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Reddy NA, Clements RG, Brooks JCW, Bright MG. Simultaneous cortical, subcortical, and brainstem mapping of sensory activation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.11.589099. [PMID: 38659741 PMCID: PMC11042175 DOI: 10.1101/2024.04.11.589099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Non-painful tactile sensory stimuli are processed in the cortex, subcortex, and brainstem. Recent functional magnetic resonance imaging (fMRI) studies have highlighted the value of whole-brain, systems-level investigation for examining pain processing. However, whole-brain fMRI studies are uncommon, in part due to challenges with signal to noise when studying the brainstem. Furthermore, the differentiation of small sensory brainstem structures such as the cuneate and gracile nuclei necessitates high resolution imaging. To address this gap in systems-level sensory investigation, we employed a whole-brain, multi-echo fMRI acquisition at 3T with multi-echo independent component analysis (ME-ICA) denoising and brainstem-specific modeling to enable detection of activation across the entire sensory system. In healthy participants, we examined patterns of activity in response to non-painful brushing of the right hand, left hand, and right foot, and found the expected lateralization, with distinct cortical and subcortical responses for upper and lower limb stimulation. At the brainstem level, we were able to differentiate the small, adjacent cuneate and gracile nuclei, corresponding to hand and foot stimulation respectively. Our findings demonstrate that simultaneous cortical, subcortical, and brainstem mapping at 3T could be a key tool to understand the sensory system in both healthy individuals and clinical cohorts with sensory deficits.
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Affiliation(s)
- Neha A. Reddy
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, United States
| | - Rebecca G. Clements
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, United States
| | | | - Molly G. Bright
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, United States
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Wang D, Li H, Xu M, Bo B, Pei M, Liang Z, Thompson GJ. Differential Effect of Global Signal Regression Between Awake and Anesthetized Conditions in Mice. Brain Connect 2024; 14:48-59. [PMID: 38063007 DOI: 10.1089/brain.2023.0032] [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] [Indexed: 01/11/2024] Open
Abstract
Introduction: In resting-state functional magnetic resonance imaging (rs-fMRI) studies, global signal regression (GSR) is a controversial preprocessing strategy. It effectively eliminates global noise driven by motion and respiration but also can introduce artifacts and remove functionally relevant metabolic information. Most preclinical rs-fMRI studies are performed in anesthetized animals, and anesthesia will alter both metabolic and neuronal activity. Methods: In this study, we explored the effect of GSR on rs-fMRI data collected under anesthetized and awake state in mice (n = 12). We measured global signal amplitude, and also functional connectivity (FC), functional connectivity density (FCD) maps, and brain modularity, all commonly used data-driven analysis methods to quantify connectivity patterns. Results: We found that global signal amplitude was similar between the awake and anesthetized states. However, GSR had a different impact on connectivity networks and brain modularity changes between states. We demonstrated that GSR had a more prominent impact on the anesthetized state, with a greater decrease in functional connectivity and increased brain modularity. We classified mice using the change in amplitude of brain modularity coefficient (ΔQ) before and after GSR processing. The results revealed that, when compared with the largest ΔQ group, the smallest ΔQ group had increased FCD in the cortex region in both the awake and anesthetized states. This suggests differences in individual mice may affect how GSR differentially affects awake versus anesthetized functional connectivity. Discussion: This study suggests that, for rs-fMRI studies which compare different physiological states, researchers should use GSR processing with caution.
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Affiliation(s)
- Da Wang
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Hui Li
- iHuman Institute, ShanghaiTech University, Shanghai, China
| | - Mengyang Xu
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Binshi Bo
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
| | - Mengchao Pei
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
| | - Zhifeng Liang
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
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Steel A, Silson EH, Garcia BD, Robertson CE. A retinotopic code structures the interaction between perception and memory systems. Nat Neurosci 2024; 27:339-347. [PMID: 38168931 PMCID: PMC10923171 DOI: 10.1038/s41593-023-01512-3] [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: 07/21/2022] [Accepted: 10/31/2023] [Indexed: 01/05/2024]
Abstract
Conventional views of brain organization suggest that regions at the top of the cortical hierarchy processes internally oriented information using an abstract amodal neural code. Despite this, recent reports have described the presence of retinotopic coding at the cortical apex, including the default mode network. What is the functional role of retinotopic coding atop the cortical hierarchy? Here we report that retinotopic coding structures interactions between internally oriented (mnemonic) and externally oriented (perceptual) brain areas. Using functional magnetic resonance imaging, we observed robust inverted (negative) retinotopic coding in category-selective memory areas at the cortical apex, which is functionally linked to the classic (positive) retinotopic coding in category-selective perceptual areas in high-level visual cortex. These functionally linked retinotopic populations in mnemonic and perceptual areas exhibit spatially specific opponent responses during both bottom-up perception and top-down recall, suggesting that these areas are interlocked in a mutually inhibitory dynamic. These results show that retinotopic coding structures interactions between perceptual and mnemonic neural systems, providing a scaffold for their dynamic interaction.
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Affiliation(s)
- Adam Steel
- Department of Psychology and Brain Sciences, Dartmouth College, Hanover, NH, USA.
| | - Edward H Silson
- Psychosophy, Psychology, and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - Brenda D Garcia
- Department of Psychology and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Caroline E Robertson
- Department of Psychology and Brain Sciences, Dartmouth College, Hanover, NH, USA.
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Dong Z, Wald LL, Polimeni JR, Wang F. Single-shot Echo Planar Time-resolved Imaging for multi-echo functional MRI and distortion-free diffusion imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.24.577002. [PMID: 38328081 PMCID: PMC10849706 DOI: 10.1101/2024.01.24.577002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Purpose To develop EPTI, a multi-shot distortion-free multi-echo imaging technique, into a single-shot acquisition to achieve improved robustness to motion and physiological noise, increased temporal resolution, and high SNR efficiency for dynamic imaging applications. Methods A new spatiotemporal encoding was developed to achieve single-shot EPTI by enhancing spatiotemporal correlation in k-t space. The proposed single-shot encoding improves reconstruction conditioning and sampling efficiency, with additional optimization under various accelerations to achieve optimized performance. To achieve high SNR efficiency, continuous readout with minimized deadtime was employed that begins immediately after excitation and extends for an SNR-optimized length. Moreover, k-t partial Fourier and simultaneous multi-slice acquisition were integrated to further accelerate the acquisition and achieve high spatial and temporal resolution. Results We demonstrated that ss-EPTI achieves higher tSNR efficiency than multi-shot EPTI, and provides distortion-free imaging with densely-sampled multi-echo images at resolutions ~1.25-3 mm at 3T and 7T-with high SNR efficiency and with comparable temporal resolutions to ss-EPI. The ability of ss-EPTI to eliminate dynamic distortions common in EPI also further improves temporal stability. For fMRI, ss-EPTI also provides early-TE images (e.g., 2.9ms) to recover signal-intensity and functional-sensitivity dropout in challenging regions. The multi-echo images provide TE-dependent information about functional fluctuations, successfully distinguishing noise-components from BOLD signals and further improving tSNR. For diffusion MRI, ss-EPTI provides high-quality distortion-free diffusion images and multi-echo diffusion metrics. Conclusion ss-EPTI provides distortion-free imaging with high image quality, rich multi-echo information, and enhanced efficiency within comparable temporal resolution to ss-EPI, offering a robust and efficient acquisition for dynamic imaging.
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Affiliation(s)
- Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Lawrence L. Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Jonathan R. Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
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Reddy NA, Zvolanek KM, Moia S, Caballero-Gaudes C, Bright MG. Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.19.549746. [PMID: 37503125 PMCID: PMC10370165 DOI: 10.1101/2023.07.19.549746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson's disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired BOLD signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models' performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example in a chronic stroke cohort with varying stroke location and degree of tissue damage.
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Affiliation(s)
- Neha A. Reddy
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, United States
| | - Kristina M. Zvolanek
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, United States
| | - Stefano Moia
- Basque Center on Cognition, Brain and Language, Donostia, Gipuzkoa, Spain
- Neuro-X Institute, École polytechnique fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics (DRIM), Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | | | - Molly G. Bright
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, United States
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Steel A, Silson EH, Garcia BD, Robertson CE. A retinotopic code structures the interaction between perception and memory systems. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.15.540807. [PMID: 37292758 PMCID: PMC10245578 DOI: 10.1101/2023.05.15.540807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Conventional views of brain organization suggest that the cortical apex processes internally-oriented information using an abstract, amodal neural code. Yet, recent reports have described the presence of retinotopic coding at the cortical apex, including the default mode network. What is the functional role of retinotopic coding atop the cortical hierarchy? Here, we report that retinotopic coding structures interactions between internally-oriented (mnemonic) and externally-oriented (perceptual) brain areas. Using fMRI, we observed robust, inverted (negative) retinotopic coding in category-selective memory areas at the cortical apex, which is functionally linked to the classic (positive) retinotopic coding in category-selective perceptual areas in high-level visual cortex. Specifically, these functionally-linked retinotopic populations in mnemonic and perceptual areas exhibit spatially-specific opponent responses during both bottom-up perception and top-down recall, suggesting that these areas are interlocked in a mutually-inhibitory dynamic. Together, these results show that retinotopic coding structures interactions between perceptual and mnemonic neural systems, thereby scaffolding their dynamic interaction.
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Affiliation(s)
- Adam Steel
- Department of Psychology and Brain Sciences, Dartmouth College, Hanover, NH, 03755
| | - Edward H. Silson
- Psychology, School of Philosophy, Psychology, and Language Sciences, University of Edinburgh, Edinburgh, UK EH8 9JZ
| | - Brenda D. Garcia
- Department of Psychology and Brain Sciences, Dartmouth College, Hanover, NH, 03755
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Jacob M, Ford J, Deacon T. Cognition is entangled with metabolism: relevance for resting-state EEG-fMRI. Front Hum Neurosci 2023; 17:976036. [PMID: 37113322 PMCID: PMC10126302 DOI: 10.3389/fnhum.2023.976036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 03/02/2023] [Indexed: 04/29/2023] Open
Abstract
The brain is a living organ with distinct metabolic constraints. However, these constraints are typically considered as secondary or supportive of information processing which is primarily performed by neurons. The default operational definition of neural information processing is that (1) it is ultimately encoded as a change in individual neuronal firing rate as this correlates with the presentation of a peripheral stimulus, motor action or cognitive task. Two additional assumptions are associated with this default interpretation: (2) that the incessant background firing activity against which changes in activity are measured plays no role in assigning significance to the extrinsically evoked change in neural firing, and (3) that the metabolic energy that sustains this background activity and which correlates with differences in neuronal firing rate is merely a response to an evoked change in neuronal activity. These assumptions underlie the design, implementation, and interpretation of neuroimaging studies, particularly fMRI, which relies on changes in blood oxygen as an indirect measure of neural activity. In this article we reconsider all three of these assumptions in light of recent evidence. We suggest that by combining EEG with fMRI, new experimental work can reconcile emerging controversies in neurovascular coupling and the significance of ongoing, background activity during resting-state paradigms. A new conceptual framework for neuroimaging paradigms is developed to investigate how ongoing neural activity is "entangled" with metabolism. That is, in addition to being recruited to support locally evoked neuronal activity (the traditional hemodynamic response), changes in metabolic support may be independently "invoked" by non-local brain regions, yielding flexible neurovascular coupling dynamics that inform the cognitive context. This framework demonstrates how multimodal neuroimaging is necessary to probe the neurometabolic foundations of cognition, with implications for the study of neuropsychiatric disorders.
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Affiliation(s)
- Michael Jacob
- Mental Health Service, San Francisco VA Healthcare System, San Francisco, CA, United States
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Judith Ford
- Mental Health Service, San Francisco VA Healthcare System, San Francisco, CA, United States
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Terrence Deacon
- Department of Anthropology, University of California, Berkeley, Berkeley, CA, United States
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8
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Steel A, Garcia BD, Silson EH, Robertson CE. Evaluating the efficacy of multi-echo ICA denoising on model-based fMRI. Neuroimage 2022; 264:119723. [PMID: 36328274 DOI: 10.1016/j.neuroimage.2022.119723] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 09/30/2022] [Accepted: 10/30/2022] [Indexed: 11/05/2022] Open
Abstract
fMRI is an indispensable tool for neuroscience investigation, but this technique is limited by multiple sources of physiological and measurement noise. These noise sources are particularly problematic for analysis techniques that require high signal-to-noise ratio for stable model fitting, such as voxel-wise modeling. Multi-echo data acquisition in combination with echo-time dependent ICA denoising (ME-ICA) represents one promising strategy to mitigate physiological and hardware-related noise sources as well as motion-related artifacts. However, most studies employing ME-ICA to date are resting-state fMRI studies, and therefore we have a limited understanding of the impact of ME-ICA on complex task or model-based fMRI paradigms. Here, we addressed this knowledge gap by comparing data quality and model fitting performance of data acquired during a visual population receptive field (pRF) mapping (N = 13 participants) experiment after applying one of three preprocessing procedures: ME-ICA, optimally combined multi-echo data without ICA-denoising, and typical single echo processing. As expected, multi-echo fMRI improved temporal signal-to-noise compared to single echo fMRI, with ME-ICA amplifying the improvement compared to optimal combination alone. However, unexpectedly, this boost in temporal signal-to-noise did not directly translate to improved model fitting performance: compared to single echo acquisition, model fitting was only improved after ICA-denoising. Specifically, compared to single echo acquisition, ME-ICA resulted in improved variance explained by our pRF model throughout the visual system, including anterior regions of the temporal and parietal lobes where SNR is typically low, while optimal combination without ICA did not. ME-ICA also improved reliability of parameter estimates compared to single echo and optimally combined multi-echo data without ICA-denoising. Collectively, these results suggest that ME-ICA is effective for denoising task-based fMRI data for modeling analyzes and maintains the integrity of the original data. Therefore, ME-ICA may be beneficial for complex fMRI experiments, including voxel-wise modeling and naturalistic paradigms.
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Affiliation(s)
- Adam Steel
- Department of Psychology and Brain Sciences, Dartmouth College, 3 Maynard Street, Hanover, NH 03755, US.
| | - Brenda D Garcia
- Department of Psychology and Brain Sciences, Dartmouth College, 3 Maynard Street, Hanover, NH 03755, US
| | - Edward H Silson
- Psychology, School of Philosophy, Psychology, and Language Sciences, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Caroline E Robertson
- Department of Psychology and Brain Sciences, Dartmouth College, 3 Maynard Street, Hanover, NH 03755, US
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9
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Arboit A, Krautwald K, Angenstein F. The cholinergic system modulates negative BOLD responses in the prefrontal cortex once electrical perforant pathway stimulation triggers neuronal afterdischarges in the hippocampus. J Cereb Blood Flow Metab 2022; 42:364-380. [PMID: 34590894 PMCID: PMC8795231 DOI: 10.1177/0271678x211049820] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Repeated high-frequency pulse-burst stimulations of the rat perforant pathway elicited positive BOLD responses in the right hippocampus, septum and prefrontal cortex. However, when the first stimulation period also triggered neuronal afterdischarges in the hippocampus, then a delayed negative BOLD response in the prefrontal cortex was generated. While neuronal activity and cerebral blood volume (CBV) increased in the hippocampus during the period of hippocampal neuronal afterdischarges (h-nAD), CBV decreased in the prefrontal cortex, although neuronal activity did not decrease. Only after termination of h-nAD did CBV in the prefrontal cortex increase again. Thus, h-nAD triggered neuronal activity in the prefrontal cortex that counteracted the usual neuronal activity-related functional hyperemia. This process was significantly enhanced by pilocarpine, a mACh receptor agonist, and completely blocked when pilocarpine was co-administered with scopolamine, a mACh receptor antagonist. Scopolamine did not prevent the formation of the negative BOLD response, thus mACh receptors modulate the strength of the negative BOLD response.
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Affiliation(s)
- Alberto Arboit
- Functional Imaging Group, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Magdeburg, Germany
| | - Karla Krautwald
- Functional Imaging Group, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Magdeburg, Germany
| | - Frank Angenstein
- Functional Imaging Group, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Magdeburg, Germany.,Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany.,Medical Faculty, Otto von Guericke University, Magdeburg, Germany
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10
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Rafique SA, Steeves JKE. Modulating intrinsic functional connectivity with visual cortex using low-frequency repetitive transcranial magnetic stimulation. Brain Behav 2022; 12:e2491. [PMID: 35049143 PMCID: PMC8865167 DOI: 10.1002/brb3.2491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 12/20/2021] [Accepted: 12/30/2021] [Indexed: 11/11/2022] Open
Abstract
INTRODUCTION Intrinsic network connectivity becomes altered in pathophysiology. Noninvasive brain stimulation can modulate pathological functional networks in an attempt to restore the inherent response. To determine its usefulness for visual-related disorders, we developed procedures investigating repetitive transcranial magnetic stimulation (rTMS) protocols targeting the visual cortex on modulating connectivity associated with the visual network and default mode network (DMN). METHODS We compared two low-frequency (1 Hz) rTMS protocols to the visual cortex (V1)-a single 20 min session and five successive 20 min sessions (accelerated/within-session rTMS)-using multi-echo resting-state functional magnetic resonance whole-brain imaging and resting-state functional connectivity (rsFC). We also explored the relationship between rsFC and rTMS-induced changes in key inhibitory and excitatory neurotransmitters, γ-aminobutyric acid (GABA) and glutamate. GABA (GABA+) and glutamate (Glx) concentrations were measured in vivo using magnetic resonance spectroscopy. RESULTS Acute disruption with a single rTMS session caused widespread connectivity reconfiguration with nodes of interest. Changes were not evident immediately post-rTMS but were observed at 1 h post-rTMS. Accelerated sessions resulted in weak alterations in connectivity, producing a relatively homeostatic response. Changes in GABA+ and Glx concentrations with network connectivity were dependent on the rTMS protocol. CONCLUSIONS This proof-of-concept study offers new perspectives to assess stimulation-induced neural processes involved in intrinsic functional connectivity and the potential for rTMS to modulate nodes interconnected with the visual cortex. The differential effects of single-session and accelerated rTMS on physiological markers are crucial for furthering the advancement of treatment modalities in visual cortex related disorders.
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Affiliation(s)
- Sara A Rafique
- Department of Psychology and Centre for Vision Research, York University, Toronto, Canada
| | - Jennifer K E Steeves
- Department of Psychology and Centre for Vision Research, York University, Toronto, Canada
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11
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Elliott ML, Knodt AR, Hariri AR. Striving toward translation: strategies for reliable fMRI measurement. Trends Cogn Sci 2021; 25:776-787. [PMID: 34134933 PMCID: PMC8363569 DOI: 10.1016/j.tics.2021.05.008] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 12/27/2022]
Abstract
fMRI has considerable potential as a translational tool for understanding risk, prioritizing interventions, and improving the treatment of brain disorders. However, recent studies have found that many of the most widely used fMRI measures have low reliability, undermining this potential. Here, we argue that many fMRI measures are unreliable because they were designed to identify group effects, not to precisely quantify individual differences. We then highlight four emerging strategies [extended aggregation, reliability modeling, multi-echo fMRI (ME-fMRI), and stimulus design] that build on established psychometric properties to generate more precise and reliable fMRI measures. By adopting such strategies to improve reliability, we are optimistic that fMRI can fulfill its potential as a clinical tool.
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Affiliation(s)
- Maxwell L Elliott
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA.
| | - Annchen R Knodt
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Ahmad R Hariri
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
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12
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Sheng J, Zhang L, Feng J, Liu J, Li A, Chen W, Shen Y, Wang J, He Y, Xue G. The coupling of BOLD signal variability and degree centrality underlies cognitive functions and psychiatric diseases. Neuroimage 2021; 237:118187. [PMID: 34020011 DOI: 10.1016/j.neuroimage.2021.118187] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 05/16/2021] [Accepted: 05/17/2021] [Indexed: 11/16/2022] Open
Abstract
Brain signal variability has been consistently linked to functional integration; however, whether this coupling is associated with cognitive functions and/or psychiatric diseases has not been clarified. Using multiple multimodality datasets, including resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP: N = 927) and a Beijing sample (N = 416) and cerebral blood flow (CBF) and rsfMRI data from a Hangzhou sample (N = 29), we found that, compared with the existing variability measure (i.e., SDBOLD), the mean-scaled (standardized) fractional standard deviation of the BOLD signal (mfSDBOLD) maintained very high test-retest reliability, showed greater cross-site reliability and was less affected by head motion. We also found strong reproducible couplings between the mfSDBOLD and functional integration measured by the degree centrality (DC), both cross-voxel and cross-subject, which were robust to scanning and preprocessing parameters. Moreover, both mfSDBOLD and DC were correlated with CBF, suggesting a common physiological basis for both measures. Critically, the degree of coupling between mfSDBOLD and long-range DC was positively correlated with individuals' cognitive total composite scores. Brain regions with greater mismatches between mfSDBOLD and long-range DC were more vulnerable to brain diseases. Our results suggest that BOLD signal variability could serve as a meaningful index of local function that underlies functional integration in the human brain and that a strong coupling between BOLD signal variability and functional integration may serve as a hallmark of balanced brain networks that are associated with optimal brain functions.
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Affiliation(s)
- Jintao Sheng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Liang Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Junjiao Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Jing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Anqi Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Wei Chen
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, and the Collaborative Innovation Center for Brain Science, Hangzhou, Zhejiang 310000, PR China
| | - Yuedi Shen
- The Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou, Zhejiang 310000, PR China
| | - Jinhui Wang
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Institute for Brain Research and Rehabilitation, Guangzhou 510631, PR China; Key Laboratory of Brain, Ministry of Education, Cognition and Education Sciences (South China Normal University), PR China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Gui Xue
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute of Brain Research, Beijing Normal University, Beijing 100875, PR China.
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13
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Steel A, Billings MM, Silson EH, Robertson CE. A network linking scene perception and spatial memory systems in posterior cerebral cortex. Nat Commun 2021; 12:2632. [PMID: 33976141 PMCID: PMC8113503 DOI: 10.1038/s41467-021-22848-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 04/05/2021] [Indexed: 02/03/2023] Open
Abstract
The neural systems supporting scene-perception and spatial-memory systems of the human brain are well-described. But how do these neural systems interact? Here, using fine-grained individual-subject fMRI, we report three cortical areas of the human brain, each lying immediately anterior to a region of the scene perception network in posterior cerebral cortex, that selectively activate when recalling familiar real-world locations. Despite their close proximity to the scene-perception areas, network analyses show that these regions constitute a distinct functional network that interfaces with spatial memory systems during naturalistic scene understanding. These "place-memory areas" offer a new framework for understanding how the brain implements memory-guided visual behaviors, including navigation.
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Affiliation(s)
- Adam Steel
- grid.254880.30000 0001 2179 2404Department of Psychology and Brain Sciences, Dartmouth College, Hanover, NH USA
| | - Madeleine M. Billings
- grid.254880.30000 0001 2179 2404Department of Psychology and Brain Sciences, Dartmouth College, Hanover, NH USA
| | - Edward H. Silson
- grid.4305.20000 0004 1936 7988Psychology, School of Philosophy, Psychology, and Language Sciences, University of Edinburgh, Edinburgh, EH8 9JZ UK
| | - Caroline E. Robertson
- grid.254880.30000 0001 2179 2404Department of Psychology and Brain Sciences, Dartmouth College, Hanover, NH USA
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14
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New analysis method for functional brain imaging: White noise removed T 2* variation mapping using multi-echo EPI. J Neurosci Methods 2021; 359:109218. [PMID: 33971200 DOI: 10.1016/j.jneumeth.2021.109218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 03/07/2021] [Accepted: 05/02/2021] [Indexed: 11/23/2022]
Abstract
BACKGROUND Generally, the analysis of functional magnetic resonance imaging (fMRI) using echo-planar imaging (EPI) data is based on independent component analysis (ICA) and the general linear model (GLM). The application of these two approaches is highly independent, like GLM is for task-related activation mapping, and ICA is for resting-state imaging. Herein, we propose white noise-removed T2*-variation mapping as a new analysis method for fMRI that integrates the two conventional mapping approaches. NEW METHOD We derived the standard deviation to the mean-square ratio of the true T2* signal from the multi-echo EPI (ME-EPI) dataset. For the true T2*-variation-based value, we removed the S0 (initial signal intensity) and white noise component from the variation in the EPI signal using signal-coherence analysis of the echo time (TE) dataset and slope analysis of the TE-variated coefficient of variation of the ME-EPI dataset. RESULTS The activation mapping for a visual task and resting-state imaging by the proposed method showed the reliable activation map in the visual cortex area and area for the typical default mode network, with white noise and the S0 component removed. COMPARISON WITH EXISTING METHODS Conventional analyses for fMRI cannot be applied to both activation mapping and resting-state imaging, with white noise removed, while the proposed method can be applied. CONCLUSIONS We demonstrated white noise-removed true T2*-variation-based mapping as a new functional brain analysis approach. We expect the method allows studying in which that the association between task timing and brain activity is somewhat uncertain, such as studies of emotion and awareness.
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15
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Cohen AD, Jagra AS, Yang B, Fernandez B, Banerjee S, Wang Y. Detecting Task Functional MRI Activation Using the Multiband Multiecho (MBME) Echo-Planar Imaging (EPI) Sequence. J Magn Reson Imaging 2021; 53:1366-1374. [PMID: 33210793 PMCID: PMC10937038 DOI: 10.1002/jmri.27448] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/05/2020] [Accepted: 11/06/2020] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Blood oxygen level-dependent (BOLD) functional MRI (fMRI) has been widely applied to detect brain activations. Recent advances in multiband (MB) and multiecho (ME) techniques have greatly improved fMRI methods. MB imaging improves temporal and/or spatial resolution, while ME imaging has been shown to improve BOLD sensitivity. This study aimed to evaluate the novel MBME echo planar imaging (EPI) sequence utilizing MB and ME simultaneously to determine if the MBME outperform the MB single echo (MBSE) sequence for task fMRI. PURPOSE To compare the performance of MBME with MBSE in a task fMRI study. STUDY TYPE Prospective. POPULATION A total of 29 healthy volunteers aged 20-46 years (9 male, 20 female). FIELD STRENGTH/SEQUENCE MBSE and MBME gradient-echo EPI sequences were applied at 3T. Additional T1 -weighted magnetization-prepared rapid acquisition with gradient echo (MPRAGE) was collected. ASSESSMENT A checkerboard visual task was presented during the functional MBSE and MBME scans. The MBME or MBSE signal was evaluated using the temporal signal-to-noise ratio (tSNR). Task activation was evaluated using the z-score, volume, sensitivity, and specificity. Test-retest metrics of task activation were examined with the Dice coefficient (DC) and intraclass correlation coefficient (ICC) on subjects with repeated scans. STATISTICAL TESTS A linear mixed-effects model was used to compared MBME and MBSE activation at the voxel base. The paired t-test was used to compare tSNR, activation z-score, and volume, along with sensitivity, specificity, and DC between MBSE and MBME. RESULTS While similar task activation was detected in the visual cortex, MBME showed higher activation volume and higher sensitivity compared with MBSE (P < 0.05). ICC was higher for MBME than MBSE, while there was a trend of differences in DC (P = 0.08). DATA CONCLUSION MBME resulted in higher task fMRI activation volume and sensitivity without losing specificity. Reliability was also higher for MBME scans compared with MBSE. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Alexander D. Cohen
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, United States
| | | | | | | | | | - Yang Wang
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, United States
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16
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Moia S, Termenon M, Uruñuela E, Chen G, Stickland RC, Bright MG, Caballero-Gaudes C. ICA-based denoising strategies in breath-hold induced cerebrovascular reactivity mapping with multi echo BOLD fMRI. Neuroimage 2021; 233:117914. [PMID: 33684602 PMCID: PMC8351526 DOI: 10.1016/j.neuroimage.2021.117914] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 01/25/2021] [Accepted: 02/22/2021] [Indexed: 12/19/2022] Open
Abstract
Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.
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Affiliation(s)
- Stefano Moia
- Basque Center on Cognition, Brain and Language, Donostia, Spain; University of the Basque Country UPV/EHU, Donostia, Spain.
| | - Maite Termenon
- Basque Center on Cognition, Brain and Language, Donostia, Spain
| | - Eneko Uruñuela
- Basque Center on Cognition, Brain and Language, Donostia, Spain; University of the Basque Country UPV/EHU, Donostia, Spain
| | - Gang Chen
- Scientific and Statistical Computing Core, NIMH/NIH/HHS, Bethesda, MD, United States
| | - Rachael C Stickland
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - 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
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17
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Cohen AD, Yang B, Fernandez B, Banerjee S, Wang Y. Improved resting state functional connectivity sensitivity and reproducibility using a multiband multi-echo acquisition. Neuroimage 2021; 225:117461. [PMID: 33069864 PMCID: PMC10015256 DOI: 10.1016/j.neuroimage.2020.117461] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 09/20/2020] [Accepted: 10/12/2020] [Indexed: 02/04/2023] Open
Abstract
Recent advances in functional MRI techniques include multiband (MB) imaging and multi-echo (ME) imaging. In MB imaging multiple slices are acquired simultaneously leading to significant increases in temporal and spatial resolution. Multi-echo imaging enables multiple echoes to be acquired in one shot, where the ME images can be used to denoise the BOLD time series and increase BOLD sensitivity. In this study, resting state fMRI (rs-fMRI) data were collected using a combined MBME sequence and compared to an MB single echo sequence. In total, 29 subjects were imaged, and 18 of them returned within two weeks for repeat imaging. Participants underwent one MBME scan with three echoes and one MB scan with one echo. Both datasets were processed using standard denoising and advanced denoising. Advanced denoising included multi-echo independent component analysis (ME-ICA) for the MBME data and ICA-AROMA for the MB data. Resting state functional connectivity (RSFC) was evaluated using both selective seed-based and whole grey matter (GM) region-of-interest (ROI) based approaches. The reproducibility of connectivity metrics was also analyzed in the repeat subjects. In addition, functional connectivity density (FCD), a data-driven approach that counts the number of significant connections, both within a local cluster and globally, with each voxel was analyzed. Regardless of the standard or advanced denoising technique, all seed-based RSFC was significantly higher for MBME compared to MB. Much more GM ROI combinations showed significantly higher RSFC for MBME vs. MB. Reproducibility, evaluated using the dice coefficient was significantly higher for MBME relative to MB data. Finally, FCD was also higher for MBME vs. MB data. This study showed higher RSFC for MBME vs. MB data using selected seed-based, whole GM ROI-based, and data-driven approaches. Reproducibility found also higher for MBME data. Taken together, these results indicate that MBME is a promising technique for rs-fMRI.
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Affiliation(s)
- Alexander D Cohen
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, United States
| | | | | | | | - Yang Wang
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, United States.
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18
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Hu K, De Rosa E, Anderson AK. Differential color tuning of the mesolimbic reward system. Sci Rep 2020; 10:10223. [PMID: 32576844 PMCID: PMC7311418 DOI: 10.1038/s41598-020-66574-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Accepted: 05/13/2020] [Indexed: 02/05/2023] Open
Abstract
Visual wavelengths are not only associated with the subjective experience of color but also have long been thought to regulate affect. Here we examined the attracting rewarding properties of opposite ends of the wavelength spectrum, as well as their individual variation. As reward is multifaceted, we sought convergent evidence from subjective and objective behavioral and attentional indices, as well as its neural reward system bases. On average, short (blue) relative to long (red) wavelengths were judged subjectively more pleasant and had objectively greater behavioral and attentional salience, regulating speed of simple color discriminations and perception of temporal order. Consistent with reward, these color effects were magnified following monetary reinforcement. Pronounced individual differences in color effects were related to reward but not punishment sensitivity, with blue relative to red preference associated with high relative to low reward sensitivity. An fMRI study revealed these individual differences were supported by color-dependent functional coupling between the visual cortices and mesolimbic reward circuitry. Our findings reveal the reward bases of color, demonstrating color is a potent regulator of perception, action, and neural dynamics.
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Affiliation(s)
- Kesong Hu
- Institute of Mental Health, Nanjing Xiaozhuang University, Nanjing, China. .,Department of Psychology, Lake Superior State University, Sault St. Marie, USA. .,Department of Human Development, Cornell University, Ithaca, USA. .,Human Neuroscience Institute, Cornell University, Ithaca, USA.
| | - Eve De Rosa
- Department of Human Development, Cornell University, Ithaca, USA.,Human Neuroscience Institute, Cornell University, Ithaca, USA
| | - Adam K Anderson
- Department of Human Development, Cornell University, Ithaca, USA. .,Human Neuroscience Institute, Cornell University, Ithaca, USA.
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19
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Bernas A, Breuer LEM, Lamerichs R, de Louw AJA, Aldenkamp AP, Zinger S. Accelerated Cognitive Ageing in epilepsy: exploring the effective connectivity between resting-state networks and its relation to cognitive decline. Heliyon 2020; 6:e03951. [PMID: 32529058 PMCID: PMC7283153 DOI: 10.1016/j.heliyon.2020.e03951] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 07/24/2019] [Accepted: 05/05/2020] [Indexed: 12/22/2022] Open
Abstract
Objective This study aims at understanding the dynamic functional brain organization in Accelerated Cognitive Ageing (ACA) in epilepsy. We also assess to which extend the (abnormal) effective connectivity between brain networks correlates with the (estimated) decline in IQ scores observed in the ACA patients. Material and methods Two multi-echo resting-state fMRI scans of 10 ACA patients and 14 age- and education-matched healthy controls were acquired. A task-based fMRI was acquired in-between those two scans, for possible cognitive fatigue effects on reserve capacity. Granger causality (GC), a measure of effective connectivity between brain regions, was applied on 7 major cognitive networks, and group-wise compared, using permutation testing statistics. This was performed on each of the resting-state sessions independently. We assessed the correlation between the cognitive deterioration scores (representing cognitive decline), and the paired-networks granger causality values. Results The cingulate cortex appeared to be more engaged in ACA patients. Its dynamics towards the right fronto-parietal cortex, salience network, and the dorsal attention networks (DAN) was stronger than in controls, only in the first resting-state scan session. The Granger causality from the DAN to the default mode network (DMN) and from the ventral attention network (VAN) to the left fronto-parietal network (FPL) was also stronger in ACA patients and again only in the first scans. In the second resting-state scans, only the DMN was more strongly connected with the cingulate cortex in ACA patients. A weaker GC from DMN to FPL, and stronger GC from the salience network to cingulate cortex were associated with more decline in the Full-scale IQ and more GC from DMN to VAN would lead to more decline in the Perceptual Reasoning Index in ACA. Conclusion The results are in line with the hypothesis of over-recruitment at low cognitive load, and exhaustion at higher cognitive load, as shown by the compensation-related utilization of neural circuits hypothesis (CRUNCH) model for ageing. Moreover, the DMN to VAN directed connectivity strongly correlates with the (estimated) decline in the Perceptual Reasoning Index, which is also in line with a recent study on ageing with mild cognitive impairment in elderly, and the posterior-anterior shift in aging (PASA) model. This study therefore supports the idea that the cognitive decline in our patients resembles the decline observed in healthy ageing, but in an accelerated mode. This study also sheds light on the directions of the impaired connectivity between the main networks involved in the deterioration process, which can be helpful for future development of treatment solutions.
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Affiliation(s)
- A Bernas
- Department of Electrical Engineering, University of Technology, Eindhoven, the Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, the Netherlands.,School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - L E M Breuer
- Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, the Netherlands
| | - R Lamerichs
- Department of Electrical Engineering, University of Technology, Eindhoven, the Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, the Netherlands.,Philips Research, Eindhoven, the Netherlands
| | - A J A de Louw
- Department of Electrical Engineering, University of Technology, Eindhoven, the Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, the Netherlands
| | - A P Aldenkamp
- Department of Electrical Engineering, University of Technology, Eindhoven, the Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, the Netherlands.,Department of Neurology and Clinical Neurophysiology, Maastricht University Medical Center, Maastricht, the Netherlands.,School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - S Zinger
- Department of Electrical Engineering, University of Technology, Eindhoven, the Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, the Netherlands
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20
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Sander CY, Hansen HD, Wey HY. Advances in simultaneous PET/MR for imaging neuroreceptor function. J Cereb Blood Flow Metab 2020; 40:1148-1166. [PMID: 32169011 PMCID: PMC7238372 DOI: 10.1177/0271678x20910038] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Hybrid imaging using PET/MRI has emerged as a platform for elucidating novel neurobiology, molecular and functional changes in disease, and responses to physiological or pharmacological interventions. For the central nervous system, PET/MRI has provided insights into biochemical processes, linking selective molecular targets and distributed brain function. This review highlights several examples that leverage the strengths of simultaneous PET/MRI, which includes measuring the perturbation of multi-modal imaging signals on dynamic timescales during pharmacological challenges, physiological interventions or behavioral tasks. We discuss important considerations for the experimental design of dynamic PET/MRI studies and data analysis approaches for comparing and quantifying simultaneous PET/MRI data. The primary focus of this review is on functional PET/MRI studies of neurotransmitter and receptor systems, with an emphasis on the dopamine, opioid, serotonin and glutamate systems as molecular neuromodulators. In this context, we provide an overview of studies that employ interventions to alter the activity of neuroreceptors or the release of neurotransmitters. Overall, we emphasize how the synergistic use of simultaneous PET/MRI with appropriate study design and interventions has the potential to expand our knowledge about the molecular and functional dynamics of the living human brain. Finally, we give an outlook on the future opportunities for simultaneous PET/MRI.
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Affiliation(s)
- Christin Y Sander
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, MA, USA
| | - Hanne D Hansen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, MA, USA.,Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital (Rigshospitalet), Copenhagen, Denmark
| | - Hsiao-Ying Wey
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, MA, USA
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21
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Angenstein F. The role of ongoing neuronal activity for baseline and stimulus-induced BOLD signals in the rat hippocampus. Neuroimage 2019; 202:116082. [PMID: 31425796 DOI: 10.1016/j.neuroimage.2019.116082] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 06/18/2019] [Accepted: 08/06/2019] [Indexed: 10/26/2022] Open
Abstract
To understand how ongoing neuronal activity affects baseline BOLD signals, neuronal and resultant fMRI responses were simultaneously recorded in the right hippocampus of male rats during continuous low-frequency (2 or 4 Hz) pulse stimulation of the right perforant pathway. Despite continuously increased neuronal activity, BOLD signals only transiently increased in the hippocampus and subsequently returned to either the initial level (2 Hz) or even to a consistently lower level (4 Hz). Whereas the initially transient increase in BOLD signals coincided with an increased spiking of granule cells, the subsequent reduction of BOLD signals was independent of granule cell spiking activity but coincided with persistent inhibition of granule cell excitability, i.e., with reduced postsynaptic activity and prolonged population spike latency. The decline in BOLD signals occurred in the presence of an elevated local cerebral blood volume (CBV), thus the reduction of granule cell excitability is attended by high oxygen consumption. When previous or current stimulations lessen baseline BOLD signals, subsequent short stimulation periods only elicited attenuated BOLD responses, even when actual spiking activity of granule cells was similar. Thus, the quality of stimulus-induced BOLD responses critically depends on the current existing inhibitory activity, which closely relates to baseline BOLD signals. Thus, a meaningful interpretation of stimulus-induced BOLD responses should consider slowly developing variations in baseline BOLD signals; therefore, baseline correction tools should be cautiously used for fMRI data analysis.
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Affiliation(s)
- Frank Angenstein
- Functional Imaging Group, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 39118, Magdeburg, Germany; Leibniz Institute for Neurobiology (LIN), 39118, Magdeburg, Germany; Medical Faculty, Otto von Guericke University, 39118, Magdeburg, Germany.
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22
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Caballero-Gaudes C, Moia S, Panwar P, Bandettini PA, Gonzalez-Castillo J. A deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mapping. Neuroimage 2019; 202:116081. [PMID: 31419613 DOI: 10.1016/j.neuroimage.2019.116081] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 07/01/2019] [Accepted: 08/06/2019] [Indexed: 10/26/2022] Open
Abstract
This work introduces a novel algorithm for deconvolution of the BOLD signal in multi-echo fMRI data: Multi-echo Sparse Paradigm Free Mapping (ME-SPFM). Assuming a linear dependence of the BOLD percent signal change on the echo time (TE) and using sparsity-promoting regularized least squares estimation, ME-SPFM yields voxelwise time-varying estimates of the changes in the apparent transverse relaxation (ΔR2⁎) without prior knowledge of the timings of individual BOLD events. Our results in multi-echo fMRI data collected during a multi-task event-related paradigm at 3 Tesla demonstrate that the maps of R2⁎ changes obtained with ME-SPFM at the times of the stimulus trials show high spatial and temporal concordance with the activation maps and BOLD signals obtained with standard model-based analysis. This method yields estimates of ΔR2⁎ having physiologically plausible values. Owing to its ability to blindly detect events, ME-SPFM also enables us to map ΔR2⁎ associated with spontaneous, transient BOLD responses occurring between trials. This framework is a step towards deciphering the dynamic nature of brain activity in naturalistic paradigms, resting-state or experimental paradigms with unknown timing of the BOLD events.
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Affiliation(s)
| | - Stefano Moia
- Basque Center on Cognition, Brain and Language, San Sebastian, Spain
| | - Puja Panwar
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA; Functional MRI Core, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA
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23
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Steel A, Silson EH, Stagg CJ, Baker CI. Differential impact of reward and punishment on functional connectivity after skill learning. Neuroimage 2019; 189:95-105. [PMID: 30630080 DOI: 10.1016/j.neuroimage.2019.01.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 01/03/2019] [Accepted: 01/04/2019] [Indexed: 12/20/2022] Open
Abstract
Reward and punishment shape behavior, but the mechanisms underlying their effect on skill learning are not well understood. Here, we tested whether the functional connectivity of premotor cortex (PMC), a region known to be critical for learning of sequencing skills, is altered after training when reward or punishment is given during training. Resting-state fMRI was collected in two experiments before and after participants trained on either a serial reaction time task (SRTT; n = 36) or force-tracking task (FTT; n = 36) with reward, punishment, or control feedback. In each experiment, training-related change in PMC functional connectivity was compared across feedback groups. In both tasks, we found that reward and punishment differentially affected PMC functional connectivity. On the SRTT, participants trained with reward showed an increase in functional connectivity between PMC and cerebellum as well as PMC and striatum, while participants trained with punishment showed an increase in functional connectivity between PMC and medial temporal lobe connectivity. After training on the FTT, subjects trained with control and reward showed increases in PMC connectivity with parietal and temporal cortices after training, while subjects trained with punishment showed increased PMC connectivity with ventral striatum. While the results from the two experiments overlapped in some areas, including ventral pallidum, temporal lobe, and cerebellum, these regions showed diverging patterns of results across the two tasks for the different feedback conditions. These findings suggest that reward and punishment strongly influence spontaneous brain activity after training, and that the regions implicated depend on the task learned.
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Affiliation(s)
- Adam Steel
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK; Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20814, USA.
| | - Edward H Silson
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Charlotte J Stagg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK; Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, University Department of Psychiatry, University of Oxford, Oxford, OX3 9DU, UK
| | - Chris I Baker
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20814, USA
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24
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Low frequency pulse stimulation of Schaffer collaterals in Trpm4 -/- knockout rats differently affects baseline BOLD signals in target regions of the right hippocampus but not BOLD responses at the site of stimulation. Neuroimage 2018; 188:347-356. [PMID: 30553915 DOI: 10.1016/j.neuroimage.2018.12.020] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 11/21/2018] [Accepted: 12/11/2018] [Indexed: 11/22/2022] Open
Abstract
Electrical stimulation of right Schaffer collateral in Trpm4-/- knockout and wild type rats were used to study the role of Trpm4 channels for signal processing in the hippocampal formation. Stimulation induced neuronal activity was simultaneously monitored in the CA1 region by in vivo extracellular field recordings and in the entire brain by BOLD fMRI measurements. In wild type and Trpm4-/- knockout rats, consecutive 5 Hz pulse trains elicited similar neuronal responses in the CA1 region and similar BOLD responses in the stimulated right hippocampus. Stimulus-related positive BOLD responses were also found in the left dorsal hippocampus. In contrast to the right dorsal hippocampus, baseline BOLD signals in the left hippocampus significantly decreased during consecutive stimulation trains. Similarly, slowly developing significant declines in baseline BOLD signals, in absence of any positive BOLD responses, were also observed in the right entorhinal, right piriform cortex, right basolateral amygdala and right dorsal striatum whereas baseline BOLD signals remained almost stable in the corresponding left regions. Furthermore, significant declines in baseline BOLD signals were found in the prefrontal cortex and prelimbic/infralimbic cortex. Because significant baseline BOLD declines were only observed in target regions of the right dorsal hippocampus, it might reflect functional connectivity between these regions. In all observed regions the decline in baseline BOLD signals was significantly delayed and less pronounced in Trpm4-/- knockout rats when compared to wild type rats. Thus, either Trpm4 channels are involved in mediating these baseline BOLD shifts or functional connectivity of the hippocampus is impaired in Trpm4-/- knockout rats.
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Woletz M, Hoffmann A, Tik M, Sladky R, Lanzenberger R, Robinson S, Windischberger C. Beware detrending: Optimal preprocessing pipeline for low-frequency fluctuation analysis. Hum Brain Mapp 2018; 40:1571-1582. [PMID: 30430691 PMCID: PMC6587723 DOI: 10.1002/hbm.24468] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Revised: 09/21/2018] [Accepted: 10/30/2018] [Indexed: 12/19/2022] Open
Abstract
Resting‐state functional magnetic resonance imaging (rs‐fMRI) offers the possibility to assess brain function independent of explicit tasks and individual performance. This absence of explicit stimuli in rs‐fMRI makes analyses more susceptible to nonneural signal fluctuations than task‐based fMRI. Data preprocessing is a critical procedure to minimise contamination by artefacts related to motion and physiology. We herein investigate the effects of different preprocessing strategies on the amplitude of low‐frequency fluctuations (ALFFs) and its fractional counterpart, fractional ALFF (fALFF). Sixteen artefact reduction schemes based on nuisance regression are applied to data from 82 subjects acquired at 1.5 T, 30 subjects at 3 T, and 23 subjects at 7 T, respectively. In addition, we examine test–retest variance and effects of bias correction. In total, 569 data sets are included in this study. Our results show that full artefact reduction reduced test–retest variance by up to 50%. Polynomial detrending of rs‐fMRI data has a positive effect on group‐level t‐values for ALFF but, importantly, a negative effect for fALFF. We show that the normalisation process intrinsic to fALFF calculation causes the observed reduction and introduce a novel measure for low‐frequency fluctuations denoted as high‐frequency ALFF (hfALFF). We demonstrate that hfALFF values are not affected by the negative detrending effects seen in fALFF data. Still, highest grey matter (GM) group‐level t‐values were obtained for fALFF data without detrending, even when compared to an exploratory detrending approach based on autocorrelation measures. From our results, we recommend the use of full nuisance regression including polynomial detrending in ALFF data, but to refrain from using polynomial detrending in fALFF data. Such optimised preprocessing increases GM group‐level t‐values by up to 60%.
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Affiliation(s)
- Michael Woletz
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - André Hoffmann
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Martin Tik
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Ronald Sladky
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Simon Robinson
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Christian Windischberger
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
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Luo Y, Zhang J, Chen L, Cai S, Cai C. Accelerating multi-slice spatiotemporally encoded MRI with simultaneous echo refocusing. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2018; 296:12-22. [PMID: 30195714 DOI: 10.1016/j.jmr.2018.08.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Revised: 07/25/2018] [Accepted: 08/30/2018] [Indexed: 06/08/2023]
Abstract
Single-shot spatiotemporally encoded (SPEN) ultrafast magnetic resonance imaging (MRI) is of great value to both scientific research and clinical application owing to its capability for delivering MR images with greater robustness to magnetic field inhomogeneity and chemical-shift displacement effects than conventional methods like EPI due to high effective phase-encoded bandwidth. Many SPEN MRI methods have been developed, among which multi-slice SPEN MRI arises as a promising supplement to ultrafast multi-slice sampling. In this work, we propose a new multi-slice SPEN MRI method, termed multi-echo segmented SPEN (ME-SeSPEN) method, which produces multiple images within a single train of echoes and successively samples widely separated slices. The resulting images were reconstructed using de-convolution super-resolved algorithm. The robustness and efficiency of the proposed method were demonstrated by phantom, lemon and in vivo experiments in comparison with spin-echo EPI, spin-echo simultaneous echo refocusing (SER), and segmented SPEN (SeSPEN) MRI. The results indicate that the new method effectively shortens the sampling time (20% reduction practically in comparison with SeSPEN when two slices are simultaneously sampled). ME-SeSPEN also reduces eddy current effects while maintaining the benefits of SPEN MRI, such as similar robustness to field inhomogeneity, spatial resolution and signal-to-noise ratio to SeSPEN MRI. The new method will promote the versatility of multi-slice MRI in practical applications.
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Affiliation(s)
- Yao Luo
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Jun Zhang
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Lin Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Shuhui Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China.
| | - Congbo Cai
- Department of Communication Engineering, Xiamen University, Xiamen 361005, China.
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Besseling R, Lamerichs R, Michels B, Heunis S, de Louw A, Tijhuis A, Bergmans J, Aldenkamp B. Functional network abnormalities consistent with behavioral profile in Autism Spectrum Disorder. Psychiatry Res Neuroimaging 2018; 275:43-48. [PMID: 29628271 DOI: 10.1016/j.pscychresns.2018.02.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 02/06/2018] [Accepted: 02/16/2018] [Indexed: 01/10/2023]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder in which the severity of symptoms varies over subjects. The iCAPs model (innovation-driven co-activation patterns) is a recently developed spatio-temporal model to describe fMRI data. In this study, the iCAPs model was employed to find functional imaging biomarkers for ASD in resting-state fMRI data. MRI data from 125 ASD patients and 243 healthy controls was selected from the online ABIDE data repository. Following standard fMRI preprocessing steps, the iCAP patterns were fitted to the data to obtain network time series. Furthermore, specific combinations of iCAPs were mapped to behavioral domain time series. To quantify to which extent the time series contribute to the fMRI dynamics, their (temporal) standard deviation was calculated and compared between patients and controls. Abnormalities were found in networks involving subcortical and limbic areas and default mode network regions. When mapping the network dynamics to behavioral domain time series, abnormalities were found in emotional and visual behavioral subdomains, and within the ASD spectrum were more pronounced in subjects with autism compared to Asperger's syndrome. Also a trend towards impairment in networks facilitating social cognition was found. The functional imaging abnormalities are consistent with the behavioral impairments typical for ASD.
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Affiliation(s)
- René Besseling
- Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands; Academic Center for Epileptology Kempenhaeghe, Heeze, The Netherlands.
| | - Rolf Lamerichs
- Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands; Philips Research, Eindhoven, The Netherlands
| | - Britt Michels
- Department of Biomedical Engineering, Eindhoven University of Technology, The Netherlands
| | - Stephan Heunis
- Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands; Academic Center for Epileptology Kempenhaeghe, Heeze, The Netherlands
| | - Anton de Louw
- Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands; Academic Center for Epileptology Kempenhaeghe, Heeze, The Netherlands
| | - Anton Tijhuis
- Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands
| | - Jan Bergmans
- Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands
| | - Bert Aldenkamp
- Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands; Academic Center for Epileptology Kempenhaeghe, Heeze, The Netherlands; Ghent University Hospital, Ghent, Belgium; Maastricht University Hospital, Maastricht, The Netherlands
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28
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Abstract
The ability to discriminate signal from noise plays a key role in the analysis and interpretation of functional magnetic resonance imaging (fMRI) measures of brain activity. Over the past two decades, a number of major sources of noise have been identified, including system-related instabilities, subject motion, and physiological fluctuations. This article reviews the characteristics of the various noise sources as well as the mechanisms through which they affect the fMRI signal. Approaches for distinguishing signal from noise and the associated challenges are also reviewed. These challenges reflect the fact that some noise sources, such as respiratory activity, are generated by the same underlying brain networks that give rise to functional signals that are of interest.
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Affiliation(s)
- Thomas T Liu
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA 92093, United States; Departments of Radiology, Psychiatry and Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, United States.
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Cohen AD, Nencka AS, Wang Y. Multiband multi-echo simultaneous ASL/BOLD for task-induced functional MRI. PLoS One 2018; 13:e0190427. [PMID: 29389985 PMCID: PMC5794066 DOI: 10.1371/journal.pone.0190427] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 12/14/2017] [Indexed: 11/22/2022] Open
Abstract
Typical simultaneous blood oxygenation-level dependent (BOLD) and arterial spin labeling (ASL) sequences acquire two echoes, one perfusion-sensitive and one BOLD-sensitive. However, for ASL, spatial resolution and brain coverage are limited due to the T1 decay of the labeled blood. This study applies a sequence combining a multiband acquisition with four echoes for simultaneous BOLD and pseudo-continuous ASL (pCASL) echo planar imaging (MBME ASL/BOLD) for block-design task-fMRI. A multiband acceleration of four was employed to increase brain coverage and reduce slice-timing effects on the ASL signal. Multi-echo independent component analysis (MEICA) was implemented to automatically denoise the BOLD signal by regressing non-BOLD components. This technique led to increased temporal signal-to-noise ratio (tSNR) and BOLD sensitivity. The MEICA technique was also modified to denoise the ASL signal by regressing artifact and BOLD signals from the first echo time-series. The MBME ASL/BOLD sequence was applied to a finger-tapping task functional MRI (fMRI) experiment. Signal characteristics and activation were evaluated using single echo BOLD, combined ME BOLD, combined ME BOLD after MEICA denoising, perfusion-weighted (PW), and perfusion-weighted after MEICA denoising time-series. The PW data was extracted using both surround subtraction and high-pass filtering followed by demodulation. In addition, the CBF/BOLD response ratio and CBF/BOLD coupling were analyzed. Results showed that the MEICA denoising procedure significantly improved the BOLD signal, leading to increased BOLD sensitivity, tSNR, and activation statistics compared to conventional single echo BOLD data. At the same time, the denoised PW data showed increased tSNR and activation statistics compared to the non-denoised PW data. CBF/BOLD coupling was also increased using the denoised ASL and BOLD data. Our preliminary data suggest that the MBME ASL/BOLD sequence can be employed to collect whole-brain task-fMRI with improved data quality for both BOLD and PW time series, thus improving the results of block-design task fMRI.
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Affiliation(s)
- Alexander D Cohen
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Andrew S Nencka
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Yang Wang
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
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Jangraw DC, Gonzalez-Castillo J, Handwerker DA, Ghane M, Rosenberg MD, Panwar P, Bandettini PA. A functional connectivity-based neuromarker of sustained attention generalizes to predict recall in a reading task. Neuroimage 2017; 166:99-109. [PMID: 29031531 DOI: 10.1016/j.neuroimage.2017.10.019] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 09/28/2017] [Accepted: 10/09/2017] [Indexed: 01/17/2023] Open
Abstract
Sustaining attention to the task at hand is a crucial part of everyday life, from following a lecture at school to maintaining focus while driving. Lapses in sustained attention are frequent and often problematic, with conditions such as attention deficit hyperactivity disorder affecting millions of people worldwide. Recent work has had some success in finding signatures of sustained attention in whole-brain functional connectivity (FC) measures during basic tasks, but since FC can be dynamic and task-dependent, it remains unclear how fully these signatures would generalize to a more complex and naturalistic scenario. To this end, we used a previously defined whole-brain FC network - a marker of attention that was derived from a sustained attention task - to predict the ability of participants to recall material during a free-viewing reading task. Though the predictive network was trained on a different task and set of participants, the strength of FC in the sustained attention network predicted reading recall significantly better than permutation tests where behavior was scrambled to simulate chance performance. To test the generalization of the method used to derive the sustained attention network, we applied the same method to our reading task data to find a new FC network whose strength specifically predicts reading recall. Even though the sustained attention network provided significant prediction of recall, the reading network was more predictive of recall accuracy. The new reading network's spatial distribution indicates that reading recall is highest when temporal pole regions have higher FC with left occipital regions and lower FC with bilateral supramarginal gyrus. Right cerebellar to right frontal connectivity is also indicative of poor reading recall. We examine these and other differences between the two predictive FC networks, providing new insight into the task-dependent nature of FC-based performance metrics.
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Affiliation(s)
- David C Jangraw
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA.
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Daniel A Handwerker
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Merage Ghane
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA; Department of Psychology, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | | | - Puja Panwar
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
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Cai C, Zeng Y, Zhuang Y, Cai S, Chen L, Ding X, Bao L, Zhong J, Chen Z. Single-Shot ${\text{T}}_{{2}}$ Mapping Through OverLapping-Echo Detachment (OLED) Planar Imaging. IEEE Trans Biomed Eng 2017; 64:2450-2461. [DOI: 10.1109/tbme.2017.2661840] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Caballero-Gaudes C, Reynolds RC. Methods for cleaning the BOLD fMRI signal. Neuroimage 2017; 154:128-149. [PMID: 27956209 PMCID: PMC5466511 DOI: 10.1016/j.neuroimage.2016.12.018] [Citation(s) in RCA: 320] [Impact Index Per Article: 45.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Revised: 12/05/2016] [Accepted: 12/08/2016] [Indexed: 01/13/2023] Open
Abstract
Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.
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Affiliation(s)
| | - Richard C Reynolds
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Department of Health and Human Services, USA
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Balderston NL, Hale E, Hsiung A, Torrisi S, Holroyd T, Carver FW, Coppola R, Ernst M, Grillon C. Threat of shock increases excitability and connectivity of the intraparietal sulcus. eLife 2017; 6. [PMID: 28555565 PMCID: PMC5478270 DOI: 10.7554/elife.23608] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Accepted: 05/29/2017] [Indexed: 11/30/2022] Open
Abstract
Anxiety disorders affect approximately 1 in 5 (18%) Americans within a given 1 year period, placing a substantial burden on the national health care system. Therefore, there is a critical need to understand the neural mechanisms mediating anxiety symptoms. We used unbiased, multimodal, data-driven, whole-brain measures of neural activity (magnetoencephalography) and connectivity (fMRI) to identify the regions of the brain that contribute most prominently to sustained anxiety. We report that a single brain region, the intraparietal sulcus (IPS), shows both elevated neural activity and global brain connectivity during threat. The IPS plays a key role in attention orienting and may contribute to the hypervigilance that is a common symptom of pathological anxiety. Hyperactivation of this region during elevated state anxiety may account for the paradoxical facilitation of performance on tasks that require an external focus of attention, and impairment of performance on tasks that require an internal focus of attention. DOI:http://dx.doi.org/10.7554/eLife.23608.001 Anxiety disorders affect around one in five Americans, and in many cases people experience anxiety so intensely that they have difficulties performing day-to-day activities. To help these people, it is important to understand how anxiety works. Current research suggests that anxiety disorders are caused when the connections in the brain that control our response to threat are either excessively or inappropriately activated. However, it was not clear what causes the anxiety to last for long periods. To better understand this phenomenon, Balderston et al. studied the brains of over 30 volunteers using two types of measurements called magnetoencephalography and fMRI. In the each experiment, participants experienced periods of threat, where they could receive unpredictable electric shocks. In the first experiment, Balderston et al. measured the brain activity by recording the magnetic fields generated in the brain. In the second experiment, they used fMRI to record changes in the blood flow throughout the brain to measure how the different regions in the brain communicate. The recordings identified a single part of the brain that increased its activity and changed its communication pattern with the other regions in the brain, when people are anxious. This region in a part of the brain called parietal lobe, is also important for processing attention, which suggests that anxiety might make people also more aware of their surroundings. However, this extra awareness might also make it more difficult for people to concentrate. Future studies may be able to stimulate this area of the brain through the scalp to potentially reduce anxiety, as the affected area is close to the skull. DOI:http://dx.doi.org/10.7554/eLife.23608.002
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Affiliation(s)
- Nicholas L Balderston
- Section on Neurobiology of Fear and Anxiety, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Elizabeth Hale
- Section on Neurobiology of Fear and Anxiety, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Abigail Hsiung
- Section on Neurobiology of Fear and Anxiety, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Salvatore Torrisi
- Section on Neurobiology of Fear and Anxiety, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Tom Holroyd
- MEG Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Frederick W Carver
- MEG Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Richard Coppola
- MEG Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Monique Ernst
- Section on Neurobiology of Fear and Anxiety, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Christian Grillon
- Section on Neurobiology of Fear and Anxiety, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
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Kundu P, Voon V, Balchandani P, Lombardo MV, Poser BA, Bandettini PA. Multi-echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals. Neuroimage 2017; 154:59-80. [PMID: 28363836 DOI: 10.1016/j.neuroimage.2017.03.033] [Citation(s) in RCA: 159] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Revised: 03/15/2017] [Accepted: 03/16/2017] [Indexed: 01/16/2023] Open
Abstract
In recent years the field of fMRI research has enjoyed expanded technical abilities related to resolution, as well as use across many fields of brain research. At the same time, the field has also dealt with uncertainty related to many known and unknown effects of artifact in fMRI data. In this review we discuss an emerging fMRI technology, called multi-echo (ME)-fMRI, which focuses on improving the fidelity and interpretability of fMRI. Where the essential problem of standard single-echo fMRI is the indeterminacy of sources of signals, whether BOLD or artifact, this is not the case for ME-fMRI. By acquiring multiple echo images per slice, the ME approach allows T2* decay to be modeled at every voxel at every time point. Since BOLD signals arise by changes in T2* over time, an fMRI experiment sampling the T2* signal decay can be analyzed to distinguish BOLD from artifact signal constituents. While the ME approach has a long history of use in theoretical and validation studies, modern MRI systems enable whole-brain multi-echo fMRI at high resolution. This review covers recent multi-echo fMRI acquisition methods, and the analysis steps for this data to make fMRI at once more principled, straightforward, and powerful. After a brief overview of history and theory, T2* modeling and applications will be discussed. These applications include T2* mapping and combining echoes from ME data to increase BOLD contrast and mitigate dropout artifacts. Next, the modeling of fMRI signal changes to detect signal origins in BOLD-related T2* versus artifact-related S0 changes will be reviewed. A focus is on the use of ME-fMRI data to extract and classify components from spatial ICA, called multi-echo ICA (ME-ICA). After describing how ME-fMRI and ME-ICA lead to a general model for analysis of fMRI signals, applications in animal and human imaging will be discussed. Applications include removing motion artifacts in resting state data at subject and group level. New imaging methods such as multi-band multi-echo fMRI and imaging at 7T are demonstrated throughout the review, and a practical analysis pipeline is described. The review culminates with evidence from recent studies of major boosts in statistical power from using multi-echo fMRI for detecting activation and connectivity in healthy individuals and patients with neuropsychiatric disease. In conclusion, the review shows evidence that the multi-echo approach expands the range of experiments that is practicable using fMRI. These findings suggest a compelling future role of the multi-echo approach in subject-level and clinical fMRI.
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Affiliation(s)
- Prantik Kundu
- Departments of Radiology and Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Valerie Voon
- Behavioral and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Priti Balchandani
- Departments of Radiology and Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael V Lombardo
- Department of Psychology and Center for Applied Neuroscience, University of Cyprus, Nicosia, Cyprus
| | - Benedikt A Poser
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, NL, The Netherlands
| | - Peter A Bandettini
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, USA
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Karageorgiou E, Vossel KA. Brain rhythm attractor breakdown in Alzheimer's disease: Functional and pathologic implications. Alzheimers Dement 2017; 13:1054-1067. [PMID: 28302453 DOI: 10.1016/j.jalz.2017.02.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2016] [Revised: 01/30/2017] [Accepted: 02/04/2017] [Indexed: 12/11/2022]
Abstract
This perspective binds emerging evidence on the bidirectional relationship between Alzheimer's disease (AD) and sleep disorders through a model of brain rhythm attractor breakdown. This approach explains behavioral-cognitive changes in AD across the sleep-wake cycle and supports a causal association between early brainstem tau pathology and subsequent cortical amyloid β accumulation. Specifically, early tau dysregulation within brainstem-hypothalamic nuclei leads to breakdown of sleep-wake attractor networks, with patients displaying an attenuated range of behavioral and electrophysiological activity patterns, a "twilight zone" of constant activity between deep rest and full alertness. This constant cortical activity promotes activity-dependent amyloid β accumulation in brain areas that modulate their activity across sleep-wake states, especially the medial prefrontal cortex. In addition, the accompanying breakdown of hippocampal-medial prefrontal cortex interplay across sleep stages could explain deficient memory consolidation through dysregulation of synaptic plasticity. Clinical implications include the potential therapeutic benefit of attractor consolidation (e.g., slow-wave sleep enhancers) in delaying AD progression.
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Affiliation(s)
- Elissaios Karageorgiou
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA; Neurological Institute of Athens, Athens, Greece.
| | - Keith A Vossel
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA; Gladstone Institute of Neurological Disease, San Francisco, CA, USA
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36
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Cohen AD, Nencka AS, Lebel RM, Wang Y. Multiband multi-echo imaging of simultaneous oxygenation and flow timeseries for resting state connectivity. PLoS One 2017; 12:e0169253. [PMID: 28253268 PMCID: PMC5333818 DOI: 10.1371/journal.pone.0169253] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Accepted: 12/14/2016] [Indexed: 11/18/2022] Open
Abstract
A novel sequence has been introduced that combines multiband imaging with a multi-echo acquisition for simultaneous high spatial resolution pseudo-continuous arterial spin labeling (ASL) and blood-oxygenation-level dependent (BOLD) echo-planar imaging (MBME ASL/BOLD). Resting-state connectivity in healthy adult subjects was assessed using this sequence. Four echoes were acquired with a multiband acceleration of four, in order to increase spatial resolution, shorten repetition time, and reduce slice-timing effects on the ASL signal. In addition, by acquiring four echoes, advanced multi-echo independent component analysis (ME-ICA) denoising could be employed to increase the signal-to-noise ratio (SNR) and BOLD sensitivity. Seed-based and dual-regression approaches were utilized to analyze functional connectivity. Cerebral blood flow (CBF) and BOLD coupling was also evaluated by correlating the perfusion-weighted timeseries with the BOLD timeseries. These metrics were compared between single echo (E2), multi-echo combined (MEC), multi-echo combined and denoised (MECDN), and perfusion-weighted (PW) timeseries. Temporal SNR increased for the MECDN data compared to the MEC and E2 data. Connectivity also increased, in terms of correlation strength and network size, for the MECDN compared to the MEC and E2 datasets. CBF and BOLD coupling was increased in major resting-state networks, and that correlation was strongest for the MECDN datasets. These results indicate our novel MBME ASL/BOLD sequence, which collects simultaneous high-resolution ASL/BOLD data, could be a powerful tool for detecting functional connectivity and dynamic neurovascular coupling during the resting state. The collection of more than two echoes facilitates the use of ME-ICA denoising to greatly improve the quality of resting state functional connectivity MRI.
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Affiliation(s)
- Alexander D Cohen
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Andrew S Nencka
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | | | - Yang Wang
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
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Liu TT, Nalci A, Falahpour M. The global signal in fMRI: Nuisance or Information? Neuroimage 2017; 150:213-229. [PMID: 28213118 DOI: 10.1016/j.neuroimage.2017.02.036] [Citation(s) in RCA: 234] [Impact Index Per Article: 33.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Revised: 02/05/2017] [Accepted: 02/13/2017] [Indexed: 01/17/2023] Open
Abstract
The global signal is widely used as a regressor or normalization factor for removing the effects of global variations in the analysis of functional magnetic resonance imaging (fMRI) studies. However, there is considerable controversy over its use because of the potential bias that can be introduced when it is applied to the analysis of both task-related and resting-state fMRI studies. In this paper we take a closer look at the global signal, examining in detail the various sources that can contribute to the signal. For the most part, the global signal has been treated as a nuisance term, but there is growing evidence that it may also contain valuable information. We also examine the various ways that the global signal has been used in the analysis of fMRI data, including global signal regression, global signal subtraction, and global signal normalization. Furthermore, we describe new ways for understanding the effects of global signal regression and its relation to the other approaches.
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Affiliation(s)
- Thomas T Liu
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA 92093, United States; Departments of Radiology, Psychiatry, and Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, United States.
| | - Alican Nalci
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA 92093, United States; Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, United States.
| | - Maryam Falahpour
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA 92093, United States.
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Kettinger Á, Hill C, Vidnyánszky Z, Windischberger C, Nagy Z. Investigating the Group-Level Impact of Advanced Dual-Echo fMRI Combinations. Front Neurosci 2016; 10:571. [PMID: 28018165 PMCID: PMC5149566 DOI: 10.3389/fnins.2016.00571] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Accepted: 11/28/2016] [Indexed: 11/16/2022] Open
Abstract
Multi-echo fMRI data acquisition has been widely investigated and suggested to optimize sensitivity for detecting the BOLD signal. Several methods have also been proposed for the combination of data with different echo times. The aim of the present study was to investigate whether these advanced echo combination methods provide advantages over the simple averaging of echoes when state-of-the-art group-level random-effect analyses are performed. Both resting-state and task-based dual-echo fMRI data were collected from 27 healthy adult individuals (14 male, mean age = 25.75 years) using standard echo-planar acquisition methods at 3T. Both resting-state and task-based data were subjected to a standard image pre-processing pipeline. Subsequently the two echoes were combined as a weighted average, using four different strategies for calculating the weights: (1) simple arithmetic averaging, (2) BOLD sensitivity weighting, (3) temporal-signal-to-noise ratio weighting and (4) temporal BOLD sensitivity weighting. Our results clearly show that the simple averaging of data with the different echoes is sufficient. Advanced echo combination methods may provide advantages on a single-subject level but when considering random-effects group level statistics they provide no benefit regarding sensitivity (i.e., group-level t-values) compared to the simple echo-averaging approach. One possible reason for the lack of clear advantages may be that apart from increasing the average BOLD sensitivity at the single-subject level, the advanced weighted averaging methods also inflate the inter-subject variance. As the echo combination methods provide very similar results, the recommendation is to choose between them depending on the availability of time for collecting additional resting-state data or whether subject-level or group-level analyses are planned.
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Affiliation(s)
- Ádám Kettinger
- Department of Nuclear Techniques, Budapest University of Technology and EconomicsBudapest, Hungary; Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of SciencesBudapest, Hungary
| | - Christopher Hill
- Laboratory for Social and Neural Systems Research, University of Zurich Zurich, Switzerland
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of Sciences Budapest, Hungary
| | - Christian Windischberger
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna Vienna, Austria
| | - Zoltán Nagy
- Laboratory for Social and Neural Systems Research, University of Zurich Zurich, Switzerland
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39
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Human brain responses to gastrointestinal nutrients and gut hormones. Curr Opin Pharmacol 2016; 31:8-12. [DOI: 10.1016/j.coph.2016.08.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Revised: 07/13/2016] [Accepted: 08/11/2016] [Indexed: 01/01/2023]
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40
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Liu TT. Noise contributions to the fMRI signal: An overview. Neuroimage 2016; 143:141-151. [PMID: 27612646 DOI: 10.1016/j.neuroimage.2016.09.008] [Citation(s) in RCA: 139] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Revised: 09/01/2016] [Accepted: 09/03/2016] [Indexed: 01/21/2023] Open
Abstract
The ability to discriminate signal from noise plays a key role in the analysis and interpretation of functional magnetic resonance imaging (fMRI) measures of brain activity. Over the past two decades, a number of major sources of noise have been identified, including system-related instabilities, subject motion, and physiological fluctuations. This article reviews the characteristics of the various noise sources as well as the mechanisms through which they affect the fMRI signal. Approaches for distinguishing signal from noise and the associated challenges are also reviewed. These challenges reflect the fact that some noise sources, such as respiratory activity, are generated by the same underlying brain networks that give rise to functional signals that are of interest.
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Affiliation(s)
- Thomas T Liu
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA 92093, United States; Departments of Radiology, Psychiatry and Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, United States.
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41
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Silson EH, Steel AD, Baker CI. Scene-Selectivity and Retinotopy in Medial Parietal Cortex. Front Hum Neurosci 2016; 10:412. [PMID: 27588001 PMCID: PMC4988988 DOI: 10.3389/fnhum.2016.00412] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Accepted: 08/03/2016] [Indexed: 01/21/2023] Open
Abstract
Functional imaging studies in human reliably identify a trio of scene-selective regions, one on each of the lateral [occipital place area (OPA)], ventral [parahippocampal place area (PPA)], and medial [retrosplenial complex (RSC)] cortical surfaces. Recently, we demonstrated differential retinotopic biases for the contralateral lower and upper visual fields within OPA and PPA, respectively. Here, using functional magnetic resonance imaging, we combine detailed mapping of both population receptive fields (pRF) and category-selectivity, with independently acquired resting-state functional connectivity analyses, to examine scene and retinotopic processing within medial parietal cortex. We identified a medial scene-selective region, which was contained largely within the posterior and ventral bank of the parieto-occipital sulcus (POS). While this region is typically referred to as RSC, the spatial extent of our scene-selective region typically did not extend into retrosplenial cortex, and thus we adopt the term medial place area (MPA) to refer to this visually defined scene-selective region. Intriguingly MPA co-localized with a region identified solely on the basis of retinotopic sensitivity using pRF analyses. We found that MPA demonstrates a significant contralateral visual field bias, coupled with large pRF sizes. Unlike OPA and PPA, MPA did not show a consistent bias to a single visual quadrant. MPA also co-localized with a region identified by strong differential functional connectivity with PPA and the human face-selective fusiform face area (FFA), commensurate with its functional selectivity. Functional connectivity with OPA was much weaker than with PPA, and similar to that with face-selective occipital face area (OFA), suggesting a closer link with ventral than lateral cortex. Consistent with prior research, we also observed differential functional connectivity in medial parietal cortex for anterior over posterior PPA, as well as a region on the lateral surface, the caudal inferior parietal lobule (cIPL). However, the differential connectivity in medial parietal cortex was found principally anterior of MPA. We suggest that there is posterior–anterior gradient within medial parietal cortex, with posterior regions in the POS showing retinotopically based scene-selectivity and more anterior regions showing connectivity that may be more reflective of abstract, navigationally pertinent and possibly mnemonic representations.
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Affiliation(s)
- Edward H Silson
- Laboratory of Brain and Cognition, National institute of Mental Health Bethesda, MD, USA
| | - Adam D Steel
- Laboratory of Brain and Cognition, National institute of Mental HealthBethesda, MD, USA; Physiological Neuroimaging Group FMRIB Centre, John Radcliffe Hospital, University of OxfordOxford, UK
| | - Chris I Baker
- Laboratory of Brain and Cognition, National institute of Mental Health Bethesda, MD, USA
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Gonzalez-Castillo J, Panwar P, Buchanan LC, Caballero-Gaudes C, Handwerker DA, Jangraw DC, Zachariou V, Inati S, Roopchansingh V, Derbyshire JA, Bandettini PA. Evaluation of multi-echo ICA denoising for task based fMRI studies: Block designs, rapid event-related designs, and cardiac-gated fMRI. Neuroimage 2016; 141:452-468. [PMID: 27475290 DOI: 10.1016/j.neuroimage.2016.07.049] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Revised: 07/21/2016] [Accepted: 07/25/2016] [Indexed: 10/21/2022] Open
Abstract
Multi-echo fMRI, particularly the multi-echo independent component analysis (ME-ICA) algorithm, has previously proven useful for increasing the sensitivity and reducing false positives for functional MRI (fMRI) based resting state connectivity studies. Less is known about its efficacy for task-based fMRI, especially at the single subject level. This work, which focuses exclusively on individual subject results, compares ME-ICA to single-echo fMRI and a voxel-wise T2(⁎) weighted combination of multi-echo data for task-based fMRI under the following scenarios: cardiac-gated block designs, constant repetition time (TR) block designs, and constant TR rapid event-related designs. Performance is evaluated primarily in terms of sensitivity (i.e., activation extent, activation magnitude, percent detected trials and effect size estimates) using five different tasks expected to evoke neuronal activity in a distributed set of regions. The ME-ICA algorithm significantly outperformed all other evaluated processing alternatives in all scenarios. Largest improvements were observed for the cardiac-gated dataset, where ME-ICA was able to reliably detect and remove non-neural T1 signal fluctuations caused by non-constant repetition times. Although ME-ICA also outperformed the other options in terms of percent detection of individual trials for rapid event-related experiments, only 46% of all events were detected after ME-ICA; suggesting additional improvements in sensitivity are required to reliably detect individual short event occurrences. We conclude the manuscript with a detailed evaluation of ME-ICA outcomes and a discussion of how the ME-ICA algorithm could be further improved. Overall, our results suggest that ME-ICA constitutes a versatile, powerful approach for advanced denoising of task-based fMRI, not just resting-state data.
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Affiliation(s)
- Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States.
| | - Puja Panwar
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Laura C Buchanan
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | | | - Daniel A Handwerker
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - David C Jangraw
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Valentinos Zachariou
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Souheil Inati
- Functional MRI Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Vinai Roopchansingh
- Functional MRI Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - John A Derbyshire
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States; Functional MRI Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
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43
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Lombardo MV, Auyeung B, Holt RJ, Waldman J, Ruigrok ANV, Mooney N, Bullmore ET, Baron-Cohen S, Kundu P. Improving effect size estimation and statistical power with multi-echo fMRI and its impact on understanding the neural systems supporting mentalizing. Neuroimage 2016; 142:55-66. [PMID: 27417345 PMCID: PMC5102698 DOI: 10.1016/j.neuroimage.2016.07.022] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2015] [Revised: 06/04/2016] [Accepted: 07/09/2016] [Indexed: 12/27/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) research is routinely criticized for being statistically underpowered due to characteristically small sample sizes and much larger sample sizes are being increasingly recommended. Additionally, various sources of artifact inherent in fMRI data can have detrimental impact on effect size estimates and statistical power. Here we show how specific removal of non-BOLD artifacts can improve effect size estimation and statistical power in task-fMRI contexts, with particular application to the social-cognitive domain of mentalizing/theory of mind. Non-BOLD variability identification and removal is achieved in a biophysical and statistically principled manner by combining multi-echo fMRI acquisition and independent components analysis (ME-ICA). Without smoothing, group-level effect size estimates on two different mentalizing tasks were enhanced by ME-ICA at a median rate of 24% in regions canonically associated with mentalizing, while much more substantial boosts (40-149%) were observed in non-canonical cerebellar areas. Effect size boosting occurs via reduction of non-BOLD noise at the subject-level and consequent reductions in between-subject variance at the group-level. Smoothing can attenuate ME-ICA-related effect size improvements in certain circumstances. Power simulations demonstrate that ME-ICA-related effect size enhancements enable much higher-powered studies at traditional sample sizes. Cerebellar effects observed after applying ME-ICA may be unobservable with conventional imaging at traditional sample sizes. Thus, ME-ICA allows for principled design-agnostic non-BOLD artifact removal that can substantially improve effect size estimates and statistical power in task-fMRI contexts. ME-ICA could mitigate some issues regarding statistical power in fMRI studies and enable novel discovery of aspects of brain organization that are currently under-appreciated and not well understood.
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Affiliation(s)
- Michael V Lombardo
- Center for Applied Neuroscience, Department of Psychology, University of Cyprus, Cyprus; Autism Research Centre, Department of Psychiatry, University of Cambridge, UK.
| | - Bonnie Auyeung
- Autism Research Centre, Department of Psychiatry, University of Cambridge, UK; Department of Psychology, School of Philosophy, Psychology, and Language Sciences, University of Edinburgh, UK
| | - Rosemary J Holt
- Autism Research Centre, Department of Psychiatry, University of Cambridge, UK
| | - Jack Waldman
- Autism Research Centre, Department of Psychiatry, University of Cambridge, UK
| | - Amber N V Ruigrok
- Autism Research Centre, Department of Psychiatry, University of Cambridge, UK
| | - Natasha Mooney
- Autism Research Centre, Department of Psychiatry, University of Cambridge, UK
| | - Edward T Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, UK
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, UK
| | - Prantik Kundu
- Section on Advanced Functional Neuroimaging, Departments of Radiology & Psychiatry, Icahn School of Medicine at Mount Sinai, USA.
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Chang C, Raven EP, Duyn JH. Brain-heart interactions: challenges and opportunities with functional magnetic resonance imaging at ultra-high field. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2016; 374:rsta.2015.0188. [PMID: 27044994 PMCID: PMC4822447 DOI: 10.1098/rsta.2015.0188] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/05/2016] [Indexed: 05/24/2023]
Abstract
Magnetic resonance imaging (MRI) at ultra-high field (UHF) strengths (7 T and above) offers unique opportunities for studying the human brain with increased spatial resolution, contrast and sensitivity. However, its reliability can be compromised by factors such as head motion, image distortion and non-neural fluctuations of the functional MRI signal. The objective of this review is to provide a critical discussion of the advantages and trade-offs associated with UHF imaging, focusing on the application to studying brain-heart interactions. We describe how UHF MRI may provide contrast and resolution benefits for measuring neural activity of regions involved in the control and mediation of autonomic processes, and in delineating such regions based on anatomical MRI contrast. Limitations arising from confounding signals are discussed, including challenges with distinguishing non-neural physiological effects from the neural signals of interest that reflect cardiorespiratory function. We also consider how recently developed data analysis techniques may be applied to high-field imaging data to uncover novel information about brain-heart interactions.
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Affiliation(s)
- Catie Chang
- Advanced Magnetic Resonance Imaging Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Erika P Raven
- Advanced Magnetic Resonance Imaging Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA Center for Functional and Molecular Imaging, Georgetown University Medical Center, Washington, DC 20007, USA
| | - Jeff H Duyn
- Advanced Magnetic Resonance Imaging Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
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Robust resting state fMRI processing for studies on typical brain development based on multi-echo EPI acquisition. Brain Imaging Behav 2016; 9:56-73. [PMID: 25592183 DOI: 10.1007/s11682-014-9346-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Several methodological challenges affect the study of typical brain development based on resting state blood oxygenation level dependent (BOLD) functional MRI (fMRI). One such challenge is mitigating artifacts such as those from head motion, known to be more substantial in younger subjects than older subjects. Other challenges include controlling for potential age-dependence in cerebrospinal fluid (CSF) volume affecting anatomical-functional coregistration; in vascular density affecting BOLD contrast-to-noise; and in CSF pulsation creating time series artifacts. Historically, these confounds have been approached through incorporating artifact-specific temporal and/or spatial filtering into preprocessing pipelines. However, such paths often come with new confounds or limitations. In this study we take the approach of a bottom-up revision of fMRI methodology based on acquisition of multi-echo fMRI and comprehensive utilization of the information in the TE-domain to enhance several aspects of fMRI analysis in the context of a developmental study. We show in a cohort of 25 healthy subjects, aged 9 to 43 years, that the analysis of multi-echo fMRI data eliminates a number of arbitrary processing steps such as bandpass filtering and spatial smoothing, while enabling procedures such as [Formula: see text] mapping, BOLD contrast normalization and signal dropout recovery, precise anatomical-functional coregistration based on [Formula: see text] measurements, automatic denoising through removing subject motion, scanner-related signal drifts and physiology, as well as statistical inference for seed-based connectivity. These enhancements are of both theoretical significance and practical benefit in the study of typical brain development.
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