddha Das
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, New York, United States of America
- * E-mail:
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148
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de la Cruz F, Schumann A, Köhler S, Reichenbach JR, Wagner G, Bär KJ. The relationship between heart rate and functional connectivity of brain regions involved in autonomic control. Neuroimage 2019; 196:318-328. [PMID: 30981856 DOI: 10.1016/j.neuroimage.2019.04.014] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 03/27/2019] [Accepted: 04/03/2019] [Indexed: 12/15/2022] Open
Abstract
The peripheral autonomic nervous system (ANS) adjusts the heart rate (HR) to intrinsic and extrinsic demands. It is controlled by a group of functionally connected brain regions assembling the so-called central autonomic network (CAN). More specifically, forebrain cortical regions, limbic and brainstem structures within the CAN have been identified as important components of circuits involved in HR regulation. The present study aimed to investigate whether functional connectivity (FC) between these regions varies in subjects with different heart rates. Thus, 84 healthy subjects were separated according to their HR in slow, medium and fast. We observed a direct association between HR and FC in CAN regions, where stronger FC was related to slower HR. This relationship, however, is non-linear, follows an exponential course and is not restricted to CAN areas only. The network-based analysis (NBS) using time series from 262 independent anatomical ROIs revealed significantly increased functional connectivity in subjects with slow HR compared to subjects with fast HR mainly in regions being part of the salience network, but also of the default-mode network. We additionally simulated the effect of aliasing on the functional connectivity using several TRs and heart rates to exclude the possibility that FC differences might be due to different aliasing effects in the data. The result of the simulation indicated that aliasing cannot explain our findings. Thus, present results imply a functionally meaningful coupling between FC and HR that need to be accounted for in future studies. Moreover, given the established link between HR and emotional, cognitive and social processes, present findings may also be considered to explain individual differences in brain activation or connectivity when using corresponding paradigms in the MR scanner to investigate such processes.
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Affiliation(s)
- Feliberto de la Cruz
- Psychiatric Brain and Body Research Group, Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Andy Schumann
- Psychiatric Brain and Body Research Group, Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Stefanie Köhler
- Psychiatric Brain and Body Research Group, Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Jürgen R Reichenbach
- Medical Physics Group, Department of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany; Michael Stifel Center for Data-driven and Simulation Science Jena, Friedrich Schiller University, Jena, Germany
| | - Gerd Wagner
- Psychiatric Brain and Body Research Group, Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Karl-Jürgen Bär
- Psychiatric Brain and Body Research Group, Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.
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149
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Li J, Kong R, Liégeois R, Orban C, Tan Y, Sun N, Holmes AJ, Sabuncu MR, Ge T, Yeo BTT. Global signal regression strengthens association between resting-state functional connectivity and behavior. Neuroimage 2019; 196:126-141. [PMID: 30974241 PMCID: PMC6585462 DOI: 10.1016/j.neuroimage.2019.04.016] [Citation(s) in RCA: 212] [Impact Index Per Article: 42.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 04/01/2019] [Accepted: 04/04/2019] [Indexed: 01/02/2023] Open
Abstract
Global signal regression (GSR) is one of the most debated preprocessing strategies for resting-state functional MRI. GSR effectively removes global artifacts driven by motion and respiration, but also discards globally distributed neural information and introduces negative correlations between certain brain regions. The vast majority of previous studies have focused on the effectiveness of GSR in removing imaging artifacts, as well as its potential biases. Given the growing interest in functional connectivity fingerprinting, here we considered the utilitarian question of whether GSR strengthens or weakens associations between resting-state functional connectivity (RSFC) and multiple behavioral measures across cognition, personality and emotion. By applying the variance component model to the Brain Genomics Superstruct Project (GSP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 47% across 23 behavioral measures after GSR. In the Human Connectome Project (HCP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 40% across 58 behavioral measures, when GSR was applied after ICA-FIX de-noising. To ensure generalizability, we repeated our analyses using kernel regression. GSR improved behavioral prediction accuracies by an average of 64% and 12% in the GSP and HCP datasets respectively. Importantly, the results were consistent across methods. A behavioral measure with greater RSFC-explained variance (using the variance component model) also exhibited greater prediction accuracy (using kernel regression). A behavioral measure with greater improvement in behavioral variance explained after GSR (using the variance component model) also enjoyed greater improvement in prediction accuracy after GSR (using kernel regression). Furthermore, GSR appeared to benefit task performance measures more than self-reported measures. Since GSR was more effective at removing motion-related and respiratory-related artifacts, GSR-related increases in variance explained and prediction accuracies were unlikely the result of motion-related or respiratory-related artifacts. However, it is worth emphasizing that the current study focused on whole-brain RSFC, so it remains unclear whether GSR improves RSFC-behavioral associations for specific connections or networks. Overall, our results suggest that at least in the case for young healthy adults, GSR strengthens the associations between RSFC and most (although not all) behavioral measures. Code for the variance component model and ridge regression can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/preprocessing/Li2019_GSR.
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Affiliation(s)
- Jingwei Li
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
| | - Ru Kong
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
| | - Raphaël Liégeois
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
| | - Csaba Orban
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
| | - Yanrui Tan
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
| | - Nanbo Sun
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
| | | | - Mert R Sabuncu
- School of Electrical and Computer Engineering, Cornell University, USA
| | - Tian Ge
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore; NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore.
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150
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Kung YC, Li CW, Chen S, Chen SCJ, Lo CYZ, Lane TJ, Biswal B, Wu CW, Lin CP. Instability of brain connectivity during nonrapid eye movement sleep reflects altered properties of information integration. Hum Brain Mapp 2019; 40:3192-3202. [PMID: 30941797 DOI: 10.1002/hbm.24590] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 03/05/2019] [Accepted: 03/11/2019] [Indexed: 02/01/2023] Open
Abstract
Nonrapid eye movement (NREM) sleep is associated with fading consciousness in humans. Recent neuroimaging studies have demonstrated the spatiotemporal alterations of the brain functional connectivity (FC) in NREM sleep, suggesting the changes of information integration in the sleeping brain. However, the common stationarity assumption in FC does not satisfactorily explain the dynamic process of information integration during sleep. The dynamic FC (dFC) across brain networks is speculated to better reflect the time-varying information propagation during sleep. Accordingly, we conducted simultaneous EEG-fMRI recordings involving 12 healthy men during sleep and observed dFC across sleep stages using the sliding-window approach. We divided dFC into two aspects: mean dFC (dFCmean ) and variance dFC (dFCvar ). A high dFCmean indicates stable brain network integrity, whereas a high dFCvar indicates instability of information transfer within and between functional networks. For the network-based dFC, the dFCvar were negatively correlated with the dFCmean across the waking and three NREM sleep stages. As sleep deepened, the dFCmean decreased (N0~N1 > N2 > N3), whereas the dFCvar peaked during the N2 stage (N0~N1 < N3 < N2). The highest dFCvar during the N2 stage indicated the unstable synchronizations across the entire brain. In the N3 stage, the overall disrupted network integration was observed through the lowest dFCmean and elevated dFCvar, compared with N0 and N1. Conclusively, when the network specificity (dFCmean ) breaks down, the consciousness dissipates with increasing variability of information exchange (dFCvar ).
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Affiliation(s)
- Yi-Chia Kung
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Chia-Wei Li
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Shuo Chen
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Sharon Chia-Ju Chen
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chun-Yi Z Lo
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Timothy J Lane
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Center, Shuang-Ho Hospital, New Taipei, Taiwan.,Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey.,Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Changwei W Wu
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Center, Shuang-Ho Hospital, New Taipei, Taiwan.,Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan
| | - Ching-Po Lin
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan.,Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
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