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Ritz T, Kroll JL, Khan DA, Yezhuvath US, Aslan S, Pinkham A, Rosenfield D, Brown ES. fMRI BOLD responses to film stimuli and their association with exhaled nitric oxide in asthma and health. Psychophysiology 2024; 61:e14513. [PMID: 38339852 DOI: 10.1111/psyp.14513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 09/02/2023] [Accepted: 10/03/2023] [Indexed: 02/12/2024]
Abstract
Little is known about central nervous system (CNS) responses to emotional stimuli in asthma. Nitric oxide in exhaled breath (FENO) is elevated in asthma due to allergic immune processes, but endogenous nitric oxide is also known to modulate CNS activity. We measured fMRI blood oxygen-dependent (BOLD) brain activation to negative (blood-injection-injury themes) and neutral films in 31 participants (15 with asthma). Regions-of-interest analysis was performed on key areas relevant to central adaptive control, threat processing, or salience networks, with dorsolateral prefrontal cortex (PFC), anterior insula, dorsal anterior cingulate cortex (dACC), amygdala, ventral striatum, ventral tegmentum, and periaqueductal gray, as well as top-down modulation of emotion, with ventrolateral and ventromedial PFC. Both groups showed less BOLD deactivation from fixation cross-baseline in the left anterior insula and bilateral ventromedial PFC for negative than neutral films, and for an additional number of areas, including the fusiform gyrus, for film versus recovery phases. Less deactivation during films followed by less recovery from deactivation was found in asthma compared to healthy controls. Changes in PCO2 did not explain these findings. FENO was positively related to BOLD activation in general, but more pronounced in healthy controls and more likely in neutral film processing. Thus, asthma is associated with altered processing of film stimuli across brain regions not limited to central adaptive control, threat processing, or salience networks. Higher levels of NO appear to facilitate CNS activity, but only in healthy controls, possibly due to allergy's masking effects on FENO.
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Affiliation(s)
- Thomas Ritz
- Department of Psychology, Southern Methodist University, Dallas, Texas, USA
| | - Juliet L Kroll
- Department of Psychology, Southern Methodist University, Dallas, Texas, USA
- Department of Palliative, Rehabilitation and Integrative Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - David A Khan
- Department of Internal Medicine, Division of Allergy and Immunology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | | | - Sina Aslan
- Department of Internal Medicine, Division of Allergy and Immunology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Advance MRI LLC, Frisco, Texas, USA
- Department of Psychology, University of Texas at Dallas, Dallas, Texas, USA
| | - Amy Pinkham
- Department of Psychology, University of Texas at Dallas, Dallas, Texas, USA
| | - David Rosenfield
- Department of Psychology, Southern Methodist University, Dallas, Texas, USA
| | - E Sherwood Brown
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, Texas, USA
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Xu B, Vu C, Borzage M, González-Zacarías C, Shen J, Wood J. Improved cerebrovascular reactivity mapping using coherence weighted general linear model in the frequency domain. Neuroimage 2023; 284:120448. [PMID: 37952392 PMCID: PMC10822713 DOI: 10.1016/j.neuroimage.2023.120448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 10/25/2023] [Accepted: 11/06/2023] [Indexed: 11/14/2023] Open
Abstract
Cerebrovascular reactivity (CVR) is a prognostic indicator of cerebrovascular health. Estimating CVR from endogenous end-tidal carbon dioxide (CO2) fluctuation and MRI signal recorded under resting state can be difficult due to the poor signal-to-noise ratio (SNR) of signals. Thus, we aimed to improve the method of estimating CVR from end-tidal CO2 and MRI signals. We proposed a coherence weighted general linear model (CW-GLM) to estimate CVR from the Fourier coefficients weighted by the signal coherence in frequency domain, which confers two advantages. First, it requires no signal alignment in time domain, which simplifies experimental methods. Second, it limits the GLM analysis within the frequency band where CO2 and MRI signals are highly correlated, which automatically suppresses noise and nuisance signals. We compared the performance of our method with time-domain GLM (TD-GLM) and frequency-domain GLM (FD-GLM) in both synthetic and in-vivo data; wherein we calculated CVR from signals recorded under both resting state and sinusoidal stimulus. In synthetic data, CW-GLM has a remarkable performance on CVR estimation from narrow band signals with a mean-absolute error of 0.7 % (gray matter) and 1.2 % (white matter), which was lower than all the other methods. Meanwhile, CW-GLM maintains a comparable performance on CVR estimation from resting signals, with a mean-absolute error of 4.1 % (gray matter) and 8 % (white matter). The superior performance was maintained across the 36 in-vivo measurements, with CW-GLM exhibiting limits of agreement of -16.7 % - 9.5 % between CVR calculated from the resting and sinusoidal CO2 paradigms which was 12 % - 209 % better than current time-domain methods. Evaluating of the cross-coherence spectrum revealed highest signal coherence within the frequency band from 0.01 Hz to 0.05 Hz, which overlaps with previously recommended frequency band (0.02 Hz to 0.04 Hz) for CVR analysis. Our data demonstrates that CW-GLM can work as a self-adaptive band-pass filter to improve CVR robustness, while also avoiding the need for signal temporal alignment.
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Affiliation(s)
- Botian Xu
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States; Department of Pediatrics and Radiology, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - Chau Vu
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States; Department of Pediatrics and Radiology, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - Matthew Borzage
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States; Division of Neonatology, Department of Pediatrics, Fetal and Neonatal Institute, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - Clio González-Zacarías
- Department of Pediatrics and Radiology, Children's Hospital Los Angeles, Los Angeles, CA, United States; Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States
| | - Jian Shen
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States; Department of Pediatrics and Radiology, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - John Wood
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States; Department of Pediatrics and Radiology, Children's Hospital Los Angeles, Los Angeles, CA, United States.
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Marchena-Romero KJ, Ji X, Sommer R, Centen A, Ramirez J, Poulin JM, Mikulis D, Thrippleton M, Wardlaw J, Lim A, Black SE, MacIntosh BJ. Examining temporal features of BOLD-based cerebrovascular reactivity in clinical populations. Front Neurol 2023; 14:1199805. [PMID: 37396759 PMCID: PMC10310960 DOI: 10.3389/fneur.2023.1199805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 05/25/2023] [Indexed: 07/04/2023] Open
Abstract
Background Conventional cerebrovascular reactivity (CVR) estimation has demonstrated that many brain diseases and/or conditions are associated with altered CVR. Despite the clinical potential of CVR, characterization of temporal features of a CVR challenge remains uncommon. This work is motivated by the need to develop CVR parameters that characterize individual temporal features of a CVR challenge. Methods Data were collected from 54 adults and recruited based on these criteria: (1) Alzheimer's disease diagnosis or subcortical Vascular Cognitive Impairment, (2) sleep apnea, and (3) subjective cognitive impairment concerns. We investigated signal changes in blood oxygenation level dependent (BOLD) contrast images with respect to hypercapnic and normocapnic CVR transition periods during a gas manipulation paradigm. We developed a model-free, non-parametric CVR metric after considering a range of responses through simulations to characterize BOLD signal changes that occur when transitioning from normocapnia to hypercapnia. The non-parametric CVR measure was used to examine regional differences across the insula, hippocampus, thalamus, and centrum semiovale. We also examined the BOLD signal transition from hypercapnia back to normocapnia. Results We found a linear association between isolated temporal features of successive CO2 challenges. Our study concluded that the transition rate from hypercapnia to normocapnia was significantly associated with the second CVR response across all regions of interest (p < 0.001), and this association was highest in the hippocampus (R2 = 0.57, p < 0.0125). Conclusion This study demonstrates that it is feasible to examine individual responses associated with normocapnic and hypercapnic transition periods of a BOLD-based CVR experiment. Studying these features can provide insight on between-subject differences in CVR.
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Affiliation(s)
- Kayley-Jasmin Marchena-Romero
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Xiang Ji
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Toronto, ON, Canada
| | - Rosa Sommer
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Andrew Centen
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Joel Ramirez
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Toronto, ON, Canada
| | - Joshua M. Poulin
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Toronto, ON, Canada
| | - David Mikulis
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Division of Neuroradiology, Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Michael Thrippleton
- Brain Research Imaging Centre, Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre, The University of Edinburgh, Edinburgh, United Kingdom
| | - Joanna Wardlaw
- Brain Research Imaging Centre, Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre, The University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew Lim
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Sandra E. Black
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Toronto, ON, Canada
| | - Bradley J. MacIntosh
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Toronto, ON, Canada
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Cowdrick KR, Urner T, Sathialingam E, Fang Z, Quadri A, Turrentine K, Yup Lee S, Buckley EM. Agreement in cerebrovascular reactivity assessed with diffuse correlation spectroscopy across experimental paradigms improves with short separation regression. NEUROPHOTONICS 2023; 10:025002. [PMID: 37034012 PMCID: PMC10079775 DOI: 10.1117/1.nph.10.2.025002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 03/07/2023] [Indexed: 06/19/2023]
Abstract
Significance Cerebrovascular reactivity (CVR), i.e., the ability of cerebral vasculature to dilate or constrict in response to vasoactive stimuli, is a biomarker of vascular health. Exogenous administration of inhaled carbon dioxide, i.e., hypercapnia (HC), remains the "gold-standard" intervention to assess CVR. More tolerable paradigms that enable CVR quantification when HC is difficult/contraindicated have been proposed. However, because these paradigms feature mechanistic differences in action, an assessment of agreement of these more tolerable paradigms to HC is needed. Aim We aim to determine the agreement of CVR assessed during HC, breath-hold (BH), and resting state (RS) paradigms. Approach Healthy adults were subject to HC, BH, and RS paradigms. End tidal carbon dioxide (EtCO2) and cerebral blood flow (CBF, assessed with diffuse correlation spectroscopy) were monitored continuously. CVR (%/mmHg) was quantified via linear regression of CBF versus EtCO2 or via a general linear model (GLM) that was used to minimize the influence of systemic and extracerebral signal contributions. Results Strong agreement ( CCC ≥ 0.69 ; R ≥ 0.76 ) among CVR paradigms was demonstrated when utilizing a GLM to regress out systemic/extracerebral signal contributions. Linear regression alone showed poor agreement across paradigms ( CCC ≤ 0.35 ; R ≤ 0.45 ). Conclusions More tolerable experimental paradigms coupled with regression of systemic/extracerebral signal contributions may offer a viable alternative to HC for assessing CVR.
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Affiliation(s)
- Kyle R. Cowdrick
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Tara Urner
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Eashani Sathialingam
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Zhou Fang
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Ayesha Quadri
- Children’s Healthcare of Atlanta and Emory University School of Medicine, Department of Pediatrics, Atlanta, Georgia, United States
- Morehouse School of Medicine, Atlanta, Georgia, United States
| | - Katherine Turrentine
- Children’s Healthcare of Atlanta and Emory University School of Medicine, Department of Pediatrics, Atlanta, Georgia, United States
| | - Seung Yup Lee
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Kennesaw State University, Department of Electrical and Computer Engineering, Marietta, Georgia, United States
| | - Erin M. Buckley
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Children’s Healthcare of Atlanta and Emory University School of Medicine, Department of Pediatrics, Atlanta, Georgia, United States
- Children’s Healthcare of Atlanta, Children’s Research Scholar, Atlanta, Georgia, United States
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Fitzgerald B, Yao JF, Hocke LM, Frederick BD, van Niftrik CHB, Tong Y. Using carpet plots to analyze blood transit times in the brain during hypercapnic challenge magnetic resonance imaging. Front Physiol 2023; 14:1134804. [PMID: 36875021 PMCID: PMC9975721 DOI: 10.3389/fphys.2023.1134804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 02/01/2023] [Indexed: 02/17/2023] Open
Abstract
Blood arrival time and blood transit time are useful metrics in characterizing hemodynamic behaviors in the brain. Functional magnetic resonance imaging in combination with a hypercapnic challenge has been proposed as a non-invasive imaging tool to determine blood arrival time and replace dynamic susceptibility contrast (DSC) magnetic resonance imaging, a current gold-standard imaging tool with the downsides of invasiveness and limited repeatability. Using a hypercapnic challenge, blood arrival times can be computed by cross-correlating the administered CO2 signal with the fMRI signal, which increases during elevated CO2 due to vasodilation. However, whole-brain transit times derived from this method can be significantly longer than the known cerebral transit time for healthy subjects (nearing 20 s vs. the expected 5-6 s). To address this unrealistic measurement, we here propose a novel carpet plot-based method to compute improved blood transit times derived from hypercapnic blood oxygen level dependent fMRI, demonstrating that the method reduces estimated blood transit times to an average of 5.32 s. We also investigate the use of hypercapnic fMRI with cross-correlation to compute the venous blood arrival times in healthy subjects and compare the computed delay maps with DSC-MRI time to peak maps using the structural similarity index measure (SSIM). The strongest delay differences between the two methods, indicated by low structural similarity index measure, were found in areas of deep white matter and the periventricular region. SSIM measures throughout the remainder of the brain reflected a similar arrival sequence derived from the two methods despite the exaggerated spread of voxel delays computed using CO2 fMRI.
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Affiliation(s)
- Bradley Fitzgerald
- Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States
| | - Jinxia Fiona Yao
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
| | - Lia M Hocke
- McLean Imaging Center, McLean Hospital, Belmont, MA, United States.,Department of Psychiatry, Harvard Medical School, Boston, MA, , United States
| | - Blaise deB Frederick
- McLean Imaging Center, McLean Hospital, Belmont, MA, United States.,Department of Psychiatry, Harvard Medical School, Boston, MA, , United States
| | | | - Yunjie Tong
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
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Shams S, Prokopiou P, Esmaelbeigi A, Mitsis GD, Chen JJ. Modeling the dynamics of cerebrovascular reactivity to carbon dioxide in fMRI under task and resting-state conditions. Neuroimage 2023; 265:119758. [PMID: 36442732 DOI: 10.1016/j.neuroimage.2022.119758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 11/11/2022] [Accepted: 11/18/2022] [Indexed: 11/26/2022] Open
Abstract
Conventionally, cerebrovascular reactivity (CVR) is estimated as the amplitude of the hemodynamic response to vascular stimuli, most commonly carbon dioxide (CO2). While the CVR amplitude has established clinical utility, the temporal characteristics of CVR (dCVR) have been increasingly explored and may yield even more pathology-sensitive parameters. This work is motivated by the current need to evaluate the feasibility of dCVR modeling in various experimental conditions. In this work, we present a comparison of several recently published/utilized model-based deconvolution (response estimation) approaches for estimating the CO2 response function h(t), including maximum a posteriori likelihood (MAP), inverse logit (IL), canonical correlation analysis (CCA), and basis expansion (using Gamma and Laguerre basis sets). To aid the comparison, we devised a novel simulation framework that incorporates a wide range of SNRs, ranging from 10 to -7 dB, representative of both task and resting-state CO2 changes. In addition, we built ground-truth h(t) into our simulation framework, overcoming the conventional limitation that the true h(t) is unknown. Moreover, to best represent realistic noise found in fMRI scans, we extracted noise from in-vivo resting-state scans. Furthermore, we introduce a simple optimization of the CCA method (CCAopt) and compare its performance to these existing methods. Our findings suggest that model-based methods can accurately estimate dCVR even amidst high noise (i.e. resting-state), and in a manner that is largely independent of the underlying model assumptions for each method. We also provide a quantitative basis for making methodological choices, based on the desired dCVR parameters, the estimation accuracy and computation time. The BEL method provided the highest accuracy and robustness, followed by the CCAopt and IL methods. Of the three, the CCAopt method has the lowest computational requirements. These findings lay the foundation for wider adoption of dCVR estimation in CVR mapping.
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Affiliation(s)
- Seyedmohammad Shams
- Rotman Research Institute, Baycrest Health Sciences, Canada; Department of Neurology, Henry Ford Health, USA
| | - Prokopis Prokopiou
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | | | - J Jean Chen
- Rotman Research Institute, Baycrest Health Sciences, Canada; Department of Bioengineering, McGill University, Canada; Department of Medical Biophysics, University of Toronto, Canada; Institute of Biomedical Engineering, University of Toronto, Canada.
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Ciumas C, Rheims S, Ryvlin P. fMRI studies evaluating central respiratory control in humans. Front Neural Circuits 2022; 16:982963. [PMID: 36213203 PMCID: PMC9537466 DOI: 10.3389/fncir.2022.982963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 09/01/2022] [Indexed: 11/13/2022] Open
Abstract
A plethora of neural centers in the central nervous system control the fundamental respiratory pattern. This control is ensured by neurons that act as pacemakers, modulating activity through chemical control driven by changes in the O2/CO2 balance. Most of the respiratory neural centers are located in the brainstem, but difficult to localize on magnetic resonance imaging (MRI) due to their small size, lack of visually-detectable borders with neighboring areas, and significant physiological noise hampering detection of its activity with functional MRI (fMRI). Yet, several approaches make it possible to study the normal response to different abnormal stimuli or conditions such as CO2 inhalation, induced hypercapnia, volitional apnea, induced hypoxia etc. This review provides a comprehensive overview of the majority of available studies on central respiratory control in humans.
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Affiliation(s)
- Carolina Ciumas
- Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Lyon Neuroscience Research Center, Institut National de la Santé et de la Recherche Médicale U1028/CNRS UMR 5292 Lyon 1 University, Bron, France
- IDEE Epilepsy Institute, Lyon, France
- *Correspondence: Carolina Ciumas
| | - Sylvain Rheims
- Lyon Neuroscience Research Center, Institut National de la Santé et de la Recherche Médicale U1028/CNRS UMR 5292 Lyon 1 University, Bron, France
- IDEE Epilepsy Institute, Lyon, France
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon, Lyon, France
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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Whittaker JR, Steventon JJ, Venzi M, Murphy K. The Spatiotemporal Dynamics of Cerebral Autoregulation in Functional Magnetic Resonance Imaging. Front Neurosci 2022; 16:795683. [PMID: 35873811 PMCID: PMC9304653 DOI: 10.3389/fnins.2022.795683] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 06/20/2022] [Indexed: 12/02/2022] Open
Abstract
The thigh-cuff release (TCR) maneuver is a physiological challenge that is widely used to assess dynamic cerebral autoregulation (dCA). It is often applied in conjunction with Transcranial Doppler ultrasound (TCD), which provides temporal information of the global flow response in the brain. This established method can only yield very limited insights into the regional variability of dCA, whereas functional MRI (fMRI) has the ability to reveal the spatial distribution of flow responses in the brain with high spatial resolution. The aim of this study was to use whole-brain blood-oxygenation-level-dependent (BOLD) fMRI to characterize the spatiotemporal dynamics of the flow response to the TCR challenge, and thus pave the way toward mapping dCA in the brain. We used a data driven approach to derive a novel basis set that was then used to provide a voxel-wise estimate of the TCR associated haemodynamic response function (HRF TCR ). We found that the HRF TCR evolves with a specific spatiotemporal pattern, with gray and white matter showing an asynchronous response, which likely reflects the anatomical structure of cerebral blood supply. Thus, we propose that TCR challenge fMRI is a promising method for mapping spatial variability in dCA, which will likely prove to be clinically advantageous.
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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
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jessica J. Steventon
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Marcello Venzi
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Kevin Murphy
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
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Li Y, Zhou Z, Li Q, Li T, Julian IN, Guo H, Chen J. Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network. Front Neurosci 2022; 16:889105. [PMID: 35578623 PMCID: PMC9106560 DOI: 10.3389/fnins.2022.889105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 04/01/2022] [Indexed: 11/13/2022] Open
Abstract
The brain network structure is highly uncertain due to the noise in imaging signals and evaluation methods. Recent works have shown that uncertain brain networks could capture uncertain information with regards to functional connections. Most of the existing research studies covering uncertain brain networks used graph mining methods for analysis; for example, the mining uncertain subgraph patterns (MUSE) method was used to mine frequent subgraphs and the discriminative feature selection for uncertain graph classification (DUG) method was used to select discriminant subgraphs. However, these methods led to a lack of effective discriminative information; this reduced the classification accuracy for brain diseases. Therefore, considering these problems, we propose an approximate frequent subgraph mining algorithm based on pattern growth of frequent edge (unFEPG) for uncertain brain networks and a novel discriminative feature selection method based on statistical index (dfsSI) to perform graph mining and selection. Results showed that compared with the conventional methods, the unFEPG and dfsSI methods achieved a higher classification accuracy. Furthermore, to demonstrate the efficacy of the proposed method, we used consistent discriminative subgraph patterns based on thresholding and weighting approaches to compare the classification performance of uncertain networks and certain networks in a bidirectional manner. Results showed that classification performance of the uncertain network was superior to that of the certain network within a defined sparsity range. This indicated that if a better classification performance is to be achieved, it is necessary to select a certain brain network with a higher threshold or an uncertain brain network model. Moreover, if the uncertain brain network model was selected, it is necessary to make full use of the uncertain information of its functional connection.
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Affiliation(s)
- Yao Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Zihao Zhou
- College of Mathematics, Taiyuan University of Technology, Taiyuan, China
| | - Qifan Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Tao Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Ibegbu Nnamdi Julian
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Hao Guo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Junjie Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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Deng S, Franklin CG, O'Boyle M, Zhang W, Heyl BL, Jerabek PA, Lu H, Fox PT. Hemodynamic and metabolic correspondence of resting-state voxel-based physiological metrics in healthy adults. Neuroimage 2022; 250:118923. [PMID: 35066157 PMCID: PMC9201851 DOI: 10.1016/j.neuroimage.2022.118923] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 01/07/2022] [Accepted: 01/11/2022] [Indexed: 12/18/2022] Open
Abstract
Voxel-based physiological (VBP) variables derived from blood oxygen level dependent (BOLD) fMRI time-course variations include: amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity (ReHo). Although these BOLD-derived variables can detect between-group (e.g. disease vs control) spatial pattern differences, physiological interpretations are not well established. The primary objective of this study was to quantify spatial correspondences between BOLD VBP variables and PET measurements of cerebral metabolic rate and hemodynamics, being well-validated physiological standards. To this end, quantitative, whole-brain PET images of metabolic rate of glucose (MRGlu; 18FDG) and oxygen (MRO2; 15OO), blood flow (BF; H215O) and blood volume (BV; C15O) were obtained in 16 healthy controls. In the same subjects, BOLD time-courses were obtained for computation of ALFF, fALFF and ReHo images. PET variables were compared pair-wise with BOLD variables. In group-averaged, across-region analyses, ALFF corresponded significantly only with BV (R = 0.64; p < 0.0001). fALFF corresponded most strongly with MRGlu (R = 0.79; p < 0.0001), but also significantly (p < 0.0001) with MRO2 (R = 0.68), BF (R = 0.68) and BV (R=0.68). ReHo performed similarly to fALFF, with significant strong correspondence (p < 0.0001) with MRGlu (R = 0.78), MRO2 (R = 0.54), and, but less strongly with BF (R = 0.50) and BV (R=0.50). Mutual information analyses further clarified these physiological interpretations. When conditioned by BV, ALFF retained no significant MRGlu, MRO2 or BF information. When conditioned by MRGlu, fALFF and ReHo retained no significant MRO2, BF or BV information. Of concern, however, the strength of PET-BOLD correspondences varied markedly by brain region, which calls for future investigation on physiological interpretations at a regional and per-subject basis.
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Affiliation(s)
- Shengwen Deng
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA; Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Crystal G Franklin
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Michael O'Boyle
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Wei Zhang
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Betty L Heyl
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Paul A Jerabek
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; South Texas Veterans Health Care System, San Antonio, TX, USA.
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11
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Kassinopoulos M, Mitsis GD. A multi-measure approach for assessing the performance of fMRI preprocessing strategies in resting-state functional connectivity. Magn Reson Imaging 2021; 85:228-250. [PMID: 34715292 DOI: 10.1016/j.mri.2021.10.028] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 09/17/2021] [Accepted: 10/17/2021] [Indexed: 12/17/2022]
Abstract
It is well established that head motion and physiological processes (e.g. cardiac and breathing activity) should be taken into consideration when analyzing and interpreting results in fMRI studies. However, even though recent studies aimed to evaluate the performance of different preprocessing pipelines there is still no consensus on the optimal strategy. This is partly due to the fact that the quality control (QC) metrics used to evaluate differences in performance across pipelines have often yielded contradictory results. Furthermore, preprocessing techniques based on physiological recordings or data decomposition techniques (e.g. aCompCor) have not been comprehensively examined. Here, to address the aforementioned issues, we propose a framework that summarizes the scores from eight previously proposed and novel QC metrics to a reduced set of two QC metrics that reflect the signal-to-noise ratio and the reduction in motion artifacts and biases in the preprocessed fMRI data. Using this framework, we evaluate the performance of three commonly used practices on the quality of data: 1) Removal of nuisance regressors from fMRI data, 2) discarding motion-contaminated volumes (i.e., scrubbing) before regression, and 3) low-pass filtering the data and the nuisance regressors before their removal. Using resting-state fMRI data from the Human Connectome Project, we show that the scores of the examined QC metrics improve the most when the global signal (GS) and about 17% of principal components from white matter (WM) are removed from the data. Finally, we observe a small further improvement with low-pass filtering at 0.20 Hz and milder variants of WM denoising, but not with scrubbing.
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Affiliation(s)
- Michalis Kassinopoulos
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada.
| | - Georgios D Mitsis
- Department of Bioengineering, McGill University, Montreal, QC, Canada
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12
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Kassinopoulos M, Mitsis GD. Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph. Neuroimage 2021; 242:118467. [PMID: 34390877 DOI: 10.1016/j.neuroimage.2021.118467] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/21/2021] [Accepted: 08/10/2021] [Indexed: 02/06/2023] Open
Abstract
The blood oxygenation level-dependent (BOLD) contrast mechanism allows the noninvasive monitoring of changes in deoxyhemoglobin content. As such, it is commonly used in functional magnetic resonance imaging (fMRI) to study brain activity since levels of deoxyhemoglobin are indirectly related to local neuronal activity through neurovascular coupling mechanisms. However, the BOLD signal is severely affected by physiological processes as well as motion. Due to this, several noise correction techniques have been developed to correct for the associated confounds. The present study focuses on cardiac pulsatility fMRI confounds, aiming to refine model-based techniques that utilize the photoplethysmograph (PPG) signal. Specifically, we propose a new technique based on convolution filtering, termed cardiac pulsatility model (CPM) and compare its performance with the cardiac-related RETROICOR (Card-RETROICOR), which is a technique commonly used to model fMRI fluctuations due to cardiac pulsatility. Further, we investigate whether variations in the amplitude of the PPG pulses (PPG-Amp) covary with variations in amplitude of pulse-related fMRI fluctuations, as well as with the systemic low frequency oscillations (SLFOs) component of the fMRI global signal (GS - defined as the mean signal across all gray matter voxels). Capitalizing on 3T fMRI data from the Human Connectome Project, CPM was found to explain a significantly larger fraction of the fMRI signal variance compared to Card-RETROICOR, particularly for subjects with larger heart rate variability during the scan. The amplitude of the fMRI pulse-related fluctuations did not covary with PPG-Amp; however, PPG-Amp explained significant variance in the GS that was not attributed to variations in heart rate or breathing patterns. Our results suggest that the proposed approach can model high-frequency fluctuations due to pulsation as well as low-frequency physiological fluctuations more accurately compared to model-based techniques commonly employed in fMRI studies.
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Affiliation(s)
- Michalis Kassinopoulos
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada.
| | - Georgios D Mitsis
- Department of Bioengineering, McGill University, Montreal, QC, Canada
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13
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Cauzzo S, Callara AL, Morelli MS, Hartwig V, Esposito F, Montanaro D, Passino C, Emdin M, Giannoni A, Vanello N. Mapping dependencies of BOLD signal change to end-tidal CO 2: linear and nonlinear modeling, and effect of physiological noise correction. J Neurosci Methods 2021; 362:109317. [PMID: 34380051 DOI: 10.1016/j.jneumeth.2021.109317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 07/28/2021] [Accepted: 08/06/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND Disentangling physiological noise and signal of interest is a major issue when evaluating BOLD-signal changes in response to breath holding. Currently-adopted approaches for retrospective noise correction are general-purpose, and have non-negligible effects in studies on hypercapnic challenges. NEW METHOD We provide a novel approach to the analysis of specific and non-specific BOLD-signal changes related to end-tidal CO2 (PETCO2) in breath-hold fMRI studies. Multiple-order nonlinear predictors for PETCO2 model a region-dependent nonlinear input-output relationship hypothesized in literature and possibly playing a crucial role in disentangling noise. We explore Retrospective Image-based Correction (RETROICOR) effects on the estimated BOLD response, applying our analysis both with and without RETROICOR and analyzing the linear and non-linear correlation between PETCO2 and RETROICOR regressors. RESULTS The RETROICOR model of noise related to respiratory activity correlated with PETCO2 both linearly and non-linearly. The correction affected the shape of the estimated BOLD response to hypercapnia but allowed to discard spurious activity in ventricles and white matter. Activation clusters were best detected using non-linear components in the BOLD response model. COMPARISON WITH EXISTING METHOD We evaluated the side-effects of standard physiological noise correction procedure, tailoring our analysis on challenging understudied brainstem and subcortical regions. Our novel approach allowed to characterize delays and non-linearities in BOLD response. CONCLUSIONS RETROICOR successfully avoided false positives, still broadly affecting the estimated non-linear BOLD responses. Non-linearities in the model better explained CO2-related BOLD signal fluctuations. The necessity to modify the standard procedure for physiological-noise correction in breath-hold studies was addressed, stating its crucial importance.
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Affiliation(s)
- Simone Cauzzo
- Institute of Life Sciences, Sant'Anna School of Advanced Studies, Pisa, Italy.
| | | | - Maria Sole Morelli
- Institute of Life Sciences, Sant'Anna School of Advanced Studies, Pisa, Italy
| | - Valentina Hartwig
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Fabrizio Esposito
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | | | - Claudio Passino
- Institute of Life Sciences, Sant'Anna School of Advanced Studies, Pisa, Italy
| | - Michele Emdin
- Institute of Life Sciences, Sant'Anna School of Advanced Studies, Pisa, Italy
| | - Alberto Giannoni
- Institute of Life Sciences, Sant'Anna School of Advanced Studies, Pisa, Italy; Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Nicola Vanello
- Dipartimento di Ingegneria dell'Informazione, University of Pisa, Pisa, Italy
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14
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Xifra-Porxas A, Kassinopoulos M, Mitsis GD. Physiological and motion signatures in static and time-varying functional connectivity and their subject identifiability. eLife 2021; 10:e62324. [PMID: 34342582 PMCID: PMC8378847 DOI: 10.7554/elife.62324] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 08/02/2021] [Indexed: 02/06/2023] Open
Abstract
Human brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.
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Affiliation(s)
- Alba Xifra-Porxas
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montréal, Canada
| | - Michalis Kassinopoulos
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montréal, Canada
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15
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Yao JF, Yang HCS, Wang JH, Liang Z, Talavage TM, Tamer GG, Jang I, Tong Y. A novel method of quantifying hemodynamic delays to improve hemodynamic response, and CVR estimates in CO2 challenge fMRI. J Cereb Blood Flow Metab 2021; 41:1886-1898. [PMID: 33444087 PMCID: PMC8327112 DOI: 10.1177/0271678x20978582] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Elevated carbon dioxide (CO2) in breathing air is widely used as a vasoactive stimulus to assess cerebrovascular functions under hypercapnia (i.e., "stress test" for the brain). Blood-oxygen-level-dependent (BOLD) is a contrast mechanism used in functional magnetic resonance imaging (fMRI). BOLD is used to study CO2-induced cerebrovascular reactivity (CVR), which is defined as the voxel-wise percentage BOLD signal change per mmHg change in the arterial partial pressure of CO2 (PaCO2). Besides the CVR, two additional important parameters reflecting the cerebrovascular functions are the arrival time of arterial CO2 at each voxel, and the waveform of the local BOLD signal. In this study, we developed a novel analytical method to accurately calculate the arrival time of elevated CO2 at each voxel using the systemic low frequency oscillations (sLFO: 0.01-0.1 Hz) extracted from the CO2 challenge data. In addition, 26 candidate hemodynamic response functions (HRF) were used to quantitatively describe the temporal brain reactions to a CO2 stimulus. We demonstrated that our approach improved the traditional method by allowing us to accurately map three perfusion-related parameters: the relative arrival time of blood, the hemodynamic response function, and CVR during a CO2 challenge.
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Affiliation(s)
- Jinxia Fiona Yao
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Ho-Ching Shawn Yang
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - James H Wang
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Zhenhu Liang
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.,School of Electrical Engineering, Yanshan University, Qinhuangdao, China
| | - Thomas M Talavage
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.,School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
| | - Gregory G Tamer
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Ikbeom Jang
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
| | - Yunjie Tong
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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16
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Fitzgerald B, Yao JF, Talavage TM, Hocke LM, Frederick BD, Tong Y. Using carpet plots to analyze transit times of low frequency oscillations in resting state fMRI. Sci Rep 2021; 11:7011. [PMID: 33772060 PMCID: PMC7998022 DOI: 10.1038/s41598-021-86402-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 03/09/2021] [Indexed: 01/05/2023] Open
Abstract
A "carpet plot" is a 2-dimensional plot (time vs. voxel) of scaled fMRI voxel intensity values. Low frequency oscillations (LFOs) can be successfully identified from BOLD fMRI and used to study characteristics of neuronal and physiological activity. Here, we evaluate the use of carpet plots paired with a developed slope-detection algorithm as a means to study LFOs in resting state fMRI (rs-fMRI) data with the help of dynamic susceptibility contrast (DSC) MRI data. Carpet plots were constructed by ordering voxels according to signal delay time for each voxel. The slope-detection algorithm was used to identify and calculate propagation times, or "transit times", of tilted vertical edges across which a sudden signal change was observed. We aim to show that this metric has applications in understanding LFOs in fMRI data, possibly reflecting changes in blood flow speed during the scan, and for evaluating alternative blood-tracking contrast agents such as inhaled CO2. We demonstrate that the propagations of LFOs can be visualized and automatically identified in a carpet plot as tilted lines of sudden intensity change. Resting state carpet plots produce edges with transit times similar to those of DSC carpet plots. Additionally, resting state carpet plots indicate that edge transit times vary at different time points during the scan.
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Affiliation(s)
- Bradley Fitzgerald
- grid.169077.e0000 0004 1937 2197School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907 USA
| | - Jinxia Fiona Yao
- grid.169077.e0000 0004 1937 2197Weldon School of Biomedical Engineering, Purdue University, 206 S. Martin Jischke Drive, West Lafayette, IN 47907-2032 USA
| | - Thomas M. Talavage
- grid.169077.e0000 0004 1937 2197Weldon School of Biomedical Engineering, Purdue University, 206 S. Martin Jischke Drive, West Lafayette, IN 47907-2032 USA ,grid.169077.e0000 0004 1937 2197School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907 USA
| | - Lia M. Hocke
- grid.38142.3c000000041936754XMclean Hospital, Harvard Medical School, Belmont, MA 02478 USA
| | - Blaise deB Frederick
- grid.38142.3c000000041936754XMclean Hospital, Harvard Medical School, Belmont, MA 02478 USA
| | - Yunjie Tong
- Weldon School of Biomedical Engineering, Purdue University, 206 S. Martin Jischke Drive, West Lafayette, IN, 47907-2032, USA.
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17
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Tanrıtanır AC, Villringer K, Galinovic I, Grittner U, Kirilina E, Fiebach JB, Villringer A, Khalil AA. The Effect of Scan Length on the Assessment of BOLD Delay in Ischemic Stroke. Front Neurol 2020; 11:381. [PMID: 32431665 PMCID: PMC7214917 DOI: 10.3389/fneur.2020.00381] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 04/15/2020] [Indexed: 01/21/2023] Open
Abstract
Objectives: To evaluate the impact of resting-state functional MRI scan length on the diagnostic accuracy, image quality and lesion volume estimation of BOLD delay maps used for brain perfusion assessment in acute ischemic stroke. Methods: Sixty-three acute ischemic stroke patients received a 340 s resting-state functional MRI within 24 h of stroke symptom onset. BOLD delay maps were calculated from the full scan and four shortened versions (68 s, 136 s, 204 s, 272 s). The BOLD delay lesions on these maps were compared in terms of spatial overlap and volumetric agreement with the lesions derived from the full scans and with time-to-maximum (Tmax) lesions derived from DSC-MRI in a subset of patients (n = 10). In addition, the interpretability and quality of these maps were compared across different scan lengths using mixed models. Results: Shortened BOLD delay scans showed a small volumetric bias (ranging from 0.05 to 5.3 mL; between a 0.13% volumetric underestimation and a 7.7% overestimation relative to the mean of the volumes, depending on scan length) compared to the full scan. Decreased scan length was associated with decreased spatial overlap with both the BOLD delay lesions derived from the full scans and with Tmax lesions. Only the two shortest scan lengths (68 and 136 s) were associated with substantially decreased interpretability, decreased structure clarity, and increased noisiness of BOLD delay maps. Conclusions: BOLD delay maps derived from resting-state fMRI scans lasting 272 and 204 s provide sufficient diagnostic quality and adequate assessment of perfusion lesion volumes. Such shortened scans may be helpful in situations where quick clinical decisions need to be made.
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Affiliation(s)
| | - Kersten Villringer
- Center for Stroke Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ivana Galinovic
- Center for Stroke Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ulrike Grittner
- Institute of Biometry and Clinical Epidemiology, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany
| | - Evgeniya Kirilina
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Center for Cognitive Neuroscience Berlin, Free University, Berlin, Germany
| | - Jochen B Fiebach
- Center for Stroke Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Arno Villringer
- Berlin School of Mind and Brain, Humboldt Universität zu Berlin, Berlin, Germany.,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ahmed A Khalil
- Center for Stroke Research, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany.,Berlin School of Mind and Brain, Humboldt Universität zu Berlin, Berlin, Germany.,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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18
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Impaired endothelium-mediated cerebrovascular reactivity promotes anxiety and respiration disorders in mice. Proc Natl Acad Sci U S A 2020; 117:1753-1761. [PMID: 31896584 DOI: 10.1073/pnas.1907467117] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Carbon dioxide (CO2), the major product of metabolism, has a strong impact on cerebral blood vessels, a phenomenon known as cerebrovascular reactivity. Several vascular risk factors such as hypertension or diabetes dampen this response, making cerebrovascular reactivity a useful diagnostic marker for incipient vascular pathology, but its functional relevance, if any, is still unclear. Here, we found that GPR4, an endothelial H+ receptor, and endothelial Gαq/11 proteins mediate the CO2/H+ effect on cerebrovascular reactivity in mice. CO2/H+ leads to constriction of vessels in the brainstem area that controls respiration. The consequential washout of CO2, if cerebrovascular reactivity is impaired, reduces respiration. In contrast, CO2 dilates vessels in other brain areas such as the amygdala. Hence, an impaired cerebrovascular reactivity amplifies the CO2 effect on anxiety. Even at atmospheric CO2 concentrations, impaired cerebrovascular reactivity caused longer apneic episodes and more anxiety, indicating that cerebrovascular reactivity is essential for normal brain function. The site-specific reactivity of vessels to CO2 is reflected by regional differences in their gene expression and the release of vasoactive factors from endothelial cells. Our data suggest the central nervous system (CNS) endothelium as a target to treat respiratory and affective disorders associated with vascular diseases.
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19
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Hoiland RL, Fisher JA, Ainslie PN. Regulation of the Cerebral Circulation by Arterial Carbon Dioxide. Compr Physiol 2019; 9:1101-1154. [DOI: 10.1002/cphy.c180021] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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