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Siffredi V, Liverani MC, Fernandez N, Freitas LGA, Borradori Tolsa C, Van De Ville D, Hüppi PS, Ha-Vinh Leuchter R. Impact of a mindfulness-based intervention on neurobehavioral functioning and its association with large-scale brain networks in preterm young adolescents. Psychiatry Clin Neurosci 2024; 78:416-425. [PMID: 38757554 DOI: 10.1111/pcn.13675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 04/15/2024] [Accepted: 04/18/2024] [Indexed: 05/18/2024]
Abstract
AIM Adolescents born very preterm (VPT; <32 weeks of gestation) face an elevated risk of executive, behavioral, and socioemotional difficulties. Evidence suggests beneficial effects of mindfulness-based intervention (MBI) on these abilities. This study seeks to investigate the association between the effects of MBI on executive, behavioral, and socioemotional functioning and reliable changes in large-scale brain networks dynamics during rest in VPT young adolescents who completed an 8-week MBI program. METHODS Neurobehavioral assessments and resting-state functional magnetic resonance imaging were performed before and after MBI in 32 VPT young adolescents. Neurobehavioral abilities in VPT participants were compared with full-term controls. In the VPT group, dynamic functional connectivity was extracted by using the innovation-driven coactivation patterns framework. The reliable change index was used to quantify change after MBI. A multivariate data-driven approach was used to explore associations between MBI-related changes on neurobehavioral measures and temporal brain dynamics. RESULTS Compared with term-born controls, VPT adolescents showed reduced executive and socioemotional functioning before MBI. After MBI, a significant improvement was observed for all measures that were previously reduced in the VPT group. The increase in executive functioning, only, was associated with reliable changes in the duration of activation of large-scale brain networks, including frontolimbic, amygdala-hippocampus, dorsolateral prefrontal, and visual networks. CONCLUSION The improvement in executive functioning after an MBI was associated with reliable changes in large-scale brain network dynamics during rest. These changes encompassed frontolimbic, amygdala-hippocampus, dorsolateral prefrontal, and visual networks that are related to different executive processes including self-regulation, attentional control, and attentional awareness of relevant sensory stimuli.
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Affiliation(s)
- Vanessa Siffredi
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
- Neuro-X Institute, École polytechnique fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Maria Chiara Liverani
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
- SensoriMotor, Affective and Social Development Laboratory, Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
| | - Natalia Fernandez
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Lorena G A Freitas
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
- Neuro-X Institute, École polytechnique fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Cristina Borradori Tolsa
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Dimitri Van De Ville
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
- Neuro-X Institute, École polytechnique fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Petra Susan Hüppi
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Russia Ha-Vinh Leuchter
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
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Das SK, Sao AK, Biswal BB. Estimation of static and dynamic functional connectivity in resting-state fMRI using zero-frequency resonator. Hum Brain Mapp 2024; 45:e26606. [PMID: 38895977 PMCID: PMC11187872 DOI: 10.1002/hbm.26606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 11/28/2023] [Accepted: 12/29/2023] [Indexed: 06/21/2024] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly being used to infer the functional organization of the brain. Blood oxygen level-dependent (BOLD) features related to spontaneous neuronal activity, are yet to be clearly understood. Prior studies have hypothesized that rs-fMRI is spontaneous event-related and these events convey crucial information about the neuronal activity in estimating resting state functional connectivity (FC). Attempts have been made to extract these temporal events using a predetermined threshold. However, the thresholding methods in addition to being very sensitive to noise, may consider redundant events or exclude the low-valued inflection points. Here, we extract the event-related temporal onsets from the rs-fMRI time courses using a zero-frequency resonator (ZFR). The ZFR reflects the transient behavior of the BOLD events at its output. The conditional rate (CR) of the BOLD events occurring in a time course with respect to a seed time course is used to derive static FC. The temporal activity around the estimated events called high signal-to-noise ratio (SNR) segments are also obtained in the rs-fMRI time course and are then used to compute static and dynamic FCs during rest. Coactivation pattern (CAP) is the dynamic FC obtained using the high SNR segments driven by the ZFR. The static FC demonstrates that the ZFR-based CR distinguishes the coactivation and non-coactivation scores well in the distribution. CAP analysis demonstrated the stable and longer dwell time dominant resting state functional networks with high SNR segments driven by the ZFR. Static and dynamic FC analysis underpins that the ZFR-driven temporal onsets of BOLD events derive reliable and consistent FCs in the resting brain using a subset of the time points.
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Affiliation(s)
- Sukesh Kumar Das
- School of Computing and Electrical EngineeringIndian Institute of Technology MandiMandiHimachal PradeshIndia
| | - Anil K. Sao
- Department of Computer Science and EngineeringIndian Institute of Technology BhilaiBhilaiChhattisgarhIndia
| | - Bharat B. Biswal
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
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Ensel S, Uhrig L, Ozkirli A, Hoffner G, Tasserie J, Dehaene S, Van De Ville D, Jarraya B, Pirondini E. Transient brain activity dynamics discriminate levels of consciousness during anesthesia. Commun Biol 2024; 7:716. [PMID: 38858589 PMCID: PMC11164921 DOI: 10.1038/s42003-024-06335-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 05/15/2024] [Indexed: 06/12/2024] Open
Abstract
The awake mammalian brain is functionally organized in terms of large-scale distributed networks that are constantly interacting. Loss of consciousness might disrupt this temporal organization leaving patients unresponsive. We hypothesize that characterizing brain activity in terms of transient events may provide a signature of consciousness. For this, we analyze temporal dynamics of spatiotemporally overlapping functional networks obtained from fMRI transient activity across different anesthetics and levels of anesthesia. We first show a striking homology in spatial organization of networks between monkeys and humans, indicating cross-species similarities in resting-state fMRI structure. We then track how network organization shifts under different anesthesia conditions in macaque monkeys. While the spatial aspect of the networks is preserved, their temporal dynamics are highly affected by anesthesia. Networks express for longer durations and co-activate in an anesthetic-specific configuration. Additionally, hierarchical brain organization is disrupted with a consciousness-level-signature role of the default mode network. In conclusion, large-scale brain network temporal dynamics capture differences in anesthetic-specific consciousness-level, paving the way towards a clinical translation of these cortical signature.
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Affiliation(s)
- Scott Ensel
- Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lynn Uhrig
- NeuroSpin Center, Institute of BioImaging Commissariat à l'Energie Atomique, Gif/Yvette, France
- Cognitive Neuroimaging Unit, INSERM, U992, Gif/Yvette, France
- Department of Anesthesiology and Critical Care, Necker Hospital, AP-HP, Université Paris Cité, Paris, France
| | - Ayberk Ozkirli
- Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Guylaine Hoffner
- NeuroSpin Center, Institute of BioImaging Commissariat à l'Energie Atomique, Gif/Yvette, France
- Cognitive Neuroimaging Unit, INSERM, U992, Gif/Yvette, France
| | - Jordy Tasserie
- Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Stanislas Dehaene
- Cognitive Neuroimaging Unit, INSERM, U992, Gif/Yvette, France
- Collège de France, Paris, France
| | - Dimitri Van De Ville
- Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Béchir Jarraya
- NeuroSpin Center, Institute of BioImaging Commissariat à l'Energie Atomique, Gif/Yvette, France
- Cognitive Neuroimaging Unit, INSERM, U992, Gif/Yvette, France
- Université Paris-Saclay (UVSQ), Saclay, France
- Neuroscience Pole, Foch Hospital, Suresnes, France
| | - Elvira Pirondini
- Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
- Department of Physical Medicine & Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA.
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Uruñuela E, Gonzalez-Castillo J, Zheng C, Bandettini P, Caballero-Gaudes C. Whole-brain multivariate hemodynamic deconvolution for functional MRI with stability selection. Med Image Anal 2024; 91:103010. [PMID: 37950937 PMCID: PMC10843584 DOI: 10.1016/j.media.2023.103010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 10/11/2023] [Accepted: 10/17/2023] [Indexed: 11/13/2023]
Abstract
Conventionally, analysis of functional MRI (fMRI) data relies on available information about the experimental paradigm to establish hypothesized models of brain activity. However, this information can be inaccurate, incomplete or unavailable in multiple scenarios such as resting-state, naturalistic paradigms or clinical conditions. In these cases, blind estimates of neuronal-related activity can be obtained with paradigm-free analysis methods such as hemodynamic deconvolution. Yet, current formulations of the hemodynamic deconvolution problem have three important limitations: (1) their efficacy strongly depends on the appropriate selection of regularization parameters, (2) being univariate, they do not take advantage of the information present across the brain, and (3) they do not provide any measure of statistical certainty associated with each detected event. Here we propose a novel approach that addresses all these limitations. Specifically, we introduce multivariate sparse paradigm free mapping (Mv-SPFM), a novel hemodynamic deconvolution algorithm that operates at the whole brain level and adds spatial information via a mixed-norm regularization term over all voxels. Additionally, Mv-SPFM employs a stability selection procedure that removes the need to select regularization parameters and also lets us obtain an estimate of the true probability of having a neuronal-related BOLD event at each voxel and time-point based on the area under the curve (AUC) of the stability paths. Besides, we present a formulation tailored for multi-echo fMRI acquisitions (MvME-SPFM), which allows us to better isolate fluctuations of BOLD origin on the basis of their linear dependence with the echo time (TE) and to assign physiologically interpretable units (i.e., changes in the apparent transverse relaxation ΔR2∗) to the resulting deconvolved events. Remarkably, we demonstrate that Mv-SPFM achieves comparable performance even when using a single-echo formulation. We demonstrate that this algorithm outperforms existing state-of-the-art deconvolution approaches, and shows higher spatial and temporal agreement with the activation maps and BOLD signals obtained with a standard model-based linear regression approach, even at the level of individual neuronal events. Furthermore, we show that by employing stability selection, the performance of the algorithm depends less on the selection of temporal and spatial regularization parameters λ and ρ. Consequently, the proposed algorithm provides more reliable estimates of neuronal-related activity, here in terms of ΔR2∗, for the study of the dynamics of brain activity when no information about the timings of the BOLD events is available. This algorithm will be made publicly available as part of the splora Python package.
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Affiliation(s)
- Eneko Uruñuela
- Basque Center on Cognition, Brain and Language, Donostia - San Sebastián, Spain; University of the Basque Country (EHU/UPV), Donostia-San Sebastián, Spain.
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD 20892, United States
| | - Charles Zheng
- Machine Learning Team, Functional Magnetic Resonance Imaging Facility, National Institute of Mental Health, Bethesda, MD 20892, United States
| | - Peter Bandettini
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD 20892, United States
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5
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Morgenroth E, Vilaclara L, Muszynski M, Gaviria J, Vuilleumier P, Van De Ville D. Probing neurodynamics of experienced emotions-a Hitchhiker's guide to film fMRI. Soc Cogn Affect Neurosci 2023; 18:nsad063. [PMID: 37930850 PMCID: PMC10656947 DOI: 10.1093/scan/nsad063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 08/04/2023] [Accepted: 11/01/2023] [Indexed: 11/08/2023] Open
Abstract
Film functional magnetic resonance imaging (fMRI) has gained tremendous popularity in many areas of neuroscience. However, affective neuroscience remains somewhat behind in embracing this approach, even though films lend themselves to study how brain function gives rise to complex, dynamic and multivariate emotions. Here, we discuss the unique capabilities of film fMRI for emotion research, while providing a general guide of conducting such research. We first give a brief overview of emotion theories as these inform important design choices. Next, we discuss films as experimental paradigms for emotion elicitation and address the process of annotating them. We then situate film fMRI in the context of other fMRI approaches, and present an overview of results from extant studies so far with regard to advantages of film fMRI. We also give an overview of state-of-the-art analysis techniques including methods that probe neurodynamics. Finally, we convey limitations of using film fMRI to study emotion. In sum, this review offers a practitioners' guide to the emerging field of film fMRI and underscores how it can advance affective neuroscience.
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Affiliation(s)
- Elenor Morgenroth
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva 1202, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva 1202, Switzerland
- Swiss Center for Affective Sciences, University of Geneva, Geneva 1202, Switzerland
| | - Laura Vilaclara
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva 1202, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva 1202, Switzerland
| | - Michal Muszynski
- Department of Basic Neurosciences, University of Geneva, Geneva 1202, Switzerland
| | - Julian Gaviria
- Swiss Center for Affective Sciences, University of Geneva, Geneva 1202, Switzerland
- Department of Basic Neurosciences, University of Geneva, Geneva 1202, Switzerland
- Department of Psychiatry, University of Geneva, Geneva 1202, Switzerland
| | - Patrik Vuilleumier
- Swiss Center for Affective Sciences, University of Geneva, Geneva 1202, Switzerland
- Department of Basic Neurosciences, University of Geneva, Geneva 1202, Switzerland
- CIBM Center for Biomedical Imaging, Geneva 1202, Switzerland
| | - Dimitri Van De Ville
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva 1202, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva 1202, Switzerland
- CIBM Center for Biomedical Imaging, Geneva 1202, Switzerland
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6
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Rangaprakash D, David O, Barry RL, Deshpande G. Comparison of hemodynamic response functions obtained from resting-state functional MRI and invasive electrophysiological recordings in rats. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.27.530359. [PMID: 37961471 PMCID: PMC10634675 DOI: 10.1101/2023.02.27.530359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Resting-state functional MRI (rs-fMRI) is a popular technology that has enriched our understanding of brain and spinal cord functioning, including how different regions communicate (connectivity). But fMRI is an indirect measure of neural activity capturing blood hemodynamics. The hemodynamic response function (HRF) interfaces between the unmeasured neural activity and measured fMRI time series. The HRF is variable across brain regions and individuals, and is modulated by non-neural factors. Ignoring this HRF variability causes errors in FC estimates. Hence, it is crucial to reliably estimate the HRF from rs-fMRI data. Robust techniques have emerged to estimate the HRF from fMRI time series. Although such techniques have been validated non-invasively using simulated and empirical fMRI data, thorough invasive validation using simultaneous electrophysiological recordings, the gold standard, has been elusive. This report addresses this gap in the literature by comparing HRFs derived from invasive intracranial electroencephalogram recordings with HRFs estimated from simultaneously acquired fMRI data in six epileptic rats. We found that the HRF shape parameters (HRF amplitude, latency and width) were not significantly different (p>0.05) between ground truth and estimated HRFs. In the single pathological region, the HRF width was marginally significantly different (p=0.03). Our study provides preliminary invasive validation for the efficacy of the HRF estimation technique in reliably estimating the HRF non-invasively from rs-fMRI data directly. This has a notable impact on rs-fMRI connectivity studies, and we recommend that HRF deconvolution be performed to minimize HRF variability and improve connectivity estimates.
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Affiliation(s)
- D Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Olivier David
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institute of Neuroscience, F-38000, Grenoble, France
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Robert L Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
- Harvard-Massachusetts Institute of Technology Division of Health Sciences & Technology, Cambridge, Massachusetts, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
- Department of Psychological Sciences, Auburn University, Auburn, AL, USA
- Center for Neuroscience, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA
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7
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Rangaprakash D, Barry RL, Deshpande G. The confound of hemodynamic response function variability in human resting-state functional MRI studies. Front Neurosci 2023; 17:934138. [PMID: 37521709 PMCID: PMC10375034 DOI: 10.3389/fnins.2023.934138] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 04/07/2023] [Indexed: 08/01/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) is an indirect measure of neural activity with the hemodynamic response function (HRF) coupling it with unmeasured neural activity. The HRF, modulated by several non-neural factors, is variable across brain regions, individuals and populations. Yet, a majority of human resting-state fMRI connectivity studies continue to assume a non-variable HRF. In this article, with supportive prior evidence, we argue that HRF variability cannot be ignored as it substantially confounds within-subject connectivity estimates and between-subjects connectivity group differences. We also discuss its clinical relevance with connectivity impairments confounded by HRF aberrations in several disorders. We present limited data on HRF differences between women and men, which resulted in a 15.4% median error in functional connectivity estimates in a group-level comparison. We also discuss the implications of HRF variability for fMRI studies in the spinal cord. There is a need for more dialogue within the community on the HRF confound, and we hope that our article is a catalyst in the process.
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Affiliation(s)
- D. Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Robert L. Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, United States
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychological Sciences, Auburn University, Auburn, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Birmingham, AL, United States
- Key Laboratory for Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Centre for Brain Research, Indian Institute of Science, Bangalore, India
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8
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Kinany N, Khatibi A, Lungu O, Finsterbusch J, Büchel C, Marchand-Pauvert V, Ville DVD, Vahdat S, Doyon J. Decoding cerebro-spinal signatures of human behavior: application to motor sequence learning. Neuroimage 2023; 275:120174. [PMID: 37201642 DOI: 10.1016/j.neuroimage.2023.120174] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/12/2023] [Accepted: 05/15/2023] [Indexed: 05/20/2023] Open
Abstract
Mapping the neural patterns that drive human behavior is a key challenge in neuroscience. Even the simplest of our everyday actions stem from the dynamic and complex interplay of multiple neural structures across the central nervous system (CNS). Yet, most neuroimaging research has focused on investigating cerebral mechanisms, while the way the spinal cord accompanies the brain in shaping human behavior has been largely overlooked. Although the recent advent of functional magnetic resonance imaging (fMRI) sequences that can simultaneously target the brain and spinal cord has opened up new avenues for studying these mechanisms at multiple levels of the CNS, research to date has been limited to inferential univariate techniques that cannot fully unveil the intricacies of the underlying neural states. To address this, we propose to go beyond traditional analyses and instead use a data-driven multivariate approach leveraging the dynamic content of cerebro-spinal signals using innovation-driven coactivation patterns (iCAPs). We demonstrate the relevance of this approach in a simultaneous brain-spinal cord fMRI dataset acquired during motor sequence learning (MSL), to highlight how large-scale CNS plasticity underpins rapid improvements in early skill acquisition and slower consolidation after extended practice. Specifically, we uncovered cortical, subcortical and spinal functional networks, which were used to decode the different stages of learning with a high accuracy and, thus, delineate meaningful cerebro-spinal signatures of learning progression. Our results provide compelling evidence that the dynamics of neural signals, paired with a data-driven approach, can be used to disentangle the modular organization of the CNS. While we outline the potential of this framework to probe the neural correlates of motor learning, its versatility makes it broadly applicable to explore the functioning of cerebro-spinal networks in other experimental or pathological conditions.
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Affiliation(s)
- N Kinany
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, 1211, Switzerland; Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, 1202, Switzerland.
| | - A Khatibi
- Center of Precision Rehabilitation for Spinal Pain, School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Edgbaston, B15 2TT, United Kingdom
| | - O Lungu
- McConnell Brain Imaging Center, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - J Finsterbusch
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany
| | - C Büchel
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany
| | - V Marchand-Pauvert
- Sorbonne Université, Inserm, CNRS, Laboratoire d'Imagerie biomédicale, Paris F-75006, France
| | - D Van De Ville
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, 1211, Switzerland; Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, 1202, Switzerland
| | - S Vahdat
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, Florida, FL 32611, United States
| | - J Doyon
- McConnell Brain Imaging Center, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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Onoda K, Akama H. Complex of global functional network as the core of consciousness. Neurosci Res 2023; 190:67-77. [PMID: 36535365 DOI: 10.1016/j.neures.2022.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 11/20/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
Finding the neural basis of consciousness is challenging, and the distribution location of the core of consciousness remains inconclusive. Integrated information theory (IIT) argues that the posterior part of the brain is the hot zone of consciousness, especially phenological consciousness. The IIT has proposed a "main complex", a set of elements determined such that the information loss in a hierarchical partition approach is the largest among those of any other supersets and subsets, as the core of consciousness in a dynamic system. This approach may be applicable not only to phenomenal but also to access-consciousness. This study estimated the main complex of brain dynamics using functional magnetic resonance imaging in Human Connectome Project (HCP) and sleep datasets. The complex analyses revealed the common networks across various tasks and rest-state in HCP, composed of executive control, salience, and dorsal/ventral attention networks. The set of networks of the main complex was maintained during sleep. However, compared with the wakefulness stage, the amount of information of these networks and the default mode network, was reduced for the hypnagogic stage. The global interconnected structure composed of major functional networks can comprise the core of consciousness.
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Affiliation(s)
- Keiichi Onoda
- Department of Psychology, Otemon Gakuin University, Ibaraki, Osaka 567-8502, Japan.
| | - Hiroyuki Akama
- Department of Life Science and Technology, Tokyo Institute of Technology, Meguro, Tokyo 152-8550, Japan
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Das SK, Sao AK, Biswal BB. Estimation of neuronal task information in fMRI using zero frequency resonator. Neuroimage 2023; 267:119865. [PMID: 36610681 PMCID: PMC10635735 DOI: 10.1016/j.neuroimage.2023.119865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 12/14/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
In functional magnetic resonance imaging (fMRI), temporal onsets of BOLD events contain crucial information on activity-inducing signals and make a significant impact in the analysis of functional connectivity (FC). In literature, the estimation of the onsets of the BOLD events from the acquired blood oxygen level-dependent (BOLD) signal using fMRI is mostly performed by choosing locations with a high value of the BOLD signal. This approach may give false onset points because it can incorporate redundant onsets which could be due to non-neuronal activity or can exclude true low-valued BOLD signals. In this study, we present a novel approach to estimating the temporal onsets of the BOLD events using a zero frequency resonator (ZFR) without necessitating information regarding the experimental paradigm (EP). The proposed approach exploits the impulse-like characteristic of activity-inducing signal to estimate the temporal onset points of BOLD events using ZFR which has been widely studied in the area of speech signal processing to estimate the glottal closure instances. The idea behind the approach is that an ideal neuronal impulse has, in principle, equal energy at all frequencies, including around the zero frequency, and will preserve the information of the temporal onsets of the BOLD events at its output. The ZFR-based approach estimates two important features, namely: 1) task-induced temporal onsets of the BOLD events in the fMRI time course and 2) high SNR (HSNR) regions around the estimated BOLD events. Both the estimated features are used to obtain the FC. Results are demonstrated using both the synthetic and experimental (event-related finger tapping and block design working memory) data. We show that a small number of plausible time points, estimated by ZFR, can convey sufficient information indicating the associated activation pattern. The method also illustrates its significance over the conventional correlation and threshold-based conditional rate analysis to estimate FC. The study demonstrates that ZFR-estimated BOLD events and HSNR regions can produce sufficient functionality of the brain in the task paradigm.
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Affiliation(s)
- Sukesh Kumar Das
- Indian Institute of Technology Mandi, Mandi, HP 175005, Himachal Pradesh, India.
| | - Anil K Sao
- Indian Institute of Technology Bhilai, Bhilai, Chhattisgarh 492015, Chhattisgarh, India.
| | - Bharat B Biswal
- New Jersey Institute of Technology, Newark, NJ 07102, New Jersey, USA.
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11
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Zhong S, Chen N, Lai S, Shan Y, Li Z, Chen J, Luo A, Zhang Y, Lv S, He J, Wang Y, Yao Z, Jia Y. Association between cognitive impairments and aberrant dynamism of overlapping brain sub-networks in unmedicated major depressive disorder: A resting-state MEG study. J Affect Disord 2023; 320:576-589. [PMID: 36179776 DOI: 10.1016/j.jad.2022.09.069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 08/24/2022] [Accepted: 09/20/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Little is known about the pathogenesis underlying cognitive impairment in major depressive disorder (MDD). We aimed to explore the mechanisms of cognitive impairments among patients with MDD by investigating the dynamics of overlapping brain sub-networks. METHODS Forty unmedicated patients with MDD and 28 healthy controls (HC) were enrolled in this study. Cognitive function was measured using the Chinese versions of MATRICS Consensus Cognitive Battery (MCCB). All participants were scanned using a whole-head resting-state magnetoencephalography (MEG) machine. The dynamism of neural sub-networks was analyzed based on the detection of overlapping communities in five frequency bands of oscillatory brain signals. RESULTS MDD demonstrated poorer cognitive performance in six domains compared to HC. The difference in community detection (functional integration mode) in MDD was frequency-dependent. MDD showed significantly decreased community dynamics in all frequency bands compared to HC. Specifically, differences in the visual network (VN) and default mode network (DMN) were detected in all frequency bands, differences in the cognitive control network (CCN) were detected in the alpha2 and beta frequency bands, and differences in the bilateral limbic network (BLN) were only detected in the beta frequency band. Moreover, community dynamics in the alpha2 frequency band were positively correlated with verbal learning and reasoning problem solving abilities in MDD. CONCLUSIONS Our study found that decreasing in the dynamics of overlapping sub-networks may differ by frequency bands. The aberrant dynamics of overlapping neural sub-networks revealed by frequency-specific MEG signals may provide new information on the mechanism of cognitive impairments that result from MDD.
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Affiliation(s)
- Shuming Zhong
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Nan Chen
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Shunkai Lai
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Yanyan Shan
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Zhinan Li
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Junhao Chen
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Aiming Luo
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Yiliang Zhang
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Sihui Lv
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Jiali He
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Ying Wang
- Medical Imaging Center, First Affiliated Hospital, Jinan University, Guangzhou 510630, China.
| | - Zhijun Yao
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
| | - Yanbin Jia
- Department of Psychiatry, First Affiliated Hospital, Jinan University, Guangzhou 510630, China.
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12
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Erol A, Soloukey C, Generowicz B, van Dorp N, Koekkoek S, Kruizinga P, Hunyadi B. Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition. Neuroinformatics 2022; 21:247-265. [PMID: 36378467 PMCID: PMC10085969 DOI: 10.1007/s12021-022-09613-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/06/2022] [Indexed: 11/16/2022]
Abstract
Functional ultrasound (fUS) indirectly measures brain activity by detecting changes in cerebral blood volume following neural activation. Conventional approaches model such functional neuroimaging data as the convolution between an impulse response, known as the hemodynamic response function (HRF), and a binarized representation of the input signal based on the stimulus onsets, the so-called experimental paradigm (EP). However, the EP may not characterize the whole complexity of the activity-inducing signals that evoke the hemodynamic changes. Furthermore, the HRF is known to vary across brain areas and stimuli. To achieve an adaptable framework that can capture such dynamics of the brain function, we model the multivariate fUS time-series as convolutive mixtures and apply block-term decomposition on a set of lagged fUS autocorrelation matrices, revealing both the region-specific HRFs and the source signals that induce the hemodynamic responses. We test our approach on two mouse-based fUS experiments. In the first experiment, we present a single type of visual stimulus to the mouse, and deconvolve the fUS signal measured within the mouse brain's lateral geniculate nucleus, superior colliculus and visual cortex. We show that the proposed method is able to recover back the time instants at which the stimulus was displayed, and we validate the estimated region-specific HRFs based on prior studies. In the second experiment, we alter the location of the visual stimulus displayed to the mouse, and aim at differentiating the various stimulus locations over time by identifying them as separate sources.
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Affiliation(s)
- Aybüke Erol
- Circuits and Systems (CAS), Department of Microelectronics, Delft University of Technology, Mekelweg 5, Delft, 2628 CD, The Netherlands.
| | - Chagajeg Soloukey
- Center for Ultrasound and Brain imaging at Erasmus MC (CUBE), Department of Neuroscience, Erasmus Medical Center, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - Bastian Generowicz
- Center for Ultrasound and Brain imaging at Erasmus MC (CUBE), Department of Neuroscience, Erasmus Medical Center, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - Nikki van Dorp
- Center for Ultrasound and Brain imaging at Erasmus MC (CUBE), Department of Neuroscience, Erasmus Medical Center, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - Sebastiaan Koekkoek
- Center for Ultrasound and Brain imaging at Erasmus MC (CUBE), Department of Neuroscience, Erasmus Medical Center, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - Pieter Kruizinga
- Center for Ultrasound and Brain imaging at Erasmus MC (CUBE), Department of Neuroscience, Erasmus Medical Center, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - Borbála Hunyadi
- Circuits and Systems (CAS), Department of Microelectronics, Delft University of Technology, Mekelweg 5, Delft, 2628 CD, The Netherlands
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13
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Visualization of the Dynamic Brain Activation Pattern during a Decision-Making Task. Brain Sci 2022; 12:brainsci12111468. [DOI: 10.3390/brainsci12111468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/17/2022] [Accepted: 10/26/2022] [Indexed: 11/16/2022] Open
Abstract
Decision making is a complex process involving various parts of the brain which are active during different times. It is challenging to measure externally the exact instant when any given region becomes active during the decision-making process. Here, we propose the development and validation of an algorithm to extract and visualize the dynamic functional brain activation information from the observed fMRI data. We propose the use of a regularized deconvolution model to simultaneously map various activation regions within the brain and track how different activation regions changes with time, thus providing both spatial and temporal brain activation information. The proposed technique was validated using simulated data and then applied to a simple decision-making task for identification of various brain regions involved in different stages of decision making. Using the results of the dynamic activation for the decision-making task, we were able to identify key brain regions involved in some of the phases of decision making. The visualization aspect of the algorithm allows us to actually see the flow of activation (and deactivation) in the form of a motion picture. The dynamic estimate may aid in understanding the causality of activation between various brain regions in a better way in future fMRI brain studies.
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14
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Sun L, Zhang W, Wang M, Wang S, Li Z, Zhao C, Lin M, Si Q, Li X, Liang Y, Wei J, Zhang X, Chen R, Li C. Reading-related Brain Function Restored to Normal After Articulation Training in Patients with Cleft Lip and Palate: An fMRI Study. Neurosci Bull 2022; 38:1215-1228. [PMID: 35849311 PMCID: PMC9554179 DOI: 10.1007/s12264-022-00918-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 04/19/2022] [Indexed: 10/17/2022] Open
Abstract
Cleft lip and/or palate (CLP) are the most common craniofacial malformations in humans. Speech problems often persist even after cleft repair, such that follow-up articulation training is usually required. However, the neural mechanism behind effective articulation training remains largely unknown. We used fMRI to investigate the differences in brain activation, functional connectivity, and effective connectivity across CLP patients with and without articulation training and matched normal participants. We found that training promoted task-related brain activation among the articulation-related brain networks, as well as the global attributes and nodal efficiency in the functional-connectivity-based graph of the network. Our results reveal the neural correlates of effective articulation training in CLP patients, and this could contribute to the future improvement of the post-repair articulation training program.
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Affiliation(s)
- Liwei Sun
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
| | - Wenjing Zhang
- Beijing Stomatological Hospital, Capital Medical University, Beijing, 100050, China
| | - Mengyue Wang
- School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Songjian Wang
- Beijing Institute of Otolaryngology-Head and Neck Surgery, Beijing, 100005, China
- Key Laboratory of Otolaryngology-Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing, 100005, China
- Beijing Tongren Hospital, Capital Medical University, Beijing, 100005, China
| | - Zhen Li
- Department of Ultrasound, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, 100026, China
| | - Cui Zhao
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
| | - Meng Lin
- Peking University First Hospital, Beijing, 100034, China
| | - Qian Si
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
| | - Xia Li
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
| | - Ying Liang
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
| | - Jing Wei
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
| | - Xu Zhang
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, 100069, China.
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
| | - Renji Chen
- Beijing Stomatological Hospital, Capital Medical University, Beijing, 100050, China.
| | - Chunlin Li
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, 100069, China.
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
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15
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Ashourvan A, Pequito S, Bertolero M, Kim JZ, Bassett DS, Litt B. External drivers of BOLD signal's non-stationarity. PLoS One 2022; 17:e0257580. [PMID: 36121808 PMCID: PMC9484685 DOI: 10.1371/journal.pone.0257580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 09/01/2022] [Indexed: 11/19/2022] Open
Abstract
A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. This dynamical systems model of the brain offers information on the structure and dimensionality of the BOLD signal's external drivers and shines a light on the likely external sources contributing to the BOLD signal's non-stationarity. Our findings show the role of exogenous inputs in the BOLD dynamics and highlight the importance of accounting for external inputs to unravel the brain's time-varying functional dynamics.
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Affiliation(s)
- Arian Ashourvan
- Department of Psychology, University of Kansas, Lawrence, KS, United States of America
| | - Sérgio Pequito
- Delft Center for Systems and Control, Delft University of Technology, Delft, Netherlands
| | - Maxwell Bertolero
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Jason Z. Kim
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Danielle S. Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States of America
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Brian Litt
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States of America
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, United States of America
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16
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Pirondini E, Kinany N, Sueur CL, Griffis JC, Shulman GL, Corbetta M, Ville DVD. Post-stroke reorganization of transient brain activity characterizes deficits and recovery of cognitive functions. Neuroimage 2022; 255:119201. [PMID: 35405342 DOI: 10.1016/j.neuroimage.2022.119201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 03/24/2022] [Accepted: 04/07/2022] [Indexed: 02/06/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) has been widely employed to study stroke pathophysiology. In particular, analyses of fMRI signals at rest were directed at quantifying the impact of stroke on spatial features of brain networks. However, brain networks have intrinsic time features that were, so far, disregarded in these analyses. In consequence, standard fMRI analysis failed to capture temporal imbalance resulting from stroke lesions, hence restricting their ability to reveal the interdependent pathological changes in structural and temporal network features following stroke. Here, we longitudinally analyzed hemodynamic-informed transient activity in a large cohort of stroke patients (n = 103) to assess spatial and temporal changes of brain networks after stroke. Metrics extracted from the hemodynamic-informed transient activity were replicable within- and between-individuals in healthy participants, hence supporting their robustness and their clinical applicability. While large-scale spatial patterns of brain networks were preserved after stroke, their durations were altered, with stroke subjects exhibiting a varied pattern of longer and shorter network activations compared to healthy individuals. Specifically, patients showed a longer duration in the lateral precentral gyrus and anterior cingulum, and a shorter duration in the occipital lobe and in the cerebellum. These temporal alterations were associated with white matter damage in projection and association pathways. Furthermore, they were tied to deficits in specific behavioral domains as restoration of healthy brain dynamics paralleled recovery of cognitive functions (attention, language and spatial memory), but was not significantly correlated to motor recovery. These findings underscore the critical importance of network temporal properties in dissecting the pathophysiology of brain changes after stroke, thus shedding new light on the clinical potential of time-resolved methods for fMRI analysis.
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Affiliation(s)
- Elvira Pirondini
- Department of Radiology and Medical Informatics, University of Geneva; 1211 Geneva, Switzerland; Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland; Department of Physical Medicine and Rehabilitation, University of Pittsburgh; Pittsburgh, PA, USA; Rehabilitation Neural Engineering Laboratories, University of Pittsburgh; Pittsburgh, PA, USA; Department of BioEngineering, University of Pittsburgh; Pittsburgh, PA, USA.
| | - Nawal Kinany
- Department of Radiology and Medical Informatics, University of Geneva; 1211 Geneva, Switzerland; Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland; Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, Institute of Bioengineerin, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland
| | - Cécile Le Sueur
- Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland
| | - Joseph C Griffis
- Department of Neurology, Washington University School of Medicine, St. Louis; MO, 63110, USA
| | - Gordon L Shulman
- Department of Neurology, Washington University School of Medicine, St. Louis; MO, 63110, USA
| | - Maurizio Corbetta
- Department of Neurology, Washington University School of Medicine, St. Louis; MO, 63110, USA; Department of Radiology, Washington University School of Medicine, St. Louis; MO, 63110, USA; Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis; MO, 63110, USA; Department of Bioengineering, Washington University School of Medicine, St. Louis; MO, 63110, USA; Department of Neuroscience and Padua Neuroscience Center, University of Padua; Padua, Italy; Venetian Institute of Molecular Medicine (VIMM); Padua, Italy
| | - Dimitri Van De Ville
- Department of Radiology and Medical Informatics, University of Geneva; 1211 Geneva, Switzerland; Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland.
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17
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Costantini I, Deriche R, Deslauriers-Gauthier S. An Anisotropic 4D Filtering Approach to Recover Brain Activation From Paradigm-Free Functional MRI Data. FRONTIERS IN NEUROIMAGING 2022; 1:815423. [PMID: 37555185 PMCID: PMC10406250 DOI: 10.3389/fnimg.2022.815423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 02/11/2022] [Indexed: 08/10/2023]
Abstract
CONTEXT Functional Magnetic Resonance Imaging (fMRI) is a non-invasive imaging technique that provides an indirect view into brain activity via the blood oxygen level dependent (BOLD) response. In particular, resting-state fMRI poses challenges to the recovery of brain activity without prior knowledge on the experimental paradigm, as it is the case for task fMRI. Conventional methods to infer brain activity from the fMRI signals, for example, the general linear model (GLM), require the knowledge of the experimental paradigm to define regressors and estimate the contribution of each voxel's time course to the task. To overcome this limitation, approaches to deconvolve the BOLD response and recover the underlying neural activations without a priori information on the task have been proposed. State-of-the-art techniques, and in particular the total activation (TA), formulate the deconvolution as an optimization problem with decoupled spatial and temporal regularization and an optimization strategy that alternates between the constraints. APPROACH In this work, we propose a paradigm-free regularization algorithm named Anisotropic 4D-fMRI (A4D-fMRI) that is applied on the 4D fMRI image, acting simultaneously in the 3D space and 1D time dimensions. Based on the idea that large image variations should be preserved as they occur during brain activations, whereas small variations considered as noise should be removed, the A4D-fMRI applies an anisotropic regularization, thus recovering the location and the duration of brain activations. RESULTS Using the experimental paradigm as ground truth, the A4D-fMRI is validated on synthetic and real task-fMRI data from 51 subjects, and its performance is compared to the TA. Results show higher correlations of the recovered time courses with the ground truth compared to the TA and lower computational times. In addition, we show that the A4D-fMRI recovers activity that agrees with the GLM, without requiring or using any knowledge of the experimental paradigm.
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18
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Network-specific differences in transient brain activity at rest are associated with age-related reductions in motor performance. Neuroimage 2022; 252:119025. [PMID: 35202812 DOI: 10.1016/j.neuroimage.2022.119025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 02/15/2022] [Accepted: 02/20/2022] [Indexed: 11/20/2022] Open
Abstract
Multiple functional changes occur in the brain with increasing age. Among those, older adults typically display more restricted fluctuations of brain activity, both during resting-state and task execution. These altered dynamic patterns have been linked to reduced task performance across multiple behavioral domains. Windowed functional connectivity, which is typically employed in the study of connectivity dynamics, however, might not be able to properly characterize moment-to-moment variations of individual networks. In the present study, we used innovation-driven co-activation patterns (ICAP) to overcome this limitation and investigate the length (duration) and frequency (innovation) in which various brain networks emerged across the adult lifespan (N= 92) during a resting-state period. We identified a link between increasing age and a tendency to engage brain areas with distinct functional associations simultaneously as a single network. The emergence of isolated and spatially well-defined visual, motor, frontoparietal, and posterior networks decreased with increased age. This reduction in dynamics of specialized networks mediated age-related performance decreases (i.e., increases in interlimb interference) in a bimanual motor task. Altogether, our findings demonstrated that older compared to younger adults tend to activate fewer network configurations, which include multiple functionally distinct brain areas. The reduction in independent emergence of functionally well-defined and task-relevant networks may reflect an expression of brain dedifferentiation and is likely associated with functional modulatory deficits, negatively impacting motor behavior.
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19
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Towards reliable spinal cord fMRI: assessment of common imaging protocols. Neuroimage 2022; 250:118964. [DOI: 10.1016/j.neuroimage.2022.118964] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 01/07/2022] [Accepted: 02/01/2022] [Indexed: 01/29/2023] Open
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20
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Wu GR, Colenbier N, Van Den Bossche S, Clauw K, Johri A, Tandon M, Marinazzo D. rsHRF: A toolbox for resting-state HRF estimation and deconvolution. Neuroimage 2021; 244:118591. [PMID: 34560269 DOI: 10.1016/j.neuroimage.2021.118591] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 06/25/2021] [Accepted: 09/16/2021] [Indexed: 10/20/2022] Open
Abstract
The hemodynamic response function (HRF) greatly influences the intra- and inter-subject variability of brain activation and connectivity, and might confound the estimation of temporal precedence in connectivity analyses, making its estimation necessary for a correct interpretation of neuroimaging studies. Additionally, the HRF shape itself is a useful local measure. However, most algorithms for HRF estimation are specific for task-related fMRI data, and only a few can be directly applied to resting-state protocols. Here we introduce rsHRF, a Matlab and Python toolbox that implements HRF estimation and deconvolution from the resting-state BOLD signal. We first provide an overview of the main algorithm, practical implementations, and then demonstrate the feasibility and usefulness of rsHRF by validation experiments with a publicly available resting-state fMRI dataset. We also provide tools for statistical analyses and visualization. We believe that this toolbox may significantly contribute to a better analysis and understanding of the components and variability of BOLD signals.
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Affiliation(s)
- Guo-Rong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing 400715, China; Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium.
| | - Nigel Colenbier
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium; Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven 3001, Belgium; Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice 30126, Italy
| | - Sofie Van Den Bossche
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
| | - Kenzo Clauw
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
| | - Amogh Johri
- International Institute of Information Technology, Bangalore 560100, India
| | - Madhur Tandon
- Indraprastha Institute of Information Technology, Delhi 110020, India
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium; Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice 30126, Italy
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21
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Van De Ville D, Farouj Y, Preti MG, Liégeois R, Amico E. When makes you unique: Temporality of the human brain fingerprint. SCIENCE ADVANCES 2021; 7:eabj0751. [PMID: 34652937 PMCID: PMC8519575 DOI: 10.1126/sciadv.abj0751] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/20/2021] [Indexed: 05/30/2023]
Abstract
The extraction of “fingerprints” from human brain connectivity data has become a new frontier in neuroscience. However, the time scales of human brain identifiability are still largely unexplored. We here investigate the dynamics of brain fingerprints along two complementary axes: (i) What is the optimal time scale at which brain fingerprints integrate information and (ii) when best identification happens. Using dynamic identifiability, we show that the best identification emerges at longer time scales; however, short transient “bursts of identifiability,” associated with neuronal activity, persist even when looking at shorter functional interactions. Furthermore, we report evidence that different parts of connectome fingerprints relate to different time scales, i.e., more visual-somatomotor at short temporal windows and more frontoparietal-DMN driven at increasing temporal windows. Last, different cognitive functions appear to be meta-analytically implicated in dynamic fingerprints across time scales. We hope that this investigation will advance our understanding of what makes our brains unique.
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Affiliation(s)
- Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Vaud, Switzerland
| | - Younes Farouj
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Maria Giulia Preti
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Vaud, Switzerland
| | - Raphaël Liégeois
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
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22
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Piguet C, Karahanoğlu FI, Saccaro LF, Van De Ville D, Vuilleumier P. Mood disorders disrupt the functional dynamics, not spatial organization of brain resting state networks. Neuroimage Clin 2021; 32:102833. [PMID: 34619652 PMCID: PMC8498469 DOI: 10.1016/j.nicl.2021.102833] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 09/10/2021] [Accepted: 09/19/2021] [Indexed: 12/24/2022]
Abstract
Spontaneous fluctuations in the blood oxygenation level dependent signal measured through resting-state functional magnetic resonance imaging have been corroborated to aggregate into multiple functional networks. Abnormal resting brain activity is observed in mood disorder patients, however with inconsistent results. How do such alterations relate to clinical symptoms; e.g., level of depression and rumination tendencies? Here we recovered spatially and temporally overlapping functional networks from 31 mood disorder patients and healthy controls during rest, by applying novel methods that identify transient changes in spontaneous brain activity. Our unique approach disentangles the dynamic engagement of resting-state networks unconstrained by the slow hemodynamic response. This time-varying characterization provides moment-to-moment information about functional networks in terms of their durations and dynamic coupling, and offers novel evidence for selective contributionsto particular clinical symptoms. Patients showed increased duration of default-mode network (DMN), increased duration and occurrence of posterior DMN as well as insula- and amygdala-centered networks, but decreased occurrence of visual and anterior salience networks. Coupling between limbic (insula and amygdala) networks was also reduced. Depression level modulated DMN duration, whereas intrusive thoughts correlated with occurrence of insula and posterior DMN. Anatomical network organization was similar to controls. In sum, altered brain dynamics in mood disorder patients appear to mediate distinct clinical dimensions including increased self-processing, and decreased attention to external world.
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Affiliation(s)
- Camille Piguet
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Switzerland
| | - Fikret Işık Karahanoğlu
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Department of Radiology, Harvard Medical School, MA, USA
| | | | - Dimitri Van De Ville
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland
- Institute of Bioengineering, School of Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Patrik Vuilleumier
- Swiss Center for Affective Sciences, Campus Biotech, Geneva, Switzerland
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23
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Siffredi V, Farouj Y, Tarun A, Anderson V, Wood AG, McIlroy A, Leventer RJ, Spencer-Smith MM, Ville DVD. Large-scale functional network dynamics in human callosal agenesis: Increased subcortical involvement and preserved laterality. Neuroimage 2021; 243:118471. [PMID: 34455063 DOI: 10.1016/j.neuroimage.2021.118471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 07/20/2021] [Accepted: 08/11/2021] [Indexed: 10/20/2022] Open
Abstract
In the human brain, the corpus callosum is the major white-matter commissural tract enabling the transmission of sensory-motor, and higher level cognitive information between homotopic regions of the two cerebral hemispheres. Despite developmental absence (i.e., agenesis) of the corpus callosum (AgCC), functional connectivity is preserved, including interhemispheric connectivity. Subcortical structures have been hypothesised to provide alternative pathways to enable this preservation. To test this hypothesis, we used functional Magnetic Resonance Imaging (fMRI) recordings in children with AgCC and typically developing children, and a time-resolved approach to retrieve temporal characteristics of whole-brain functional networks. We observed an increased engagement of the cerebellum and amygdala/hippocampus networks in children with AgCC compared to typically developing children. There was little evidence that laterality of activation networks was affected in AgCC. Our findings support the hypothesis that subcortical structures play an essential role in the functional reconfiguration of the brain in the absence of a corpus callosum.
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Affiliation(s)
- Vanessa Siffredi
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland.
| | - Younes Farouj
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Anjali Tarun
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Vicki Anderson
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Neuroscience Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; School of Psychological Sciences, University of Melbourne, Melbourne, Australia; Department of Psychology, Royal Children's Hospital, Melbourne, Australia
| | - Amanda G Wood
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; School of Life and Health Sciences & Aston Neuroscience Institute, Aston University, Birmingham, B4 7ET UK; School of Psychology, Faculty of Health, Melbourne Burwood Campus, Deakin University, Geelong, Victoria, Australia
| | - Alissandra McIlroy
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia
| | - Richard J Leventer
- Neuroscience Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Australia; Department of Neurology, Royal Children's Hospital, Melbourne, Australia
| | - Megan M Spencer-Smith
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Dimitri Van De Ville
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
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24
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Multivariate semi-blind deconvolution of fMRI time series. Neuroimage 2021; 241:118418. [PMID: 34303793 DOI: 10.1016/j.neuroimage.2021.118418] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 07/17/2021] [Accepted: 07/20/2021] [Indexed: 12/16/2022] Open
Abstract
Whole brain estimation of the haemodynamic response function (HRF) in functional magnetic resonance imaging (fMRI) is critical to get insight on the global status of the neurovascular coupling of an individual in healthy or pathological condition. Most of existing approaches in the literature works on task-fMRI data and relies on the experimental paradigm as a surrogate of neural activity, hence remaining inoperative on resting-stage fMRI (rs-fMRI) data. To cope with this issue, recent works have performed either a two-step analysis to detect large neural events and then characterize the HRF shape or a joint estimation of both the neural and haemodynamic components in an univariate fashion. In this work, we express the neural activity signals as a combination of piece-wise constant temporal atoms associated with sparse spatial maps and introduce an haemodynamic parcellation of the brain featuring a temporally dilated version of a given HRF model in each parcel with unknown dilation parameters. We formulate the joint estimation of the HRF shapes and spatio-temporal neural representations as a multivariate semi-blind deconvolution problem in a paradigm-free setting and introduce constraints inspired from the dictionary learning literature to ease its identifiability. A fast alternating minimization algorithm, along with its efficient implementation, is proposed and validated on both synthetic and real rs-fMRI data at the subject level. To demonstrate its significance at the population level, we apply this new framework to the UK Biobank data set, first for the discrimination of haemodynamic territories between balanced groups (n=24 individuals in each) patients with an history of stroke and healthy controls and second, for the analysis of normal aging on the neurovascular coupling. Overall, we statistically demonstrate that a pathology like stroke or a condition like normal brain aging induce longer haemodynamic delays in certain brain areas (e.g. Willis polygon, occipital, temporal and frontal cortices) and that this haemodynamic feature may be predictive with an accuracy of 74 % of the individual's age in a supervised classification task performed on n=459 subjects.
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25
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Tardiff N, Medaglia JD, Bassett DS, Thompson-Schill SL. The modulation of brain network integration and arousal during exploration. Neuroimage 2021; 240:118369. [PMID: 34242784 PMCID: PMC8507424 DOI: 10.1016/j.neuroimage.2021.118369] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 07/01/2021] [Accepted: 07/05/2021] [Indexed: 11/08/2022] Open
Abstract
There is growing interest in how neuromodulators shape brain networks. Recent neuroimaging studies provide evidence that brainstem arousal systems, such as the locus coeruleus-norepinephrine system (LC-NE), influence functional connectivity and brain network topology, suggesting they have a role in flexibly reconfiguring brain networks in order to adapt behavior and cognition to environmental demands. To date, however, the relationship between brainstem arousal systems and functional connectivity has not been assessed within the context of a task with an established relationship between arousal and behavior, with most prior studies relying on incidental variations in arousal or pharmacological manipulation and static brain networks constructed over long periods of time. These factors have likely contributed to a heterogeneity of effects across studies. To address these issues, we took advantage of the association between LC-NE-linked arousal and exploration to probe the relationships between exploratory choice, arousal—as measured indirectly via pupil diameter—and brain network dynamics. Exploration in a bandit task was associated with a shift toward fewer, more weakly connected modules that were more segregated in terms of connectivity and topology but more integrated with respect to the diversity of cognitive systems represented in each module. Functional connectivity strength decreased, and changes in connectivity were correlated with changes in pupil diameter, in line with the hypothesis that brainstem arousal systems influence the dynamic reorganization of brain networks. More broadly, we argue that carefully aligning dynamic network analyses with task designs can increase the temporal resolution at which behaviorally- and cognitively-relevant modulations can be identified, and offer these results as a proof of concept of this approach.
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Affiliation(s)
- Nathan Tardiff
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States.
| | - John D Medaglia
- Department of Psychology, Drexel University, Philadelphia, PA, United States; Department of Neurology, Drexel University, Philadelphia, PA, United States; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Danielle S Bassett
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, United States; Santa Fe Institute, Santa Fe, NM, United States
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26
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Strindberg M, Fransson P, Cabral J, Ådén U. Spatiotemporally flexible subnetworks reveal the quasi-cyclic nature of integration and segregation in the human brain. Neuroimage 2021; 239:118287. [PMID: 34153450 DOI: 10.1016/j.neuroimage.2021.118287] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 06/14/2021] [Accepted: 06/17/2021] [Indexed: 11/30/2022] Open
Abstract
Though the organization of functional brain networks is modular at its core, modularity does not capture the full range of dynamic interactions between individual brain areas nor at the level of subnetworks. In this paper we present a hierarchical model that represents both flexible and modular aspects of intrinsic brain organization across time by constructing spatiotemporally flexible subnetworks. We also demonstrate that segregation and integration are complementary and simultaneous events. The method is based on combining the instantaneous phase synchrony analysis (IPSA) framework with community detection to identify a small, yet representative set of subnetwork components at the finest level of spatial granularity. At the next level, subnetwork components are combined into spatiotemporally flexibly subnetworks where temporal lag in the recruitment of areas within subnetworks is captured. Since individual brain areas are permitted to be part of multiple interleaved subnetworks, both modularity as well as more flexible tendencies of connectivity are accommodated for in the model. Importantly, we show that assignment of subnetworks to the same community (integration) corresponds to positive phase coherence within and between subnetworks, while assignment to different communities (segregation) corresponds to negative phase coherence or orthogonality. Together with disintegration, i.e. the breakdown of internal coupling within subnetwork components, orthogonality facilitates reorganization between subnetworks. In addition, we show that the duration of periods of integration is a function of the coupling strength within subnetworks and subnetwork components which indicates an underlying metastable dynamical regime. Based on the main tendencies for either integration or segregation, subnetworks are further clustered into larger meta-networks that are shown to correspond to combinations of core resting-state networks. We also demonstrate that subnetworks and meta-networks are coarse graining strategies that captures the quasi-cyclic recurrence of global patterns of integration and segregation in the brain. Finally, the method allows us to estimate in broad terms the spectrum of flexible and/or modular tendencies for individual brain areas.
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Affiliation(s)
- Marika Strindberg
- Department of Women's and Children's health, Karolinska Institutet, Sweden.
| | - Peter Fransson
- Department of Clinical Neuroscience, Karolinska Institutet, Sweden
| | - Joana Cabral
- Life and health Sciences Research Institute (ICVS), University of Minho, Portugal; Department of Psychiatry, University of Oxford, UK
| | - Ulrika Ådén
- Department of Women's and Children's health, Karolinska Institutet, Sweden
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27
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Zöller D, Sandini C, Schaer M, Eliez S, Bassett DS, Van De Ville D. Structural control energy of resting-state functional brain states reveals less cost-effective brain dynamics in psychosis vulnerability. Hum Brain Mapp 2021; 42:2181-2200. [PMID: 33566395 PMCID: PMC8046160 DOI: 10.1002/hbm.25358] [Citation(s) in RCA: 13] [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: 07/09/2020] [Revised: 12/01/2020] [Accepted: 01/05/2021] [Indexed: 12/19/2022] Open
Abstract
How the brain's white-matter anatomy constrains brain activity is an open question that might give insights into the mechanisms that underlie mental disorders such as schizophrenia. Chromosome 22q11.2 deletion syndrome (22q11DS) is a neurodevelopmental disorder with an extremely high risk for psychosis providing a test case to study developmental aspects of schizophrenia. In this study, we used principles from network control theory to probe the implications of aberrant structural connectivity for the brain's functional dynamics in 22q11DS. We retrieved brain states from resting-state functional magnetic resonance images of 78 patients with 22q11DS and 85 healthy controls. Then, we compared them in terms of persistence control energy; that is, the control energy that would be required to persist in each of these states based on individual structural connectivity and a dynamic model. Persistence control energy was altered in a broad pattern of brain states including both energetically more demanding and less demanding brain states in 22q11DS. Further, we found a negative relationship between persistence control energy and resting-state activation time, which suggests that the brain reduces energy by spending less time in energetically demanding brain states. In patients with 22q11DS, this behavior was less pronounced, suggesting a deficiency in the ability to reduce energy through brain activation. In summary, our results provide initial insights into the functional implications of altered structural connectivity in 22q11DS, which might improve our understanding of the mechanisms underlying the disease.
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Affiliation(s)
- Daniela Zöller
- Medical Image Processing LaboratoryInstitute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
- Institute of Neuromodulation and NeurotechnologyUniversity of TübingenTübingenGermany
- Developmental Imaging an Psychopathology Laboratory, Department of PsychiatryUniversity of GenevaGenevaSwitzerland
| | - Corrado Sandini
- Institute of Neuromodulation and NeurotechnologyUniversity of TübingenTübingenGermany
| | - Marie Schaer
- Institute of Neuromodulation and NeurotechnologyUniversity of TübingenTübingenGermany
| | - Stephan Eliez
- Institute of Neuromodulation and NeurotechnologyUniversity of TübingenTübingenGermany
| | - Danielle S. Bassett
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Electrical & Systems EngineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Physics & AstronomyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Dimitri Van De Ville
- Medical Image Processing LaboratoryInstitute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
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28
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Hütel M, Antonelli M, Melbourne A, Ourselin S. Hemodynamic matrix factorization for functional magnetic resonance imaging. Neuroimage 2021; 231:117814. [PMID: 33549748 PMCID: PMC8210649 DOI: 10.1016/j.neuroimage.2021.117814] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 01/10/2021] [Accepted: 01/24/2021] [Indexed: 11/30/2022] Open
Abstract
The General Linear Model (GLM) used in task-fMRI relates activated brain areas to extrinsic task conditions. The translation of resulting neural activation into a hemodynamic response is commonly approximated with a linear convolution model using a hemodynamic response function (HRF). There are two major limitations in GLM analysis. Firstly, the GLM assumes that neural activation is either on or off and matches the exact stimulus duration in the corresponding task timings. Secondly, brain networks observed in resting-state fMRI experiments present also during task experiments, but the GLM approach models these task-unrelated brain activity as noise. A novel kernel matrix factorization approach, called hemodynamic matrix factorization (HMF), is therefore proposed that addresses both limitations by assuming that task-related and task-unrelated brain activity can be modeled with the same convolution model as in GLM analysis. By contrast to the GLM, the proposed HMF is a blind source separation (BSS) technique, which decomposes fMRI data into modes. Each mode comprises of a neural activation time course and a spatial mapping. Two versions of HMF are proposed in which the neural activation time course of each mode is convolved with either the canonical HRF or predetermined subject-specific HRFs. Firstly, HMF with the canonical HRF is applied to two open-source cohorts. These cohorts comprise of several task experiments including motor, incidental memory, spatial coherence discrimination, verbal discrimination task and a very short localization task, engaging multiple parts of the eloquent cortex. HMF modes were obtained whose neural activation time course followed original task timings and whose corresponding spatial map matched cortical areas known to be involved in the respective task processing. Secondly, the alignment of these neural activation time courses to task timings were further improved by replacing the canonical HRF with subject-specific HRFs during HMF mode computation. In addition to task-related modes, HMF also produced seemingly task-unrelated modes whose spatial maps matched known resting-state networks. The validity of a fMRI task experiment relies on the assumption that the exposure to a stimulus for a given time causes an imminent increase in neural activation of equal duration. The proposed HMF is an attempt to falsify this assumption and allows to identify subject task participation that does not comply with the experiment instructions.
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Affiliation(s)
- Michael Hütel
- Department of Medical Physics and Biomedical Engineering, UCL, United Kingdom; School of Biomedical Engineering & Imaging Sciences, KCL, United Kingdom.
| | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, KCL, United Kingdom
| | - Andrew Melbourne
- School of Biomedical Engineering & Imaging Sciences, KCL, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, KCL, United Kingdom
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29
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Gonzalez-Castillo J, Kam JWY, Hoy CW, Bandettini PA. How to Interpret Resting-State fMRI: Ask Your Participants. J Neurosci 2021; 41:1130-1141. [PMID: 33568446 PMCID: PMC7888219 DOI: 10.1523/jneurosci.1786-20.2020] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 11/17/2020] [Accepted: 11/21/2020] [Indexed: 12/20/2022] Open
Abstract
Resting-state fMRI (rsfMRI) reveals brain dynamics in a task-unconstrained environment as subjects let their minds wander freely. Consequently, resting subjects navigate a rich space of cognitive and perceptual states (i.e., ongoing experience). How this ongoing experience shapes rsfMRI summary metrics (e.g., functional connectivity) is unknown, yet likely to contribute uniquely to within- and between-subject differences. Here we argue that understanding the role of ongoing experience in rsfMRI requires access to standardized, temporally resolved, scientifically validated first-person descriptions of those experiences. We suggest best practices for obtaining those descriptions via introspective methods appropriately adapted for use in fMRI research. We conclude with a set of guidelines for fusing these two data types to answer pressing questions about the etiology of rsfMRI.
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Affiliation(s)
- Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, Maryland, 20892
| | - Julia W Y Kam
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada, T2N 1N4
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada, T2N 4N1
| | - Colin W Hoy
- Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, California, 94720
| | - Peter A Bandettini
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, Maryland, 20892
- FMRI Core, National Institute of Mental Health, Bethesda, Maryland, 20892
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30
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Chen N, Shi J, Li Y, Ji S, Zou Y, Yang L, Yao Z, Hu B. Decreased dynamism of overlapping brain sub-networks in Major Depressive Disorder. J Psychiatr Res 2021; 133:197-204. [PMID: 33360426 DOI: 10.1016/j.jpsychires.2020.12.018] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 11/09/2020] [Accepted: 12/09/2020] [Indexed: 12/26/2022]
Abstract
Major Depressive Disorder (MDD) is increasingly recognized as a common brain disorder with aberrant brain networks. Alterations in dynamic functional brain networks have been widely reported in MDD. However, previous studies mainly focused on detecting non-overlapping sub-networks/communities, neglecting the possibility that one brain region may belong to multiple sub-networks/communities. In the present work, we utilized tensor decomposition method to detect overlapping communities and study the dynamism of overlapping sub-networks through 58 patients with MDD and 63 age- and sex-matched healthy controls (HC). The strength vectors of communities were calculated and two-sample t-test was performed to investigate the statistical significance of the differences in dynamism of MDD and HC groups. We found that communities detected in two groups were pairwise region-matching but overlapped brain regions were almost totally different. We considered two region-matching communities in the two groups as a sub-network. Compared to HCs, MDD patients showed significantly decreased dynamism in five sub-networks which could be functionally mapped to Visual Network (VN), Default Mode Network (DMN), Cognitive Control Network (CCN), Bilateral Limbic Network (BLN) and Auditory Network (AN). The results showed that MDD might only have a marginal effect on the holistic detection of communities and the changes of overlapped brain regions in MDD patients might be put down to the alteration of hubs. Further statistical analysis on nine sub-networks showed decreased dynamism of five sub-networks in MDD patients, which might help us achieve a better understanding of mechanism in MDD.
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Affiliation(s)
- Nan Chen
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Jie Shi
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yongchao Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Shanling Ji
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Ying Zou
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Lin Yang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China; Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University), Ministry of Education, Lanzhou, China.
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31
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Tarun A, Wainstein-Andriano D, Sterpenich V, Bayer L, Perogamvros L, Solms M, Axmacher N, Schwartz S, Van De Ville D. NREM sleep stages specifically alter dynamical integration of large-scale brain networks. iScience 2020; 24:101923. [PMID: 33409474 PMCID: PMC7773861 DOI: 10.1016/j.isci.2020.101923] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 11/07/2020] [Accepted: 12/07/2020] [Indexed: 02/07/2023] Open
Abstract
Functional dissociations in the brain observed during non-rapid eye movement (NREM) sleep have been associated with reduced information integration and impaired consciousness that accompany increasing sleep depth. Here, we explored the dynamical properties of large-scale functional brain networks derived from transient brain activity using functional magnetic resonance imaging. Spatial brain maps generally display significant modifications in terms of their tendency to occur across wakefulness and NREM sleep. Unexpectedly, almost all networks predominated in activity during NREM stage 2 before an abrupt loss of activity is observed in NREM stage 3. Yet, functional connectivity and mutual dependencies between these networks progressively broke down with increasing sleep depth. Thus, the efficiency of information transfer during NREM stage 2 is low despite the high attempt to communicate. Critically, our approach provides relevant data for evaluating functional brain network integrity and our findings robustly support a significant advance in our neural models of human sleep and consciousness.
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Affiliation(s)
- Anjali Tarun
- École Polytechnique Fédérale de Lausanne (Institute of Bioengineering, Medical Image Processing Laboratory), Geneva 1202, Switzerland.,University of Geneva (Department of Radiology and Medical Informatics), Geneva 1202, Switzerland
| | - Danyal Wainstein-Andriano
- University of Cape Town (Psychology Department, Faculty of Humanities), Cape Town 7701, South Africa.,Ruhr-Universität Bochum (Institute of Cognitive Neuroscience, Faculty of Psychology), Ruhr 44801, Germany
| | - Virginie Sterpenich
- University of Geneva, (Department of Basic Neurosciences), Geneva 1202, Switzerland
| | - Laurence Bayer
- University Hospitals of Geneva (Center for Sleep Medicine, Department of Medicine), Geneva 1202, Switzerland
| | - Lampros Perogamvros
- University of Geneva, (Department of Basic Neurosciences), Geneva 1202, Switzerland.,University Hospitals of Geneva (Center for Sleep Medicine, Department of Medicine), Geneva 1202, Switzerland
| | - Mark Solms
- University of Cape Town (Psychology Department, Faculty of Humanities), Cape Town 7701, South Africa
| | - Nikolai Axmacher
- Ruhr-Universität Bochum (Institute of Cognitive Neuroscience, Faculty of Psychology), Ruhr 44801, Germany
| | - Sophie Schwartz
- University of Geneva, (Department of Basic Neurosciences), Geneva 1202, Switzerland
| | - Dimitri Van De Ville
- École Polytechnique Fédérale de Lausanne (Institute of Bioengineering, Medical Image Processing Laboratory), Geneva 1202, Switzerland.,University of Geneva (Department of Radiology and Medical Informatics), Geneva 1202, Switzerland
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32
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Kinany N, Pirondini E, Micera S, Van De Ville D. Dynamic Functional Connectivity of Resting-State Spinal Cord fMRI Reveals Fine-Grained Intrinsic Architecture. Neuron 2020; 108:424-435.e4. [PMID: 32910894 DOI: 10.1016/j.neuron.2020.07.024] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 06/23/2020] [Accepted: 07/18/2020] [Indexed: 12/24/2022]
Abstract
The neuroimaging community has shown tremendous interest in exploring the brain's spontaneous activity using functional magnetic resonance imaging (fMRI). On the contrary, the spinal cord has been largely overlooked despite its pivotal role in processing sensorimotor signals. Only a handful of studies have probed the organization of spinal resting-state fluctuations, always using static measures of connectivity. Many innovative approaches have emerged for analyzing dynamics of brain fMRI, but they have not yet been applied to the spinal cord, although they could help disentangle its functional architecture. Here, we leverage a dynamic connectivity method based on the clustering of hemodynamic-informed transients to unravel the rich dynamic organization of spinal resting-state signals. We test this approach in 19 healthy subjects, uncovering fine-grained spinal components and highlighting their neuroanatomical and physiological nature. We provide a versatile tool, the spinal innovation-driven co-activation patterns (SpiCiCAP) framework, to characterize spinal circuits during rest and task, as well as their disruption in neurological disorders.
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Affiliation(s)
- Nawal Kinany
- Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland
| | - Elvira Pirondini
- Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland
| | - Silvestro Micera
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56025 Pontedera, Italy.
| | - Dimitri Van De Ville
- Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland.
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33
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Iraji A, Faghiri A, Lewis N, Fu Z, Rachakonda S, Calhoun VD. Tools of the trade: estimating time-varying connectivity patterns from fMRI data. Soc Cogn Affect Neurosci 2020; 16:849-874. [PMID: 32785604 PMCID: PMC8343585 DOI: 10.1093/scan/nsaa114] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 06/24/2020] [Accepted: 08/05/2020] [Indexed: 01/04/2023] Open
Abstract
Given the dynamic nature of the brain, there has always been a motivation to move beyond 'static' functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain's dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.
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Affiliation(s)
- Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Noah Lewis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Srinivas Rachakonda
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
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34
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Bolton T, Urunuela E, Tian Y, Zalesky A, Caballero-Gaudes C, Van De Ville D. Sparse coupled logistic regression to estimate co-activation and modulatory influences of brain regions. J Neural Eng 2020; 17. [PMID: 32662774 DOI: 10.1088/1741-2552/aba55e] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Accepted: 07/13/2020] [Indexed: 11/11/2022]
Abstract
Accurate mapping of the functional interactions between remote brain areas with resting-state functional magnetic resonance imaging requires the quantification of their underlying dynamics. In conventional methodological pipelines, a spatial scale of interest is first selected, and dynamic analysis then proceeds at this hypothesised level of complexity. If large-scale functional networks or states are studied, more local regional rearrangements are then not described, potentially missing important neurobiological information. Here, we propose a novel mathematical framework that jointly estimates resting-state functional networks, and spatially more localised cross-regional modulations. To do so, the changes in activity of each brain region are modelled by a logistic regression including co-activation coefficients (reflective of network assignment, as they highlight simultaneous activations across areas) and causal interplays (denoting finer regional cross-talks, when one region active at timetmodulates thettot+1 transition likelihood of another area). A two-parameter L1 regularisation scheme is used to make these two sets of coefficients sparse: one controls overall sparsity, while the other governs the trade-off between co-activations and causal interplays, enabling to properly fit the data despite the yet unknown balance between both types of couplings. Across a range of simulation settings, we show that the framework successfully retrieves the two types of cross-regional interactions at once. Performance across noise and sample size settings was globally on par with that of other existing methods, with the potential to reveal more precise information missed by alternative approaches. Preliminary application to experimental data revealed that in the resting brain, co-activations and causal modulations co-exist with a varying balance across regions. Our methodological pipeline offers a conceptually elegant alternative for the assessment of functional brain dynamics, and can be downloaded at https://c4science.ch/source/Sparse_logistic_regression.git.
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Affiliation(s)
- Thomas Bolton
- Institute of Bioengineering, EPFL, Lausanne, SWITZERLAND
| | - Eneko Urunuela
- Basque Center on Cognition Brain and Language, San Sebastian, Pais Vasco, SPAIN
| | - Ye Tian
- Department of Psychiatry, The University of Melbourne Melbourne Neuropsychiatry Centre - Parkville Campus, Carlton, Victoria, AUSTRALIA
| | - Andrew Zalesky
- The University of Melbourne Melbourne Neuropsychiatry Centre - Parkville Campus, Carlton, Victoria, AUSTRALIA
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35
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Urunuela E, Jones S, Crawford A, Shin W, Oh S, Lowe M, Caballero-Gaudes C. Stability-Based Sparse Paradigm Free Mapping Algorithm for Deconvolution of Functional MRI Data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1092-1095. [PMID: 33018176 DOI: 10.1109/embc44109.2020.9176137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Neuronal-related activity can be estimated from functional magnetic resonance imaging (fMRI) data with no knowledge of the timings of blood oxygenation level-dependent (BOLD) events by means of deconvolution with regularized least-squares. This work proposes two improvements on the deconvolution algorithm of sparse paradigm free mapping (SPFM): a new formulation that enables the estimation of neuronal events with long, sustained activity; and the implementation of a subsampling approach based on stability selection that avoids the choice of any regularization parameter. The proposed method is evaluated on real fMRI data and compared with both the original SPFM algorithm and conventional analysis with a general linear model (GLM) that is aware of the temporal model of the neuronal-related activity. We demonstrate that the novel stability-based SPFM algorithm yields activation maps with higher resemblance to the maps obtained with GLM analyses and offers improved detection of neuronal-related events over SPFM, particularly in scenarios with low contrast-to-noise ratio.
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36
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Farouj Y, Karahanoglu FI, Van De Ville D. Deconvolution of Sustained Neural Activity From Large-Scale Calcium Imaging Data. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1094-1103. [PMID: 31545714 DOI: 10.1109/tmi.2019.2942765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Recent technological advances in light-sheet microscopy make it possible to perform whole-brain functional imaging at the cellular level with the use of Ca2+ indicators. The outstanding spatial extent and resolution of this type of data open unique opportunities for understanding the complex organization of neuronal circuits across the brain. However, the analysis of this data remains challenging because the observed variations in fluorescence are, in fact, noisy indirect measures of the neuronal activity. Moreover, measuring over large field-of-view negatively impact temporal resolution and signal-to-noise ratio, which further impedes conventional spike inference. Here we argue that meaningful information can be extracted from large-scale functional imaging data by deconvolving with the calcium response and by modeling moments of sustained neuronal activity instead of individual spikes. Specifically, we characterize the calcium response by a linear system of which the inverse is a differential operator. This operator is then included in a regularization term promoting sparsity of activity transients through generalized total variation. Our results illustrate the numerical performance of the algorithm on simulated signals; i.e., we show the firing rate phase transition at which our model outperforms spike inference. Finally, we apply the proposed algorithm to experimental data from zebrafish larvæ. In particular, we show that, when applied to a specific group of neurons, the algorithm retrieves neural activation that matches the locomotor behavior unknown to the method.
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37
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Freitas LGA, Bolton TAW, Krikler BE, Jochaut D, Giraud AL, Hüppi PS, Van De Ville D. Time-resolved effective connectivity in task fMRI: Psychophysiological interactions of Co-Activation patterns. Neuroimage 2020; 212:116635. [PMID: 32105884 DOI: 10.1016/j.neuroimage.2020.116635] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 02/10/2020] [Accepted: 02/12/2020] [Indexed: 12/12/2022] Open
Abstract
Investigating context-dependent modulations of Functional Connectivity (FC) with functional magnetic resonance imaging is crucial to reveal the neurological underpinnings of cognitive processing. Most current analysis methods hypothesise sustained FC within the duration of a task, but this assumption has been shown too limiting by recent imaging studies. While several methods have been proposed to study functional dynamics during rest, task-based studies are yet to fully disentangle network modulations. Here, we propose a seed-based method to probe task-dependent modulations of brain activity by revealing Psychophysiological Interactions of Co-activation Patterns (PPI-CAPs). This point process-based approach temporally decomposes task-modulated connectivity into dynamic building blocks which cannot be captured by current methods, such as PPI or Dynamic Causal Modelling. Additionally, it identifies the occurrence of co-activation patterns at single frame resolution as opposed to window-based methods. In a naturalistic setting where participants watched a TV program, we retrieved several patterns of co-activation with a posterior cingulate cortex seed whose occurrence rates and polarity varied depending on the context; on the seed activity; or on an interaction between the two. Moreover, our method exposed the consistency in effective connectivity patterns across subjects and time, allowing us to uncover links between PPI-CAPs and specific stimuli contained in the video. Our study reveals that explicitly tracking connectivity pattern transients is paramount to advance our understanding of how different brain areas dynamically communicate when presented with a set of cues.
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Affiliation(s)
- Lorena G A Freitas
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland; Division of Development and Growth, Department of Pediatrics, University of Geneva, Switzerland.
| | - Thomas A W Bolton
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | | | - Delphine Jochaut
- Department of Basic Neurosciences, University of Geneva, Switzerland
| | - Anne-Lise Giraud
- Department of Basic Neurosciences, University of Geneva, Switzerland
| | - Petra S Hüppi
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland
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38
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Ant Colony Clustering for ROI Identification in Functional Magnetic Resonance Imaging. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2019:5259643. [PMID: 32082371 PMCID: PMC7012274 DOI: 10.1155/2019/5259643] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 11/28/2019] [Accepted: 12/03/2019] [Indexed: 11/18/2022]
Abstract
Brain network analysis using functional magnetic resonance imaging (fMRI) is a widely used technique. The first step of brain network analysis in fMRI is to detect regions of interest (ROIs). The signals from these ROIs are then used to evaluate neural networks and quantify neuronal dynamics. The two main methods to identify ROIs are based on brain atlas registration and clustering. This work proposes a bioinspired method that combines both paradigms. The method, dubbed HAnt, consists of an anatomical clustering of the signal followed by an ant clustering step. The method is evaluated empirically in both in silico and in vivo experiments. The results show a significantly better performance of the proposed approach compared to other brain parcellations obtained using purely clustering-based strategies or atlas-based parcellations.
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39
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Precise Estimation of Resting State Functional Connectivity Using Empirical Mode Decomposition. Brain Inform 2020. [DOI: 10.1007/978-3-030-59277-6_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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40
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Zöller D, Sandini C, Karahanoğlu FI, Padula MC, Schaer M, Eliez S, Van De Ville D. Large-Scale Brain Network Dynamics Provide a Measure of Psychosis and Anxiety in 22q11.2 Deletion Syndrome. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:881-892. [DOI: 10.1016/j.bpsc.2019.04.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 04/06/2019] [Indexed: 12/21/2022]
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41
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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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42
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Caballero-Gaudes C, Moia S, Panwar P, Bandettini PA, Gonzalez-Castillo J. A deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mapping. Neuroimage 2019; 202:116081. [PMID: 31419613 DOI: 10.1016/j.neuroimage.2019.116081] [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] [Received: 02/22/2019] [Revised: 07/01/2019] [Accepted: 08/06/2019] [Indexed: 10/26/2022] Open
Abstract
This work introduces a novel algorithm for deconvolution of the BOLD signal in multi-echo fMRI data: Multi-echo Sparse Paradigm Free Mapping (ME-SPFM). Assuming a linear dependence of the BOLD percent signal change on the echo time (TE) and using sparsity-promoting regularized least squares estimation, ME-SPFM yields voxelwise time-varying estimates of the changes in the apparent transverse relaxation (ΔR2⁎) without prior knowledge of the timings of individual BOLD events. Our results in multi-echo fMRI data collected during a multi-task event-related paradigm at 3 Tesla demonstrate that the maps of R2⁎ changes obtained with ME-SPFM at the times of the stimulus trials show high spatial and temporal concordance with the activation maps and BOLD signals obtained with standard model-based analysis. This method yields estimates of ΔR2⁎ having physiologically plausible values. Owing to its ability to blindly detect events, ME-SPFM also enables us to map ΔR2⁎ associated with spontaneous, transient BOLD responses occurring between trials. This framework is a step towards deciphering the dynamic nature of brain activity in naturalistic paradigms, resting-state or experimental paradigms with unknown timing of the BOLD events.
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Affiliation(s)
| | - Stefano Moia
- Basque Center on Cognition, Brain and Language, San Sebastian, Spain
| | - Puja Panwar
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA; Functional MRI Core, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA
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43
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Multivariate graph learning for detecting aberrant connectivity of dynamic brain networks in autism. Med Image Anal 2019; 56:11-25. [DOI: 10.1016/j.media.2019.05.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2018] [Revised: 04/01/2019] [Accepted: 05/24/2019] [Indexed: 01/24/2023]
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44
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Dynamic mode decomposition of resting-state and task fMRI. Neuroimage 2019; 194:42-54. [DOI: 10.1016/j.neuroimage.2019.03.019] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Accepted: 03/08/2019] [Indexed: 12/19/2022] Open
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45
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Prokopiou PC, Mitsis GD. Modeling of the BOLD signal using event-related simultaneous EEG-fMRI and convolutional sparse coding analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:181-184. [PMID: 31945873 DOI: 10.1109/embc.2019.8857311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In this work, we employ simultaneous EEG-fMRI data acquired during a visually-guided attention task along with convolutional sparse coding (CSC) analysis to extract transient events from the EEG. Subsequently, we use these events in a standard voxel-wise fMRI analysis and compare the resultant activation maps with maps obtained using the subjects' response time (RT) in detection of visual target stimuli. We also employ FIR models to obtain HRF estimates using the detected CSC events. Our results show concordance between the resultant activation maps and consistent HRF shapes for most of the subjects, suggesting that CSC can be used as a tool for the detection of reliable events in the EEG.
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46
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Wang H, Zhao S, Dong Q, Cui Y, Chen Y, Han J, Xie L, Liu T. Recognizing Brain States Using Deep Sparse Recurrent Neural Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1058-1068. [PMID: 30369441 PMCID: PMC6508593 DOI: 10.1109/tmi.2018.2877576] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Brain activity is a dynamic combination of different sensory responses and thus brain activity/state is continuously changing over time. However, the brain's dynamical functional states recognition at fast time-scales in task fMRI data have been rarely explored. In this paper, we propose a novel 5-layer deep sparse recurrent neural network (DSRNN) model to accurately recognize the brain states across the whole scan session. Specifically, the DSRNN model includes an input layer, one fully-connected layer, two recurrent layers, and a softmax output layer. The proposed framework has been tested on seven task fMRI data sets of Human Connectome Project. Extensive experiment results demonstrate that the proposed DSRNN model can accurately identify the brain's state in different task fMRI data sets and significantly outperforms other auto-correlation methods or non-temporal approaches in the dynamic brain state recognition accuracy. In general, the proposed DSRNN offers a new methodology for basic neuroscience and clinical research.
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Affiliation(s)
- Han Wang
- College of Bio-medical Engineering & Instrument Science,
Zhejiang University, 310027, Hangzhou, P. R. China
| | - Shijie Zhao
- School of Automation, Northwestern Polytechnical University,
Xi’an, 710072, China
| | - Qinglin Dong
- Cortical Architecture Imaging and Discovery Lab, Department of
Computer Science and Bioimaging Research Center, The University of Georgia,
Athens, GA, 30602 USA
| | - Yan Cui
- College of Bio-medical Engineering & Instrument Science,
Zhejiang University, 310027, Hangzhou, P. R. China
| | - Yaowu Chen
- College of Bio-medical Engineering & Instrument Science,
Zhejiang University, 310027, Hangzhou, P. R. China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University,
Xi’an, 710072, China
| | - Li Xie
- College of Bio-medical Engineering & Instrument Science,
Zhejiang University, 310027, Hangzhou, P. R. China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of
Computer Science and Bioimaging Research Center, The University of Georgia,
Athens, GA, 30602 USA (corresponding author; phone: (706) 542-3478;
)
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47
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Cao X, Sandstede B, Luo X. A Functional Data Method for Causal Dynamic Network Modeling of Task-Related fMRI. Front Neurosci 2019; 13:127. [PMID: 30872989 PMCID: PMC6402339 DOI: 10.3389/fnins.2019.00127] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Accepted: 02/05/2019] [Indexed: 01/15/2023] Open
Abstract
Functional MRI (fMRI) is a popular approach to investigate brain connections and activations when human subjects perform tasks. Because fMRI measures the indirect and convoluted signals of brain activities at a lower temporal resolution, complex differential equation modeling methods (e.g., Dynamic Causal Modeling) are usually employed to infer the neuronal processes and to fit the resulting fMRI signals. However, this modeling strategy is computationally expensive and remains to be mostly a confirmatory or hypothesis-driven approach. One major statistical challenge here is to infer, in a data-driven fashion, the underlying differential equation models from fMRI data. In this paper, we propose a causal dynamic network (CDN) method to estimate brain activations and connections simultaneously. Our method links the observed fMRI data with the latent neuronal states modeled by an ordinary differential equation (ODE) model. Using the basis function expansion approach in functional data analysis, we develop an optimization-based criterion that combines data-fitting errors and ODE fitting errors. We also develop and implement a block coordinate-descent algorithm to compute the ODE parameters efficiently. We illustrate the numerical advantages of our approach using data from realistic simulations and two task-related fMRI experiments. Compared with various effective connectivity methods, our method achieves higher estimation accuracy while improving the computational speed by from tens to thousands of times. Though our method is developed for task-related fMRI, we also demonstrate the potential applicability of our method (with a simple modification) to resting-state fMRI, by analyzing both simulated and real data from medium-sized networks.
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Affiliation(s)
- Xuefei Cao
- Division of Applied Mathematics, Brown University, Providence, RI, United States
| | - Björn Sandstede
- Division of Applied Mathematics, Brown University, Providence, RI, United States
| | - Xi Luo
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
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48
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Zoller DM, Bolton TAW, Karahanoglu FI, Eliez S, Schaer M, Van De Ville D. Robust Recovery of Temporal Overlap Between Network Activity Using Transient-Informed Spatio-Temporal Regression. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:291-302. [PMID: 30188815 DOI: 10.1109/tmi.2018.2863944] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Functional magnetic resonance imaging is a non-invasive tomographic imaging modality that has provided insights into system-level brain function. New analysis methods are emerging to study the dynamic behavior of brain activity. The innovation-driven co-activation pattern (iCAP) approach is one such approach that relies on the detection of timepoints with a significant transient activity to subsequently retrieve spatially and temporally overlapping large-scale brain networks. To recover temporal profiles of the iCAPs for further time-resolved analysis, spatial patterns are fitted back to the activity-inducing signals. In this crucial step, spatial dependences can hinder the recovery of temporal overlapping activity. To overcome this effect, we propose a novel back-projection method that optimally fits activity-inducing signals given a set of transient timepoints and spatial maps of iCAPs, thus taking into account both spatial and temporal constraints. Validation on simulated data shows that transient-based constraints improve the quality of fitted time courses. Further evaluation on experimental data demonstrates that overfitting and underfitting are prevented by the use of optimized spatio-temporal constraints. Spatial and temporal properties of resulting iCAPs support that brain activity is characterized by the recurrent co-activation and co-deactivation of spatially overlapping large-scale brain networks. This new approach opens new avenues to explore the brain's dynamic core.
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Blanco B, Molnar M, Caballero-Gaudes C. Effect of prewhitening in resting-state functional near-infrared spectroscopy data. NEUROPHOTONICS 2018; 5:040401. [PMID: 30397629 PMCID: PMC6200149 DOI: 10.1117/1.nph.5.4.040401] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Accepted: 09/24/2018] [Indexed: 05/15/2023]
Abstract
Near-infrared spectroscopy (NIRS) offers the potential to characterize resting-state functional connectivity (RSFC) in populations that are not easily assessed otherwise, such as young infants. In addition to the advantages of NIRS, one should also consider that the RS-NIRS signal requires specific data preprocessing and analysis. In particular, the RS-NIRS signal shows a colored frequency spectrum, which can be observed as temporal autocorrelation, thereby introducing spurious correlations. To address this issue, prewhitening of the RS-NIRS signal has been recently proposed as a necessary step to remove the signal temporal autocorrelation and therefore reduce false-discovery rates. However, the impact of this step on the analysis of experimental RS-NIRS data has not been thoroughly assessed prior to the present study. Here, the results of a standard preprocessing pipeline in a RS-NIRS dataset acquired in infants are compared with the results after incorporating two different prewhitening algorithms. Our results with a standard preprocessing replicated previous studies. Prewhitening altered RSFC patterns and disrupted the antiphase relationship between oxyhemoglobin and deoxyhemoglobin. We conclude that a better understanding of the effect of prewhitening on RS-NIRS data is still needed before directly considering its incorporation to the standard preprocessing pipeline.
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Affiliation(s)
- Borja Blanco
- Basque Center on Cognition, Brain and Language (BCBL), Donostia-San Sebastián, Spain
- Address all correspondence to: Borja Blanco, E-mail:
| | - Monika Molnar
- Basque Center on Cognition, Brain and Language (BCBL), Donostia-San Sebastián, Spain
- University of Toronto, Department of Speech-Language Pathology, Faculty of Medicine, Toronto, Ontario, Canada
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Keilholz S, Caballero-Gaudes C, Bandettini P, Deco G, Calhoun V. Time-Resolved Resting-State Functional Magnetic Resonance Imaging Analysis: Current Status, Challenges, and New Directions. Brain Connect 2018; 7:465-481. [PMID: 28874061 DOI: 10.1089/brain.2017.0543] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Time-resolved analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data allows researchers to extract more information about brain function than traditional functional connectivity analysis, yet a number of challenges in data analysis and interpretation remain. This article briefly summarizes common methods for time-resolved analysis and presents some of the pressing issues and opportunities in the field. From there, the discussion moves to interpretation of the network dynamics observed with rs-fMRI and the role that rs-fMRI can play in elucidating the large-scale organization of brain activity.
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Affiliation(s)
- Shella Keilholz
- 1 Department of Biomedical Engineering, Emory University/Georgia Institute of Technology , Atlanta, Georgia
| | | | - Peter Bandettini
- 3 Section on Functional Imaging Methods, NIMH, NIH, Bethesda, Maryland.,4 Functional MRI Core Facility, NIMH, NIH, Bethesda, Maryland
| | - Gustavo Deco
- 5 Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra , Barcelona, Spain .,6 Institució Catalana de la Recerca i Estudis Avançats (ICREA) , Barcelona, Spain.,7 Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences , Leipzig, Germany .,8 School of Psychological Sciences, Monash University , Melbourne, Australia
| | - Vince Calhoun
- 9 The Mind Research Network, Albuquerque, New Mexico.,10 Department of Electrical and Computer Engineering, The University of New Mexico , Albuquerque, New Mexico
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