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Barrett J, Meng H, Zhang Z, Chen SM, Zhao L, Alsop DC, Qiao X, Dai W. An improved spectral clustering method for accurate detection of brain resting-state networks. Neuroimage 2024; 299:120811. [PMID: 39214436 DOI: 10.1016/j.neuroimage.2024.120811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 08/20/2024] [Accepted: 08/22/2024] [Indexed: 09/04/2024] Open
Abstract
This paper proposes a data-driven analysis method to accurately partition large-scale resting-state functional brain networks from fMRI data. The method is based on a spectral clustering algorithm and combines eigenvector direction selection with Pearson correlation clustering in the spectral space. The method is an improvement on available spectral clustering methods, capable of robustly identifying active brain networks consistent with those from model-driven methods at different noise levels, even at the noise level of real fMRI data.
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Affiliation(s)
- Jason Barrett
- Department of Computer Science, State University of New York at Binghamton, Binghamton, NY, USA
| | - Haomiao Meng
- Department of Mathematics and Statistics, State University of New York at Binghamton, Binghamton, NY, USA
| | - Zongpai Zhang
- Department of Computer Science, State University of New York at Binghamton, Binghamton, NY, USA
| | - Song M Chen
- Department of Computer Science, State University of New York at Binghamton, Binghamton, NY, USA
| | - Li Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - David C Alsop
- Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Xingye Qiao
- Department of Mathematics and Statistics, State University of New York at Binghamton, Binghamton, NY, USA
| | - Weiying Dai
- Department of Computer Science, State University of New York at Binghamton, Binghamton, NY, USA.
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2
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Korponay C, Janes AC, Frederick BB. Brain-wide functional connectivity artifactually inflates throughout functional magnetic resonance imaging scans. Nat Hum Behav 2024; 8:1568-1580. [PMID: 38898230 DOI: 10.1038/s41562-024-01908-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 05/03/2024] [Indexed: 06/21/2024]
Abstract
Functional magnetic resonance imaging (fMRI) is a central tool for investigating human brain function, organization and disease. Here, we show that fMRI-based estimates of functional brain connectivity artifactually inflate at spatially heterogeneous rates during resting-state and task-based scans. This produces false positive connection strength changes and spatial distortion of brain connectivity maps. We demonstrate that this artefact is driven by temporal inflation of the non-neuronal, systemic low-frequency oscillation (sLFO) blood flow signal during fMRI scanning and is not addressed by standard denoising procedures. We provide evidence that sLFO inflation reflects perturbations in cerebral blood flow by respiration and heart rate changes that accompany diminishing arousal during scanning, although the mechanisms of this pathway are uncertain. Finally, we show that adding a specialized sLFO denoising procedure to fMRI processing pipelines mitigates the artifactual inflation of functional connectivity, enhancing the validity and within-scan reliability of fMRI findings.
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Affiliation(s)
- Cole Korponay
- Department of Psychiatry, Harvard University Medical School, Boston, MA, USA.
- McLean Hospital Brain Imaging Center, Belmont, MA, USA.
| | - Amy C Janes
- Neuroimaging Research Branch, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Blaise B Frederick
- Department of Psychiatry, Harvard University Medical School, Boston, MA, USA
- McLean Hospital Brain Imaging Center, Belmont, MA, USA
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3
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Notte C, Alionte C, Strubakos CD. The efficacy and methodology of using near-infrared spectroscopy to determine resting-state brain networks. J Neurophysiol 2024; 131:668-677. [PMID: 38416714 DOI: 10.1152/jn.00357.2023] [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: 09/27/2023] [Revised: 02/14/2024] [Accepted: 02/19/2024] [Indexed: 03/01/2024] Open
Abstract
Functional connectivity is a critical aspect of brain function and is essential for understanding, diagnosing, and treating neurological and psychiatric disorders. It refers to the synchronous activity between different regions of the brain, which gives rise to communication and information processing. Resting-state functional connectivity is a subarea of study that allows researchers to examine brain activity in the absence of a task or stimulus. This can provide insight into the brain's intrinsic functional architecture and help identify neural networks that are active during rest. Thus, determining functional connectivity topography is valuable both clinically and in research. Traditional methods using functional magnetic resonance imaging have proven to be effective, however, they have their limitations. In this review, we investigate the feasibility of using functional near-infrared spectroscopy (fNIRS) as a low-cost, portable alternative for measuring functional connectivity. We first establish fNIRS' ability to detect localized brain activity during task-based experiments. Next, we verify its use in resting-state studies with results showing a high degree of correspondence with resting-state functional magnetic resonance imaging (rs-fMRI). Also discussed are various data-processing methods and the validity of filtering the global signal, which is the current standard for analysis. We consider the possible origins of the global signal, if it contains pertinent neuronal information that could be of importance in better understanding neuronal networks, and what we believe is the best method of approaching signal analysis and regression.
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Affiliation(s)
- Christian Notte
- Department of Physics, University of Windsor, Windsor, Ontario, Canada
| | - Caroline Alionte
- Department of Physics, University of Windsor, Windsor, Ontario, Canada
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4
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Chen H, Zhan L, Li Q, Meng C, Quan X, Chen X, Hao Z, Li J, Gao Y, Li H, Jia X, Li M, Liang Z. Frequency specific alterations of the degree centrality in patients with acute basal ganglia ischemic stroke: a resting-state fMRI study. Brain Imaging Behav 2024; 18:19-33. [PMID: 37821673 PMCID: PMC10844151 DOI: 10.1007/s11682-023-00806-1] [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] [Accepted: 09/14/2023] [Indexed: 10/13/2023]
Abstract
This study intended to investigate the frequency specific brain oscillation activity in patients with acute basal ganglia ischemic stroke (BGIS) by using the degree centrality (DC) method. A total of 34 acute BGIS patients and 44 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. The DC values in three frequency bands (conventional band: 0.01-0.08 Hz, slow‑4 band: 0.027-0.073 Hz, slow‑5 band: 0.01-0.027 Hz) were calculated. A two-sample t-test was used to explore the between-group differences in the conventional frequency band. A two-way repeated-measures analysis of variance (ANOVA) was used to analyze the DC differences between groups (BGIS patients, HCs) and bands (slow‑4, slow‑5). Moreover, correlations between DC values and clinical indicators were performed. In conventional band, the DC value in the right middle temporal gyrus was decreased in BGIS patients compared with HCs. Significant differences of DC were observed between the two bands mainly in the bilateral cortical brain regions. Compared with the HCs, the BGIS patients showed increased DC in the right superior temporal gyrus and the left precuneus, but decreased mainly in the right inferior temporal gyrus, right inferior occipital gyrus, right precentral, and right supplementary motor area. Furthermore, the decreased DC in the right rolandic operculum in slow-4 band and the right superior temporal gyrus in slow-5 band were found by post hoc two-sample t-test of main effect of group. There was no significant correlation between DC values and clinical scales after Bonferroni correction. Our findings showed that the DC changes in BGIS patients were frequency specific. Functional abnormalities in local brain regions may help us to understand the underlying pathogenesis mechanism of brain functional reorganization of BGIS patients.
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Affiliation(s)
- Hao Chen
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Linlin Zhan
- Faculty of Western Languages, Heilongjiang University, Heilongjiang, China
| | - Qianqian Li
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chaoguo Meng
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xuemei Quan
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Department of Neurology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Xiaoling Chen
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zeqi Hao
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Jing Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Yanyan Gao
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Huayun Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Xize Jia
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Mengting Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China.
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China.
| | - Zhijian Liang
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
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5
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Schneider SC, Kaczmarz S, Göttler J, Kufer J, Zott B, Priller J, Kallmayer M, Zimmer C, Sorg C, Preibisch C. Stronger influence of systemic than local hemodynamic-vascular factors on resting-state BOLD functional connectivity. Neuroimage 2023; 281:120380. [PMID: 37741595 DOI: 10.1016/j.neuroimage.2023.120380] [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: 04/06/2023] [Revised: 08/28/2023] [Accepted: 09/13/2023] [Indexed: 09/25/2023] Open
Abstract
Correlated fluctuations in the blood oxygenation level dependent (BOLD) signal of resting-state functional MRI (i.e., BOLD-functional connectivity, BOLD-FC) reflect a spectrum of neuronal and non-neuronal processes. In particular, there are multiple hemodynamic-vascular influences on BOLD-FC on both systemic (e.g., perfusion delay) and local levels (e.g., neurovascular coupling). While the influence of individual factors has been studied extensively, combined and comparative studies of systemic and local hemodynamic-vascular factors on BOLD-FC are scarce, notably in humans. We employed a multi-modal MRI approach to investigate and compare distinct hemodynamic-vascular processes and their impact on homotopic BOLD-FC in healthy controls and patients with unilateral asymptomatic internal carotid artery stenosis (ICAS). Asymptomatic ICAS is a cerebrovascular disorder, in which neuronal functioning is largely preserved but hemodynamic-vascular processes are impaired, mostly on the side of stenosis. Investigated indicators for local hemodynamic-vascular processes comprise capillary transit time heterogeneity (CTH) and cerebral blood volume (CBV) from dynamic susceptibility contrast (DSC) MRI, and cerebral blood flow (CBF) from pseudo-continuous arterial spin labeling (pCASL). Indicators for systemic processes are time-to-peak (TTP) from DSC MRI and BOLD lags from functional MRI. For each of these parameters, their influence on BOLD-FC was estimated by a comprehensive linear mixed model. Equally across groups, we found that individual mean BOLD-FC, local (CTH, CBV, and CBF) and systemic (TTP and BOLD lag) hemodynamic-vascular factors together explain 40.7% of BOLD-FC variance, with 20% of BOLD-FC variance explained by hemodynamic-vascular factors, with an about two-times larger contribution of systemic versus local factors. We conclude that regional differences in blood supply, i.e., systemic perfusion delays, exert a stronger influence on BOLD-FC than impairments in local neurovascular coupling.
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Affiliation(s)
- Sebastian C Schneider
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Clinic for Psychiatry, Ismaningerstr. 22, 81675 Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675 Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675 Munich, Munich, Germany.
| | - Stephan Kaczmarz
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675 Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675 Munich, Munich, Germany; Philips GmbH Market DACH, Hamburg, Germany
| | - Jens Göttler
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675 Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675 Munich, Munich, Germany
| | - Jan Kufer
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675 Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675 Munich, Munich, Germany; Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, United States of America
| | - Benedikt Zott
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675 Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675 Munich, Munich, Germany
| | - Josef Priller
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Clinic for Psychiatry, Ismaningerstr. 22, 81675 Munich, Germany
| | - Michael Kallmayer
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Clinic for vascular surgery, Ismaningerstr. 22, 81675 Munich, Munich, Germany
| | - Claus Zimmer
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675 Munich, Munich, Germany
| | - Christian Sorg
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Clinic for Psychiatry, Ismaningerstr. 22, 81675 Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675 Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675 Munich, Munich, Germany
| | - Christine Preibisch
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675 Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675 Munich, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Clinic for Neurology, Ismaningerstr. 22, 81675 Munich, Munich, Germany
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6
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Raud L, Sneve MH, Vidal-Piñeiro D, Sørensen Ø, Folvik L, Ness HT, Mowinckel AM, Grydeland H, Walhovd KB, Fjell AM. Hippocampal-cortical functional connectivity during memory encoding and retrieval. Neuroimage 2023; 279:120309. [PMID: 37544416 DOI: 10.1016/j.neuroimage.2023.120309] [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: 04/04/2023] [Revised: 07/16/2023] [Accepted: 08/04/2023] [Indexed: 08/08/2023] Open
Abstract
Memory encoding and retrieval are critical sub-processes of episodic memory. While the hippocampus is involved in both, less is known about its connectivity with the neocortex during memory processing in humans. This is partially due to variations in demands in common memory tasks, which inevitably recruit cognitive processes other than episodic memory. Conjunctive analysis of data from different tasks with the same core elements of encoding and retrieval can reduce the intrusion of patterns related to subsidiary perceptual and cognitive processing. Leveraging data from two large-scale functional resonance imaging studies with different episodic memory tasks (514 and 237 participants), we identified hippocampal-cortical networks active during memory tasks. Whole-brain functional connectivity maps were similar during resting state, encoding, and retrieval. Anterior and posterior hippocampus had distinct connectivity profiles, which were also stable across resting state and memory tasks. When contrasting encoding and retrieval connectivity, conjunctive encoding-related connectivity was sparse. During retrieval hippocampal connectivity was increased with areas known to be active during recollection, including medial prefrontal, inferior parietal, and parahippocampal cortices. This indicates that the stable functional connectivity of the hippocampus along its longitudinal axis is superposed by increased functional connectivity with the recollection network during retrieval, while auxiliary encoding connectivity likely reflects contextual factors.
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Affiliation(s)
- Liisa Raud
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway.
| | - Markus H Sneve
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Didac Vidal-Piñeiro
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Øystein Sørensen
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Line Folvik
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Hedda T Ness
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Athanasia M Mowinckel
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Håkon Grydeland
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Kristine B Walhovd
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway; Department of Radiology and Nuclear Medicine, Oslo University Hospital, 0372 Oslo, Norway
| | - Anders M Fjell
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway; Department of Radiology and Nuclear Medicine, Oslo University Hospital, 0372 Oslo, Norway
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7
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Korponay C, Janes AC, Frederick BB. Brain-wide functional connectivity artifactually inflates throughout fMRI scans: a problem and solution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.08.556939. [PMID: 37745340 PMCID: PMC10515781 DOI: 10.1101/2023.09.08.556939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The fMRI blood oxygen level-dependent (BOLD) signal is a mainstay of neuroimaging assessment of neuronal activity and functional connectivity in vivo. Thus, a chief priority is maximizing this signal's reliability and validity. To this end, the fMRI community has invested considerable effort into optimizing both experimental designs and physiological denoising procedures to improve the accuracy, across-scan reproducibility, and subject discriminability of BOLD-derived metrics like functional connectivity. Despite these advances, we discover that a substantial and ubiquitous defect remains in fMRI datasets: functional connectivity throughout the brain artifactually inflates during the course of fMRI scans - by an average of more than 70% in 15 minutes of scan time - at spatially heterogeneous rates, producing both spatial and temporal distortion of brain connectivity maps. We provide evidence that this inflation is driven by a previously unrecognized time-dependent increase of non-neuronal, systemic low-frequency oscillation (sLFO) blood flow signal during fMRI scanning. This signal is not removed by standard denoising procedures such as independent component analysis (ICA). However, we demonstrate that a specialized sLFO denoising procedure - Regressor Interpolation at Progressive Time Delays (RIPTiDe) - can be added to standard denoising pipelines to significantly attenuate functional connectivity inflation. We confirm the presence of sLFO-driven functional connectivity inflation in multiple independent fMRI datasets - including the Human Connectome Project - as well as across resting-state, task, and sleep-state conditions, and demonstrate its potential to produce false positive findings. Collectively, we present evidence for a previously unknown physiological phenomenon that spatiotemporally distorts estimates of brain connectivity in human fMRI datasets, and present a solution for mitigating this artifact.
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Affiliation(s)
- Cole Korponay
- Department of Psychiatry, Harvard University Medical School, 25 Shattuck St, Boston, MA, 02115, USA
- McLean Hospital Brain Imaging Center, 115 Mill St., Belmont, MA, 02478, USA
| | - Amy C. Janes
- Neuroimaging Research Branch, National Institute on Drug Abuse (NIDA), Intramural Research Program, National Institutes of Health, Baltimore, Maryland, USA
| | - Blaise B. Frederick
- Department of Psychiatry, Harvard University Medical School, 25 Shattuck St, Boston, MA, 02115, USA
- McLean Hospital Brain Imaging Center, 115 Mill St., Belmont, MA, 02478, USA
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8
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Gong J, Stickland RC, Bright MG. Hemodynamic timing in resting-state and breathing-task BOLD fMRI. Neuroimage 2023; 274:120120. [PMID: 37072074 PMCID: PMC10208394 DOI: 10.1016/j.neuroimage.2023.120120] [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: 11/14/2022] [Revised: 04/06/2023] [Accepted: 04/15/2023] [Indexed: 04/20/2023] Open
Abstract
The blood flow response to a vasoactive stimulus demonstrates regional heterogeneity across both the healthy brain and in cerebrovascular pathology. The timing of a regional hemodynamic response is emerging as an important biomarker of cerebrovascular dysfunction, as well as a confound within fMRI analyses. Previous research demonstrated that hemodynamic timing is more robustly characterized when a larger systemic vascular response is evoked by a breathing challenge, compared to when only spontaneous fluctuations in vascular physiology are present (i.e., in resting-state data). However, it is not clear whether hemodynamic delays in these two conditions are physiologically interchangeable, and how methodological signal-to-noise factors may limit their agreement. To address this, we generated whole-brain maps of hemodynamic delays in nine healthy adults. We assessed the agreement of voxel-wise gray matter (GM) hemodynamic delays between two conditions: resting-state and breath-holding. We found that delay values demonstrated poor agreement when considering all GM voxels, but increasingly greater agreement when limiting analyses to voxels showing strong correlation with the GM mean time-series. Voxels showing the strongest agreement with the GM mean time-series were primarily located near large venous vessels, however these voxels explain some, but not all, of the observed agreement in timing. Increasing the degree of spatial smoothing of the fMRI data enhanced the correlation between individual voxel time-series and the GM mean time-series. These results suggest that signal-to-noise factors may be limiting the accuracy of voxel-wise timing estimates and hence their agreement between the two data segments. In conclusion, caution must be taken when using voxel-wise delay estimates from resting-state and breathing-task data interchangeably, and additional work is needed to evaluate their relative sensitivity and specificity to aspects of vascular physiology and pathology.
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Affiliation(s)
- Jingxuan Gong
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, United States of America.
| | - Rachael C Stickland
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America
| | - Molly G Bright
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, United States of America; Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America
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9
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Zvolanek KM, Moia S, Dean JN, Stickland RC, Caballero-Gaudes C, Bright MG. Comparing end-tidal CO 2, respiration volume per time (RVT), and average gray matter signal for mapping cerebrovascular reactivity amplitude and delay with breath-hold task BOLD fMRI. Neuroimage 2023; 272:120038. [PMID: 36958618 DOI: 10.1016/j.neuroimage.2023.120038] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/27/2023] [Accepted: 03/14/2023] [Indexed: 03/25/2023] Open
Abstract
Cerebrovascular reactivity (CVR), defined as the cerebral blood flow response to a vasoactive stimulus, is an imaging biomarker with demonstrated utility in a range of diseases and in typical development and aging processes. A robust and widely implemented method to map CVR involves using a breath-hold task during a BOLD fMRI scan. Recording end-tidal CO2 (PETCO2) changes during the breath-hold task is recommended to be used as a reference signal for modeling CVR amplitude in standard units (%BOLD/mmHg) and CVR delay in seconds. However, obtaining reliable PETCO2 recordings requires equipment and task compliance that may not be achievable in all settings. To address this challenge, we investigated two alternative reference signals to map CVR amplitude and delay in a lagged general linear model (lagged-GLM) framework: respiration volume per time (RVT) and average gray matter BOLD response (GM-BOLD). In 8 healthy adults with multiple scan sessions, we compare spatial agreement of CVR maps from RVT and GM-BOLD to those generated with PETCO2. We define a threshold to determine whether a PETCO2 recording has "sufficient" quality for CVR mapping and perform these comparisons in 16 datasets with sufficient PETCO2 and 6 datasets with insufficient PETCO2. When PETCO2 quality is sufficient, both RVT and GM-BOLD produce CVR amplitude maps that are nearly identical to those from PETCO2 (after accounting for differences in scale), with the caveat they are not in standard units to facilitate between-group comparisons. CVR delays are comparable to PETCO2 with an RVT regressor but may be underestimated with the average GM-BOLD regressor. Importantly, when PETCO2 quality is insufficient, RVT and GM-BOLD CVR recover reasonable CVR amplitude and delay maps, provided the participant attempted the breath-hold task. Therefore, our framework offers a solution for achieving high quality CVR maps in both retrospective and prospective studies where sufficient PETCO2 recordings are not available and especially in populations where obtaining reliable measurements is a known challenge (e.g., children). Our results have the potential to improve the accessibility of CVR mapping and to increase the prevalence of this promising metric of vascular health.
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Affiliation(s)
- Kristina M Zvolanek
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, USA.
| | - Stefano Moia
- Basque Center on Cognition, Brain and Language, Donostia, Gipuzkoa, Spain; Medical Imaging Processing Lab (MIP:Lab), Neuro-X institute, EPFL, Geneva, Switzerland
| | - Joshua N Dean
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, USA
| | - Rachael C Stickland
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Molly G Bright
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, USA
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10
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Cao L, Kohut SJ, Frederick BD. Estimating and mitigating the effects of systemic low frequency oscillations (sLFO) on resting state networks in awake non-human primates using time lag dependent methodology. FRONTIERS IN NEUROIMAGING 2023; 1:1031991. [PMID: 37555145 PMCID: PMC10406257 DOI: 10.3389/fnimg.2022.1031991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 12/15/2022] [Indexed: 08/10/2023]
Abstract
AIM Resting-state fMRI (rs-fMRI) is often used to infer regional brain interactions from the degree of temporal correlation between spontaneous low-frequency fluctuations, thought to reflect local changes in the BOLD signal due to neuronal activity. One complication in the analysis and interpretation of rs-fMRI data is the existence of non-neuronal low frequency physiological noise (systemic low frequency oscillations; sLFOs) which occurs within the same low frequency band as the signal used to compute functional connectivity. Here, we demonstrate the use of a time lag mapping technique to estimate and mitigate the effects of the sLFO signal on resting state functional connectivity of awake squirrel monkeys. METHODS Twelve squirrel monkeys (6 male/6 female) were acclimated to awake scanning procedures; whole-brain fMRI images were acquired with a 9.4 Tesla scanner. Rs-fMRI data was preprocessed using an in-house pipeline and sLFOs were detected using a seed regressor generated by averaging BOLD signal across all voxels in the brain, which was then refined recursively within a time window of -16-12 s. The refined regressor was then used to estimate the voxel-wise sLFOs; these regressors were subsequently included in the general linear model to remove these moving hemodynamic components from the rs-fMRI data using general linear model filtering. Group level independent component analysis (ICA) with dual regression was used to detect resting-state networks and compare networks before and after sLFO denoising. RESULTS Results show sLFOs constitute ~64% of the low frequency fMRI signal in squirrel monkey gray matter; they arrive earlier in regions in proximity to the middle cerebral arteries (e.g., somatosensory cortex) and later in regions close to draining vessels (e.g., cerebellum). Dual regression results showed that the physiological noise was significantly reduced after removing sLFOs and the extent of reduction was determined by the brain region contained in the resting-state network. CONCLUSION These results highlight the need to estimate and remove sLFOs from fMRI data before further analysis.
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Affiliation(s)
- Lei Cao
- Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, MA, United States
- McLean Imaging Center, McLean Hospital, Belmont, MA, United States
| | - Stephen J. Kohut
- Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, MA, United States
- McLean Imaging Center, McLean Hospital, Belmont, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Blaise deB. Frederick
- McLean Imaging Center, McLean Hospital, Belmont, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Opto-Magnetic Group, McLean Hospital, Belmont, MA, United States
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11
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Wanger TJ, Janes AC, Frederick BB. Spatial variation of changes in test-retest reliability of functional connectivity after global signal regression: The effect of considering hemodynamic delay. Hum Brain Mapp 2022; 44:668-678. [PMID: 36214198 PMCID: PMC9842913 DOI: 10.1002/hbm.26091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 08/24/2022] [Accepted: 09/07/2022] [Indexed: 01/25/2023] Open
Abstract
Global signal regression (GSR) is a controversial analysis method, since its removal of signal has been observed to reduce the reliability of functional connectivity estimates. Here, we used test-retest reliability to characterize potential differences in spatial patterns between conventional, static GSR (sGSR) and a novel dynamic form of GSR (dGSR). In contrast with sGSR, dGSR models the global signal at a time delay to correct for blood arrival time. Thus, dGSR accounts for greater variation in global signal, removes blood-flow-related nuisance signal, and leaves higher quality neuronal signal remaining. We used intraclass correlation coefficients (ICCs) to estimate the reliability of functional connectivity in 462 healthy controls from the Human Connectome Project. We tested across two factors: denoising method used (control, sGSR, and dGSR), and interacquisition interval (between days, or within session while varying phase encoding direction). Reliability was estimated regionally to identify topographic patterns for each condition. sGSR and dGSR provided global reductions in reliability compared with the non-GSR control. Test-retest reliability was highest in the frontoparietal and default mode regions, and lowest in sensorimotor cortex for all conditions. dGSR provides more effective denoising in regions where both strategies greatly reduce reliability. Both GSR methods substantially reduced test-retest reliability, which was most evident in brain regions that had low reliability prior to denoising. These findings suggest that reliability of interregional correlation is likely inflated by the global signal, which is thought to primarily reflect dynamic blood flow.
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Affiliation(s)
- Timothy J. Wanger
- McLean Imaging CenterMcLean HospitalBelmontMassachusettsUSA,Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - Amy C. Janes
- Neuroimaging Research Branch, National Institute on Drug Abuse (NIDA)Intramural Research Program, National Institutes of HealthBaltimoreMarylandUSA
| | - Blaise B. Frederick
- McLean Imaging CenterMcLean HospitalBelmontMassachusettsUSA,Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
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12
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Amemiya S, Takao H, Watanabe Y, Miyawaki S, Koizumi S, Saito N, Abe O. Reliability and Sensitivity to Alterered Hemodynamics Measured with Resting-state fMRI Metrics: Comparison with 123I-IMP SPECT. Neuroimage 2022; 263:119654. [PMID: 36180009 DOI: 10.1016/j.neuroimage.2022.119654] [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: 08/02/2022] [Revised: 09/16/2022] [Accepted: 09/26/2022] [Indexed: 11/24/2022] Open
Abstract
Blood oxygenation level-dependent (BOLD) contrast is sensitive to local hemodynamic changes and thus is applicable to imaging perfusion or vascular reactivity. However, knowledge about its measurement characteristics compared to reference standard perfusion imaging is limited. This study longitudinally evaluated perfusion in patients with steno-occlusive disease using resting-state functional MRI (rsfMRI) acquired before and within nine days of anterior circulation revascularization in patients with large cerebral artery steno-occlusive diseases. The reliability and sensitivity to longitudinal changes of rsfMRI temporal correlation (Rc) and time delay (TDc) relative to the cerebellar signal were examined voxel-wise in comparison with single-photon emission CT (SPECT) cerebral blood flow (CBF) using the within-subject standard deviation (Sw) and intraclass correlation coefficients (ICCs). For statistical comparisons, the standard deviation (SD) of longitudinal changes within the cerebellum, the number of voxels with significant changes in the left middle cerebral artery territory ipsilateral to surgery, and their average changes relative to the cerebellar SD were evaluated. The test-retest reliability of the fMRI metrics was also similarly evaluated using the human connectome project (HCP) healthy young adult dataset. The test-retest time interval was 31 ± 18 days. Test-retest reliability was significantly higher for SPECT (cerebellar SD: -2.59 ± 0.20) than for fMRI metrics (cerebellar SD: Rc, -2.34 ± 0.24, p = 0.04; TDc, -2.19 ± 0.21, p = 0.003). Sensitivity to postoperative changes, which was evaluated as the number of voxels, was significantly higher for fMRI TDc (8.78 ± 0.72) than for Rc (7.42 ± 1.48, p = 0.03) or SPECT CBF (6.88 ± 0.67, p < 0.001). The ratio between the average Rc, TDc, and SPECT CBF changes within the left MCA target region and cerebellar SD was also significantly higher for fMRI TDc (1.21 ± 0.79) than Rc (0.48 ± 0.94, p = 0.006) or SPECT CBF (0.23 ± 0.57, p = 0.001). The measurement variability of time delay was also larger than that of temporal correlation in HCP data within the cerebellum (t = -8.7, p < 0.001) or in the whole-brain (t = -27.4, p < 0.001) gray matter. These data suggest that fMRI time delay is more sensitive to the hemodynamic changes than SPECT CBF, although the reliability is lower. The implication for fMRI connectivity studies is that temporal correlation can be significantly decreased due to altered hemodynamics, even in cases with normal CBF.
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Affiliation(s)
- Shiori Amemiya
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, JAPAN.
| | - Hidemasa Takao
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, JAPAN
| | - Yusuke Watanabe
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, JAPAN
| | - Satoru Miyawaki
- Department of Neurosurgery, Graduate School of Medicine, University of Tokyo, Tokyo, JAPAN
| | - Satoshi Koizumi
- Department of Neurosurgery, Graduate School of Medicine, University of Tokyo, Tokyo, JAPAN
| | - Nobuhito Saito
- Department of Neurosurgery, Graduate School of Medicine, University of Tokyo, Tokyo, JAPAN
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, JAPAN
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13
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Tachibana A, Ikoma Y, Hirano Y, Kershaw J, Obata T. Separating neuronal activity and systemic low-frequency oscillation related BOLD responses at nodes of the default mode network during resting-state fMRI with multiband excitation echo-planar imaging. Front Neurosci 2022; 16:961686. [PMID: 36213741 PMCID: PMC9534563 DOI: 10.3389/fnins.2022.961686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) evaluates brain activity using blood oxygenation level-dependent (BOLD) contrast. Resting-state fMRI (rsfMRI) examines spontaneous brain function using BOLD in the absence of a task, and the default mode network (DMN) has been identified from that. The DMN is a set of nodes within the brain that appear to be active and in communication when the subject is in an awake resting state. In addition to signal changes related to neural activity, it is thought that the BOLD signal may be affected by systemic low-frequency oscillations (SysLFOs) that are non-neuronal in source and likely propagate throughout the brain to arrive at different regions at different times. However, it may be difficult to distinguish between the response due to neuronal activity and the arrival of a SysLFO in specific regions. Conventional single-shot EPI (Conv) acquisition requires a longish repetition time, but faster image acquisition has recently become possible with multiband excitation EPI (MB). In this study, we evaluated the time-lag between nodes of the DMN using both Conv and MB protocols to determine whether it is possible to distinguish between neuronal activity and SysLFO related responses during rsfMRI. While the Conv protocol data suggested that SysLFOs substantially influence the apparent time-lag of neuronal activity, the MB protocol data implied that the effects of SysLFOs and neuronal activity on the BOLD response may be separated. Using a higher time-resolution acquisition for rsfMRI might help to distinguish neuronal activity induced changes to the BOLD response from those induced by non-neuronal sources.
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Affiliation(s)
- Atsushi Tachibana
- Department of Molecular Imaging and Theranostics, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Yoko Ikoma
- Department of Molecular Imaging and Theranostics, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, Chiba, Japan
- *Correspondence: Yoko Ikoma,
| | - Yoshiyuki Hirano
- Department of Molecular Imaging and Theranostics, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, Chiba, Japan
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
- United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Suita, Japan
| | - Jeff Kershaw
- Department of Molecular Imaging and Theranostics, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Takayuki Obata
- Department of Molecular Imaging and Theranostics, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, Chiba, Japan
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
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14
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Colenbier N, Marino M, Arcara G, Frederick B, Pellegrino G, Marinazzo D, Ferrazzi G. WHOCARES: WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions. J Neural Eng 2022; 19:10.1088/1741-2552/ac8bff. [PMID: 35998568 PMCID: PMC9673276 DOI: 10.1088/1741-2552/ac8bff] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 08/23/2022] [Indexed: 11/12/2022]
Abstract
Objective. To spatio-temporally resolve cardiac signals in functional magnetic resonance imaging (fMRI) time-series of the human brain using neither external physiological measurements nor ad hoc modelling assumptions.Approach. Cardiac pulsation is a physiological confound of fMRI time-series that introduces spurious signal fluctuations in proximity to blood vessels. fMRI alone is not sufficiently fast to resolve cardiac pulsation. Depending on the ratio between the instantaneous heart-rate and the acquisition sampling frequency (1/TR, with TR being the repetition time), the cardiac signal may alias into the frequency band of neural activation so that its removal through spectral filtering techniques is generally not possible. In this paper, we show that it is feasible to temporally and spatially resolve cardiac signals throughout the brain even when cardiac aliasing occurs by combining fMRI hyper-sampling with simultaneous multislice (SMS) imaging. The technique, which we name WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions (WHOCARES), was developed on 695 healthy subjects selected from the Human Connectome Project and its performance validated against the RETROICOR, HAPPY and the pulse oxymeter signal regression methods.Main results.WHOCARES is capable of retrieving voxel-wise cardiac signal regressors. This is achieved without employing external physiological recordings nor through ad hoc modelling assumptions. The performance of WHOCARES was, on average, superior to RETROICOR, HAPPY and the pulse oxymeter regression methods.Significance.WHOCARES holds basis for the reliable mapping of cardiac activity in fMRI time-series. WHOCARES can be employed for the retrospective removal of cardiac noise in publicly available fMRI datasets where physiological recordings are not available. WHOCARES is freely available athttps://github.com/gferrazzi/WHOCARES.
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Affiliation(s)
- Nigel Colenbier
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
| | - Marco Marino
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, 3001, Belgium
| | - Giorgio Arcara
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
| | - Blaise Frederick
- Brain Imaging Center, McLean Hospital, 115 Mill St., Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard University Medical School, 25 Shattuck St., Boston, MA, 02115, USA
| | | | - Daniele Marinazzo
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
| | - Giulio Ferrazzi
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
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15
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Lipkin B, Tuckute G, Affourtit J, Small H, Mineroff Z, Kean H, Jouravlev O, Rakocevic L, Pritchett B, Siegelman M, Hoeflin C, Pongos A, Blank IA, Struhl MK, Ivanova A, Shannon S, Sathe A, Hoffmann M, Nieto-Castañón A, Fedorenko E. Probabilistic atlas for the language network based on precision fMRI data from >800 individuals. Sci Data 2022; 9:529. [PMID: 36038572 PMCID: PMC9424256 DOI: 10.1038/s41597-022-01645-3] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 08/09/2022] [Indexed: 11/13/2022] Open
Abstract
Two analytic traditions characterize fMRI language research. One relies on averaging activations across individuals. This approach has limitations: because of inter-individual variability in the locations of language areas, any given voxel/vertex in a common brain space is part of the language network in some individuals but in others, may belong to a distinct network. An alternative approach relies on identifying language areas in each individual using a functional 'localizer'. Because of its greater sensitivity, functional resolution, and interpretability, functional localization is gaining popularity, but it is not always feasible, and cannot be applied retroactively to past studies. To bridge these disjoint approaches, we created a probabilistic functional atlas using fMRI data for an extensively validated language localizer in 806 individuals. This atlas enables estimating the probability that any given location in a common space belongs to the language network, and thus can help interpret group-level activation peaks and lesion locations, or select voxels/electrodes for analysis. More meaningful comparisons of findings across studies should increase robustness and replicability in language research.
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Affiliation(s)
- Benjamin Lipkin
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Greta Tuckute
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Josef Affourtit
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hannah Small
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA
| | - Zachary Mineroff
- Human-computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Hope Kean
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Olessia Jouravlev
- Department of Cognitive Science, Carleton University, Ottawa, ON, Canada
| | - Lara Rakocevic
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Brianna Pritchett
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Caitlyn Hoeflin
- Harris School of Public Policy, University of Chicago, Chicago, IL, USA
| | - Alvincé Pongos
- Department of Bioengineering, University of California, Berkeley, CA, USA
| | - Idan A Blank
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Melissa Kline Struhl
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Anna Ivanova
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Steven Shannon
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Aalok Sathe
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Malte Hoffmann
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Cambridge, MA, USA
| | - Alfonso Nieto-Castañón
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA, USA
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Speech, Hearing, Bioscience, and Technology, Harvard University, Cambridge, MA, USA.
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16
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Kato S, Bagarinao E, Isoda H, Koyama S, Watanabe H, Maesawa S, Hara K, Katsuno M, Naganawa S, Ozaki N, Sobue G. Reproducibility of functional connectivity metrics estimated from resting-state functional MRI with differences in days, coils, and global signal regression. Radiol Phys Technol 2022; 15:298-310. [PMID: 35960494 DOI: 10.1007/s12194-022-00670-6] [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] [Received: 04/03/2022] [Revised: 08/03/2022] [Accepted: 08/04/2022] [Indexed: 11/26/2022]
Abstract
In multisite studies, differences in imaging acquisition systems could affect the reproducibility of the results when examining changes in brain function using resting-state functional magnetic resonance imaging (rs-fMRI). This is also important for longitudinal studies, in which changes in equipment settings can occur. This study examined the reproducibility of functional connectivity (FC) metrics estimated from rs-fMRI data acquired using scanner receiver coils with different numbers of channels. This study involved 80 rs-fMRI datasets from 20 healthy volunteers scanned in two independent imaging sessions using both 12- and 32-channel coils for each session. We used independent component analysis (ICA) to evaluate the FC of canonical resting-state networks (RSNs) and graph theory to calculate several whole-brain network metrics. The effect of global signal regression (GSR) as a preprocessing step was also considered. Comparisons within and between receiver coils were performed. Irrespective of the GSR, RSNs derived from rs-fMRI data acquired using the same receiver coil were reproducible, but not from different receiver coils. However, both the GSR and the channel count of the receiver coil have discernible effects on the reproducibility of network metrics estimated using whole-brain network analysis. The data acquired using the 32-channel coil tended to have better reproducibility than those acquired using the 12-channel coil. Our findings suggest that the reproducibility of FC metrics estimated from rs-fMRI data acquired using different receiver coils showed some level of dependence on the preprocessing method and the type of analysis performed.
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Affiliation(s)
- Sanae Kato
- Department of Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Epifanio Bagarinao
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko Minami, Higashi-ku, Nagoya, Aichi, 461-8673, Japan.
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan.
| | - Haruo Isoda
- Department of Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko Minami, Higashi-ku, Nagoya, Aichi, 461-8673, Japan
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
| | - Shuji Koyama
- Department of Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko Minami, Higashi-ku, Nagoya, Aichi, 461-8673, Japan
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
| | - Hirohisa Watanabe
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
- Department of Neurology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Satoshi Maesawa
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Kazuhiro Hara
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Masahisa Katsuno
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Shinji Naganawa
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Norio Ozaki
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Gen Sobue
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
- Department of Neurology, Aichi Medical University, Nagakute, Aichi, Japan
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17
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Bolt T, Nomi JS, Bzdok D, Salas JA, Chang C, Thomas Yeo BT, Uddin LQ, Keilholz SD. A parsimonious description of global functional brain organization in three spatiotemporal patterns. Nat Neurosci 2022; 25:1093-1103. [PMID: 35902649 DOI: 10.1038/s41593-022-01118-1] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 06/13/2022] [Indexed: 12/15/2022]
Abstract
Resting-state functional magnetic resonance imaging (MRI) has yielded seemingly disparate insights into large-scale organization of the human brain. The brain's large-scale organization can be divided into two broad categories: zero-lag representations of functional connectivity structure and time-lag representations of traveling wave or propagation structure. In this study, we sought to unify observed phenomena across these two categories in the form of three low-frequency spatiotemporal patterns composed of a mixture of standing and traveling wave dynamics. We showed that a range of empirical phenomena, including functional connectivity gradients, the task-positive/task-negative anti-correlation pattern, the global signal, time-lag propagation patterns, the quasiperiodic pattern and the functional connectome network structure, are manifestations of these three spatiotemporal patterns. These patterns account for much of the global spatial structure that underlies functional connectivity analyses and unifies phenomena in resting-state functional MRI previously thought distinct.
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Affiliation(s)
- Taylor Bolt
- Emory University/Georgia Institute of Technology, Atlanta, GA, USA. .,Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Jason S Nomi
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Danilo Bzdok
- The Neuro (Montreal Neurological Institute), McGill University & Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Jorge A Salas
- Departments of Electrical and Computer Engineering, Computer Science, and Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Catie Chang
- Departments of Electrical and Computer Engineering, Computer Science, and Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - B T Thomas Yeo
- Department of Electrical & Computer Engineering, Centre for Translational MR Research, Centre for Sleep & Cognition, N.1 Institute for Health and Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
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18
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Bhogal AA, Sayin ES, Poublanc J, Duffin J, Fisher JA, Sobcyzk O, Mikulis DJ. Quantifying cerebral blood arrival times using hypoxia-mediated arterial BOLD contrast. Neuroimage 2022; 261:119523. [PMID: 35907499 DOI: 10.1016/j.neuroimage.2022.119523] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 07/20/2022] [Accepted: 07/25/2022] [Indexed: 11/19/2022] Open
Abstract
Cerebral blood arrival and tissue transit times are sensitive measures of the efficiency of tissue perfusion and can provide clinically meaningful information on collateral blood flow status. We exploit the arterial blood oxygen level dependent (BOLD) signal contrast established by precisely decreasing, and then increasing, arterial hemoglobin saturation using respiratory re-oxygenation challenges to quantify arterial blood arrival times throughout the brain. We term this approach the Step Hemoglobin re-Oxygenation Contrast Stimulus (SHOCS). Carpet plot analysis yielded measures of signal onset (blood arrival), global transit time (gTT) and calculations of relative total blood volume. Onset times averaged across 12 healthy subjects were 1.1 ± 0.4 and 1.9 ± 0.6 for cortical gray and deep white matter, respectively. The average whole brain gTT was 4.5 ± 0.9 seconds. The SHOCS response was 1.7 fold higher in grey versus white matter; in line with known differences in tissue-specific blood volume fraction. SHOCS was also applied in a patient with unilateral carotid artery occlusion revealing ipsilateral prolonged signal onset with normal perfusion in the unaffected hemisphere. We anticipate that SHOCS will further inform on the extent of collateral blood flow in patients with upstream steno-occlusive vascular disease, including those already known to manifest reductions in vasodilatory reserve capacity or vascular steal.
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Affiliation(s)
- Alex A Bhogal
- Center of Imaging Sciences, High Field Department, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, CX 3584, the Netherlands.
| | - Ece Su Sayin
- Department of Physiology, University of Toronto, Toronto, Canada
| | - Julien Poublanc
- Joint Department of Medical Imaging and the Functional Neuroimaging Lab, University Health Network, Toronto, ON, Canada
| | - James Duffin
- Department of Physiology, University of Toronto, Toronto, Canada; Toronto General Hospital Research Institute, Toronto, Canada
| | - Joseph A Fisher
- Department of Physiology, University of Toronto, Toronto, Canada; Department of Anesthesiology and Pain Medicine, University Health Network and University of Toronto, Toronto, Canada
| | - Olivia Sobcyzk
- Joint Department of Medical Imaging and the Functional Neuroimaging Lab, University Health Network, Toronto, ON, Canada; Department of Anesthesiology and Pain Medicine, University Health Network and University of Toronto, Toronto, Canada
| | - David J Mikulis
- Joint Department of Medical Imaging and the Functional Neuroimaging Lab, University Health Network, Toronto, ON, Canada
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19
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Guilbert J, Légaré A, De Koninck P, Desrosiers P, Desjardins M. Toward an integrative neurovascular framework for studying brain networks. NEUROPHOTONICS 2022; 9:032211. [PMID: 35434179 PMCID: PMC8989057 DOI: 10.1117/1.nph.9.3.032211] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 03/11/2022] [Indexed: 05/28/2023]
Abstract
Brain functional connectivity based on the measure of blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signals has become one of the most widely used measurements in human neuroimaging. However, the nature of the functional networks revealed by BOLD fMRI can be ambiguous, as highlighted by a recent series of experiments that have suggested that typical resting-state networks can be replicated from purely vascular or physiologically driven BOLD signals. After going through a brief review of the key concepts of brain network analysis, we explore how the vascular and neuronal systems interact to give rise to the brain functional networks measured with BOLD fMRI. This leads us to emphasize a view of the vascular network not only as a confounding element in fMRI but also as a functionally relevant system that is entangled with the neuronal network. To study the vascular and neuronal underpinnings of BOLD functional connectivity, we consider a combination of methodological avenues based on multiscale and multimodal optical imaging in mice, used in combination with computational models that allow the integration of vascular information to explain functional connectivity.
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Affiliation(s)
- Jérémie Guilbert
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Université Laval, Centre de recherche du CHU de Québec, Québec, Canada
| | - Antoine Légaré
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Centre de recherche CERVO, Québec, Canada
- Université Laval, Department of Biochemistry, Microbiology, and Bioinformatics, Québec, Canada
| | - Paul De Koninck
- Centre de recherche CERVO, Québec, Canada
- Université Laval, Department of Biochemistry, Microbiology, and Bioinformatics, Québec, Canada
| | - Patrick Desrosiers
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Centre de recherche CERVO, Québec, Canada
| | - Michèle Desjardins
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Université Laval, Centre de recherche du CHU de Québec, Québec, Canada
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20
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Adaptation of stimulation duration to enhance auditory response in fNIRS block design. Hear Res 2022; 424:108593. [DOI: 10.1016/j.heares.2022.108593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 07/19/2022] [Accepted: 07/26/2022] [Indexed: 11/04/2022]
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21
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Schneider SC, Archila-Meléndez ME, Göttler J, Kaczmarz S, Zott B, Priller J, Kallmayer M, Zimmer C, Sorg C, Preibisch C. Resting-state BOLD functional connectivity depends on the heterogeneity of capillary transit times in the human brain A combined lesion and simulation study about the influence of blood flow response timing. Neuroimage 2022; 255:119208. [PMID: 35427773 DOI: 10.1016/j.neuroimage.2022.119208] [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: 11/04/2021] [Revised: 02/23/2022] [Accepted: 04/11/2022] [Indexed: 11/25/2022] Open
Abstract
Functional connectivity (FC) derived from blood oxygenation level dependent (BOLD) functional magnetic resonance imaging at rest (rs-fMRI), is commonly interpreted as indicator of neuronal connectivity. In a number of brain disorders, however, metabolic, vascular, and hemodynamic impairments can be expected to alter BOLD-FC independently from neuronal activity. By means of a neurovascular coupling (NVC) model of BOLD-FC, we recently demonstrated that aberrant timing of cerebral blood flow (CBF) responses may influence BOLD-FC. In the current work, we support and extend this finding by empirically linking BOLD-FC with capillary transit time heterogeneity (CTH), which we consider as an indicator of delayed and broadened CBF responses. We assessed 28 asymptomatic patients with unilateral high-grade internal carotid artery stenosis (ICAS) as a hemodynamic lesion model with largely preserved neurocognitive functioning and 27 age-matched healthy controls. For each participant, we obtained rs-fMRI, arterial spin labeling, and dynamic susceptibility contrast MRI to study the dependence of left-right homotopic BOLD-FC on local perfusion parameters. Additionally, we investigated the dependency of BOLD-FC on CBF response timing by detailed simulations. Homotopic BOLD-FC was negatively associated with increasing CTH differences between homotopic brain areas. This relation was more pronounced in asymptomatic ICAS patients even after controlling for baseline CBF and relative cerebral blood volume influences. These findings match simulation results that predict an influence of delayed and broadened CBF responses on BOLD-FC. Results demonstrate that increasing CTH differences between homotopic brain areas lead to BOLD-FC reductions. Simulations suggest that CTH increases correspond to broadened and delayed CBF responses to fluctuations in ongoing neuronal activity.
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Affiliation(s)
- Sebastian C Schneider
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675, Munich, Germany
| | - Mario E Archila-Meléndez
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675, Munich, Germany
| | - Jens Göttler
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675, Munich, Germany
| | - Stephan Kaczmarz
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675, Munich, Germany; Philips GmbH Market DACH, Hamburg, Germany
| | - Benedikt Zott
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675, Munich, Germany
| | - Josef Priller
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Psychiatry, Ismaningerstr. 22, 81675, Munich, Munich, Germany
| | - Michael Kallmayer
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Vascular and Endovascular Surgery, Ismaningerstr. 22, 81675, Munich, Munich, Germany
| | - Claus Zimmer
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675, Munich, Germany
| | - Christian Sorg
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675, Munich, Germany
| | - Christine Preibisch
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Neurology, Ismaningerstr. 22, 81675, Munich, Munich, Germany.
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22
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LIONirs: flexible Matlab toolbox for fNIRS data analysis. J Neurosci Methods 2022; 370:109487. [DOI: 10.1016/j.jneumeth.2022.109487] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 01/19/2022] [Accepted: 01/21/2022] [Indexed: 11/21/2022]
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23
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Malik-Moraleda S, Cucu T, Lipkin B, Fedorenko E. The Domain-General Multiple Demand Network Is More Active in Early Balanced Bilinguals Than Monolinguals During Executive Processing. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2021; 2:647-664. [PMID: 37214622 PMCID: PMC10158558 DOI: 10.1162/nol_a_00058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 09/23/2021] [Indexed: 05/24/2023]
Abstract
The bilingual experience may place special cognitive demands on speakers and has been argued to lead to improvements in domain-general executive abilities, like cognitive control and working memory. Such improvements have been argued for based on both behavioral and brain imaging evidence. However, the empirical landscape is complex and ridden with controversy. Here we attempt to shed light on this question through an fMRI investigation of relatively large, relatively homogeneous, and carefully matched samples of early balanced bilinguals (n = 55) and monolinguals (n = 54), using robust, previously validated individual-level markers of neural activity in the domain-general multiple demand (MD) network, which supports executive functions. We find that the bilinguals, compared to the monolinguals, show significantly stronger neural responses to an executive (spatial working memory) task, and a larger difference between a harder and an easier condition of the task, across the MD network. These stronger neural responses are accompanied by better behavioral performance on the working memory task. We further show that the bilingual-vs.-monolingual difference in neural responses is not ubiquitous across the brain as no group difference in magnitude is observed in primary visual areas, which also respond to the task. Although the neural group difference in the MD network appears robust, it remains difficult to causally link it to bilingual experience specifically.
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Affiliation(s)
- Saima Malik-Moraleda
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Boston, MA, USA
| | - Theodor Cucu
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Benjamin Lipkin
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Boston, MA, USA
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24
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Kassinopoulos M, Mitsis GD. A multi-measure approach for assessing the performance of fMRI preprocessing strategies in resting-state functional connectivity. Magn Reson Imaging 2021; 85:228-250. [PMID: 34715292 DOI: 10.1016/j.mri.2021.10.028] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 09/17/2021] [Accepted: 10/17/2021] [Indexed: 12/17/2022]
Abstract
It is well established that head motion and physiological processes (e.g. cardiac and breathing activity) should be taken into consideration when analyzing and interpreting results in fMRI studies. However, even though recent studies aimed to evaluate the performance of different preprocessing pipelines there is still no consensus on the optimal strategy. This is partly due to the fact that the quality control (QC) metrics used to evaluate differences in performance across pipelines have often yielded contradictory results. Furthermore, preprocessing techniques based on physiological recordings or data decomposition techniques (e.g. aCompCor) have not been comprehensively examined. Here, to address the aforementioned issues, we propose a framework that summarizes the scores from eight previously proposed and novel QC metrics to a reduced set of two QC metrics that reflect the signal-to-noise ratio and the reduction in motion artifacts and biases in the preprocessed fMRI data. Using this framework, we evaluate the performance of three commonly used practices on the quality of data: 1) Removal of nuisance regressors from fMRI data, 2) discarding motion-contaminated volumes (i.e., scrubbing) before regression, and 3) low-pass filtering the data and the nuisance regressors before their removal. Using resting-state fMRI data from the Human Connectome Project, we show that the scores of the examined QC metrics improve the most when the global signal (GS) and about 17% of principal components from white matter (WM) are removed from the data. Finally, we observe a small further improvement with low-pass filtering at 0.20 Hz and milder variants of WM denoising, but not with scrubbing.
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Affiliation(s)
- Michalis Kassinopoulos
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada.
| | - Georgios D Mitsis
- Department of Bioengineering, McGill University, Montreal, QC, Canada
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25
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Allegra M, Favaretto C, Metcalf N, Corbetta M, Brovelli A. Stroke-related alterations in inter-areal communication. NEUROIMAGE-CLINICAL 2021; 32:102812. [PMID: 34544032 PMCID: PMC8453222 DOI: 10.1016/j.nicl.2021.102812] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 08/02/2021] [Accepted: 08/29/2021] [Indexed: 01/03/2023]
Abstract
We used covariance-based Granger Causality on resting-state fMRI of stroke patients. Stroke determines an overall decrease of homotopic Granger causality (GC) Stroke determines a decrease of GC from and within the lesioned hemisphere. Stroke causes imbalances in GC between the lesioned and the healthy hemisphere. GC anomalies correlate with impaired performance in several behavioral domains.
Beyond causing local ischemia and cell damage at the site of injury, stroke strongly affects long-range anatomical connections, perturbing the functional organization of brain networks. Several studies reported functional connectivity abnormalities parallelling both behavioral deficits and functional recovery across different cognitive domains. FC alterations suggest that long-range communication in the brain is altered after stroke. However, standard FC analyses cannot reveal the directionality and time scale of inter-areal information transfer. We used resting-state fMRI and covariance-based Granger causality analysis to quantify network-level information transfer and its alteration in stroke. Two main large-scale anomalies were observed in stroke patients. First, inter-hemispheric information transfer was significantly decreased with respect to healthy controls. Second, stroke caused inter-hemispheric asymmetries, as information transfer within the affected hemisphere and from the affected to the intact hemisphere was significantly reduced. Both anomalies were more prominent in resting-state networks related to attention and language, and they correlated with impaired performance in several behavioral domains. Overall, our findings support the hypothesis that stroke provokes asymmetries between the affected and spared hemisphere, with different functional consequences depending on which hemisphere is lesioned.
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Affiliation(s)
- Michele Allegra
- Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, Marseille 13005, France.
| | - Chiara Favaretto
- Department of Neuroscience, Neurological Clinic, University of Padua, Padua, Italy; Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Nicholas Metcalf
- Department of Neurology, Radiology, and Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
| | - Maurizio Corbetta
- Department of Neuroscience, Neurological Clinic, University of Padua, Padua, Italy; Padova Neuroscience Center, University of Padua, Padua, Italy; Department of Neurology, Radiology, and Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, Marseille 13005, France.
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26
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Dowdle LT, Ghose G, Chen CCC, Ugurbil K, Yacoub E, Vizioli L. Statistical power or more precise insights into neuro-temporal dynamics? Assessing the benefits of rapid temporal sampling in fMRI. Prog Neurobiol 2021; 207:102171. [PMID: 34492308 DOI: 10.1016/j.pneurobio.2021.102171] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 08/09/2021] [Accepted: 09/02/2021] [Indexed: 01/25/2023]
Abstract
Functional magnetic resonance imaging (fMRI), a non-invasive and widely used human neuroimaging method, is most known for its spatial precision. However, there is a growing interest in its temporal sensitivity. This is despite the temporal blurring of neuronal events by the blood oxygen level dependent (BOLD) signal, the peak of which lags neuronal firing by 4-6 seconds. Given this, the goal of this review is to answer a seemingly simple question - "What are the benefits of increased temporal sampling for fMRI?". To answer this, we have combined fMRI data collected at multiple temporal scales, from 323 to 1000 milliseconds, with a review of both historical and contemporary temporal literature. After a brief discussion of technological developments that have rekindled interest in temporal research, we next consider the potential statistical and methodological benefits. Most importantly, we explore how fast fMRI can uncover previously unobserved neuro-temporal dynamics - effects that are entirely missed when sampling at conventional 1 to 2 second rates. With the intrinsic link between space and time in fMRI, this temporal renaissance also delivers improvements in spatial precision. Far from producing only statistical gains, the array of benefits suggest that the continued temporal work is worth the effort.
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Affiliation(s)
- Logan T Dowdle
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States; Department of Neurosurgery, University of Minnesota, 500 SE Harvard St, Minneapolis, MN, 55455, United States; Department of Neuroscience, University of Minnesota, 321 Church St SE, Minneapolis, MN, 55455, United States.
| | - Geoffrey Ghose
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States; Department of Neuroscience, University of Minnesota, 321 Church St SE, Minneapolis, MN, 55455, United States
| | - Clark C C Chen
- Department of Neurosurgery, University of Minnesota, 500 SE Harvard St, Minneapolis, MN, 55455, United States
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States
| | - Luca Vizioli
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States; Department of Neurosurgery, University of Minnesota, 500 SE Harvard St, Minneapolis, MN, 55455, United States.
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27
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Abramian D, Larsson M, Eklund A, Aganj I, Westin CF, Behjat H. Diffusion-informed spatial smoothing of fMRI data in white matter using spectral graph filters. Neuroimage 2021; 237:118095. [PMID: 34000402 PMCID: PMC8356807 DOI: 10.1016/j.neuroimage.2021.118095] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 03/07/2021] [Accepted: 04/13/2021] [Indexed: 12/15/2022] Open
Abstract
Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detectability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing fMRI data in WM that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the BOLD signal in WM is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the BOLD signal in WM. The fundamental element in the proposed method is a graph-based description of WM that encodes the underlying anisotropy observed across WM, derived from diffusion-weighted MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subject's unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth fMRI data in WM, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of simulated phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Project's 100-unrelated subject dataset, across seven functional tasks, showing that the proposed method enables the detection of streamline-like activations within axonal bundles.
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Affiliation(s)
- David Abramian
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
| | - Martin Larsson
- Centre of Mathematical Sciences, Lund University, Lund, Sweden
| | - Anders Eklund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden; Department of Computer and Information Science, Linköping University, Linköping, Sweden
| | - Iman Aganj
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Hamid Behjat
- Department of Biomedical Engineering, Lund University, Lund, Sweden; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA.
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28
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Hu JY, Kirilina E, Nierhaus T, Ovadia-Caro S, Livne M, Villringer K, Margulies D, Fiebach JB, Villringer A, Khalil AA. A novel approach for assessing hypoperfusion in stroke using spatial independent component analysis of resting-state fMRI. Hum Brain Mapp 2021; 42:5204-5216. [PMID: 34323339 PMCID: PMC8519861 DOI: 10.1002/hbm.25610] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 07/09/2021] [Accepted: 07/20/2021] [Indexed: 01/03/2023] Open
Abstract
Individualized treatment of acute stroke depends on the timely detection of ischemia and potentially salvageable tissue in the brain. Using functional MRI (fMRI), it is possible to characterize cerebral blood flow from blood‐oxygen‐level‐dependent (BOLD) signals without the administration of exogenous contrast agents. In this study, we applied spatial independent component analysis to resting‐state fMRI data of 37 stroke patients scanned within 24 hr of symptom onset, 17 of whom received follow‐up scans the next day. Our analysis revealed “Hypoperfusion spatially‐Independent Components” (HICs) whose spatial patterns of BOLD signal resembled regions of delayed perfusion depicted by dynamic susceptibility contrast MRI. These HICs were detected even in the presence of excessive patient motion, and disappeared following successful tissue reperfusion. The unique spatial and temporal features of HICs allowed them to be distinguished with high accuracy from other components in a user‐independent manner (area under the curve = 0.93, balanced accuracy = 0.90, sensitivity = 1.00, and specificity = 0.85). Our study therefore presents a new, noninvasive method for assessing blood flow in acute stroke that minimizes interpretative subjectivity and is robust to severe patient motion.
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Affiliation(s)
- Jiun-Yiing Hu
- Department of Internal Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Evgeniya Kirilina
- Department of Neurophysics, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Neurocomputation and Neuroimaging Unit, Center for Cognitive Neuroscience Berlin (CCNB), Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | - Till Nierhaus
- Neurocomputation and Neuroimaging Unit, Center for Cognitive Neuroscience Berlin (CCNB), Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany.,Department of Neurology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | | | - Michelle Livne
- Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Kersten Villringer
- Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Daniel Margulies
- Centre National de la Recherche Scientifique (CNRS) UMR 7225, Frontlab, Institut du Cerveau et de la Moelle Épinière, Paris, France
| | - Jochen B Fiebach
- Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Arno Villringer
- Department of Neurology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Berlin School of Mind and Brain, Humboldt Universität zu Berlin, Berlin, Germany
| | - Ahmed A Khalil
- Department of Neurology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Berlin School of Mind and Brain, Humboldt Universität zu Berlin, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany
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29
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Raitamaa L, Huotari N, Korhonen V, Helakari H, Koivula A, Kananen J, Kiviniemi V. Spectral analysis of physiological brain pulsations affecting the BOLD signal. Hum Brain Mapp 2021; 42:4298-4313. [PMID: 34037278 PMCID: PMC8356994 DOI: 10.1002/hbm.25547] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/18/2021] [Accepted: 05/19/2021] [Indexed: 12/17/2022] Open
Abstract
Physiological pulsations have been shown to affect the global blood oxygen level dependent (BOLD) signal in human brain. While these pulsations have previously been regarded as noise, recent studies show their potential as biomarkers of brain pathology. We used the extended 5 Hz spectral range of magnetic resonance encephalography (MREG) data to investigate spatial and frequency distributions of physiological BOLD signal sources. Amplitude spectra of the global image signals revealed cardiorespiratory envelope modulation (CREM) peaks, in addition to the previously known very low frequency (VLF) and cardiorespiratory pulsations. We then proceeded to extend the amplitude of low frequency fluctuations (ALFF) method to each of these pulsations. The respiratory pulsations were spatially dominating over most brain structures. The VLF pulsations overcame the respiratory pulsations in frontal and parietal gray matter, whereas cardiac and CREM pulsations had this effect in central cerebrospinal fluid (CSF) spaces and major blood vessels. A quasi‐periodic pattern (QPP) analysis showed that the CREM pulsations propagated as waves, with a spatiotemporal pattern differing from that of respiratory pulsations, indicating them to be distinct intracranial physiological phenomenon. In conclusion, the respiration has a dominant effect on the global BOLD signal and directly modulates cardiovascular brain pulsations.
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Affiliation(s)
- Lauri Raitamaa
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu
| | - Niko Huotari
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu
| | - Vesa Korhonen
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu
| | - Heta Helakari
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu
| | - Anssi Koivula
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu
| | - Janne Kananen
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu
| | - Vesa Kiviniemi
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu
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30
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Bonkhoff AK, Schirmer MD, Bretzner M, Etherton M, Donahue K, Tuozzo C, Nardin M, Giese A, Wu O, D. Calhoun V, Grefkes C, Rost NS. Abnormal dynamic functional connectivity is linked to recovery after acute ischemic stroke. Hum Brain Mapp 2021; 42:2278-2291. [PMID: 33650754 PMCID: PMC8046120 DOI: 10.1002/hbm.25366] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 01/26/2021] [Accepted: 01/29/2021] [Indexed: 12/30/2022] Open
Abstract
The aim of the current study was to explore the whole-brain dynamic functional connectivity patterns in acute ischemic stroke (AIS) patients and their relation to short and long-term stroke severity. We investigated resting-state functional MRI-based dynamic functional connectivity of 41 AIS patients two to five days after symptom onset. Re-occurring dynamic connectivity configurations were obtained using a sliding window approach and k-means clustering. We evaluated differences in dynamic patterns between three NIHSS-stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we built Bayesian hierarchical models to evaluate the predictive capacity of dynamic connectivity and examine the interrelation with clinical measures, such as white matter hyperintensity lesions. Finally, we established correlation analyses between dynamic connectivity and AIS severity as well as 90-day neurological recovery (ΔNIHSS). We identified three distinct dynamic connectivity configurations acutely post-stroke. More severely affected patients spent significantly more time in a configuration that was characterized by particularly strong connectivity and isolated processing of functional brain domains (three-level ANOVA: p < .05, post hoc t tests: p < .05, FDR-corrected). Configuration-specific time estimates possessed predictive capacity of stroke severity in addition to the one of clinical measures. Recovery, as indexed by the realized change of the NIHSS over time, was significantly linked to the dynamic connectivity between bilateral intraparietal lobule and left angular gyrus (Pearson's r = -.68, p = .003, FDR-corrected). Our findings demonstrate transiently increased isolated information processing in multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first 3 months poststroke.
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Affiliation(s)
- Anna K. Bonkhoff
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
- Cognitive NeuroscienceInstitute of Neuroscience and Medicine (INM‐3), Research Centre JuelichJuelichGermany
| | - Markus D. Schirmer
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
- Department of Population Health SciencesGerman Centre for Neurodegenerative Diseases (DZNE)Germany
| | - Martin Bretzner
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
- Neurosciences and CognitionUniversity of LilleLilleFrance
| | - Mark Etherton
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
| | - Kathleen Donahue
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
| | - Carissa Tuozzo
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
| | - Marco Nardin
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
| | - Anne‐Katrin Giese
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of TechnologyEmory UniversityAtlantaGeorgiaUSA
| | - Christian Grefkes
- Cognitive NeuroscienceInstitute of Neuroscience and Medicine (INM‐3), Research Centre JuelichJuelichGermany
- Department of NeurologyUniversity Hospital CologneCologneGermany
| | - Natalia S. Rost
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
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31
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Betta M, Handjaras G, Leo A, Federici A, Farinelli V, Ricciardi E, Siclari F, Meletti S, Ballotta D, Benuzzi F, Bernardi G. Cortical and subcortical hemodynamic changes during sleep slow waves in human light sleep. Neuroimage 2021; 236:118117. [PMID: 33940148 DOI: 10.1016/j.neuroimage.2021.118117] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 04/09/2021] [Accepted: 04/18/2021] [Indexed: 12/22/2022] Open
Abstract
EEG slow waves, the hallmarks of NREM sleep are thought to be crucial for the regulation of several important processes, including learning, sensory disconnection and the removal of brain metabolic wastes. Animal research indicates that slow waves may involve complex interactions within and between cortical and subcortical structures. Conventional EEG in humans, however, has a low spatial resolution and is unable to accurately describe changes in the activity of subcortical and deep cortical structures. To overcome these limitations, here we took advantage of simultaneous EEG-fMRI recordings to map cortical and subcortical hemodynamic (BOLD) fluctuations time-locked to slow waves of light sleep. Recordings were performed in twenty healthy adults during an afternoon nap. Slow waves were associated with BOLD-signal increases in the posterior brainstem and in portions of thalamus and cerebellum characterized by preferential functional connectivity with limbic and somatomotor areas, respectively. At the cortical level, significant BOLD-signal decreases were instead found in several areas, including insula and somatomotor cortex. Specifically, a slow signal increase preceded slow-wave onset and was followed by a delayed, stronger signal decrease. Similar hemodynamic changes were found to occur at different delays across most cortical brain areas, mirroring the propagation of electrophysiological slow waves, from centro-frontal to inferior temporo-occipital cortices. Finally, we found that the amplitude of electrophysiological slow waves was positively related to the magnitude and inversely related to the delay of cortical and subcortical BOLD-signal changes. These regional patterns of brain activity are consistent with theoretical accounts of the functions of sleep slow waves.
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Affiliation(s)
- Monica Betta
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Piazza San Francesco, 19, Lucca 55100, Italy
| | - Giacomo Handjaras
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Piazza San Francesco, 19, Lucca 55100, Italy
| | - Andrea Leo
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Piazza San Francesco, 19, Lucca 55100, Italy
| | - Alessandra Federici
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Piazza San Francesco, 19, Lucca 55100, Italy
| | - Valentina Farinelli
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Emiliano Ricciardi
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Piazza San Francesco, 19, Lucca 55100, Italy
| | - Francesca Siclari
- Center for Investigation and Research on Sleep, Lausanne University Hospital, Lausanne, Switzerland
| | - Stefano Meletti
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy; Neurology Dept., Azienda Ospedaliera Universitaria di Modena, Modena, Italy
| | - Daniela Ballotta
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Francesca Benuzzi
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Giulio Bernardi
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Piazza San Francesco, 19, Lucca 55100, Italy.
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32
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Pinto J, Bright MG, Bulte DP, Figueiredo P. Cerebrovascular Reactivity Mapping Without Gas Challenges: A Methodological Guide. Front Physiol 2021; 11:608475. [PMID: 33536935 PMCID: PMC7848198 DOI: 10.3389/fphys.2020.608475] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 12/02/2020] [Indexed: 01/08/2023] Open
Abstract
Cerebrovascular reactivity (CVR) is defined as the ability of vessels to alter their caliber in response to vasoactive factors, by means of dilating or constricting, in order to increase or decrease regional cerebral blood flow (CBF). Importantly, CVR may provide a sensitive biomarker for pathologies where vasculature is compromised. Furthermore, the spatiotemporal dynamics of CVR observed in healthy subjects, reflecting regional differences in cerebral vascular tone and response, may also be important in functional MRI studies based on neurovascular coupling mechanisms. Assessment of CVR is usually based on the use of a vasoactive stimulus combined with a CBF measurement technique. Although transcranial Doppler ultrasound has been frequently used to obtain global flow velocity measurements, MRI techniques are being increasingly employed for obtaining CBF maps. For the vasoactive stimulus, vasodilatory hypercapnia is usually induced through the manipulation of respiratory gases, including the inhalation of increased concentrations of carbon dioxide. However, most of these methods require an additional apparatus and complex setups, which not only may not be well-tolerated by some populations but are also not widely available. For these reasons, strategies based on voluntary breathing fluctuations without the need for external gas challenges have been proposed. These include the task-based methodologies of breath holding and paced deep breathing, as well as a new generation of methods based on spontaneous breathing fluctuations during resting-state. Despite the multitude of alternatives to gas challenges, existing literature lacks definitive conclusions regarding the best practices for the vasoactive modulation and associated analysis protocols. In this work, we perform an extensive review of CVR mapping techniques based on MRI and CO2 variations without gas challenges, focusing on the methodological aspects of the breathing protocols and corresponding data analysis. Finally, we outline a set of practical guidelines based on generally accepted practices and available data, extending previous reports and encouraging the wider application of CVR mapping methodologies in both clinical and academic MRI settings.
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Affiliation(s)
- Joana Pinto
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Molly G. Bright
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States
| | - Daniel P. Bulte
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Patrícia Figueiredo
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
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33
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Linke AC, Mash LE, Fong CH, Kinnear MK, Kohli JS, Wilkinson M, Tung R, Jao Keehn RJ, Carper RA, Fishman I, Müller RA. Dynamic time warping outperforms Pearson correlation in detecting atypical functional connectivity in autism spectrum disorders. Neuroimage 2020; 223:117383. [PMID: 32949710 PMCID: PMC9851773 DOI: 10.1016/j.neuroimage.2020.117383] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 09/12/2020] [Indexed: 01/21/2023] Open
Abstract
Resting state fMRI (rsfMRI) is frequently used to study brain function, including in clinical populations. Similarity of blood-oxygen-level-dependent (BOLD) fluctuations during rsfMRI between brain regions is thought to reflect intrinsic functional connectivity (FC), potentially due to history of coactivation. To quantify similarity, studies have almost exclusively relied on Pearson correlation, which assumes linearity and can therefore underestimate FC if the hemodynamic response function differs regionally or there is BOLD signal lag between timeseries. Here we show in three cohorts of children, adolescents and adults, with and without autism spectrum disorders (ASDs), that measuring the similarity of BOLD signal fluctuations using non-linear dynamic time warping (DTW) is more robust to global signal regression (GSR), has higher test-retest reliability and is more sensitive to task-related changes in FC. Additionally, when comparing FC between individuals with ASDs and typical controls, more group differences are detected using DTW. DTW estimates are also more related to ASD symptom severity and executive function, while Pearson correlation estimates of FC are more strongly associated with respiration during rsfMRI. Together these findings suggest that non-linear methods such as DTW improve estimation of resting state FC, particularly when studying clinical populations whose hemodynamics or neurovascular coupling may be altered compared to typical controls.
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Affiliation(s)
- A C Linke
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States.
| | - L E Mash
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
| | - C H Fong
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
| | - M K Kinnear
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States
| | - J S Kohli
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
| | - M Wilkinson
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
| | - R Tung
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States
| | - R J Jao Keehn
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States
| | - R A Carper
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
| | - I Fishman
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
| | - R-A Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
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34
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Amemiya S, Takao H, Abe O. Origin of the Time Lag Phenomenon and the Global Signal in Resting-State fMRI. Front Neurosci 2020; 14:596084. [PMID: 33250709 PMCID: PMC7673396 DOI: 10.3389/fnins.2020.596084] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 10/12/2020] [Indexed: 11/13/2022] Open
Abstract
The global mean signal of resting-state fMRI (rs-fMRI) shows a characteristic spatiotemporal pattern that is closely related to the pattern of vascular perfusion. Although being increasingly adopted in the mapping of the flow of neural activity, the mechanism that gives rise to the BOLD signal time lag remains controversial. In the present study, we compared the time lag of the global mean signal with those of the local network components obtained by applying temporal independent component analysis to the resting-state fMRI data, as well as by using simultaneous wide-field visual stimulation, and demonstrated that the time lag patterns are highly similar across all types of data. These results suggest that the time lag of the rs-fMRI signal reflects the local variance of the hemodynamic responses rather than the arrival or transit time of the stimulus, whether the trigger is neuronal or non-neuronal in origin as long as it is mediated by local hemodynamic responses. Examinations of the internal carotid artery signal further confirmed that the arterial signal is tightly inversely coupled with the global mean signal in accordance with previous studies, presumably reflecting the blood flow or blood pressure changes that are occurring almost simultaneously in the internal carotid artery and the cerebral pial/capillary arteries, within the low-frequency component in human rs-fMRI.
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Affiliation(s)
- Shiori Amemiya
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hidemasa Takao
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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35
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Li M, Gao Y, Gao F, Anderson AW, Ding Z, Gore JC. Functional engagement of white matter in resting-state brain networks. Neuroimage 2020; 220:117096. [PMID: 32599266 PMCID: PMC7594260 DOI: 10.1016/j.neuroimage.2020.117096] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 06/21/2020] [Accepted: 06/24/2020] [Indexed: 01/12/2023] Open
Abstract
The topological characteristics of functional networks, derived from measurements of resting-state connectivity in gray matter (GM), are associated with individual cognitive abilities or specific dysfunctions. However, blood oxygen level-dependent (BOLD) signals in white matter (WM) are usually ignored or even regressed out as nuisance factors in the data analyses that underlie network models. Recent studies have demonstrated reliable detection of WM BOLD signals and imply these reflect associated neural activities. Here we evaluate quantitatively the contributions of individual WM voxels to the identification of functional networks, which we term their engagement (or conceptually, their importance). We quantify the engagement by measuring the reductions of connectivity, produced by ignoring the signal fluctuations within each WM voxel, with respect to both the entire network (global) or a single GM node (local). We observed highly reproducible spatial distributions of global engagement maps, as well as a trend toward increased relevance of deep WM voxels at delayed times. Local engagement maps exhibit homogeneous spatial distributions with respect to internal nodes that constitute a well-recognized sub-functional network, but inhomogeneous distributions with respect to other nodes. WM voxels show distinct distributions of engagement depending on their anatomical locations. These findings demonstrate the important role of WM in network modeling, thus supporting the need for changes of conventional views that WM signal variations represent only physiological noise.
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Affiliation(s)
- Muwei Li
- Vanderbilt University Institute of Imaging Science, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN, 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, 1161 21st Ave. S, Medical Center North, Nashville, TN, 37232, USA.
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN, 37232, USA; Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN, 37235, USA
| | - Fei Gao
- Shandong Medical Imaging Research Institute, Shandong University, Jinan, China
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN, 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, 1161 21st Ave. S, Medical Center North, Nashville, TN, 37232, USA; Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN, 37235, USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN, 37232, USA; Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN, 37235, USA; Department of Electrical Engineering and Computer Science, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN, 37235, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN, 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, 1161 21st Ave. S, Medical Center North, Nashville, TN, 37232, USA; Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN, 37235, USA
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36
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Wang T, Wilkes DM, Li M, Wu X, Gore JC, Ding Z. Hemodynamic Response Function in Brain White Matter in a Resting State. Cereb Cortex Commun 2020; 1:tgaa056. [PMID: 33073237 PMCID: PMC7552822 DOI: 10.1093/texcom/tgaa056] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 08/24/2020] [Accepted: 08/24/2020] [Indexed: 11/14/2022] Open
Abstract
The hemodynamic response function (HRF) characterizes temporal variations of blood oxygenation level-dependent (BOLD) signals. Although a variety of HRF models have been proposed for gray matter responses to functional demands, few studies have investigated HRF profiles in white matter particularly under resting conditions. In the present work we quantified the nature of the HRFs that are embedded in resting state BOLD signals in white matter, and which modulate the temporal fluctuations of baseline signals. We demonstrate that resting state HRFs in white matter could be derived by referencing to intrinsic avalanches in gray matter activities, and the derived white matter HRFs had reduced peak amplitudes and delayed peak times as compared with those in gray matter. Distributions of the time delays and correlation profiles in white matter depend on gray matter activities as well as white matter tract distributions, indicating that resting state BOLD signals in white matter encode neural activities associated with those of gray matter. This is the first investigation of derivations and characterizations of resting state HRFs in white matter and their relations to gray matter activities. Findings from this work have important implications for analysis of BOLD signals in the brain.
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Affiliation(s)
- Ting Wang
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, Sichuan 610225, China
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
| | - D Mitchell Wilkes
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA
| | - Muwei Li
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Xi Wu
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, Sichuan 610225, China
| | - John C Gore
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA
| | - Zhaohua Ding
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA
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37
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Mutlu MC, Erdoğan SB, Öztürk OC, Canbeyli R, Saybaşιlι H. Functional Near-Infrared Spectroscopy Indicates That Asymmetric Right Hemispheric Activation in Mental Rotation of a Jigsaw Puzzle Decreases With Task Difficulty. Front Hum Neurosci 2020; 14:252. [PMID: 32694987 PMCID: PMC7339288 DOI: 10.3389/fnhum.2020.00252] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 06/08/2020] [Indexed: 11/29/2022] Open
Abstract
Mental rotation (MR) is a cognitive skill whose neural dynamics are still a matter of debate as previous neuroimaging studies have produced controversial results. In order to investigate the underlying neurophysiology of MR, hemodynamic responses from the prefrontal cortex of 14 healthy subjects were recorded with functional near-infrared spectroscopy (fNIRS) during a novel MR task that had three categorical difficulty levels. Hemodynamic activity strength (HAS) parameter, which reflects the ratio of brain activation during the task to the baseline activation level, was used to assess the prefrontal cortex activation localization and strength. Behavioral data indicated that the MR requiring conditions are more difficult than the condition that did not require MR. The right dorsolateral prefrontal cortex (DLPFC) was found to be active in all conditions and to be the dominant region in the easiest task while more complex tasks showed widespread bilateral prefrontal activation. A significant increase in left DLPFC activation was observed with increasing task difficulty. Significantly higher right DLPFC activation was observed when the incongruent trials were contrasted against the congruent trials, which implied the possibility of a robust error or conflict-monitoring process during the incongruent trials. Our results showed that the right DLPFC is a core region for the processing of MR tasks regardless of the task complexity and that the left DLPFC is involved to a greater extent with increasing task complexity, which is consistent with the previous neuroimaging literature.
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Affiliation(s)
- Murat Can Mutlu
- Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Turkey
| | - Sinem Burcu Erdoğan
- Department of Medical Engineering, Acιbadem Mehmet Ali Aydιnlar University, Istanbul, Turkey
| | - Ozan Cem Öztürk
- School of Sport Exercise and Health Sciences, Loughborough University, Leicestershire, United Kingdom
| | - Reşit Canbeyli
- Department of Psychology, Boğaziçi University, Istanbul, Turkey
| | - Hale Saybaşιlι
- Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Turkey
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38
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Disambiguating the role of blood flow and global signal with partial information decomposition. Neuroimage 2020; 213:116699. [PMID: 32179104 DOI: 10.1016/j.neuroimage.2020.116699] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 02/24/2020] [Accepted: 02/29/2020] [Indexed: 12/12/2022] Open
Abstract
Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patterns in unpredicted ways. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proven to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon, while using hemodynamic and calcium mouse recordings we were able to confirm the presence of vascular effects, as calcium recordings lack hemodynamic information. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improving de-noising methods. Additionally, and beyond the mere issue of data denoising, we quantify the diverse and complementary effect of global and vessel BOLD signals on the dynamics of cortical areas.
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39
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Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem. Neuroimage 2020; 212:116614. [PMID: 32084564 DOI: 10.1016/j.neuroimage.2020.116614] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 02/05/2020] [Accepted: 02/05/2020] [Indexed: 12/13/2022] Open
Abstract
One of the most controversial procedures in the analysis of resting-state functional magnetic resonance imaging (rsfMRI) data is global signal regression (GSR): the removal, via linear regression, of the mean signal averaged over the entire brain. On one hand, the global mean signal contains variance associated with respiratory, scanner-, and motion-related artifacts, and its removal via GSR improves various quality-control metrics, enhances the anatomical specificity of functional-connectivity patterns, and can increase the behavioral variance explained by such patterns. On the other hand, GSR alters the distribution of regional signal correlations in the brain, can induce artifactual anticorrelations, may remove real neural signal, and can distort case-control comparisons of functional-connectivity measures. Global signal fluctuations can be identified visually from a matrix of colour-coded signal intensities, called a carpet plot, in which rows represent voxels and columns represent time. Prior to GSR, large, periodic bands of coherent signal changes that affect most of the brain are often apparent; after GSR, these apparently global changes are greatly diminished. Here, using three independent datasets, we show that reordering carpet plots to emphasize cluster structure in the data reveals a greater diversity of spatially widespread signal deflections (WSDs) than previously thought. Their precise form varies across time and participants, and GSR is only effective in removing specific kinds of WSDs. We present an alternative, iterative correction method called Diffuse Cluster Estimation and Regression (DiCER), that identifies representative signals associated with large clusters of coherent voxels. DiCER is more effective than GSR at removing diverse WSDs as visualized in carpet plots, reduces correlations between functional connectivity and head-motion estimates, reduces inter-individual variability in global correlation structure, and results in comparable or improved identification of canonical functional-connectivity networks. Using task fMRI data across 47 contrasts from 7 tasks in the Human Connectome Project, we also present evidence that DiCER is more successful than GSR in preserving the spatial structure of expected task-related activation patterns. Our findings indicate that care must be exercised when examining WSDs (and their possible removal) in rsfMRI data, and that DiCER is a viable alternative to GSR for removing anatomically widespread and temporally coherent signals. All code for implementing DiCER and replicating our results is available at https://github.com/BMHLab/DiCER.
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Tsurumi K, Aso T, Kawada R, Murai T, Takahashi H. A positive shift in resting-state functional connectivity between the insula and default mode network regions reflects the duration of illness in gambling disorder patients without lifetime substance abuse. Psychiatry Res Neuroimaging 2020; 295:111018. [PMID: 31770702 DOI: 10.1016/j.pscychresns.2019.111018] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 10/07/2019] [Accepted: 11/13/2019] [Indexed: 11/22/2022]
Abstract
The insula is considered an important structure involved in addiction and in the context of dynamic activity switching between large-scale brain networks, such as the default mode network (DMN) and the central executive network. Although insular-DMN resting-state functional connectivity (rsFC), which could affect such switching, has been examined in substance addiction populations, the results have been inconsistent, partly because of the confounding neural effects of the abused substances. To investigate this subject, using MRI, we examined insular-DMN rsFC in gambling disorder (GD) patients without a history of substance use. We examined rsFC between insular seeds and DMN regions of interest during rest in 23 GD patients and 27 age-, sex-, handedness-, and high education rate-matched healthy control (HC) subjects. We found a positive shift in insular-DMN rsFC in GD patients compared with HC subjects. Furthermore, the connectivity strength between insular seed regions and DMN regions was positively correlated with illness duration in GD patients. This alteration might affect switching between large-scale brain networks, potentially leading to a preoccupation with gambling as well as various types of cognitive impairments. Our results could clarify the controversial findings regarding substance addiction and enhance the system-level understanding of addiction.
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Affiliation(s)
- Kosuke Tsurumi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, 54 Shogoin-Kawahararacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Toshihiko Aso
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3, Minatojimaminamimachi, Chuo-ku, Kobe-city, Hyogo 650-0047, Japan
| | - Ryosaku Kawada
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, 54 Shogoin-Kawahararacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Toshiya Murai
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, 54 Shogoin-Kawahararacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, 54 Shogoin-Kawahararacho, Sakyo-ku, Kyoto 606-8507, Japan; Medical Institute of Developmental Disabilities Research, Showa University Karasuyama Hospital, 6-11-11 Kitakarasuyama, Setagaya-ku, Tokyo 157-8577, Japan; Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.
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Almgren H, Van de Steen F, Razi A, Friston K, Marinazzo D. The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI. Neuroimage 2019; 208:116435. [PMID: 31816423 PMCID: PMC7014820 DOI: 10.1016/j.neuroimage.2019.116435] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 09/30/2019] [Accepted: 12/02/2019] [Indexed: 12/19/2022] Open
Abstract
The influence of global BOLD fluctuations on resting state functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between resting state networks (RSNs) - as estimated with dynamic causal modelling (DCM) for resting state fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we considered four extensive longitudinal resting state fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of information gain with and without GSR. Our results showed negligible to small effects of GSR on effective connectivity within small (separately estimated) RSNs. However, although the effect sizes were small, there was substantial to conclusive evidence for an effect of GSR on connectivity parameters. For between-network connectivity, we found two important effects: the effect of GSR on between-network effective connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. In the cross-sectional (but not in the longitudinal) data, some connections showed substantial to conclusive evidence for an effect of GSR. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters characterising (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small resting state networks the precision of posterior estimates was greater after GSR. In conclusion, GSR is a minor concern in DCM studies; however, quantitative interpretation of between-network connections (as opposed to average between-network connectivity) and noise parameters should be treated with some caution. The Kullback-Leibler divergence of the posterior from the prior (i.e., information gain) - together with the precision of posterior estimates - might offer a useful measure to assess the appropriateness of GSR in resting state fMRI. We investigated the effect of GSR on parameters estimated with DCM. We found negligible to small effects of GSR in small-scale resting state networks. Small effects of GSR on the net influence between networks were observed. Results showed non-negligible effects of GSR on individual between-network connections. The effect of GSR on noise parameters was either absent or unspecific, depending on design.
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Affiliation(s)
- Hannes Almgren
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Henri Dunantlaan 2, 9000, Gent, Belgium.
| | - Frederik Van de Steen
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Henri Dunantlaan 2, 9000, Gent, Belgium.
| | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash University, 770 Blackburn Road, Building 220, Monash University, Clayton, VIC, 3800, Australia; The Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London, WC1N 3AR, United Kingdom; Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, University Road, Karachi, 75270, Pakistan.
| | - Karl Friston
- The Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London, WC1N 3AR, United Kingdom.
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Henri Dunantlaan 2, 9000, Gent, Belgium.
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Aso T, Urayama S, Fukuyama H, Murai T. Axial variation of deoxyhemoglobin density as a source of the low-frequency time lag structure in blood oxygenation level-dependent signals. PLoS One 2019; 14:e0222787. [PMID: 31545839 PMCID: PMC6756514 DOI: 10.1371/journal.pone.0222787] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 09/06/2019] [Indexed: 01/24/2023] Open
Abstract
Perfusion-related information is reportedly embedded in the low-frequency component of a blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal. The blood-propagation pattern through the cerebral vascular tree is detected as an interregional lag variation of spontaneous low-frequency oscillations (sLFOs). Mapping of this lag, or phase, has been implicitly treated as a projection of the vascular tree structure onto real space. While accumulating evidence supports the biological significance of this signal component, the physiological basis of the “perfusion lag structure,” a requirement for an integrative resting-state fMRI-signal model, is lacking. In this study, we conducted analyses furthering the hypothesis that the sLFO is not only largely of systemic origin, but also essentially intrinsic to blood, and hence behaves as a virtual tracer. By summing the small fluctuations of instantaneous phase differences between adjacent vascular regions, a velocity response to respiratory challenges was detected. Regarding the relationship to neurovascular coupling, the removal of the whole lag structure, which can be considered as an optimized global-signal regression, resulted in a reduction of inter-individual variance while preserving the fMRI response. Examination of the T2* and S0, or non-BOLD, components of the fMRI signal revealed that the lag structure is deoxyhemoglobin dependent, while paradoxically presenting a signal-magnitude reduction in the venous side of the cerebral vasculature. These findings provide insight into the origin of BOLD sLFOs, suggesting that they are highly intrinsic to the circulating blood.
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Affiliation(s)
- Toshihiko Aso
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
- Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan
- * E-mail:
| | - Shinnichi Urayama
- Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Research and Educational Unit of Leaders for Integrated Medical System, Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, Kyoto, Japan
| | - Hidenao Fukuyama
- Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Research and Educational Unit of Leaders for Integrated Medical System, Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, Kyoto, Japan
| | - Toshiya Murai
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan
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Takeda Y, Itahashi T, Sato MA, Yamashita O. Estimating repetitive spatiotemporal patterns from many subjects' resting-state fMRIs. Neuroimage 2019; 203:116182. [PMID: 31525496 DOI: 10.1016/j.neuroimage.2019.116182] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 08/20/2019] [Accepted: 09/10/2019] [Indexed: 01/06/2023] Open
Abstract
Recently, we proposed a method to estimate repetitive spatiotemporal patterns from resting-state brain activity data (SpatioTemporal Pattern estimation, STeP) (Takeda et al., 2016). From such resting-state data as functional MRI (fMRI), STeP can estimate several spatiotemporal patterns and their onsets even if they are overlapping. Nowadays, a growing number of resting-state data are publicly available from such databases as the Autism Brain Imaging Data Exchange (ABIDE), which promote a better understanding of resting-state brain activities. In this study, we extend STeP to make it applicable to such big databases, thus proposing the method we call BigSTeP. From many subjects' resting-state data, BigSTeP estimates spatiotemporal patterns that are common across subjects (common spatiotemporal patterns) as well as the corresponding spatiotemporal patterns in each subject (subject-specific spatiotemporal patterns). After verifying the performance of BigSTeP by simulation tests, we applied it to over 1,000 subjects' resting-state fMRIs (rsfMRIs) obtained from ABIDE I. This revealed two common spatiotemporal patterns and the corresponding subject-specific spatiotemporal patterns. The common spatiotemporal patterns included spatial patterns resembling the default mode (DMN), sensorimotor, auditory, and visual networks, suggesting that these networks are time-locked with each other. We compared the subject-specific spatiotemporal patterns between autism spectrum disorder (ASD) and typically developed (TD) groups. As a result, significant differences were concentrated at a specific time in a pattern, when the DMN exhibited large positive activity. This suggests that the differences are context-dependent, that is, the differences in fMRI activities between ASDs and TDs do not always occur during the resting state but tend to occur when the DMN exhibits large positive activity. All of these results demonstrate the usefulness of BigSTeP in extracting inspiring hypotheses from big databases in a data-driven way.
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Affiliation(s)
- Yusuke Takeda
- Computational Brain Dynamics Team, RIKEN Center for Advanced Intelligence Project, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan; Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan.
| | - Takashi Itahashi
- Medical Institute of Developmental Disabilities Research, Showa University, 6-11-11 Kita-karasuyama, Setagaya-ku, Tokyo, 157-8577, Japan
| | - Masa-Aki Sato
- Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan
| | - Okito Yamashita
- Computational Brain Dynamics Team, RIKEN Center for Advanced Intelligence Project, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan; Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan
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Aslan S, Hocke L, Schwarz N, Frederick B. Extraction of the cardiac waveform from simultaneous multislice fMRI data using slice sorted averaging and a deep learning reconstruction filter. Neuroimage 2019; 198:303-316. [PMID: 31129302 PMCID: PMC6592732 DOI: 10.1016/j.neuroimage.2019.05.049] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 04/07/2019] [Accepted: 05/17/2019] [Indexed: 02/05/2023] Open
Abstract
Cardiac signal contamination has long confounded the analysis of blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI). Cardiac pulsation results in significant BOLD signal changes, especially in and around blood vessels. Until the advent of simultaneous multislice echo-planar imaging (EPI) acquisition, the time resolution of whole brain EPI was insufficient to avoid cardiac aliasing (and acquisitions with repetition times (TRs) under 400-500 ms are still uncommon). As a result, direct detection and removal of the cardiac signal with spectral filters is generally not possible. Modelling methods have been developed to mitigate cardiac contamination, and recently developed techniques permit the visualization of cardiac signal propagation through the brain in undersampled data (e.g., TRs > 1s), which is useful in its own right for finding blood vessels. However, both of these techniques require data from which to estimate cardiac phase, which is generally not available for the data in many large databases of existing imaging data, and even now is not routinely recorded in many fMRI experiments. Here we present a method to estimate the cardiac waveform directly from a multislice fMRI dataset, without additional physiological measurements, such as plethysmograms. The pervasive spatial extent and temporal structure of the cardiac contamination signal across the brain offers an opportunity to exploit the nature of multislice imaging to extract this signal from the fMRI data itself. While any particular slice is recorded at the TR of the imaging experiment, slices are recorded much more quickly - typically from 10 to 20 Hz - sufficiently fast to fully sample the cardiac signal. Using the fairly permissive assumptions that the cardiac signal is a) pseudoperiodic b) somewhat coherent within any given slice, and c) is similarly shaped throughout the brain, we can extract a good estimate of the cardiac phase as a function of time from fMRI data alone. If we make further assumptions about the shape and consistency of cardiac waveforms, we can develop a deep learning filter to greatly improve our estimate of the cardiac waveform.
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Affiliation(s)
- Serdar Aslan
- Brain Imaging Center, McLean Hospital, 115 Mill Street, Belmont, MA, 02478, USA; Department of Psychiatry, Harvard University Medical School, Boston, MA, 02115, USA
| | - Lia Hocke
- Brain Imaging Center, McLean Hospital, 115 Mill Street, Belmont, MA, 02478, USA; Department of Psychiatry, Harvard University Medical School, Boston, MA, 02115, USA
| | - Nicolette Schwarz
- Brain Imaging Center, McLean Hospital, 115 Mill Street, Belmont, MA, 02478, USA; Department of Psychiatry, Harvard University Medical School, Boston, MA, 02115, USA
| | - Blaise Frederick
- Brain Imaging Center, McLean Hospital, 115 Mill Street, Belmont, MA, 02478, USA; Department of Psychiatry, Harvard University Medical School, Boston, MA, 02115, USA.
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Tong Y, Hocke LM, Frederick BB. Low Frequency Systemic Hemodynamic "Noise" in Resting State BOLD fMRI: Characteristics, Causes, Implications, Mitigation Strategies, and Applications. Front Neurosci 2019; 13:787. [PMID: 31474815 PMCID: PMC6702789 DOI: 10.3389/fnins.2019.00787] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Accepted: 07/15/2019] [Indexed: 01/06/2023] Open
Abstract
Advances in functional magnetic resonance imaging (fMRI) acquisition have improved signal to noise to the point where the physiology of the subject is the dominant noise source in resting state fMRI data (rsfMRI). Among these systemic, non-neuronal physiological signals, respiration and to some degree cardiac fluctuations can be removed through modeling, or in the case of newer, faster acquisitions such as simultaneous multislice acquisition, simple spectral filtering. However, significant low frequency physiological oscillation (∼0.01-0.15 Hz) remains in the signal. This is problematic, as it is the precise frequency band occupied by the neuronally modulated hemodynamic responses used to study brain connectivity, precluding its removal by spectral filtering. The source of this signal, and its method of production and propagation in the body, have not been conclusively determined. Here, we summarize the defining characteristics of the systemic low frequency noise signal, and review some current theories about the signal source and the evidence supporting them. The strength and distribution of the systemic LFO signal make characterizing and removing it essential for accurate quantification, especially for resting state connectivity, when no stimulation can be compared with the signal. Widespread correlated non-neuronal signals obscure and distort the more localized patterns of neuronal correlations between interacting brain regions; they may even cause apparent connectivity between regions with no neuronal interaction. Here, we discuss a simple method we have developed to parse the global, moving, blood-borne signal from the stationary, neuronal connectivity signals, substantially reducing the negative correlations that result from global signal regression. Finally, we will discuss some of the uses to which the moving systemic low frequency oscillation can be put if we consider it a "signal" carrying information, rather than simply "noise" complicating the interpretation of resting state connectivity. Properly utilizing this signal may offer insights into subtle hemodynamic alterations that can be used as early indicators of circulatory dysfunction in a number of neuropsychiatric conditions, such as prodromal stroke, moyamoya, and Alzheimer's disease.
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Affiliation(s)
- Yunjie Tong
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
| | - Lia M. Hocke
- McLean Imaging Center, McLean Hospital, Belmont, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Blaise B. Frederick
- McLean Imaging Center, McLean Hospital, Belmont, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
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Moradi N, Dousty M, Sotero RC. Spatiotemporal Empirical Mode Decomposition of Resting-State fMRI Signals: Application to Global Signal Regression. Front Neurosci 2019; 13:736. [PMID: 31396032 PMCID: PMC6664052 DOI: 10.3389/fnins.2019.00736] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 07/02/2019] [Indexed: 12/15/2022] Open
Abstract
Resting-state functional connectivity MRI (rs-fcMRI) is a common method for mapping functional brain networks. However, estimation of these networks is affected by the presence of a common global systemic noise, or global signal (GS). Previous studies have shown that the common preprocessing steps of removing the GS may create spurious correlations between brain regions. In this paper, we decompose fMRI signals into 5 spatial and 3 temporal intrinsic mode functions (SIMF and TIMF, respectively) by means of the empirical mode decomposition (EMD), which is an adaptive data-driven method widely used to analyze non-linear and non-stationary phenomena. For each SIMF, functional connectivity matrices were computed by means of Pearson correlation between TIMFs of different brain areas. Thus, instead of a single connectivity matrix, we obtained 5 × 3 = 15 functional connectivity matrices. Given the high correlation and global efficiency values of the connectivity matrices related to the low spatial maps (SIMF3, SIMF4, and SIMF5), our results suggest that these maps can be considered as spatial global signal masks. Thus, by summing up the first two SIMFs extracted from the fMRI signals, we have automatically excluded the GS which is now voxel-specific. We compared the performance of our method with the conventional GS regression and to the results when the GS was not removed. While the correlation pattern identified by the other methods suffers from a low level of precision in identifying the correct brain network connectivity, our approach demonstrated expected connectivity patterns for the default mode network and task-positive network.
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Affiliation(s)
- Narges Moradi
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Computational Neurophysics Lab, Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Mehdy Dousty
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada.,KITE, Toronto Rehab, University Health Network, Toronto, ON, Canada
| | - Roberto C Sotero
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Computational Neurophysics Lab, Department of Radiology, University of Calgary, Calgary, AB, Canada
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Yamasaki T, Aso T, Kaseda Y, Mimori Y, Doi H, Matsuoka N, Takamiya N, Torii T, Takahashi T, Ohshita T, Yamashita H, Doi H, Inamizu S, Chatani H, Tobimatsu S. Decreased stimulus-driven connectivity of the primary visual cortex during visual motion stimulation in amnestic mild cognitive impairment: An fMRI study. Neurosci Lett 2019; 711:134402. [PMID: 31356844 DOI: 10.1016/j.neulet.2019.134402] [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: 03/28/2019] [Revised: 06/26/2019] [Accepted: 07/22/2019] [Indexed: 10/26/2022]
Abstract
Motion perceptual deficits are common in Alzheimer's disease (AD). Although the posterior parietal cortex is thought to play a critical role in these deficits, it is currently unclear whether the primary visual cortex (V1) contributes to these deficits in AD. To elucidate this issue, we investigated the net activity or connectivity within V1 in 17 amnestic mild cognitive impairment (aMCI) patients, 17 AD patients and 17 normal controls (NC) using functional magnetic resonance imaging (fMRI). fMRI was recorded under two conditions: visual motion stimulation and resting-state. The net activity or connectivity within V1 extracted by independent component analysis (ICA) was significantly increased during visual motion stimuli compared with that of the resting-state condition in NC, but not in aMCI or AD patients. These findings suggest the alteration of the net activity or connectivity within V1, which may contribute to the previously reported motion perceptual deficits in aMCI and AD. Therefore, the decreased net V1 activity measured as the strength of the ICA component may provide a new disease biomarker for early detection of AD.
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Affiliation(s)
- Takao Yamasaki
- Department of Clinical Neurophysiology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Department of Neurology, Minkodo Minohara Hospital, Fukuoka, Japan.
| | - Toshihiko Aso
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yumiko Kaseda
- Department of Neurology, Hiroshima City Rehabilitation Hospital, Hiroshima, Japan
| | - Yasuyo Mimori
- Department of Rehabilitation, Faculty of Rehabilitation, Hiroshima International University, Hiroshima, Japan
| | - Hikaru Doi
- Doi Clinic Internal Medicine/Neurology, Hiroshima, Japan
| | | | - Naomi Takamiya
- Department of Clinical Neurophysiology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Department of Physical Therapy, Faculty of Health and Welfare, Prefectural University of Hiroshima, Hiroshima, Japan
| | - Tsuyoshi Torii
- Department of Neurology, National Hospital Organization Kure Medical Center, Hiroshima, Japan
| | - Tetsuya Takahashi
- Department of Clinical Neuroscience and Therapeutics, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan
| | - Tomohiko Ohshita
- Department of Neurology, Suiseikai Kajikawa Hospital, Hiroshima, Japan
| | - Hiroshi Yamashita
- Department of Neurology, Hiroshima City Asa Citizens Hospital, Hiroshima, Japan
| | - Hitoka Doi
- Doi Clinic Internal Medicine/Neurology, Hiroshima, Japan
| | - Saeko Inamizu
- Department of Clinical Neurophysiology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Department of Neurology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hiroshi Chatani
- Department of Clinical Neurophysiology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Department of Neurology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Shozo Tobimatsu
- Department of Clinical Neurophysiology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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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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Jahanian H, Christen T, Moseley ME, Zaharchuk G. Erroneous Resting-State fMRI Connectivity Maps Due to Prolonged Arterial Arrival Time and How to Fix Them. Brain Connect 2019; 8:362-370. [PMID: 29886781 DOI: 10.1089/brain.2018.0610] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
In resting-state functional MRI (rs-fMRI), functional networks are assessed utilizing the temporal correlation between spontaneous blood oxygen level-dependent signal fluctuations of spatially remote brain regions. Recently, several groups have shown that temporal shifts are present in rs-fMRI maps in patients with cerebrovascular disease due to spatial differences in arterial arrival times, and that this can be exploited to map arrival times in the brain. This suggests that rs-fMRI connectivity mapping may be similarly sensitive to such temporal shifts, and that standard rs-fMRI analysis methods may fail to identify functional connectivity networks. To investigate this, we studied the default mode network (DMN) in Moyamoya disease patients and compared it with normal healthy volunteers. Our results show that using standard independent component analysis (ICA) and seed-based approaches, arterial arrival delays lead to inaccurate incomplete characterization of functional connectivity within the DMN in Moyamoya disease patients. Furthermore, we propose two techniques to correct these errors, for seed-based and ICA methods, respectively. Using these methods, we demonstrate that it is possible to mitigate the deleterious effects of arterial arrival time on the assessment of functional connectivity of the DMN. As these corrections have not been applied to the vast majority of >200 prior rs-fMRI studies in patients with cerebrovascular disease, we suggest that they be interpreted with great caution. Correction methods should be applied in any rs-fMRI connectivity study of subjects expected to have abnormally delayed arterial arrival times.
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Affiliation(s)
| | - Thomas Christen
- 2 Department of Radiology, Stanford University , Stanford, California
| | - Michael E Moseley
- 2 Department of Radiology, Stanford University , Stanford, California
| | - Greg Zaharchuk
- 2 Department of Radiology, Stanford University , Stanford, California
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Abstract
Global signal regression is a controversial processing step for resting-state functional magnetic resonance imaging, partly because the source of the global blood oxygen level-dependent (BOLD) signal remains unclear. On the one hand, nuisance factors such as motion can readily introduce coherent BOLD changes across the whole brain. On the other hand, the global signal has been linked to neural activity and vigilance levels, suggesting that it contains important neurophysiological information and should not be discarded. Any widespread pattern of coordinated activity is likely to contribute appreciably to the global signal. Such patterns may include large-scale quasiperiodic spatiotemporal patterns, known also to be tied to performance on vigilance tasks. This uncertainty surrounding the separability of the global BOLD signal from concurrent neurological processes motivated an examination of the global BOLD signal's spatial distribution. The results clarify that although the global signal collects information from all tissue classes, a diverse subset of the BOLD signal's independent components contribute the most to the global signal. Further, the timing of each network's contribution to the global signal is not consistent across volunteers, confirming the independence of a constituent process that comprises the global signal.
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Affiliation(s)
- Jacob Billings
- 1 Program in Neuroscience, Graduate Division of Biological and Biomedical Sciences, Emory University , Atlanta, Georgia
| | - Shella Keilholz
- 1 Program in Neuroscience, Graduate Division of Biological and Biomedical Sciences, Emory University , Atlanta, Georgia .,2 Department of Biomedical Engineering, Emory/Georgia Institute of Technology , Atlanta, Georgia
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