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Reddy NA, Clements RG, Brooks JCW, Bright MG. Simultaneous cortical, subcortical, and brainstem mapping of sensory activation. bioRxiv 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Hemmerling KJ, Hoggarth MA, Sandhu MS, Parrish TB, Bright MG. MRI mapping of hemodynamics in the human spinal cord. bioRxiv 2024:2024.02.22.581606. [PMID: 38464194 PMCID: PMC10925078 DOI: 10.1101/2024.02.22.581606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Impaired spinal cord vascular function contributes to numerous neurological pathologies, making it important to be able to noninvasively characterize these changes. Here, we propose a functional magnetic resonance imaging (fMRI)-based method to map spinal cord vascular reactivity (SCVR). We used a hypercapnic breath-holding task, monitored with end-tidal CO2 (PETCO2), to evoke a systemic vasodilatory response during concurrent blood oxygenation level-dependent (BOLD) fMRI. SCVR amplitude and hemodynamic delay were mapped at the group level in 27 healthy participants as proof-of-concept of the approach, and then in two highly-sampled participants to probe feasibility/stability of individual SCVR mapping. Across the group and the highly-sampled individuals, a strong ventral SCVR amplitude was initially observed without accounting for local regional variation in the timing of the vasodilatory response. Shifted breathing traces (PETCO2) were used to account for temporal differences in the vasodilatory response across the spinal cord, producing maps of SCVR delay. These delay maps reveal an earlier ventral and later dorsal response and demonstrate distinct gray matter regions concordant with territories of arterial supply. The SCVR fMRI methods described here enable robust mapping of spatiotemporal hemodynamic properties of the human spinal cord. This noninvasive approach has exciting potential to provide early insight into pathology-driven vascular changes in the cord, which may precede and predict future irreversible tissue damage and guide the treatment of several neurological pathologies involving the spine.
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Affiliation(s)
- Kimberly J. Hemmerling
- 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, Northwestern University, Evanston, IL, United States
| | - Mark A. Hoggarth
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Department of Physical Therapy, North Central College, Naperville, IL, United States
| | - Milap S. Sandhu
- Shirley Ryan Ability Lab, Chicago, IL, United States
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Todd B. Parrish
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, 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, Northwestern University, Evanston, IL, United States
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Hemmerling KJ, Hoggarth MA, Sandhu MS, Parrish TB, Bright MG. Spatial distribution of hand-grasp motor task activity in spinal cord functional magnetic resonance imaging. Hum Brain Mapp 2023; 44:5567-5581. [PMID: 37608682 PMCID: PMC10619382 DOI: 10.1002/hbm.26458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/17/2023] [Accepted: 08/05/2023] [Indexed: 08/24/2023] Open
Abstract
Upper extremity motor paradigms during spinal cord functional magnetic resonance imaging (fMRI) can provide insight into the functional organization of the cord. Hand-grasping is an important daily function with clinical significance, but previous studies of similar squeezing movements have not reported consistent areas of activity and are limited by sample size and simplistic analysis methods. Here, we study spinal cord fMRI activation using a unimanual isometric hand-grasping task that is calibrated to participant maximum voluntary contraction (MVC). Two task modeling methods were considered: (1) a task regressor derived from an idealized block design (Ideal) and (2) a task regressor based on the recorded force trace normalized to individual MVC (%MVC). Across these two methods, group motor activity was highly lateralized to the hemicord ipsilateral to the side of the task. Activation spanned C5-C8 and was primarily localized to the C7 spinal cord segment. Specific differences in spatial distribution are also observed, such as an increase in C8 and dorsal cord activity when using the %MVC regressor. Furthermore, we explored the impact of data quantity and spatial smoothing on sensitivity to hand-grasp motor task activation. This analysis shows a large increase in number of active voxels associated with the number of fMRI runs, sample size, and spatial smoothing, demonstrating the impact of experimental design choices on motor activation.
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Affiliation(s)
- Kimberly J. Hemmerling
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of Biomedical Engineering, McCormick School of EngineeringNorthwestern UniversityEvanstonIllinoisUSA
| | - Mark A. Hoggarth
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of Physical TherapyNorth Central CollegeNapervilleIllinoisUSA
| | - Milap S. Sandhu
- Shirley Ryan Ability LabChicagoIllinoisUSA
- Department of Physical Medicine and Rehabilitation, Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
| | - Todd B. Parrish
- Department of Biomedical Engineering, McCormick School of EngineeringNorthwestern UniversityEvanstonIllinoisUSA
- Department of Radiology, Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
| | - Molly G. Bright
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of Biomedical Engineering, McCormick School of EngineeringNorthwestern UniversityEvanstonIllinoisUSA
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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 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Hemmerling KJ, Hoggarth MA, Sandhu MS, Parrish TB, Bright MG. Spatial distribution of hand-grasp motor task activity in spinal cord functional magnetic resonance imaging. bioRxiv 2023:2023.04.25.537883. [PMID: 37503173 PMCID: PMC10370018 DOI: 10.1101/2023.04.25.537883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Upper extremity motor paradigms during spinal cord functional magnetic resonance imaging (fMRI) can provide insight into the functional organization of the cord. Hand-grasping is an important daily function with clinical significance, but previous studies of similar squeezing movements have not reported consistent areas of activity and are limited by sample size and simplistic analysis methods. Here, we study spinal cord fMRI activation using a unimanual isometric hand-grasping task that is calibrated to participant maximum voluntary contraction (MVC). Two task modeling methods were considered: (1) a task regressor derived from an idealized block design (Ideal) and (2) a task regressor based on the recorded force trace normalized to individual MVC (%MVC). Across these two methods, group motor activity was highly lateralized to the hemicord ipsilateral to the side of the task. Activation spanned C5-C8 and was primarily localized to the C7 spinal cord segment. Specific differences in spatial distribution are also observed, such as an increase in C8 and dorsal cord activity when using the %MVC regressor. Furthermore, we explored the impact of data quantity and spatial smoothing on sensitivity to hand-grasp motor task activation. This analysis shows a large increase in number of active voxels associated with the number of fMRI runs, sample size, and spatial smoothing, demonstrating the impact of experimental design choices on motor activation.
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Affiliation(s)
- Kimberly J. Hemmerling
- 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, Northwestern University, Evanston, IL, United States
| | - Mark A. Hoggarth
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Department of Physical Therapy, North Central College, Naperville, IL, United States
| | - Milap S. Sandhu
- Shirley Ryan Ability Lab, Chicago, IL, United States
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Todd B. Parrish
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, 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, Northwestern University, Evanston, IL, United States
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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 DOI: 10.1016/j.neuroimage.2023.120120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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, USA
| | - Rachael C Stickland
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Molly G Bright
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, USA; Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
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Zvolanek KM, Moia S, Dean JN, Stickland RC, Caballero-Gaudes C, Bright MG. Comparing end-tidal CO 2, respiration volume per time (RVT), and average gray matter signal for mapping cerebrovascular reactivity amplitude and delay with breath-hold task BOLD fMRI. Neuroimage 2023; 272:120038. [PMID: 36958618 DOI: 10.1016/j.neuroimage.2023.120038] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/27/2023] [Accepted: 03/14/2023] [Indexed: 03/25/2023] Open
Abstract
Cerebrovascular reactivity (CVR), defined as the cerebral blood flow response to a vasoactive stimulus, is an imaging biomarker with demonstrated utility in a range of diseases and in typical development and aging processes. A robust and widely implemented method to map CVR involves using a breath-hold task during a BOLD fMRI scan. Recording end-tidal CO2 (PETCO2) changes during the breath-hold task is recommended to be used as a reference signal for modeling CVR amplitude in standard units (%BOLD/mmHg) and CVR delay in seconds. However, obtaining reliable PETCO2 recordings requires equipment and task compliance that may not be achievable in all settings. To address this challenge, we investigated two alternative reference signals to map CVR amplitude and delay in a lagged general linear model (lagged-GLM) framework: respiration volume per time (RVT) and average gray matter BOLD response (GM-BOLD). In 8 healthy adults with multiple scan sessions, we compare spatial agreement of CVR maps from RVT and GM-BOLD to those generated with PETCO2. We define a threshold to determine whether a PETCO2 recording has "sufficient" quality for CVR mapping and perform these comparisons in 16 datasets with sufficient PETCO2 and 6 datasets with insufficient PETCO2. When PETCO2 quality is sufficient, both RVT and GM-BOLD produce CVR amplitude maps that are nearly identical to those from PETCO2 (after accounting for differences in scale), with the caveat they are not in standard units to facilitate between-group comparisons. CVR delays are comparable to PETCO2 with an RVT regressor but may be underestimated with the average GM-BOLD regressor. Importantly, when PETCO2 quality is insufficient, RVT and GM-BOLD CVR recover reasonable CVR amplitude and delay maps, provided the participant attempted the breath-hold task. Therefore, our framework offers a solution for achieving high quality CVR maps in both retrospective and prospective studies where sufficient PETCO2 recordings are not available and especially in populations where obtaining reliable measurements is a known challenge (e.g., children). Our results have the potential to improve the accessibility of CVR mapping and to increase the prevalence of this promising metric of vascular health.
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Affiliation(s)
- Kristina M Zvolanek
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, USA.
| | - Stefano Moia
- Basque Center on Cognition, Brain and Language, Donostia, Gipuzkoa, Spain; Medical Imaging Processing Lab (MIP:Lab), Neuro-X institute, EPFL, Geneva, Switzerland
| | - Joshua N Dean
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, USA
| | - Rachael C Stickland
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Molly G Bright
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, USA
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Hoggarth MA, Wang MC, Hemmerling KJ, Vigotsky AD, Smith ZA, Parrish TB, Weber KA, Bright MG. Effects of variability in manually contoured spinal cord masks on fMRI co-registration and interpretation. Front Neurol 2022; 13:907581. [PMID: 36341092 PMCID: PMC9630922 DOI: 10.3389/fneur.2022.907581] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 09/26/2022] [Indexed: 02/01/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) of the human spinal cord (SC) is a unique non-invasive method for characterizing neurovascular responses to stimuli. Group-analysis of SC fMRI data involves co-registration of subject-level data to standard space, which requires manual masking of the cord and may result in bias of group-level SC fMRI results. To test this, we examined variability in SC masks drawn in fMRI data from 21 healthy participants from a completed study mapping responses to sensory stimuli of the C7 dermatome. Masks were drawn on temporal mean functional image by eight raters with varying levels of neuroimaging experience, and the rater from the original study acted as a reference. Spatial agreement between rater and reference masks was measured using the Dice Similarity Coefficient, and the influence of rater and dataset was examined using ANOVA. Each rater's masks were used to register functional data to the PAM50 template. Gray matter-white matter signal contrast of registered functional data was used to evaluate the spatial normalization accuracy across raters. Subject- and group-level analyses of activation during left- and right-sided sensory stimuli were performed for each rater's co-registered data. Agreement with the reference SC mask was associated with both rater (F(7, 140) = 32.12, P < 2 × 10-16, η2 = 0.29) and dataset (F(20, 140) = 20.58, P < 2 × 10-16, η2 = 0.53). Dataset variations may reflect image quality metrics: the ratio between the signal intensity of spinal cord voxels and surrounding cerebrospinal fluid was correlated with DSC results (p < 0.001). As predicted, variability in the manually-drawn masks influenced spatial normalization, and GM:WM contrast in the registered data showed significant effects of rater and dataset (rater: F(8, 160) = 23.57, P < 2 × 10-16, η2 = 0.24; dataset: F(20, 160) = 22.00, P < 2 × 10-16, η2 = 0.56). Registration differences propagated into subject-level activation maps which showed rater-dependent agreement with the reference. Although group-level activation maps differed between raters, no systematic bias was identified. Increasing consistency in manual contouring of spinal cord fMRI data improved co-registration and inter-rater agreement in activation mapping, however our results suggest that improvements in image acquisition and post-processing are also critical to address.
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Affiliation(s)
- Mark A. Hoggarth
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Max C. Wang
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States
| | - Kimberly J. Hemmerling
- 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, Northwestern University, Evanston, IL, United States
| | - Andrew D. Vigotsky
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States
- Department of Statistics, Weinberg College of Arts and Sciences, Northwestern University, Evanston, IL, United States
| | - Zachary A. Smith
- Department of Neurological Surgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Todd B. Parrish
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Kenneth A. Weber
- Systems Neuroscience and Pain Lab, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, 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, Northwestern University, Evanston, IL, United States
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Stickland RC, Zvolanek KM, Moia S, Caballero-Gaudes C, Bright MG. Lag-Optimized Blood Oxygenation Level Dependent Cerebrovascular Reactivity Estimates Derived From Breathing Task Data Have a Stronger Relationship With Baseline Cerebral Blood Flow. Front Neurosci 2022; 16:910025. [PMID: 35801183 PMCID: PMC9254683 DOI: 10.3389/fnins.2022.910025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/11/2022] [Indexed: 11/13/2022] Open
Abstract
Cerebrovascular reactivity (CVR), an important indicator of cerebrovascular health, is commonly studied with the Blood Oxygenation Level Dependent functional MRI (BOLD-fMRI) response to a vasoactive stimulus. Theoretical and empirical evidence suggests that baseline cerebral blood flow (CBF) modulates BOLD signal amplitude and may influence BOLD-CVR estimates. We address how acquisition and modeling choices affect the relationship between baseline cerebral blood flow (bCBF) and BOLD-CVR: whether BOLD-CVR is modeled with the inclusion of a breathing task, and whether BOLD-CVR amplitudes are optimized for hemodynamic lag effects. We assessed between-subject correlations of average GM values and within-subject spatial correlations across cortical regions. Our results suggest that a breathing task addition to a resting-state acquisition, alongside lag-optimization within BOLD-CVR modeling, can improve BOLD-CVR correlations with bCBF, both between- and within-subjects, likely because these CVR estimates are more physiologically accurate. We report positive correlations between bCBF and BOLD-CVR, both between- and within-subjects. The physiological explanation of this positive correlation is unclear; research with larger samples and tightly controlled vasoactive stimuli is needed. Insights into what drives variability in BOLD-CVR measurements and related measurements of cerebrovascular function are particularly relevant when interpreting results in populations with altered vascular and/or metabolic baselines or impaired cerebrovascular reserve.
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Affiliation(s)
- Rachael C. Stickland
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, 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, Northwestern University, Evanston, IL, United States
| | - Stefano Moia
- Basque Center on Cognition, Brain and Language, Donostia, Spain
- University of the Basque Country EHU/UPV, Donostia, Spain
| | | | - 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, Northwestern University, Evanston, IL, United States
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Hemmerling KJ, Bright MG. A visualization tool for assessment of spinal cord functional magnetic resonance imaging data quality. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:3391-3394. [PMID: 34891967 DOI: 10.1109/embc46164.2021.9630903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Functional magnetic resonance imaging (fMRI) is an extensively used neuroimaging technique to non-invasively detect neural activity. Data quality is highly variable, and fMRI analysis typically consists of a number of complex processing steps. It is crucial to visually assess images throughout analysis to ensure that data quality at each step is satisfactory. For fMRI analysis of the brain, there is a simple tool to visualize four-dimensional data on a two-dimensional plot for qualitative analysis. Despite the practicality of this method, it cannot be directly applied to fMRI data of the spinal cord, and a comparable approach does not exist for spinal cord fMRI analysis. The additional challenges encountered in spinal cord imaging, including the small size of the cord and the influence of physiological noise sources, drive the importance of developing a similar visualization technique for spinal cord fMRI. Here, we introduce a highly versatile image analysis tool to visualize spinal cord fMRI data as a simple heatmap and to co-visualize relevant traces such as physiological or motion timeseries. We present multiple variations of the plot, data features that can be identified with the heatmap, and examples of the useful qualitative analyses that can be performed using this method. The spinal cord plot can be easily integrated into an fMRI analysis pipeline and can streamline visual inspection and qualitative analysis of functional imaging data.Clinical Relevance- Implementation of this data visualization method is a simple addition to spinal cord fMRI analysis that could be used to identify normal vs. abnormal signal variation in pathologies that impact the cord, such as spinal cord injury or multiple sclerosis.
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11
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Vu J, Nguyen BT, Bhusal B, Baraboo J, Rosenow J, Bagci U, Bright MG, Golestanirad L. Machine learning-based prediction of MRI-induced power absorption in the tissue in patients with simplified deep brain stimulation lead models. IEEE Trans Electromagn Compat 2021; 63:1757-1766. [PMID: 34898696 PMCID: PMC8654205 DOI: 10.1109/temc.2021.3106872] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Interaction of an active electronic implant such as a deep brain stimulation (DBS) system and MRI RF fields can induce excessive tissue heating, limiting MRI accessibility. Efforts to quantify RF heating mostly rely on electromagnetic (EM) simulations to assess individualized specific absorption rate (SAR), but such simulations require extensive computational resources. Here, we investigate if a predictive model using machine learning (ML) can predict the local SAR in the tissue around tips of implanted leads from the distribution of the tangential component of the MRI incident electric field, Etan. A dataset of 260 unique patient-derived and artificial DBS lead trajectories was constructed, and the 1 g-averaged SAR, 1gSARmax, at the lead-tip during 1.5 T MRI was determined by EM simulations. Etan values along each lead's trajectory and the simulated SAR values were used to train and test the ML algorithm. The resulting predictions of the ML algorithm indicated that the distribution of Etan could effectively predict 1gSARmax at the DBS lead-tip (R = 0.82). Our results indicate that ML has the potential to provide a fast method for predicting MR-induced power absorption in the tissue around tips of implanted leads such as those in active electronic medical devices.
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Affiliation(s)
- Jasmine Vu
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Bach T Nguyen
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Bhumi Bhusal
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Justin Baraboo
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Joshua Rosenow
- Department of Neurosurgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Ulas Bagci
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Molly G Bright
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Laleh Golestanirad
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
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12
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Duffin J, Bright MG, Blockley NP. Editorial: Imaging Cerebrovascular Reactivity: Physiology, Physics and Therapy. Front Physiol 2021; 12:740792. [PMID: 34483975 PMCID: PMC8414884 DOI: 10.3389/fphys.2021.740792] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 07/20/2021] [Indexed: 01/02/2023] Open
Affiliation(s)
- James Duffin
- Department of Anaesthesia and Pain Management, University of Toronto, Toronto, ON, Canada.,Department of Physiology, University of Toronto, Toronto, ON, Canada.,Thornhill Research Inc., Toronto, ON, Canada
| | - 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 and Applied Sciences, Northwestern University, Evanston, IL, United States
| | - Nicholas P Blockley
- School of Life Sciences, University of Nottingham, Nottingham, United Kingdom
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13
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Levitis E, van Praag CDG, Gau R, Heunis S, DuPre E, Kiar G, Bottenhorn KL, Glatard T, Nikolaidis A, Whitaker KJ, Mancini M, Niso G, Afyouni S, Alonso-Ortiz E, Appelhoff S, Arnatkeviciute A, Atay SM, Auer T, Baracchini G, Bayer JMM, Beauvais MJS, Bijsterbosch JD, Bilgin IP, Bollmann S, Bollmann S, Botvinik-Nezer R, Bright MG, Calhoun VD, Chen X, Chopra S, Chuan-Peng H, Close TG, Cookson SL, Craddock RC, De La Vega A, De Leener B, Demeter DV, Di Maio P, Dickie EW, Eickhoff SB, Esteban O, Finc K, Frigo M, Ganesan S, Ganz M, Garner KG, Garza-Villarreal EA, Gonzalez-Escamilla G, Goswami R, Griffiths JD, Grootswagers T, Guay S, Guest O, Handwerker DA, Herholz P, Heuer K, Huijser DC, Iacovella V, Joseph MJE, Karakuzu A, Keator DB, Kobeleva X, Kumar M, Laird AR, Larson-Prior LJ, Lautarescu A, Lazari A, Legarreta JH, Li XY, Lv J, Mansour L S, Meunier D, Moraczewski D, Nandi T, Nastase SA, Nau M, Noble S, Norgaard M, Obungoloch J, Oostenveld R, Orchard ER, Pinho AL, Poldrack RA, Qiu A, Raamana PR, Rokem A, Rutherford S, Sharan M, Shaw TB, Syeda WT, Testerman MM, Toro R, Valk SL, Van Den Bossche S, Varoquaux G, Váša F, Veldsman M, Vohryzek J, Wagner AS, Walsh RJ, White T, Wong FT, Xie X, Yan CG, Yang YF, Yee Y, Zanitti GE, Van Gulick AE, Duff E, Maumet C. Centering inclusivity in the design of online conferences-An OHBM-Open Science perspective. Gigascience 2021; 10:6355274. [PMID: 34414422 DOI: 10.1093/gigascience/giab051] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.
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Affiliation(s)
- Elizabeth Levitis
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD 20892, USA.,Centre for Medical Image Computing, Department of Computer Science, University College London, London, WC1E 6BT, UK
| | - Cassandra D Gould van Praag
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK.,Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
| | - Rémi Gau
- Institute of Psychology, Université Catholique de Louvain, Louvain la Neuve 1348, Belgium
| | - Stephan Heunis
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, 5612 AP, The Netherlands
| | - Elizabeth DuPre
- NeuroDataScience - ORIGAMI laboratory, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Gregory Kiar
- Department of Biomedical Engineering, McGill University, Montreal, QC, H3A 2B4, Canada.,Center for the Developing Brain, The Child Mind Institute, New York City, NY 10022, USA
| | | | - Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada
| | - Aki Nikolaidis
- Center for the Developing Brain, The Child Mind Institute, New York City, NY 10022, USA
| | | | - Matteo Mancini
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, BN1 9RR, UK.,Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, CF24 4HQ, UK.,NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, H3T 1J4, Canada
| | - Guiomar Niso
- Departement of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA.,ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, 28040 Madrid, Spain
| | - Soroosh Afyouni
- Big Data Institute, University of Oxford, Oxford, OX3 7LF, UK.,Department of Psychology, University of Cambridge, CB2 3EB, Cambridge, UK
| | - Eva Alonso-Ortiz
- Department of Electrical Engineering, Polytechnique Montréal, Montréal, QC, H3T 1J4, Canada
| | - Stefan Appelhoff
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin 14195, Germany
| | - Aurina Arnatkeviciute
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, VIC, Clayton 3168, Australia
| | - Selim Melvin Atay
- Neuroscience and Neurotechnology, Middle East Technical University, Ankara 06800, Turkey
| | - Tibor Auer
- School of Psychology, University of Surrey, Guildford GU2 7XH, UK
| | - Giulia Baracchini
- Department of Neurology and Neurosurgery, McGill University, Montréal, QC, H3A 2B4, Canada.,Montréal Neurological Institute, Montréal, QC, H3A 2B4, Canada
| | - Johanna M M Bayer
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, 3010, Parkville, Melbourne, Australia.,Orygen Youth Health, Melbourne, VIC, 3052, Royal Park, Melbourne, Australia
| | | | - Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Isil P Bilgin
- Department of Biomedical Engineering, Cybernetics, The School of Biological Sciences, The University of Reading, Reading, RG6 6AH, UK
| | - Saskia Bollmann
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Steffen Bollmann
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia.,ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Rotem Botvinik-Nezer
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
| | - Molly G Bright
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.,Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL 60208, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA 30303, USA
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China.,Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, 100101, Beijing, China.,International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, Beijing, China
| | - Sidhant Chopra
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, VIC, Clayton 3168, Australia
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing 210024, China
| | - Thomas G Close
- Department of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia.,National Imaging Facility, The University of Sydney, Sydney, NSW 2006, Australia
| | - Savannah L Cookson
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - R Cameron Craddock
- Department of Diagnostic Medicine, The University of Texas at Austin Dell Medical School, Austin, TX 78712, USA
| | - Alejandro De La Vega
- Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Benjamin De Leener
- Department of Computer and Software Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada.,Research Centre, Sainte-Justine University Hospital Center, Montreal, QC, H3T 1C5, Canada
| | - Damion V Demeter
- Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Paola Di Maio
- Center for Systems, Knowledge Representation and Neuroscience, Edinburgh and Taipei, UK and Taiwan.,Institute for Globally Distributed Open Research and Education (IGDORE)
| | - Erin W Dickie
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, M5T 1R8, Canada
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52425, Germany
| | - Oscar Esteban
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne 1003, Switzerland
| | - Karolina Finc
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń 87-100, Poland
| | - Matteo Frigo
- Athena Project Team, Université Côte D'Azur, Inria, 06103 Nice, France
| | - Saampras Ganesan
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, VIC 3010, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Melanie Ganz
- Neurobiology Research Unit, Rigshospitalet, Copenhagen DK-2100, Denmark.,Department of Computer Science, University of Copenhagen, Copenhagen DK-2100, Denmark
| | - Kelly G Garner
- Queensland Brain Institute, University of Queensland, St. Lucia, QLD 4072, Australia.,School of Psychology, University of Birmingham, Birmingham, B15 2TT, UK.,School of Psychology, University of Queensland, St. Lucia, QLD 4072, Australia
| | - Eduardo A Garza-Villarreal
- Laboratorio Nacional de Imagenología por Resonancia Magnética, Instituto de Neurobiología, Universidad Nacional Autónoma de México campus Juriquilla, Querétaro, Qro 76230, Mexico
| | - Gabriel Gonzalez-Escamilla
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Rohit Goswami
- Faculty of Physical Sciences, University of Iceland, 102 Reykjavík, Iceland.,Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - John D Griffiths
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada.,Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
| | - Tijl Grootswagers
- The MARCS Institute for Brain, Behaviour & Development, Western Sydney University, Sydney 2751, NSW, Australia
| | - Samuel Guay
- Department of Psychology, Université de Montréal, Montreal, QC H3C 3J7, Canada
| | - Olivia Guest
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, 6525 EN, Netherlands
| | - Daniel A Handwerker
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892-9663, USA
| | - Peer Herholz
- NeuroDataScience - ORIGAMI laboratory, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Katja Heuer
- Center for Research and Interdisciplinarity (CRI), INSERM U1284, Université de Paris, 75004 Paris, France.,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Dorien C Huijser
- Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, Rotterdam 3062, the Netherlands.,Developmental and Educational Psychology, Leiden University, Leiden 2333, the Netherlands
| | - Vittorio Iacovella
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto 38068, Italy
| | - Michael J E Joseph
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
| | - Agah Karakuzu
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, H3T 1N8, Canada.,Montréal Heart Institute, University of Montréal, Montréal, QC, H1T 1C8, Canada
| | - David B Keator
- Psychiatry and Human Behavior, University of California Irvine, Irvine, CA 92697, USA
| | - Xenia Kobeleva
- Department of Neurology, University Hospital Bonn, 53127 Bonn, Germany.,Clinical Research, German Center for Neurodegenerative Diseases, 53127 Bonn, Germany
| | - Manoj Kumar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL 33199, USA
| | - Linda J Larson-Prior
- Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.,Arkansas Children's Nutrition Center, Little Rock, AR, USA.,Department of Neurology, Pediatrics, Neuroscience & Developmental Sciences, Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Alexandra Lautarescu
- Department of Perinatal Imaging and Health, Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 7EH, UK
| | - Alberto Lazari
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Jon Haitz Legarreta
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
| | - Xue-Ying Li
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing101408, China.,CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.,Sino-Danish Center for Education and Research, Graduate University of Chinese Academy of Sciences, Beijing 101408, China.,CFIN and PET Center, Aarhus University, 8000 Aarhus, Denmark
| | - Jinglei Lv
- School of Biomedical Engineering & Brain and Mind Center, University of Sydney, Sydney, NSW 2006, Australia
| | - Sina Mansour L
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, VIC 3010, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - David Meunier
- Aix Marseille Univ, CNRS, INT, Institut de Neurosciences de la Timone, 13005 Marseille, France
| | | | - Tulika Nandi
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 7LF, UK
| | - Samuel A Nastase
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Matthias Nau
- Section on Learning and Plasticity, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892-9663, USA.,Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Stephanie Noble
- Radiology & Biomedical Imaging, Yale University, New Haven, CT 06519, USA
| | - Martin Norgaard
- Center for Reproducible Neuroscience, Department of Psychology, Stanford University, Stanford, CA 94305Ci, USA.,Neurobiology Research Unit, Department of Neurology, Rigshospitalet, Copenhagen University Hospital, Copenhagen 2100, Denmark
| | - Johnes Obungoloch
- Department of Biomedical Sciences and Engineering, Mbarara University of Science and Technology, Mbarara City, Uganda
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6500 GL, The Netherlands.,NatMEG, Karolinska Institutet, Stockholm 171 77, Sweden
| | - Edwina R Orchard
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, VIC, Clayton 3168, Australia
| | - Ana Luísa Pinho
- Université Paris-Saclay, Inria, CEA, 91120 Palaiseau, France
| | | | - Anqi Qiu
- Department of Biomedical Engineering, The N.1 Institute for Health, Smart Systems Institute, National University of Singapore, Singapore 117583, Singapore.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | | | - Ariel Rokem
- Department of Psychology & eScience Institute, University of Washington, Seattle, WA 98195, USA
| | - Saige Rutherford
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen 6525 EN, The Netherlands.,Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Thomas B Shaw
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Warda T Syeda
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, VIC 3053, Australia
| | | | - Roberto Toro
- Center for Research and Interdisciplinarity (CRI), INSERM U1284, Université de Paris, 75004 Paris, France.,Neuroscience Department, Institut Pasteur, 75015 Paris, France
| | - Sofie L Valk
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52425, Germany.,Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04303, Germany
| | - Sofie Van Den Bossche
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
| | - Gaël Varoquaux
- Université Paris-Saclay, Inria, CEA, 91120 Palaiseau, France.,Montreal Neurological Institute, McGill, Montreal, QC, H3A 2B4, Canada
| | - František Váša
- Department of Neuroimaging, Institute of Psychiatry Psychology & Neuroscience, King's College London SE5 8AF, London, UK
| | - Michele Veldsman
- Department of Experimental Psychology, University of Oxford, Oxfordshire, OX2 6GG, Oxford, UK
| | - Jakub Vohryzek
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK.,Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus 8000, Denmark
| | - Adina S Wagner
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52425, Germany
| | - Reubs J Walsh
- Department of Clinical, Neuro-, and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam,1081BT, The Netherlands.,Center for Applied Transgender Studies , Chicago, USA
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam, 3000CB, The Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus University Medical Centre, Rotterdam 3000CB, The Netherlands
| | - Fu-Te Wong
- Institute of Linguistics, Academia Sinica, Taipei, Taiwan.,Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Xihe Xie
- Department of Neuroscience, Weill Cornell Graduate School, New York City, NY 10065, USA
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, 100101 Beijing, China.,International Big-Data Center for Depression Research, Chinese Academy of Sciences, 100101, Beijing, China
| | - Yu-Fang Yang
- Department of Psychology, University of Würzburg, Würzburg 97074, Germany
| | - Yohan Yee
- Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 1L7, Canada.,Mouse Imaging Centre, The Hospital for Sick Children, Toronto, ON, M5T 3H7, Canada
| | | | - Ana E Van Gulick
- Figshare, Cambridge, MA 02139, USA.,University Libraries, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Eugene Duff
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK.,Department of Paediatrics, University of Oxford, Oxford, OX3 9DU, UK
| | - Camille Maumet
- Inria, Univ Rennes, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U 1228, 35042 Rennes, France
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14
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Stickland RC, Zvolanek KM, Moia S, Ayyagari A, Caballero-Gaudes C, Bright MG. A practical modification to a resting state fMRI protocol for improved characterization of cerebrovascular function. Neuroimage 2021; 239:118306. [PMID: 34175427 PMCID: PMC8552969 DOI: 10.1016/j.neuroimage.2021.118306] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 06/18/2021] [Accepted: 06/23/2021] [Indexed: 12/22/2022] Open
Abstract
Cerebrovascular reactivity (CVR), defined here as the Blood Oxygenation Level Dependent (BOLD) response to a CO2 pressure change, is a useful metric of cerebrovascular function. Both the amplitude and the timing (hemodynamic lag) of the CVR response can bring insight into the nature of a cerebrovascular pathology and aid in understanding noise confounds when using functional Magnetic Resonance Imaging (fMRI) to study neural activity. This research assessed a practical modification to a typical resting-state fMRI protocol, to improve the characterization of cerebrovascular function. In 9 healthy subjects, we modelled CVR and lag in three resting-state data segments, and in data segments which added a 2–3 minute breathing task to the start of a resting-state segment. Two different breathing tasks were used to induce fluctuations in arterial CO2 pressure: a breath-hold task to induce hypercapnia (CO2 increase) and a cued deep breathing task to induce hypocapnia (CO2 decrease). Our analysis produced voxel-wise estimates of the amplitude (CVR) and timing (lag) of the BOLD-fMRI response to CO2 by systematically shifting the CO2 regressor in time to optimize the model fit. This optimization inherently increases gray matter CVR values and fit statistics. The inclusion of a simple breathing task, compared to a resting-state scan only, increases the number of voxels in the brain that have a significant relationship between CO2 and BOLD-fMRI signals, and improves our confidence in the plausibility of voxel-wise CVR and hemodynamic lag estimates. We demonstrate the clinical utility and feasibility of this protocol in an incidental finding of Moyamoya disease, and explore the possibilities and challenges of using this protocol in younger populations. This hybrid protocol has direct applications for CVR mapping in both research and clinical settings and wider applications for fMRI denoising and interpretation.
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Affiliation(s)
- Rachael C Stickland
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
| | - Kristina M Zvolanek
- 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
| | - Stefano Moia
- Basque Center on Cognition, Brain and Language, Donostia, Gipuzkoa, Spain; University of the Basque Country EHU/UPV, Donostia, Gipuzkoa, Spain
| | - Apoorva Ayyagari
- 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
| | | | - 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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15
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Pinto J, Bright MG, Bulte DP, Figueiredo P. Cerebrovascular Reactivity Mapping Without Gas Challenges: A Methodological Guide. Front Physiol 2021; 11:608475. [PMID: 33536935 PMCID: PMC7848198 DOI: 10.3389/fphys.2020.608475] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 12/02/2020] [Indexed: 01/08/2023] Open
Abstract
Cerebrovascular reactivity (CVR) is defined as the ability of vessels to alter their caliber in response to vasoactive factors, by means of dilating or constricting, in order to increase or decrease regional cerebral blood flow (CBF). Importantly, CVR may provide a sensitive biomarker for pathologies where vasculature is compromised. Furthermore, the spatiotemporal dynamics of CVR observed in healthy subjects, reflecting regional differences in cerebral vascular tone and response, may also be important in functional MRI studies based on neurovascular coupling mechanisms. Assessment of CVR is usually based on the use of a vasoactive stimulus combined with a CBF measurement technique. Although transcranial Doppler ultrasound has been frequently used to obtain global flow velocity measurements, MRI techniques are being increasingly employed for obtaining CBF maps. For the vasoactive stimulus, vasodilatory hypercapnia is usually induced through the manipulation of respiratory gases, including the inhalation of increased concentrations of carbon dioxide. However, most of these methods require an additional apparatus and complex setups, which not only may not be well-tolerated by some populations but are also not widely available. For these reasons, strategies based on voluntary breathing fluctuations without the need for external gas challenges have been proposed. These include the task-based methodologies of breath holding and paced deep breathing, as well as a new generation of methods based on spontaneous breathing fluctuations during resting-state. Despite the multitude of alternatives to gas challenges, existing literature lacks definitive conclusions regarding the best practices for the vasoactive modulation and associated analysis protocols. In this work, we perform an extensive review of CVR mapping techniques based on MRI and CO2 variations without gas challenges, focusing on the methodological aspects of the breathing protocols and corresponding data analysis. Finally, we outline a set of practical guidelines based on generally accepted practices and available data, extending previous reports and encouraging the wider application of CVR mapping methodologies in both clinical and academic MRI settings.
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Affiliation(s)
- Joana Pinto
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Molly G. Bright
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States
| | - Daniel P. Bulte
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Patrícia Figueiredo
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
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Moia S, Stickland RC, Ayyagari A, Termenon M, Caballero-Gaudes C, Bright MG. Voxelwise optimization of hemodynamic lags to improve regional CVR estimates in breath-hold fMRI. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020:1489-1492. [PMID: 33018273 DOI: 10.1109/embc44109.2020.9176225] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Cerebrovascular Reactivity (CVR), the responsiveness of blood vessels to a vasodilatory stimulus, is an important indicator of cerebrovascular health. Assessing CVR with fMRI, we can measure the change in the Blood Oxygen Level Dependent (BOLD) response induced by a change in CO2 pressure (%BOLD/mmHg). However, there exists a temporal offset between the recorded CO2 pressure and the local BOLD response, due to both measurement and physiological delays. If this offset is not corrected for, voxel-wise CVR values will not be accurate. In this paper, we propose a framework for mapping hemodynamic lag in breath-hold fMRI data. As breath-hold tasks drive task-correlated head motion artifacts in BOLD fMRI data, our framework for lag estimation fits a model that includes polynomial terms and head motion parameters, as well as a shifted variant of the CO2 regressor (±9 s in 0.3 s increments), and the hemodynamic lag at each voxel is the shift producing the maximum total model R2 within physiological constraints. This approach is evaluated in 8 subjects with multi-echo fMRI data, resulting in robust maps of hemodynamic delay that show consistent regional variation across subjects, and improved contrast-to-noise compared to methods where motion regression is ignored or performed earlier in preprocessing.Clinical Relevance- We map hemodynamic lag using breathhold fMRI, providing insight into vascular transit times and improving the regional accuracy of cerebrovascular reactivity measurements.
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Abstract
We present the first evidence for vascular regulation driving fMRI signals in specific functional brain networks. Using concurrent neuronal and vascular stimuli, we collected 30 BOLD fMRI datasets in 10 healthy individuals: a working memory task, flashing checkerboard stimulus, and CO2 inhalation challenge were delivered in concurrent but orthogonal paradigms. The resulting imaging data were averaged together and decomposed using independent component analysis, and three "neuronal networks" were identified as demonstrating maximum temporal correlation with the neuronal stimulus paradigms: Default Mode Network, Task Positive Network, and Visual Network. For each of these, we observed a second network component with high spatial overlap. Using dual regression in the original 30 datasets, we extracted the time-series associated with these network pairs and calculated the percent of variance explained by the neuronal or vascular stimuli using a normalized R2 parameter. In each pairing, one network was dominated by the appropriate neuronal stimulus, and the other was dominated by the vascular stimulus as represented by the end-tidal CO2 time-series recorded in each scan. We acquired a second dataset in 8 of the original participants, where no CO2 challenge was delivered and CO2 levels fluctuated naturally with breathing variations. Although splitting of functional networks was not robust in these data, performing dual regression with the network maps from the original analysis in this new dataset successfully replicated our observations. Thus, in addition to responding to localized metabolic changes, the brain's vasculature may be regulated in a coordinated manner that mimics (and potentially supports) specific functional brain networks. Multi-modal imaging and advances in fMRI acquisition and analysis could facilitate further study of the dual nature of functional brain networks. It will be critical to understand network-specific vascular function, and the behavior of a coupled vascular-neural network, in future studies of brain pathology.
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Affiliation(s)
- Molly G Bright
- Department of Physical Therapy and Human Movement Science, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA; Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, 60201, USA.
| | - Joseph R Whittaker
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, CF24 3AA, United Kingdom
| | - Ian D Driver
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, CF10 3AT, United Kingdom
| | - Kevin Murphy
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, CF24 3AA, United Kingdom
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Whittaker JR, Driver ID, Venzi M, Bright MG, Murphy K. Corrigendum: Cerebral Autoregulation Evidenced by Synchronized Low Frequency Oscillations in Blood Pressure and Resting-State fMRI. Front Neurosci 2020; 14:544. [PMID: 32670004 PMCID: PMC7327440 DOI: 10.3389/fnins.2020.00544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 05/04/2020] [Indexed: 12/05/2022] Open
Abstract
[This corrects the article DOI: 10.3389/fnins.2019.00433.].
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Affiliation(s)
- Joseph R. Whittaker
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Ian D. Driver
- CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Marcello Venzi
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Molly G. Bright
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Kevin Murphy
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
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Whittaker JR, Driver ID, Venzi M, Bright MG, Murphy K. Cerebral Autoregulation Evidenced by Synchronized Low Frequency Oscillations in Blood Pressure and Resting-State fMRI. Front Neurosci 2019; 13:433. [PMID: 31133780 PMCID: PMC6514145 DOI: 10.3389/fnins.2019.00433] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 04/15/2019] [Indexed: 01/23/2023] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) is a widely used technique for mapping the brain’s functional architecture, so delineating the main sources of variance comprising the signal is crucial. Low frequency oscillations (LFO) that are not of neural origin, but which are driven by mechanisms related to cerebral autoregulation (CA), are present in the blood-oxygenation-level-dependent (BOLD) signal within the rs-fMRI frequency band. In this study we use a MR compatible device (Caretaker, Biopac) to obtain a non-invasive estimate of beat-to-beat mean arterial pressure (MAP) fluctuations concurrently with rs-fMRI at 3T. Healthy adult subjects (n = 9; 5 male) completed two 20-min rs-fMRI scans. MAP fluctuations were decomposed into different frequency scales using a discrete wavelet transform, and oscillations at approximately 0.1 Hz show a high degree of spatially structured correlations with matched frequency fMRI fluctuations. On average across subjects, MAP fluctuations at this scale of the wavelet decomposition explain ∼2.2% of matched frequency fMRI signal variance. Additionally, a simultaneous multi-slice multi-echo acquisition was used to collect 10-min rs-fMRI at three echo times at 7T in a separate group of healthy adults (n = 5; 5 male). Multiple echo times were used to estimate the R2∗ decay at every time point, and MAP was shown to strongly correlate with this signal, which suggests a purely BOLD (i.e., blood flow related) origin. This study demonstrates that there is a significant component of the BOLD signal that has a systemic physiological origin, and highlights the fact that not all localized BOLD signal changes necessarily reflect blood flow supporting local neural activity. Instead, these data show that a proportion of BOLD signal fluctuations in rs-fMRI are due to localized control of blood flow that is independent of local neural activity, most likely reflecting more general systemic autoregulatory processes. Thus, fMRI is a promising tool for studying flow changes associated with cerebral autoregulation with high spatial resolution.
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Affiliation(s)
- Joseph R Whittaker
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Ian D Driver
- CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Marcello Venzi
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Molly G Bright
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Kevin Murphy
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
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Dury RJ, Falah Y, Gowland PA, Evangelou N, Bright MG, Francis ST. Ultra-high-field arterial spin labelling MRI for non-contrast assessment of cortical lesion perfusion in multiple sclerosis. Eur Radiol 2018; 29:2027-2033. [PMID: 30280247 PMCID: PMC6420612 DOI: 10.1007/s00330-018-5707-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Revised: 07/09/2018] [Accepted: 08/06/2018] [Indexed: 11/27/2022]
Abstract
Objectives To assess the feasibility of using an optimised ultra-high-field high-spatial-resolution low-distortion arterial spin labelling (ASL) MRI acquisition to measure focal haemodynamic pathology in cortical lesions (CLs) in multiple sclerosis (MS). Methods Twelve MS patients (eight female, mean age 50 years; range 35–64 years) gave informed consent and were scanned on a 7 Tesla Philips Achieva scanner. Perfusion data were collected at multiple post-labelling delay times using a single-slice flow-sensitive alternating inversion recovery ASL protocol with a balanced steady-state free precession readout scheme. CLs were identified using a high-resolution Phase-Sensitive Inversion Recovery (PSIR) scan. Significant differences in perfusion within CLs compared to immediately surrounding normal appearing grey matter (NAGMlocal) and total cortical normal appearing grey matter (NAGMcortical) were assessed using paired t-tests. Results Forty CLs were identified in PSIR scans that overlapped with the ASL acquisition coverage. After excluding lesions due to small size or intravascular contamination, 27 lesions were eligible for analysis. Mean perfusion was 40 ± 25 ml/100 g/min in CLs, 53 ± 12 ml/100 g/min in NAGMlocal, and 53 ± 8 ml/100 g/min in NAGMcortical. CL perfusion was significantly reduced by 23 ± 9% (mean ± SE, p = 0.013) and 26 ± 9% (p = 0.006) relative to NAGMlocal and NAGMcortical perfusion, respectively. Conclusion This is the first ASL MRI study quantifying CL perfusion in MS at 7 Tesla, demonstrating that an optimised ASL acquisition is sensitive to focal haemodynamic pathology previously observed using dynamic susceptibility contrast MRI. ASL requires no exogenous contrast agent, making it a more appropriate tool to monitor longitudinal perfusion changes in MS, providing a new window to study lesion development. Key Points • Perfusion can be quantified within cortical lesions in multiple sclerosis using an optimised high spatial resolution arterial spin Labelling MRI acquisition at ultra-high-field. • The majority of cortical lesions assessed using arterial spin labelling are hypo-perfused compared to normal appearing grey matter, in agreement with dynamic susceptibility contrast MRI literature. • Arterial spin labelling MRI, which does not involve the injection of a contrast agent, is a safe and appropriate technique for repeat scanning of an individual patient.
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Affiliation(s)
- Richard J Dury
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Yasser Falah
- Clinical Neurology, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Queen's Medical Centre, Nottingham, NG7 2UH, UK
| | - Penny A Gowland
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Nikos Evangelou
- Clinical Neurology, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Queen's Medical Centre, Nottingham, NG7 2UH, UK
| | - Molly G Bright
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK. .,Clinical Neurology, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Queen's Medical Centre, Nottingham, NG7 2UH, UK. .,Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, 645 N. Michigan Avenue, Suite 1100, Chicago, IL, 60611, USA. .,Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, 60208, USA.
| | - Susan T Francis
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
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Bright MG, Croal PL, Blockley NP, Bulte DP. Multiparametric measurement of cerebral physiology using calibrated fMRI. Neuroimage 2017; 187:128-144. [PMID: 29277404 DOI: 10.1016/j.neuroimage.2017.12.049] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 12/14/2017] [Accepted: 12/15/2017] [Indexed: 02/07/2023] Open
Abstract
The ultimate goal of calibrated fMRI is the quantitative imaging of oxygen metabolism (CMRO2), and this has been the focus of numerous methods and approaches. However, one underappreciated aspect of this quest is that in the drive to measure CMRO2, many other physiological parameters of interest are often acquired along the way. This can significantly increase the value of the dataset, providing greater information that is clinically relevant, or detail that can disambiguate the cause of signal variations. This can also be somewhat of a double-edged sword: calibrated fMRI experiments combine multiple parameters into a physiological model that requires multiple steps, thereby providing more opportunity for error propagation and increasing the noise and error of the final derived values. As with all measurements, there is a trade-off between imaging time, spatial resolution, coverage, and accuracy. In this review, we provide a brief overview of the benefits and pitfalls of extracting multiparametric measurements of cerebral physiology through calibrated fMRI experiments.
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Affiliation(s)
- Molly G Bright
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK; Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Paula L Croal
- IBME, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Nicholas P Blockley
- FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Daniel P Bulte
- IBME, Department of Engineering Science, University of Oxford, Oxford, UK; FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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Affiliation(s)
- Molly G Bright
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Division of Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Kevin Murphy
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom; CUBRIC, School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
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Whittaker JR, Bright MG, Driver ID, Babic A, Khot S, Murphy K. Changes in arterial cerebral blood volume during lower body negative pressure measured with MRI. Neuroimage 2017; 187:166-175. [PMID: 28668343 PMCID: PMC6414398 DOI: 10.1016/j.neuroimage.2017.06.041] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 06/19/2017] [Indexed: 01/12/2023] Open
Abstract
Cerebral Autoregulation (CA), defined as the ability of the cerebral vasculature to maintain stable levels of blood flow despite changes in systemic blood pressure, is a critical factor in neurophysiological health. Magnetic resonance imaging (MRI) is a powerful technique for investigating cerebrovascular function, offering high spatial resolution and wide fields of view (FOV), yet it is relatively underutilized as a tool for assessment of CA. The aim of this study was to demonstrate the potential of using MRI to measure changes in cerebrovascular resistance in response to lower body negative pressure (LBNP). A Pulsed Arterial Spin Labeling (PASL) approach with short inversion times (TI) was used to estimate cerebral arterial blood volume (CBVa) in eight healthy subjects at baseline and −40 mmHg LBNP. We estimated group mean CBVa values of 3.13 ± 1.00 and 2.70 ± 0.38 for baseline and lbnp respectively, which were the result of a differential change in CBVa during −40 mmHg LBNP that was dependent on baseline CBVa. These data suggest that the PASL CBVa estimates are sensitive to the complex cerebrovascular response that occurs during the moderate orthostatic challenge delivered by LBNP, which we speculatively propose may involve differential changes in vascular tone within different segments of the arterial vasculature. These novel data provide invaluable insight into the mechanisms that regulate perfusion of the brain, and establishes the use of MRI as a tool for studying CA in more detail.
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Affiliation(s)
- Joseph R Whittaker
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff CF24 4HQ, United Kingdom; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Queen's Buildings, The Parade, Cardiff CF24 3AA, United Kingdom.
| | - Molly G Bright
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham NG7 2RD, United Kingdom; Division of Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham NG7 2UH, United Kingdom
| | - Ian D Driver
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff CF24 4HQ, United Kingdom
| | - Adele Babic
- Department of Anaesthesia and Intensive Care Medicine, Cardiff University School of Medicine, Cardiff CF14 4XN, United Kingdom
| | - Sharmila Khot
- Department of Anaesthesia and Intensive Care Medicine, Cardiff University School of Medicine, Cardiff CF14 4XN, United Kingdom
| | - Kevin Murphy
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff CF24 4HQ, United Kingdom; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Queen's Buildings, The Parade, Cardiff CF24 3AA, United Kingdom
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Bright MG, Tench CR, Murphy K. Potential pitfalls when denoising resting state fMRI data using nuisance regression. Neuroimage 2016; 154:159-168. [PMID: 28025128 PMCID: PMC5489212 DOI: 10.1016/j.neuroimage.2016.12.027] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 12/07/2016] [Accepted: 12/10/2016] [Indexed: 12/15/2022] Open
Abstract
In resting state fMRI, it is necessary to remove signal variance associated with noise sources, leaving cleaned fMRI time-series that more accurately reflect the underlying intrinsic brain fluctuations of interest. This is commonly achieved through nuisance regression, in which the fit is calculated of a noise model of head motion and physiological processes to the fMRI data in a General Linear Model, and the "cleaned" residuals of this fit are used in further analysis. We examine the statistical assumptions and requirements of the General Linear Model, and whether these are met during nuisance regression of resting state fMRI data. Using toy examples and real data we show how pre-whitening, temporal filtering and temporal shifting of regressors impact model fit. Based on our own observations, existing literature, and statistical theory, we make the following recommendations when employing nuisance regression: pre-whitening should be applied to achieve valid statistical inference of the noise model fit parameters; temporal filtering should be incorporated into the noise model to best account for changes in degrees of freedom; temporal shifting of regressors, although merited, should be achieved via optimisation and validation of a single temporal shift. We encourage all readers to make simple, practical changes to their fMRI denoising pipeline, and to regularly assess the appropriateness of the noise model used. By negotiating the potential pitfalls described in this paper, and by clearly reporting the details of nuisance regression in future manuscripts, we hope that the field will achieve more accurate and precise noise models for cleaning the resting state fMRI time-series.
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Affiliation(s)
- Molly G Bright
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Division of Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom.
| | - Christopher R Tench
- Division of Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Kevin Murphy
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom; CUBRIC, School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
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Tewarie P, Bright MG, Hillebrand A, Robson SE, Gascoyne LE, Morris PG, Meier J, Van Mieghem P, Brookes MJ. Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions. Neuroimage 2016; 130:273-292. [PMID: 26827811 PMCID: PMC4819720 DOI: 10.1016/j.neuroimage.2016.01.053] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Revised: 12/23/2015] [Accepted: 01/24/2016] [Indexed: 11/21/2022] Open
Abstract
Understanding the electrophysiological basis of resting state networks (RSNs) in the human brain is a critical step towards elucidating how inter-areal connectivity supports healthy brain function. In recent years, the relationship between RSNs (typically measured using haemodynamic signals) and electrophysiology has been explored using functional Magnetic Resonance Imaging (fMRI) and magnetoencephalography (MEG). Significant progress has been made, with similar spatial structure observable in both modalities. However, there is a pressing need to understand this relationship beyond simple visual similarity of RSN patterns. Here, we introduce a mathematical model to predict fMRI-based RSNs using MEG. Our unique model, based upon a multivariate Taylor series, incorporates both phase and amplitude based MEG connectivity metrics, as well as linear and non-linear interactions within and between neural oscillations measured in multiple frequency bands. We show that including non-linear interactions, multiple frequency bands and cross-frequency terms significantly improves fMRI network prediction. This shows that fMRI connectivity is not only the result of direct electrophysiological connections, but is also driven by the overlap of connectivity profiles between separate regions. Our results indicate that a complete understanding of the electrophysiological basis of RSNs goes beyond simple frequency-specific analysis, and further exploration of non-linear and cross-frequency interactions will shed new light on distributed network connectivity, and its perturbation in pathology. We introduce a mathematical model to predict fMRI-based RSNs using MEG. Our model is based on a multi-variate Taylor series expansion. The electrophysiological basis of RSNs goes beyond frequency-band specific analysis. RSNs result 1) from multiple frequency bands and cross-frequency coupling. RSNs result 2) from direct and shared electrophysiological connectivity.
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Affiliation(s)
- P Tewarie
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK.
| | - M G Bright
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - A Hillebrand
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Centre, Amsterdam, The Netherlands
| | - S E Robson
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - L E Gascoyne
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - P G Morris
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - J Meier
- Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft, The Netherlands
| | - P Van Mieghem
- Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft, The Netherlands
| | - M J Brookes
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
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Whittaker JR, Driver ID, Bright MG, Murphy K. The absolute CBF response to activation is preserved during elevated perfusion: Implications for neurovascular coupling measures. Neuroimage 2016; 125:198-207. [PMID: 26477657 PMCID: PMC4692513 DOI: 10.1016/j.neuroimage.2015.10.023] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 10/06/2015] [Accepted: 10/08/2015] [Indexed: 12/31/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) techniques in which the blood oxygenation level dependent (BOLD) and cerebral blood flow (CBF) response to a neural stimulus are measured, can be used to estimate the fractional increase in the cerebral metabolic rate of oxygen consumption (CMRO2) that accompanies evoked neural activity. A measure of neurovascular coupling is obtained from the ratio of fractional CBF and CMRO2 responses, defined as n, with the implicit assumption that relative rather than absolute changes in CBF and CMRO2 adequately characterise the flow-metabolism response to neural activity. The coupling parameter n is important in terms of its effect on the BOLD response, and as potential insight into the flow-metabolism relationship in both normal and pathological brain function. In 10 healthy human subjects, BOLD and CBF responses were measured to test the effect of baseline perfusion (modulated by a hypercapnia challenge) on the coupling parameter n during graded visual stimulation. A dual-echo pulsed arterial spin labelling (PASL) sequence provided absolute quantification of CBF in baseline and active states as well as relative BOLD signal changes, which were used to estimate CMRO2 responses to the graded visual stimulus. The absolute CBF response to the visual stimuli were constant across different baseline CBF levels, meaning the fractional CBF responses were reduced at the hyperperfused baseline state. For the graded visual stimuli, values of n were significantly reduced during hypercapnia induced hyperperfusion. Assuming the evoked neural responses to the visual stimuli are the same for both baseline CBF states, this result has implications for fMRI studies that aim to measure neurovascular coupling using relative changes in CBF. The coupling parameter n is sensitive to baseline CBF, which would confound its interpretation in fMRI studies where there may be significant differences in baseline perfusion between groups. The absolute change in CBF, as opposed to the change relative to baseline, may more closely match the underlying increase in neural activity in response to a stimulus.
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Affiliation(s)
- Joseph R Whittaker
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, CF10 3AT Cardiff, UK
| | - Ian D Driver
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, CF10 3AT Cardiff, UK
| | - Molly G Bright
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, CF10 3AT Cardiff, UK; Sir Peter Mansfield Imaging Centre, Clinical Neurology, School of Medicine, University of Nottingham, Nottingham, UK
| | - Kevin Murphy
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, CF10 3AT Cardiff, UK.
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28
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Bright MG, Murphy K. Is fMRI "noise" really noise? Resting state nuisance regressors remove variance with network structure. Neuroimage 2015; 114:158-69. [PMID: 25862264 PMCID: PMC4461310 DOI: 10.1016/j.neuroimage.2015.03.070] [Citation(s) in RCA: 112] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2014] [Revised: 03/10/2015] [Accepted: 03/27/2015] [Indexed: 12/01/2022] Open
Abstract
Noise correction is a critical step towards accurate mapping of resting state BOLD fMRI connectivity. Noise sources related to head motion or physiology are typically modelled by nuisance regressors, and a generalised linear model is applied to regress out the associated signal variance. In this study, we use independent component analysis (ICA) to characterise the data variance typically discarded in this pre-processing stage in a cohort of 12 healthy volunteers. The signal variance removed by 24, 12, 6, or only 3 head motion parameters demonstrated network structure typically associated with functional connectivity, and certain networks were discernable in the variance extracted by as few as 2 physiologic regressors. Simulated nuisance regressors, unrelated to the true data noise, also removed variance with network structure, indicating that any group of regressors that randomly sample variance may remove highly structured “signal” as well as “noise.” Furthermore, to support this we demonstrate that random sampling of the original data variance continues to exhibit robust network structure, even when as few as 10% of the original volumes are considered. Finally, we examine the diminishing returns of increasing the number of nuisance regressors used in pre-processing, showing that excessive use of motion regressors may do little better than chance in removing variance within a functional network. It remains an open challenge to understand the balance between the benefits and confounds of noise correction using nuisance regressors. Data variance removed by nuisance regressors contains network structure. Simulated regressors unrelated to noise also extract data with network structure. Random sampling of original data (as few as 10% of volumes) reveals robust networks. After optimal number, motion regressors remove similar variance as simulated ones. Excessive nuisance regressors extract random signal variance with network structure.
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Affiliation(s)
- Molly G Bright
- Division of Clinical Neurology, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Sir Peter Mansfield Imaging Centre, School of Physics, University of Nottingham, Nottingham, United Kingdom; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
| | - Kevin Murphy
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
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29
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Bright MG, Bianciardi M, de Zwart JA, Murphy K, Duyn JH. Early anti-correlated BOLD signal changes of physiologic origin. Neuroimage 2013; 87:287-96. [PMID: 24211818 DOI: 10.1016/j.neuroimage.2013.10.055] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2013] [Revised: 10/18/2013] [Accepted: 10/24/2013] [Indexed: 01/02/2023] Open
Abstract
Negative BOLD signals that are synchronous with resting state fluctuations have been observed in large vessels in the cortical sulci and surrounding the ventricles. In this study, we investigated the origin of these negative BOLD signals by applying a Cued Deep Breathing (CDB) task to create transient hypocapnia and a resultant global fMRI signal decrease. We hypothesized that a global stimulus would amplify the effect in large vessels and that using a global negative (vasoconstrictive) stimulus would test whether these voxels exhibit either inherently negative or simply anti-correlated BOLD responses. Significantly anti-correlated, but positive, BOLD signal changes during respiratory challenges were identified in voxels primarily located near edges of brain spaces containing CSF. These positive BOLD responses occurred earlier than the negative CDB response across most of gray matter voxels. These findings confirm earlier suggestions that in some brain regions, local, fractional changes in CSF volume may overwhelm BOLD-related signal changes, leading to signal anti-correlation. We show that regions with CDB anti-correlated signals coincide with most, but not all, of the regions with negative BOLD signal changes observed during a visual and motor stimulus task. Thus, the addition of a physiological challenge to fMRI experiments can help identify which negative BOLD signals are passive physiological anti-correlations and which may have a putative neuronal origin.
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Affiliation(s)
- Molly G Bright
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD, USA; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.
| | - Marta Bianciardi
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD, USA; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jacco A de Zwart
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD, USA
| | - Kevin Murphy
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Jeff H Duyn
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD, USA
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30
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Bright MG, Murphy K. Reliable quantification of BOLD fMRI cerebrovascular reactivity despite poor breath-hold performance. Neuroimage 2013; 83:559-68. [PMID: 23845426 PMCID: PMC3899001 DOI: 10.1016/j.neuroimage.2013.07.007] [Citation(s) in RCA: 117] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2013] [Revised: 06/11/2013] [Accepted: 07/02/2013] [Indexed: 11/28/2022] Open
Abstract
Cerebrovascular reactivity (CVR) can be mapped using BOLD fMRI to provide a clinical insight into vascular health that can be used to diagnose cerebrovascular disease. Breath-holds are a readily accessible method for producing the required arterial CO2 increases but their implementation into clinical studies is limited by concerns that patients will demonstrate highly variable performance of breath-hold challenges. This study assesses the repeatability of CVR measurements despite poor task performance, to determine if and how robust results could be achieved with breath-holds in patients. Twelve healthy volunteers were scanned at 3 T. Six functional scans were acquired, each consisting of 6 breath-hold challenges (10, 15, or 20 s duration) interleaved with periods of paced breathing. These scans simulated the varying breath-hold consistency and ability levels that may occur in patient data. Uniform ramps, time-scaled ramps, and end-tidal CO2 data were used as regressors in a general linear model in order to measure CVR at the grey matter, regional, and voxelwise level. The intraclass correlation coefficient (ICC) quantified the repeatability of the CVR measurement for each breath-hold regressor type and scale of interest across the variable task performances. The ramp regressors did not fully account for variability in breath-hold performance and did not achieve acceptable repeatability (ICC<0.4) in several regions analysed. In contrast, the end-tidal CO2 regressors resulted in "excellent" repeatability (ICC=0.82) in the average grey matter data, and resulted in acceptable repeatability in all smaller regions tested (ICC>0.4). Further analysis of intra-subject CVR variability across the brain (ICCspatial and voxelwise correlation) supported the use of end-tidal CO2 data to extract robust whole-brain CVR maps, despite variability in breath-hold performance. We conclude that the incorporation of end-tidal CO2 monitoring into scanning enables robust, repeatable measurement of CVR that makes breath-hold challenges suitable for routine clinical practice.
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Affiliation(s)
- Molly G Bright
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, CF10 3AT Cardiff, UK.
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31
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Bulte DP, Kelly M, Germuska M, Xie J, Chappell MA, Okell TW, Bright MG, Jezzard P. Quantitative measurement of cerebral physiology using respiratory-calibrated MRI. Neuroimage 2011; 60:582-91. [PMID: 22209811 DOI: 10.1016/j.neuroimage.2011.12.017] [Citation(s) in RCA: 162] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2011] [Revised: 12/08/2011] [Accepted: 12/11/2011] [Indexed: 11/26/2022] Open
Abstract
Functional magnetic resonance imaging typically measures signal increases arising from changes in the transverse relaxation rate over small regions of the brain and associates these with local changes in cerebral blood flow, blood volume and oxygen metabolism. Recent developments in pulse sequences and image analysis methods have improved the specificity of the measurements by focussing on changes in blood flow or changes in blood volume alone. However, FMRI is still unable to match the physiological information obtainable from positron emission tomography (PET), which is capable of quantitative measurements of blood flow and volume, and can indirectly measure resting metabolism. The disadvantages of PET are its cost, its availability, its poor spatial resolution and its use of ionising radiation. The MRI techniques introduced here address some of these limitations and provide physiological data comparable with PET measurements. We present an 18-minute MRI protocol that produces multi-slice whole-brain coverage and yields quantitative images of resting cerebral blood flow, cerebral blood volume, oxygen extraction fraction, CMRO(2), arterial arrival time and cerebrovascular reactivity of the human brain in the absence of any specific functional task. The technique uses a combined hyperoxia and hypercapnia paradigm with a modified arterial spin labelling sequence.
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Affiliation(s)
- D P Bulte
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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Bright MG, Bulte DP, Jezzard P, Duyn JH. Characterization of regional heterogeneity in cerebrovascular reactivity dynamics using novel hypocapnia task and BOLD fMRI. Neuroimage 2009; 48:166-75. [PMID: 19450694 DOI: 10.1016/j.neuroimage.2009.05.026] [Citation(s) in RCA: 90] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2009] [Revised: 04/03/2009] [Accepted: 05/07/2009] [Indexed: 11/16/2022] Open
Abstract
We offer a new method for characterizing the magnitude and dynamics of the vascular response to changes in arterial gas tensions using non-invasive blood oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI) and paradigms appropriate for clinical settings. A novel respiratory task, "Cued Deep Breathing" (CDB), consisting of two consecutive cycles of cued breaths, has been developed to cause transient hypocapnia, and consequently a strong, short-lived BOLD signal decrease. Data from CDB hypocapnia paradigms and traditional breath-holding hypercapnia paradigms were analyzed on a voxel-wise basis to map regional heterogeneity in magnitude and timing parameters. The tasks caused comparable absolute BOLD percent signal changes (approximately 0.5-3.0% in gray matter) and both datasets suggested consistent regional heterogeneity in the response timing: parts of the basal ganglia, particularly the putamen, and bilateral areas of medial cortex reached their maximum signal change several seconds earlier than remaining cortical gray matter voxels. This phenomenon and a slightly delayed response in posterior cortical regions were present in group-maps of ten healthy subjects. An auxiliary experiment in different subjects measured end-tidal CO2 changes associated with the new CDB task and quantitatively compared the resulting reactivity maps with those acquired using a traditional hypercapnia challenge of 4% CO2 gas inspiration. The CDB task caused average end-tidal CO2 decreases between 6.0+/-1.1 and 10.5+/-2.6 mm Hg, with levels returning to baseline after approximately three breaths, giving evidence that the task indeed causes transient mild hypocapnia. Similarity between resulting reactivity maps suggest CDB offers an alternative method for mapping cerebrovascular reactivity.
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Affiliation(s)
- Molly G Bright
- Laboratory for Advanced MRI, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
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