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Kish B, Jean Chen J, Tong Y. Effects of clamping end-tidal CO 2 on neurofluidic low-frequency oscillations. NMR IN BIOMEDICINE 2024; 37:e5084. [PMID: 38104563 PMCID: PMC11162899 DOI: 10.1002/nbm.5084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/17/2023] [Accepted: 11/11/2023] [Indexed: 12/19/2023]
Abstract
In recent years, low-frequency oscillations (LFOs) (0.01-0.1 Hz) have been a subject of interest in resting-state functional magnetic resonance imaging research. They are believed to have many possible driving mechanisms, from both regional and global sources. Internal fluctuations in the partial pressure of CO2 (PCO2) has long been thought of as one of these major driving forces, but its exact contributions compared with other mechanisms have yet to be fully understood. This study examined the effects of end-tidal PCO2 (PetCO2) oscillations on LF cerebral hemodynamics and cerebrospinal fluid (CSF) dynamics under "clamped PetCO2" and "free-breathing" conditions. Under clamped PetCO2, a participant's PetCO2 levels were fixed to their baseline average, whereas PetCO2 was not controlled in free breathing. Under clamped PetCO2, the fractional amplitude of hemodynamic LFOs in the occipital and sensorimotor cortex and temporal lobes were found to be significantly reduced. Additionally, the fractional amplitude of CSF LFOs, measured at the fourth ventricle, was found to be reduced by almost one-half. However, the spatiotemporal distributions of blood and CSF delay times, as measured by cross-correlation in the LF domain, were not significantly altered between conditions. This study demonstrates that, while PCO2 oscillations significantly mediate LFOs, especially those observed in the CSF, other mechanisms are able to maintain LFOs, with high correlation, even in their absence.
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Affiliation(s)
- Brianna Kish
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - J. Jean Chen
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Yunjie Tong
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
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2
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Gong J, Stickland RC, Bright MG. Hemodynamic timing in resting-state and breathing-task BOLD fMRI. Neuroimage 2023; 274:120120. [PMID: 37072074 PMCID: PMC10208394 DOI: 10.1016/j.neuroimage.2023.120120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 04/06/2023] [Accepted: 04/15/2023] [Indexed: 04/20/2023] Open
Abstract
The blood flow response to a vasoactive stimulus demonstrates regional heterogeneity across both the healthy brain and in cerebrovascular pathology. The timing of a regional hemodynamic response is emerging as an important biomarker of cerebrovascular dysfunction, as well as a confound within fMRI analyses. Previous research demonstrated that hemodynamic timing is more robustly characterized when a larger systemic vascular response is evoked by a breathing challenge, compared to when only spontaneous fluctuations in vascular physiology are present (i.e., in resting-state data). However, it is not clear whether hemodynamic delays in these two conditions are physiologically interchangeable, and how methodological signal-to-noise factors may limit their agreement. To address this, we generated whole-brain maps of hemodynamic delays in nine healthy adults. We assessed the agreement of voxel-wise gray matter (GM) hemodynamic delays between two conditions: resting-state and breath-holding. We found that delay values demonstrated poor agreement when considering all GM voxels, but increasingly greater agreement when limiting analyses to voxels showing strong correlation with the GM mean time-series. Voxels showing the strongest agreement with the GM mean time-series were primarily located near large venous vessels, however these voxels explain some, but not all, of the observed agreement in timing. Increasing the degree of spatial smoothing of the fMRI data enhanced the correlation between individual voxel time-series and the GM mean time-series. These results suggest that signal-to-noise factors may be limiting the accuracy of voxel-wise timing estimates and hence their agreement between the two data segments. In conclusion, caution must be taken when using voxel-wise delay estimates from resting-state and breathing-task data interchangeably, and additional work is needed to evaluate their relative sensitivity and specificity to aspects of vascular physiology and pathology.
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Affiliation(s)
- Jingxuan Gong
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, United States of America.
| | - Rachael C Stickland
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America
| | - Molly G Bright
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, United States of America; Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America
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3
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Cao L, Kohut SJ, Frederick BD. Estimating and mitigating the effects of systemic low frequency oscillations (sLFO) on resting state networks in awake non-human primates using time lag dependent methodology. FRONTIERS IN NEUROIMAGING 2023; 1:1031991. [PMID: 37555145 PMCID: PMC10406257 DOI: 10.3389/fnimg.2022.1031991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 12/15/2022] [Indexed: 08/10/2023]
Abstract
AIM Resting-state fMRI (rs-fMRI) is often used to infer regional brain interactions from the degree of temporal correlation between spontaneous low-frequency fluctuations, thought to reflect local changes in the BOLD signal due to neuronal activity. One complication in the analysis and interpretation of rs-fMRI data is the existence of non-neuronal low frequency physiological noise (systemic low frequency oscillations; sLFOs) which occurs within the same low frequency band as the signal used to compute functional connectivity. Here, we demonstrate the use of a time lag mapping technique to estimate and mitigate the effects of the sLFO signal on resting state functional connectivity of awake squirrel monkeys. METHODS Twelve squirrel monkeys (6 male/6 female) were acclimated to awake scanning procedures; whole-brain fMRI images were acquired with a 9.4 Tesla scanner. Rs-fMRI data was preprocessed using an in-house pipeline and sLFOs were detected using a seed regressor generated by averaging BOLD signal across all voxels in the brain, which was then refined recursively within a time window of -16-12 s. The refined regressor was then used to estimate the voxel-wise sLFOs; these regressors were subsequently included in the general linear model to remove these moving hemodynamic components from the rs-fMRI data using general linear model filtering. Group level independent component analysis (ICA) with dual regression was used to detect resting-state networks and compare networks before and after sLFO denoising. RESULTS Results show sLFOs constitute ~64% of the low frequency fMRI signal in squirrel monkey gray matter; they arrive earlier in regions in proximity to the middle cerebral arteries (e.g., somatosensory cortex) and later in regions close to draining vessels (e.g., cerebellum). Dual regression results showed that the physiological noise was significantly reduced after removing sLFOs and the extent of reduction was determined by the brain region contained in the resting-state network. CONCLUSION These results highlight the need to estimate and remove sLFOs from fMRI data before further analysis.
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Affiliation(s)
- Lei Cao
- Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, MA, United States
- McLean Imaging Center, McLean Hospital, Belmont, MA, United States
| | - Stephen J. Kohut
- Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, MA, United States
- McLean Imaging Center, McLean Hospital, Belmont, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Blaise deB. Frederick
- McLean Imaging Center, McLean Hospital, Belmont, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Opto-Magnetic Group, McLean Hospital, Belmont, MA, United States
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4
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Mann‐Krzisnik D, Mitsis GD. Extracting electrophysiological correlates of functional magnetic resonance imaging data using the canonical polyadic decomposition. Hum Brain Mapp 2022; 43:4045-4073. [PMID: 35567768 PMCID: PMC9374895 DOI: 10.1002/hbm.25902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 04/25/2022] [Accepted: 04/27/2022] [Indexed: 11/11/2022] Open
Abstract
The relation between electrophysiology and BOLD-fMRI requires further elucidation. One approach for studying this relation is to find time-frequency features from electrophysiology that explain the variance of BOLD time-series. Convolution of these features with a canonical hemodynamic response function (HRF) is often required to model neurovascular coupling mechanisms and thus account for time shifts between electrophysiological and BOLD-fMRI data. We propose a framework for extracting the spatial distribution of these time-frequency features while also estimating more flexible, region-specific HRFs. The core component of this method is the decomposition of a tensor containing impulse response functions using the Canonical Polyadic Decomposition. The outputs of this decomposition provide insight into the relation between electrophysiology and BOLD-fMRI and can be used to construct estimates of BOLD time-series. We demonstrated the performance of this method on simulated data while also examining the effects of simulated measurement noise and physiological confounds. Afterwards, we validated our method on publicly available task-based and resting-state EEG-fMRI data. We adjusted our method to accommodate the multisubject nature of these datasets, enabling the investigation of inter-subject variability with regards to EEG-to-BOLD neurovascular coupling mechanisms. We thus also demonstrate how EEG features for modelling the BOLD signal differ across subjects.
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Affiliation(s)
- Dylan Mann‐Krzisnik
- Graduate Program in Biological and Biomedical EngineeringMcGill UniversityMontréalQuebecCanada
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5
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Pfurtscheller G, Blinowska KJ, Kaminski M, Rassler B, Klimesch W. Processing of fMRI-related anxiety and information flow between brain and body revealed a preponderance of oscillations at 0.15/0.16 Hz. Sci Rep 2022; 12:9117. [PMID: 35650314 PMCID: PMC9160010 DOI: 10.1038/s41598-022-13229-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 05/23/2022] [Indexed: 11/09/2022] Open
Abstract
Slow oscillations of different center frequencies and their coupling play an important role in brain-body interactions. The crucial question analyzed by us is, whether the low frequency (LF) band (0.05-0.15 Hz) or the intermediate frequency (IMF) band (0.1-0.2 Hz) is more eminent in respect of the information flow between body (heart rate and respiration) and BOLD signals in cortex and brainstem. A recently published study with the LF band in fMRI-naïve subjects revealed an intensive information flow from the cortex to the brainstem and a weaker flow from the brainstem to the cortex. The comparison of both bands revealed a significant information flow from the middle frontal gyrus (MFG) to the precentral gyrus (PCG) and from brainstem to PCG only in the IMF band. This pattern of directed coupling between slow oscillations in the cortex and brainstem not only supports the existence of a pacemaker-like structure in brainstem, but provides first evidence that oscillations centered at 0.15/0.16 Hz can also emerge in brain networks. BOLD oscillations in resting states are dominating at ~ 0.08 Hz and respiratory rates at ~ 0.32 Hz. Therefore, the frequency component at ~ 0.16 Hz (doubling-halving 0.08 Hz or 0.32 Hz) is of special interest, because phase coupled oscillations can reduce the energy demand.
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Affiliation(s)
- Gert Pfurtscheller
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria.
| | - Katarzyna J Blinowska
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Ks. Trojdena 4 St., 02-109, Warsaw, Poland.,Faculty of Physics, University of Warsaw, Ul. Pasteura 5, 02-093, Warsaw, Poland
| | - Maciej Kaminski
- Faculty of Physics, University of Warsaw, Ul. Pasteura 5, 02-093, Warsaw, Poland
| | - Beate Rassler
- Carl-Ludwig-Institute of Physiology, University of Leipzig, Leipzig, Germany
| | - Wolfgang Klimesch
- Centre of Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
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6
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Developmental coupling of cerebral blood flow and fMRI fluctuations in youth. Cell Rep 2022; 38:110576. [PMID: 35354053 PMCID: PMC9006592 DOI: 10.1016/j.celrep.2022.110576] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/03/2022] [Accepted: 03/04/2022] [Indexed: 12/16/2022] Open
Abstract
The functions of the human brain are metabolically expensive and reliant on coupling between cerebral blood flow (CBF) and neural activity, yet how this coupling evolves over development remains unexplored. Here, we examine the relationship between CBF, measured by arterial spin labeling, and the amplitude of low-frequency fluctuations (ALFF) from resting-state magnetic resonance imaging across a sample of 831 children (478 females, aged 8-22 years) from the Philadelphia Neurodevelopmental Cohort. We first use locally weighted regressions on the cortical surface to quantify CBF-ALFF coupling. We relate coupling to age, sex, and executive functioning with generalized additive models and assess network enrichment via spin testing. We demonstrate regionally specific changes in coupling over age and show that variations in coupling are related to biological sex and executive function. Our results highlight the importance of CBF-ALFF coupling throughout development; we discuss its potential as a future target for the study of neuropsychiatric diseases.
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7
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Kavroulakis E, Simos NJ, Maris TG, Zaganas I, Panagiotakis S, Papadaki E. Evidence of Age-Related Hemodynamic and Functional Connectivity Impairment: A Resting State fMRI Study. Front Neurol 2021; 12:633500. [PMID: 33833727 PMCID: PMC8021915 DOI: 10.3389/fneur.2021.633500] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 03/01/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: To assess age-related changes in intrinsic functional brain connectivity and hemodynamics during adulthood in the context of the retrogenesis hypothesis, which states that the rate of age-related changes is higher in late-myelinating (prefrontal, lateral-posterior temporal) cerebrocortical areas as compared to early myelinating (parietal, occipital) regions. In addition, to examine the dependence of age-related changes upon concurrent subclinical depression symptoms which are common even in healthy aging. Methods: Sixty-four healthy adults (28 men) aged 23-79 years (mean 45.0, SD = 18.8 years) were examined. Resting-state functional MRI (rs-fMRI) time series were used to compute voxel-wise intrinsic connectivity contrast (ICC) maps reflecting the strength of functional connectivity between each voxel and the rest of the brain. We further used Time Shift Analysis (TSA) to estimate voxel-wise hemodynamic lead or lag for each of 22 ROIs from the automated anatomical atlas (AAL). Results: Adjusted for depression symptoms, gender and education level, reduced ICC with age was found primarily in frontal, temporal regions, and putamen, whereas the opposite trend was noted in inferior occipital cortices (p < 0.002). With the same covariates, increased hemodynamic lead with advancing age was found in superior frontal cortex and thalamus, with the opposite trend in inferior occipital cortex (p < 0.002). There was also evidence of reduced coupling between voxel-wise intrinsic connectivity and hemodynamics in the inferior parietal cortex. Conclusion: Age-related intrinsic connectivity reductions and hemodynamic changes were demonstrated in several regions-most of them part of DMN and salience networks-while impaired neurovascular coupling was, also, found in parietal regions. Age-related reductions in intrinsic connectivity were greater in anterior as compared to posterior cortices, in line with implications derived from the retrogenesis hypothesis. These effects were affected by self-reported depression symptoms, which also increased with age.
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Affiliation(s)
- Eleftherios Kavroulakis
- Department of Radiology, School of Medicine, University of Crete, University Hospital of Heraklion, Heraklion, Greece
| | - Nicholas J Simos
- Department of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece.,Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Greece
| | - Thomas G Maris
- Department of Medical Physics, School of Medicine, University of Crete, University Hospital of Heraklion, Heraklion, Greece
| | - Ioannis Zaganas
- Department of Neurology, School of Medicine, University of Crete, University Hospital of Heraklion, Heraklion, Greece
| | - Simeon Panagiotakis
- Department of Internal Medicine, University Hospital of Heraklion, Heraklion, Greece
| | - Efrosini Papadaki
- Department of Radiology, School of Medicine, University of Crete, University Hospital of Heraklion, Heraklion, Greece.,Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Greece
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8
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Rivera-Rivera LA, Cody KA, Rutkowski D, Cary P, Eisenmenger L, Rowley HA, Carlsson CM, Johnson SC, Johnson KM. Intracranial vascular flow oscillations in Alzheimer's disease from 4D flow MRI. Neuroimage Clin 2020; 28:102379. [PMID: 32871386 PMCID: PMC7476069 DOI: 10.1016/j.nicl.2020.102379] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 07/10/2020] [Accepted: 08/07/2020] [Indexed: 11/26/2022]
Abstract
Recent modeling and experimental evidence suggests clearance of soluble metabolites from the brain can be driven by low frequency flow oscillations (LFOs) through the intramural periarterial drainage (IPAD) pathway. This study investigates the use of 4D flow MRI to derive LFOs from arterial and venous measures of blood flow. 3D radial 4D flow MRI data were acquired on a 3.0 T scanner and reconstructed using a low-rank constraint to produce time resolved measurements of blood flow. Physical phantom experiments were performed to validate the time resolved 4D flow against a standard 2D phase contrast (PC) approach. To evaluate the ability of 4D flow to distinguish physiologic flow changes from noise, healthy volunteers were scanned during a breath-hold (BH) maneuver and compared against 2D PC measures. Finally, flow measures were performed in intracranial arteries and veins of 112 participants including subjects diagnosed with Alzheimer's disease (AD) clinical syndrome (n = 23), and healthy controls (n = 89) on whom apolipoprotein ɛ4 positivity (APOE4+) and parental history of AD dementia (FH+) was known. To assess LFOs, flow range, standard deviation, demeaned temporal flow changes, and power spectral density were quantified from the time series. Group differences were assessed using ANOVA followed by Tukey-Kramer method for pairwise comparison for adjusted means (P < 0.05). Significantly lower LFOs as measured from flow variation range and standard deviations were observed in the arteries of AD subjects when compared to age-matched controls (P = 0.005, P = 0.011). Results suggest altered vascular function in AD subjects. 4D flow based spontaneous LFO measures might hold potential for longitudinal studies aimed at predicting cognitive trajectories in AD and study disease mechanisms.
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Affiliation(s)
- Leonardo A Rivera-Rivera
- Department of Medical Physics, University of Wisconsin, School of Medicine and Public Health, Madison, WI, USA
| | - Karly A Cody
- Alzheimer's Disease Research Center, University of Wisconsin, School of Medicine and Public Health, Madison, WI, USA
| | - David Rutkowski
- Department of Radiology, University of Wisconsin, School of Medicine and Public Health, Madison, WI, USA
| | - Paul Cary
- Alzheimer's Disease Research Center, University of Wisconsin, School of Medicine and Public Health, Madison, WI, USA
| | - Laura Eisenmenger
- Department of Radiology, University of Wisconsin, School of Medicine and Public Health, Madison, WI, USA
| | - Howard A Rowley
- Alzheimer's Disease Research Center, University of Wisconsin, School of Medicine and Public Health, Madison, WI, USA; Department of Radiology, University of Wisconsin, School of Medicine and Public Health, Madison, WI, USA
| | - Cynthia M Carlsson
- Alzheimer's Disease Research Center, University of Wisconsin, School of Medicine and Public Health, Madison, WI, USA; Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Sterling C Johnson
- Alzheimer's Disease Research Center, University of Wisconsin, School of Medicine and Public Health, Madison, WI, USA; Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Kevin M Johnson
- Department of Medical Physics, University of Wisconsin, School of Medicine and Public Health, Madison, WI, USA; Department of Radiology, University of Wisconsin, School of Medicine and Public Health, Madison, WI, USA.
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9
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Gravel N, Renken RJ, Harvey BM, Deco G, Cornelissen FW, Gilson M. Propagation of BOLD Activity Reveals Task-dependent Directed Interactions Across Human Visual Cortex. Cereb Cortex 2020; 30:5899-5914. [PMID: 32577717 DOI: 10.1093/cercor/bhaa165] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 03/13/2020] [Accepted: 05/02/2020] [Indexed: 11/14/2022] Open
Abstract
It has recently been shown that large-scale propagation of blood-oxygen-level-dependent (BOLD) activity is constrained by anatomical connections and reflects transitions between behavioral states. It remains to be seen, however, if the propagation of BOLD activity can also relate to the brain's anatomical structure at a more local scale. Here, we hypothesized that BOLD propagation reflects structured neuronal activity across early visual field maps. To explore this hypothesis, we characterize the propagation of BOLD activity across V1, V2, and V3 using a modeling approach that aims to disentangle the contributions of local activity and directed interactions in shaping BOLD propagation. It does so by estimating the effective connectivity (EC) and the excitability of a noise-diffusion network to reproduce the spatiotemporal covariance structure of the data. We apply our approach to 7T fMRI recordings acquired during resting state (RS) and visual field mapping (VFM). Our results reveal different EC interactions and changes in cortical excitability in RS and VFM, and point to a reconfiguration of feedforward and feedback interactions across the visual system. We conclude that the propagation of BOLD activity has functional relevance, as it reveals directed interactions and changes in cortical excitability in a task-dependent manner.
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Affiliation(s)
- Nicolás Gravel
- Neural Dynamics of Visual Cognition Group, Department of Education and Psychology, Freie University Berlin, 14195 Berlin, Germany.,Laboratory of Experimental Ophthalmology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Remco J Renken
- Laboratory of Experimental Ophthalmology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands.,Cognitive Neuroscience Center, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Ben M Harvey
- Experimental Psychology, Helmholtz Institute, Utrecht University, 3584 CS Utrecht, The Netherlands
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain.,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany.,School of Psychological Sciences, Monash University, VIC 3800 Melbourne, Australia
| | - Frans W Cornelissen
- Laboratory of Experimental Ophthalmology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Matthieu Gilson
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain
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10
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Asgher U, Khalil K, Khan MJ, Ahmad R, Butt SI, Ayaz Y, Naseer N, Nazir S. Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain-Computer Interface. Front Neurosci 2020; 14:584. [PMID: 32655353 PMCID: PMC7324788 DOI: 10.3389/fnins.2020.00584] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Accepted: 05/12/2020] [Indexed: 11/30/2022] Open
Abstract
Cognitive workload is one of the widely invoked human factors in the areas of human-machine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and mental workload (MWL) is vital and requires accurate neuroimaging to monitor and evaluate the cognitive states of the brain. In this study, we have decoded four classes of MWL using long short-term memory (LSTM) with 89.31% average accuracy for brain-computer interface (BCI). The brain activity signals are acquired using functional near-infrared spectroscopy (fNIRS) from the prefrontal cortex (PFC) region of the brain. We performed a supervised MWL experimentation with four varying MWL levels on 15 participants (both male and female) and 10 trials of each MWL per participant. Real-time four-level MWL states are assessed using fNIRS system, and initial classification is performed using three strong machine learning (ML) techniques, support vector machine (SVM), k-nearest neighbor (k-NN), and artificial neural network (ANN) with obtained average accuracies of 54.33, 54.31, and 69.36%, respectively. In this study, novel deep learning (DL) frameworks are proposed, which utilizes convolutional neural network (CNN) and LSTM with 87.45 and 89.31% average accuracies, respectively, to solve high-dimensional four-level cognitive states classification problem. Statistical analysis, t-test, and one-way F-test (ANOVA) are also performed on accuracies obtained through ML and DL algorithms. Results show that the proposed DL (LSTM and CNN) algorithms significantly improve classification performance as compared with ML (SVM, ANN, and k-NN) algorithms.
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Affiliation(s)
- Umer Asgher
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Khurram Khalil
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Muhammad Jawad Khan
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Riaz Ahmad
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
- Directorate of Quality Assurance and International Collaboration, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Shahid Ikramullah Butt
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Yasar Ayaz
- School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan
- National Center of Artificial Intelligence (NCAI) – NUST, Islamabad, Pakistan
| | - Noman Naseer
- Department of Mechatronics Engineering, Air University, Islamabad, Pakistan
| | - Salman Nazir
- Training and Assessment Research Group, Department of Maritime Operations, University of South-Eastern Norway, Kongsberg, Norway
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11
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Abnormal Cortical Activation in Visual Attention Processing in Sub-Clinical Psychopathic Traits and Traumatic Brain Injury: Evidence from an fNIRS Study. JOURNAL OF PSYCHOPATHOLOGY AND BEHAVIORAL ASSESSMENT 2020. [DOI: 10.1007/s10862-020-09808-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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12
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Tanrıtanır AC, Villringer K, Galinovic I, Grittner U, Kirilina E, Fiebach JB, Villringer A, Khalil AA. The Effect of Scan Length on the Assessment of BOLD Delay in Ischemic Stroke. Front Neurol 2020; 11:381. [PMID: 32431665 PMCID: PMC7214917 DOI: 10.3389/fneur.2020.00381] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 04/15/2020] [Indexed: 01/21/2023] Open
Abstract
Objectives: To evaluate the impact of resting-state functional MRI scan length on the diagnostic accuracy, image quality and lesion volume estimation of BOLD delay maps used for brain perfusion assessment in acute ischemic stroke. Methods: Sixty-three acute ischemic stroke patients received a 340 s resting-state functional MRI within 24 h of stroke symptom onset. BOLD delay maps were calculated from the full scan and four shortened versions (68 s, 136 s, 204 s, 272 s). The BOLD delay lesions on these maps were compared in terms of spatial overlap and volumetric agreement with the lesions derived from the full scans and with time-to-maximum (Tmax) lesions derived from DSC-MRI in a subset of patients (n = 10). In addition, the interpretability and quality of these maps were compared across different scan lengths using mixed models. Results: Shortened BOLD delay scans showed a small volumetric bias (ranging from 0.05 to 5.3 mL; between a 0.13% volumetric underestimation and a 7.7% overestimation relative to the mean of the volumes, depending on scan length) compared to the full scan. Decreased scan length was associated with decreased spatial overlap with both the BOLD delay lesions derived from the full scans and with Tmax lesions. Only the two shortest scan lengths (68 and 136 s) were associated with substantially decreased interpretability, decreased structure clarity, and increased noisiness of BOLD delay maps. Conclusions: BOLD delay maps derived from resting-state fMRI scans lasting 272 and 204 s provide sufficient diagnostic quality and adequate assessment of perfusion lesion volumes. Such shortened scans may be helpful in situations where quick clinical decisions need to be made.
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Affiliation(s)
| | - Kersten Villringer
- Center for Stroke Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ivana Galinovic
- Center for Stroke Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ulrike Grittner
- Institute of Biometry and Clinical Epidemiology, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany
| | - Evgeniya Kirilina
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Center for Cognitive Neuroscience Berlin, Free University, Berlin, Germany
| | - Jochen B Fiebach
- Center for Stroke Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Arno Villringer
- Berlin School of Mind and Brain, Humboldt Universität zu Berlin, Berlin, Germany.,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ahmed A Khalil
- Center for Stroke Research, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany.,Berlin School of Mind and Brain, Humboldt Universität zu Berlin, Berlin, Germany.,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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13
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Yuen NH, Osachoff N, Chen JJ. Intrinsic Frequencies of the Resting-State fMRI Signal: The Frequency Dependence of Functional Connectivity and the Effect of Mode Mixing. Front Neurosci 2019; 13:900. [PMID: 31551676 PMCID: PMC6738198 DOI: 10.3389/fnins.2019.00900] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 08/12/2019] [Indexed: 12/22/2022] Open
Abstract
The frequency characteristics of the resting-state BOLD fMRI (rs-fMRI) signal are of increasing scientific interest, as we discover more frequency-specific biological interpretations. In this work, we use variational mode decomposition (VMD) to precisely decompose the rs-fMRI time series into its intrinsic mode functions (IMFs) in a data-driven manner. The accuracy of the VMD decomposition of constituent IMFs is verified through simulations, with higher reconstruction accuracy and much-reduced mode mixing relative to previous methods. Furthermore, we examine the relative contribution of the VMD-derived modes (frequencies) to the rs-fMRI signal as well as functional connectivity measurements. Our primary findings are: (1) The rs-fMRI signal within the 0.01–0.25 Hz range can be consistently characterized by four intrinsic frequency clusters, centered at 0.028 Hz (IMF4), 0.080 Hz (IMF3), 0.15 Hz (IMF2) and 0.22 Hz (IMF1); (2) these frequency clusters were highly reproducible, and independent of rs-fMRI data sampling rate; (3) not all frequencies were associated with equivalent network topology, in contrast to previous findings. In fact, while IMF4 is most likely associated with physiological fluctuations due to respiration and pulse, IMF3 is most likely associated with metabolic processes, and IMF2 with vasomotor activity. Both IMF3 and IMF4 could produce the brain-network topology typically observed in fMRI, whereas IMF1 and IMF2 could not. These findings provide initial evidence of feasibility in decomposing the rs-fMRI signal into its intrinsic oscillatory frequencies in a reproducible manner.
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Affiliation(s)
- Nicole H Yuen
- Rotman Research Institute at Baycrest, Toronto, ON, Canada
| | | | - J Jean Chen
- Rotman Research Institute at Baycrest, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
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14
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Tong Y, Hocke LM, Frederick BB. Low Frequency Systemic Hemodynamic "Noise" in Resting State BOLD fMRI: Characteristics, Causes, Implications, Mitigation Strategies, and Applications. Front Neurosci 2019; 13:787. [PMID: 31474815 PMCID: PMC6702789 DOI: 10.3389/fnins.2019.00787] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Accepted: 07/15/2019] [Indexed: 01/06/2023] Open
Abstract
Advances in functional magnetic resonance imaging (fMRI) acquisition have improved signal to noise to the point where the physiology of the subject is the dominant noise source in resting state fMRI data (rsfMRI). Among these systemic, non-neuronal physiological signals, respiration and to some degree cardiac fluctuations can be removed through modeling, or in the case of newer, faster acquisitions such as simultaneous multislice acquisition, simple spectral filtering. However, significant low frequency physiological oscillation (∼0.01-0.15 Hz) remains in the signal. This is problematic, as it is the precise frequency band occupied by the neuronally modulated hemodynamic responses used to study brain connectivity, precluding its removal by spectral filtering. The source of this signal, and its method of production and propagation in the body, have not been conclusively determined. Here, we summarize the defining characteristics of the systemic low frequency noise signal, and review some current theories about the signal source and the evidence supporting them. The strength and distribution of the systemic LFO signal make characterizing and removing it essential for accurate quantification, especially for resting state connectivity, when no stimulation can be compared with the signal. Widespread correlated non-neuronal signals obscure and distort the more localized patterns of neuronal correlations between interacting brain regions; they may even cause apparent connectivity between regions with no neuronal interaction. Here, we discuss a simple method we have developed to parse the global, moving, blood-borne signal from the stationary, neuronal connectivity signals, substantially reducing the negative correlations that result from global signal regression. Finally, we will discuss some of the uses to which the moving systemic low frequency oscillation can be put if we consider it a "signal" carrying information, rather than simply "noise" complicating the interpretation of resting state connectivity. Properly utilizing this signal may offer insights into subtle hemodynamic alterations that can be used as early indicators of circulatory dysfunction in a number of neuropsychiatric conditions, such as prodromal stroke, moyamoya, and Alzheimer's disease.
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Affiliation(s)
- Yunjie Tong
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
| | - Lia M. Hocke
- McLean Imaging Center, McLean Hospital, Belmont, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Blaise B. Frederick
- McLean Imaging Center, McLean Hospital, Belmont, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
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15
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Wu Z, Mazzola CA, Catania L, Owoeye O, Yaramothu C, Alvarez T, Gao Y, Li X. Altered cortical activation and connectivity patterns for visual attention processing in young adults post-traumatic brain injury: A functional near infrared spectroscopy study. CNS Neurosci Ther 2018; 24:539-548. [PMID: 29359534 PMCID: PMC6490005 DOI: 10.1111/cns.12811] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Revised: 01/03/2018] [Accepted: 01/04/2018] [Indexed: 11/29/2022] Open
Abstract
AIMS This study aimed at understanding the neurobiological mechanisms associated with inattention induced by traumatic brain injury (TBI). To eliminate the potential confounding caused by the heterogeneity of TBI, we focused on young adults postsports-related concussion (SRC). METHODS Functional near-infrared spectroscopy (fNIRS) data were collected from 27 young adults post-SRC and 27 group-matched normal controls (NCs), while performing a visual sustained attention task. Task responsive cortical activation maps and pairwise functional connectivity among six regions of interest were constructed for each subject. Correlations among the brain imaging measures and clinical measures of attention were calculated in each group. RESULTS Compared to the NCs, the SRC group showed significantly increased brain activation in left middle frontal gyrus (MFG) and increased functional connectivity between right inferior occipital cortex (IOC) bilateral calcarine gyri (CG). The left MFG activation magnitude was significantly negatively correlated with the hyperactive/impulsive symptom severity measure in the NCs, but not in the patients. The right hemisphere CG-IOC functional connectivity showed a significant positive correlation with the hyperactive/impulsive symptom severity measure in patients, but not in NCs. CONCLUSION The current data suggest that abnormal left MFG activation and hyper-communications between right IOC and bilateral CG during visual attention processing may significantly contribute to behavioral manifestations of attention deficits in patients with TBI.
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Affiliation(s)
- Ziyan Wu
- Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewarkNJUSA
| | | | - Lori Catania
- North Jersey Neurodevelopmental CenterNorth HaledonNJUSA
| | - Oyindamola Owoeye
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNJUSA
| | - Chang Yaramothu
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNJUSA
| | - Tara Alvarez
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNJUSA
| | - Yu Gao
- Department of PsychologyBrooklyn College and the Graduate Center of the City University of New YorkBrooklynNYUSA
| | - Xiaobo Li
- Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewarkNJUSA
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNJUSA
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16
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Gravel N, Harvey BM, Renken RJ, Dumoulin SO, Cornelissen FW. Phase-synchronization-based parcellation of resting state fMRI signals reveals topographically organized clusters in early visual cortex. Neuroimage 2017; 170:424-433. [PMID: 28867341 DOI: 10.1016/j.neuroimage.2017.08.063] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 08/02/2017] [Accepted: 08/28/2017] [Indexed: 01/29/2023] Open
Abstract
Resting-state fMRI is widely used to study brain function and connectivity. However, interpreting patterns of resting state (RS) fMRI activity remains challenging as they may arise from different neuronal mechanisms than those triggered by exogenous events. Currently, this limits the use of RS-fMRI for understanding cortical function in health and disease. Here, we examine the phase synchronization (PS) properties of blood-oxygen level dependent (BOLD) signals obtained during visual field mapping (VFM) and RS with 7T fMRI. This data-driven approach exploits spatiotemporal covariations in the phase of BOLD recordings to establish the presence of clusters of synchronized activity. We find that, in both VFM and RS data, selecting the most synchronized neighboring recording sites identifies spatially localized PS clusters that follow the topographic organization of the visual cortex. However, in activity obtained during VFM, PS is spatially more extensive than in RS activity, likely reflecting stimulus-driven interactions between local responses. Nevertheless, the similarity of the PS clusters obtained for RS and stimulus-driven fMRI suggest that they share a common neuroanatomical origin. Our finding justifies and facilitates direct comparison of RS and stimulus-evoked activity.
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Affiliation(s)
- Nicolás Gravel
- Laboratory of Experimental Ophthalmology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
| | - Ben M Harvey
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands; Faculty of Psychology and Education Sciences, University of Coimbra, Coimbra, Portugal
| | - Remco J Renken
- NeuroImaging Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Serge O Dumoulin
- Faculty of Psychology and Education Sciences, University of Coimbra, Coimbra, Portugal
| | - Frans W Cornelissen
- Laboratory of Experimental Ophthalmology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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17
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Kazumata K, Tha KK, Uchino H, Ito M, Nakayama N, Abumiya T. Mapping altered brain connectivity and its clinical associations in adult moyamoya disease: A resting-state functional MRI study. PLoS One 2017; 12:e0182759. [PMID: 28783763 PMCID: PMC5544229 DOI: 10.1371/journal.pone.0182759] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Accepted: 07/24/2017] [Indexed: 12/22/2022] Open
Abstract
Detection of subtle ischemic injuries in moyamoya disease may enable optimization of timing of revascularization surgery, and could potentially improve functional outcomes. Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to study functional organization of the brain, but it remains unclear whether rs-fMRI could elucidate distinct characteristics in moyamoya disease. Here, we aimed to determine changes in a conventional rs-fMRI measure and analyze any associations with clinical symptoms and cerebral hemodynamics. Thirty-one adults with moyamoya disease and 25 adult controls underwent rs-fMRI, in which we measured brain connectivity via temporal correlations of low-frequency BOLD signals. We identified the extent of between-group differences with multivoxel pattern analysis. Seed-based analysis was performed to determine associations with vascular lesions, symptoms, and regional cerebral blood flow (rCBF). There was significantly altered connectivity in the precentral gyrus, operculo-insular region, precuneus, cingulate cortex, and middle frontal gyrus in moyamoya disease. There was reduced connectivity in the left insula, left precuneus, right precentral, and right middle frontal regions, which form part of the salience, default mode, motor, and central executive networks, respectively. Patients with ischemic motor-related symptoms showed significantly decreased connectivity in precentral homotopic regions compared with those without, while there were no differences in vascular lesions or rCBF. Connectivity between the right occipital and left hippocampus was significantly associated with cognitive performance and posterior cerebral artery involvement. Our results demonstrate distinct alterations in the temporal correlations of low-frequency BOLD signals, predominantly in resting-state networks in moyamoya disease. Additionally, rs-fMRI measures were associated with ischemic motor-related symptoms and cognitive performance in the patients. Thus, rs-fMRI may offer a useful non-invasive method of acquiring additional information beyond cerebral perfusion as part of clinical investigations in patients with moyamoya disease.
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Affiliation(s)
- Ken Kazumata
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, Kita, Sapporo, Japan
- * E-mail:
| | - Khin Khin Tha
- Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, Kita, Sapporo, Japan
| | - Haruto Uchino
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, Kita, Sapporo, Japan
| | - Masaki Ito
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, Kita, Sapporo, Japan
| | - Naoki Nakayama
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, Kita, Sapporo, Japan
| | - Takeo Abumiya
- Department of Neurosurgery, Hokkaido University Graduate School of Medicine, Kita, Sapporo, Japan
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18
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Aso T, Jiang G, Urayama SI, Fukuyama H. A Resilient, Non-neuronal Source of the Spatiotemporal Lag Structure Detected by BOLD Signal-Based Blood Flow Tracking. Front Neurosci 2017; 11:256. [PMID: 28553198 PMCID: PMC5425609 DOI: 10.3389/fnins.2017.00256] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Accepted: 04/21/2017] [Indexed: 01/08/2023] Open
Abstract
Recent evidence has suggested that blood oxygenation level-dependent (BOLD) signals convey information about brain circulation via low frequency oscillation of systemic origin (sLFO) that travels through the vascular structure ("lag mapping"). Prompted by its promising application in both physiology and pathology, we examined this signal component using multiple approaches. A total of 30 healthy volunteers were recruited to perform two reproducibility experiments at 3 Tesla using multiband echo planar imaging. The first experiment investigated the effect of denoising and the second was designed to study the effect of subject behavior on lag mapping. The lag map's intersession test-retest reproducibility and image contrast were both diminished by removal of either the neuronal or the non-neuronal (e.g., cardiac, respiratory) components by independent component analysis-based denoising, suggesting that the neurovascular coupling also comprises a part of the BOLD lag structure. The lag maps were, at the same time, robust against local perfusion increases due to visuomotor task and global changes in perfusion induced by breath-holding at the same level as the intrasession reliability. The lag structure was preserved after time-locked averaging to the visuomotor task and breath-holding events, while any preceding signal changes were canceled out for the visuomotor task, consistent with the passive effect of neurovascular coupling in the venous side of the vasculature. These findings support the current assumption that lag mapping primarily reflects vascular structure despite the presence of sLFO perturbation of neuronal or non-neuronal origin and, thus, emphasize the vascular origin of the lag map, encouraging application of BOLD-based blood flow tracking.
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Affiliation(s)
- Toshihiko Aso
- Human Brain Research Center, Kyoto University Graduate School of MedicineKyoto, Japan
| | - Guanhua Jiang
- Human Brain Research Center, Kyoto University Graduate School of MedicineKyoto, Japan
| | - Shin-Ichi Urayama
- Human Brain Research Center, Kyoto University Graduate School of MedicineKyoto, Japan
| | - Hidenao Fukuyama
- Human Brain Research Center, Kyoto University Graduate School of MedicineKyoto, Japan
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19
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Kleckner IR, Zhang J, Touroutoglou A, Chanes L, Xia C, Simmons WK, Quigley KS, Dickerson BC, Barrett LF. Evidence for a Large-Scale Brain System Supporting Allostasis and Interoception in Humans. Nat Hum Behav 2017; 1:0069. [PMID: 28983518 PMCID: PMC5624222 DOI: 10.1038/s41562-017-0069] [Citation(s) in RCA: 302] [Impact Index Per Article: 43.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Accepted: 02/13/2017] [Indexed: 12/23/2022]
Abstract
Large-scale intrinsic brain systems have been identified for exteroceptive senses (e.g., sight, hearing, touch). We introduce an analogous system for representing sensations from within the body, called interoception, and demonstrate its relation to regulating peripheral systems in the body, called allostasis. Employing the recently introduced Embodied Predictive Interoception Coding (EPIC) model, we used tract-tracing studies of macaque monkeys, followed by two intrinsic functional magnetic resonance imaging samples (N = 280 and N = 270) to evaluate the existence of an intrinsic allostatic/interoceptive system in the human brain. Another sample (N = 41) allowed us to evaluate the convergent validity of the hypothesized allostatic/interoceptive system by showing that individuals with stronger connectivity between system hubs performed better on an implicit index of interoceptive ability related to autonomic fluctuations. Implications include insights for the brain's functional architecture, dissolving the artificial boundary between mind and body, and unifying mental and physical illness.
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Affiliation(s)
- Ian R. Kleckner
- Department of Psychology, Northeastern University, Boston, MA
| | - Jiahe Zhang
- Department of Psychology, Northeastern University, Boston, MA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School
- Athinoula A. Martinos Center for Biomedical Imaging
- Psychiatric Neuroimaging Division, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA
| | - Lorena Chanes
- Department of Psychology, Northeastern University, Boston, MA
- Athinoula A. Martinos Center for Biomedical Imaging
- Psychiatric Neuroimaging Division, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA
| | - Chenjie Xia
- Athinoula A. Martinos Center for Biomedical Imaging
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA
| | - W. Kyle Simmons
- Laureate Institute for Brain Research, Tulsa, OK
- School of Community Medicine, The University of Tulsa, Tulsa, OK
| | - Karen S. Quigley
- Department of Psychology, Northeastern University, Boston, MA
- Edith Nourse Rogers Memorial VA Hospital, Bedford, MA
| | - Bradford C. Dickerson
- Athinoula A. Martinos Center for Biomedical Imaging
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA
| | - Lisa Feldman Barrett
- Department of Psychology, Northeastern University, Boston, MA
- Athinoula A. Martinos Center for Biomedical Imaging
- Psychiatric Neuroimaging Division, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA
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20
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Khalil AA, Ostwaldt AC, Nierhaus T, Ganeshan R, Audebert HJ, Villringer K, Villringer A, Fiebach JB. Relationship Between Changes in the Temporal Dynamics of the Blood-Oxygen-Level-Dependent Signal and Hypoperfusion in Acute Ischemic Stroke. Stroke 2017; 48:925-931. [DOI: 10.1161/strokeaha.116.015566] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 12/24/2016] [Accepted: 12/24/2017] [Indexed: 01/22/2023]
Abstract
Background and Purpose—
Changes in the blood-oxygen-level-dependent (BOLD) signal provide a noninvasive measure of blood flow, but a detailed comparison with established perfusion parameters in acute stroke is lacking. We investigated the relationship between BOLD signal temporal delay and dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) in stroke patients.
Methods—
In 30 patients with acute (<24 hours) ischemic stroke, we performed Pearson correlation and multiple linear regression between DSC-MRI parameters (time to maximum [Tmax], mean transit time, cerebral blood flow, and cerebral blood volume) and BOLD-based parameters (BOLD delay and coefficient of BOLD variation). Prediction of severe hypoperfusion (Tmax >6 seconds) was assessed using receiver–operator characteristic (ROC) analysis.
Results—
Correlation was highest between Tmax and BOLD delay (venous sinus reference; time shift range 7; median
r
=0.60; interquartile range=0.49–0.71). Coefficient of BOLD variation correlated with cerebral blood volume (median
r
= 0.37; interquartile range=0.24–0.51). Mean
R
2
for predicting BOLD delay by DSC-MRI was 0.54 (SD=0.2) and for predicting coefficient of BOLD variation was 0.37 (SD=0.17). BOLD delay (whole-brain reference, time shift range 3) had an area under the curve of 0.76 for predicting severe hypoperfusion (sensitivity=69.2%; specificity=80%), whereas BOLD delay (venous sinus reference, time shift range 3) had an area under the curve of 0.76 (sensitivity=67.3%; specificity=83.5%).
Conclusions—
BOLD delay is related to macrovascular delay and microvascular hypoperfusion, can identify severely hypoperfused tissue in acute stroke, and is a promising alternative to gadolinium contrast agent–based perfusion assessment in acute stroke.
Clinical Trial Registration—
URL:
http://www.clinicaltrials.gov
. Unique identifier: NCT00715533 and NCT02077582.
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Affiliation(s)
- Ahmed A. Khalil
- From the Center for Stroke Research Berlin, Charité–Universitätsmedizin Berlin, Germany (A.A.K., A.-C.O., R.G., H.J.A., K.V., J.B.F.); Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (A.A.K., T.N., A.V.); Mind, Brain, Body Institute, Berlin School of Mind and Brain, Humboldt-University, Berlin, Germany (A.A.K., A.V.); and Center for Cognitive Neuroscience Berlin (CCNB), Department of Education and Psychology, Freie Universität Berlin, Germany (T
| | - Ann-Christin Ostwaldt
- From the Center for Stroke Research Berlin, Charité–Universitätsmedizin Berlin, Germany (A.A.K., A.-C.O., R.G., H.J.A., K.V., J.B.F.); Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (A.A.K., T.N., A.V.); Mind, Brain, Body Institute, Berlin School of Mind and Brain, Humboldt-University, Berlin, Germany (A.A.K., A.V.); and Center for Cognitive Neuroscience Berlin (CCNB), Department of Education and Psychology, Freie Universität Berlin, Germany (T
| | - Till Nierhaus
- From the Center for Stroke Research Berlin, Charité–Universitätsmedizin Berlin, Germany (A.A.K., A.-C.O., R.G., H.J.A., K.V., J.B.F.); Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (A.A.K., T.N., A.V.); Mind, Brain, Body Institute, Berlin School of Mind and Brain, Humboldt-University, Berlin, Germany (A.A.K., A.V.); and Center for Cognitive Neuroscience Berlin (CCNB), Department of Education and Psychology, Freie Universität Berlin, Germany (T
| | - Ramanan Ganeshan
- From the Center for Stroke Research Berlin, Charité–Universitätsmedizin Berlin, Germany (A.A.K., A.-C.O., R.G., H.J.A., K.V., J.B.F.); Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (A.A.K., T.N., A.V.); Mind, Brain, Body Institute, Berlin School of Mind and Brain, Humboldt-University, Berlin, Germany (A.A.K., A.V.); and Center for Cognitive Neuroscience Berlin (CCNB), Department of Education and Psychology, Freie Universität Berlin, Germany (T
| | - Heinrich J. Audebert
- From the Center for Stroke Research Berlin, Charité–Universitätsmedizin Berlin, Germany (A.A.K., A.-C.O., R.G., H.J.A., K.V., J.B.F.); Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (A.A.K., T.N., A.V.); Mind, Brain, Body Institute, Berlin School of Mind and Brain, Humboldt-University, Berlin, Germany (A.A.K., A.V.); and Center for Cognitive Neuroscience Berlin (CCNB), Department of Education and Psychology, Freie Universität Berlin, Germany (T
| | - Kersten Villringer
- From the Center for Stroke Research Berlin, Charité–Universitätsmedizin Berlin, Germany (A.A.K., A.-C.O., R.G., H.J.A., K.V., J.B.F.); Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (A.A.K., T.N., A.V.); Mind, Brain, Body Institute, Berlin School of Mind and Brain, Humboldt-University, Berlin, Germany (A.A.K., A.V.); and Center for Cognitive Neuroscience Berlin (CCNB), Department of Education and Psychology, Freie Universität Berlin, Germany (T
| | - Arno Villringer
- From the Center for Stroke Research Berlin, Charité–Universitätsmedizin Berlin, Germany (A.A.K., A.-C.O., R.G., H.J.A., K.V., J.B.F.); Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (A.A.K., T.N., A.V.); Mind, Brain, Body Institute, Berlin School of Mind and Brain, Humboldt-University, Berlin, Germany (A.A.K., A.V.); and Center for Cognitive Neuroscience Berlin (CCNB), Department of Education and Psychology, Freie Universität Berlin, Germany (T
| | - Jochen B. Fiebach
- From the Center for Stroke Research Berlin, Charité–Universitätsmedizin Berlin, Germany (A.A.K., A.-C.O., R.G., H.J.A., K.V., J.B.F.); Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (A.A.K., T.N., A.V.); Mind, Brain, Body Institute, Berlin School of Mind and Brain, Humboldt-University, Berlin, Germany (A.A.K., A.V.); and Center for Cognitive Neuroscience Berlin (CCNB), Department of Education and Psychology, Freie Universität Berlin, Germany (T
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