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Joliot M, Cremona S, Tzourio C, Etard O. Modulate the impact of the drowsiness on the resting state functional connectivity. Sci Rep 2024; 14:8652. [PMID: 38622265 PMCID: PMC11018752 DOI: 10.1038/s41598-024-59476-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 04/11/2024] [Indexed: 04/17/2024] Open
Abstract
This research explores different methodologies to modulate the effects of drowsiness on functional connectivity (FC) during resting-state functional magnetic resonance imaging (RS-fMRI). The study utilized a cohort of students (MRi-Share) and classified individuals into drowsy, alert, and mixed/undetermined states based on observed respiratory oscillations. We analyzed the FC group difference between drowsy and alert individuals after five different processing methods: the reference method, two based on physiological and a global signal regression of the BOLD time series signal, and two based on Gaussian standardizations of the FC distribution. According to the reference method, drowsy individuals exhibit higher cortico-cortical FC than alert individuals. First, we demonstrated that each method reduced the differences between drowsy and alert states. The second result is that the global signal regression was quantitively the most effective, minimizing significant FC differences to only 3.3% of the total FCs. However, one should consider the risks of overcorrection often associated with this methodology. Therefore, choosing a less aggressive form of regression, such as the physiological method or Gaussian-based approaches, might be a more cautious approach. Third and last, using the Gaussian-based methods, cortico-subcortical and intra-default mode network (DMN) FCs were significantly greater in alert than drowsy subjects. These findings bear resemblance to the anticipated patterns during the onset of sleep, where the cortex isolates itself to assist in transitioning into deeper slow wave sleep phases, simultaneously disconnecting the DMN.
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Affiliation(s)
- Marc Joliot
- GIN, IMN UMR5293, CEA, CNRS, Université de Bordeaux, Bordeaux, France.
| | - Sandrine Cremona
- GIN, IMN UMR5293, CEA, CNRS, Université de Bordeaux, Bordeaux, France
| | | | - Olivier Etard
- Normandie Université, UNICAEN, INSERM, COMETE U1075, CYCERON, CHU Caen, 14000, Caen, France
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2
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Goheen J, Anderson JAE, Zhang J, Northoff G. From Lung to Brain: Respiration Modulates Neural and Mental Activity. Neurosci Bull 2023; 39:1577-1590. [PMID: 37285017 PMCID: PMC10533478 DOI: 10.1007/s12264-023-01070-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 04/10/2023] [Indexed: 06/08/2023] Open
Abstract
Respiration protocols have been developed to manipulate mental states, including their use for therapeutic purposes. In this systematic review, we discuss evidence that respiration may play a fundamental role in coordinating neural activity, behavior, and emotion. The main findings are: (1) respiration affects the neural activity of a wide variety of regions in the brain; (2) respiration modulates different frequency ranges in the brain's dynamics; (3) different respiration protocols (spontaneous, hyperventilation, slow or resonance respiration) yield different neural and mental effects; and (4) the effects of respiration on the brain are related to concurrent modulation of biochemical (oxygen delivery, pH) and physiological (cerebral blood flow, heart rate variability) variables. We conclude that respiration may be an integral rhythm of the brain's neural activity. This provides an intimate connection of respiration with neuro-mental features like emotion. A respiratory-neuro-mental connection holds the promise for a brain-based therapeutic usage of respiration in mental disorders.
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Affiliation(s)
- Josh Goheen
- The Royal Ottawa Mental Health Centre, The University of Ottawa, Ottawa, K1Z 7K4, Canada.
- Department of Cognitive Science, Carleton University, Ottawa, K1S 5B6, Canada.
| | - John A E Anderson
- Department of Cognitive Science, Carleton University, Ottawa, K1S 5B6, Canada
| | - Jianfeng Zhang
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, 518060, China
- School of Psychology, Shenzhen University, Shenzhen, 518060, China
| | - Georg Northoff
- The Royal Ottawa Mental Health Centre, The University of Ottawa, Ottawa, K1Z 7K4, Canada
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3
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Dagenais R, Mitsis GD. Non-invasive estimation of arterial blood pressure fluctuations using a peripheral photoplethysmograph inside the MRI scanner. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083179 DOI: 10.1109/embc40787.2023.10340020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The blood-oxygen-level-dependent (BOLD) signal measured by functional magnetic resonance imaging (fMRI) is modulated by neural activity through the neurovascular coupling effect, as well as non-neural factors of physiological origin such as heart rate, respiration, and arterial blood pressure (ABP). While the former two effects have been previously characterized, the modulation of the BOLD signal by ABP fluctuations is still poorly understood. This is largely due to the difficulty of obtaining reliable ABP measurements in the MRI environment. Here, we propose a combined experimental and mathematical modeling framework to estimate ABP fluctuations inside the MRI scanner using photoplethysmography (PPG). Specifically, we used concurrent PPG and ABP measurements obtained outside the scanner to train the mathematical model and applied it to PPG measurements obtained inside the MRI scanner. Our results suggest good agreement between the model-predicted and experimentally measured ABP fluctuations and region specific correlations with the BOLD fluctuations.
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4
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Kassinopoulos M, Rolandi N, Alphan L, Harper RM, Oliveira J, Scott C, Kozák LR, Guye M, Lemieux L, Diehl B. Brain Connectivity Correlates of Breathing and Cardiac Irregularities in SUDEP: A Resting-State fMRI Study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.19.541412. [PMID: 37293113 PMCID: PMC10245782 DOI: 10.1101/2023.05.19.541412] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Sudden unexpected death in epilepsy (SUDEP) is the leading cause of premature mortality among people with epilepsy. Evidence from witnessed and monitored SUDEP cases indicate seizure-induced cardiovascular and respiratory failures; yet, the underlying mechanisms remain obscure. SUDEP occurs often during the night and early morning hours, suggesting that sleep or circadian rhythm-induced changes in physiology contribute to the fatal event. Resting-state fMRI studies have found altered functional connectivity between brain structures involved in cardiorespiratory regulation in later SUDEP cases and in individuals at high-risk of SUDEP. However, those connectivity findings have not been related to changes in cardiovascular or respiratory patterns. Here, we compared fMRI patterns of brain connectivity associated with regular and irregular cardiorespiratory rhythms in SUDEP cases with those of living epilepsy patients of varying SUDEP risk, and healthy controls. We analysed resting-state fMRI data from 98 patients with epilepsy (9 who subsequently succumbed to SUDEP, 43 categorized as low SUDEP risk (no tonic-clonic seizures (TCS) in the year preceding the fMRI scan), and 46 as high SUDEP risk (>3 TCS in the year preceding the scan)) and 25 healthy controls. The global signal amplitude (GSA), defined as the moving standard deviation of the fMRI global signal, was used to identify periods with regular ('low state') and irregular ('high state') cardiorespiratory rhythms. Correlation maps were derived from seeds in twelve regions with a key role in autonomic or respiratory regulation, for the low and high states. Following principal component analysis, component weights were compared between the groups. We found widespread alterations in connectivity of precuneus/posterior cingulate cortex in epilepsy compared to controls, in the low state (regular cardiorespiratory activity). In the low state, and to a lesser degree in the high state, reduced anterior insula connectivity (mainly with anterior and posterior cingulate cortex) in epilepsy appeared, relative to healthy controls. For SUDEP cases, the insula connectivity differences were inversely related to the interval between the fMRI scan and death. The findings suggest that anterior insula connectivity measures may provide a biomarker of SUDEP risk. The neural correlates of autonomic brain structures associated with different cardiorespiratory rhythms may shed light on the mechanisms underlying terminal apnea observed in SUDEP.
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Affiliation(s)
- Michalis Kassinopoulos
- UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Epilepsy Society, Chalfont St. Peter, Buckinghamshire, United Kingdom
| | - Nicolo Rolandi
- UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Epilepsy Society, Chalfont St. Peter, Buckinghamshire, United Kingdom
| | - Laren Alphan
- UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Ronald M. Harper
- UCLA Brain Research Institute, Los Angeles, CA, United States
- Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - Joana Oliveira
- UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Department of Clinical Neurophysiology, National Hospital for Neurology and Neurosurgery, UCLH, London, United Kingdom
| | - Catherine Scott
- UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Department of Clinical Neurophysiology, National Hospital for Neurology and Neurosurgery, UCLH, London, United Kingdom
| | - Lajos R. Kozák
- UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Department of Neuroradiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Maxime Guye
- Aix Marseille Univ, CNRS, CRMBM UMR 7339, Marseille, France
- APHM, Hôpital de la Timone, CEMEREM, Marseille, France
| | - Louis Lemieux
- UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Epilepsy Society, Chalfont St. Peter, Buckinghamshire, United Kingdom
| | - Beate Diehl
- UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Epilepsy Society, Chalfont St. Peter, Buckinghamshire, United Kingdom
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5
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Zvolanek KM, Moia S, Dean JN, Stickland RC, Caballero-Gaudes C, Bright MG. Comparing end-tidal CO 2, respiration volume per time (RVT), and average gray matter signal for mapping cerebrovascular reactivity amplitude and delay with breath-hold task BOLD fMRI. Neuroimage 2023; 272:120038. [PMID: 36958618 DOI: 10.1016/j.neuroimage.2023.120038] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/27/2023] [Accepted: 03/14/2023] [Indexed: 03/25/2023] Open
Abstract
Cerebrovascular reactivity (CVR), defined as the cerebral blood flow response to a vasoactive stimulus, is an imaging biomarker with demonstrated utility in a range of diseases and in typical development and aging processes. A robust and widely implemented method to map CVR involves using a breath-hold task during a BOLD fMRI scan. Recording end-tidal CO2 (PETCO2) changes during the breath-hold task is recommended to be used as a reference signal for modeling CVR amplitude in standard units (%BOLD/mmHg) and CVR delay in seconds. However, obtaining reliable PETCO2 recordings requires equipment and task compliance that may not be achievable in all settings. To address this challenge, we investigated two alternative reference signals to map CVR amplitude and delay in a lagged general linear model (lagged-GLM) framework: respiration volume per time (RVT) and average gray matter BOLD response (GM-BOLD). In 8 healthy adults with multiple scan sessions, we compare spatial agreement of CVR maps from RVT and GM-BOLD to those generated with PETCO2. We define a threshold to determine whether a PETCO2 recording has "sufficient" quality for CVR mapping and perform these comparisons in 16 datasets with sufficient PETCO2 and 6 datasets with insufficient PETCO2. When PETCO2 quality is sufficient, both RVT and GM-BOLD produce CVR amplitude maps that are nearly identical to those from PETCO2 (after accounting for differences in scale), with the caveat they are not in standard units to facilitate between-group comparisons. CVR delays are comparable to PETCO2 with an RVT regressor but may be underestimated with the average GM-BOLD regressor. Importantly, when PETCO2 quality is insufficient, RVT and GM-BOLD CVR recover reasonable CVR amplitude and delay maps, provided the participant attempted the breath-hold task. Therefore, our framework offers a solution for achieving high quality CVR maps in both retrospective and prospective studies where sufficient PETCO2 recordings are not available and especially in populations where obtaining reliable measurements is a known challenge (e.g., children). Our results have the potential to improve the accessibility of CVR mapping and to increase the prevalence of this promising metric of vascular health.
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Affiliation(s)
- Kristina M Zvolanek
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, USA.
| | - Stefano Moia
- Basque Center on Cognition, Brain and Language, Donostia, Gipuzkoa, Spain; Medical Imaging Processing Lab (MIP:Lab), Neuro-X institute, EPFL, Geneva, Switzerland
| | - Joshua N Dean
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, USA
| | - Rachael C Stickland
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Molly G Bright
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, USA
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6
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Zhang J, Northoff G. Beyond noise to function: reframing the global brain activity and its dynamic topography. Commun Biol 2022; 5:1350. [PMID: 36481785 PMCID: PMC9732046 DOI: 10.1038/s42003-022-04297-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/24/2022] [Indexed: 12/13/2022] Open
Abstract
How global and local activity interact with each other is a common question in complex systems like climate and economy. Analogously, the brain too displays 'global' activity that interacts with local-regional activity and modulates behavior. The brain's global activity, investigated as global signal in fMRI, so far, has mainly been conceived as non-neuronal noise. We here review the findings from healthy and clinical populations to demonstrate the neural basis and functions of global signal to brain and behavior. We show that global signal (i) is closely coupled with physiological signals and modulates the arousal level; and (ii) organizes an elaborated dynamic topography and coordinates the different forms of cognition. We also postulate a Dual-Layer Model including both background and surface layers. Together, the latest evidence strongly suggests the need to go beyond the view of global signal as noise by embracing a dual-layer model with background and surface layer.
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Affiliation(s)
- Jianfeng Zhang
- grid.263488.30000 0001 0472 9649Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China ,grid.263488.30000 0001 0472 9649School of Psychology, Shenzhen University, Shenzhen, China
| | - Georg Northoff
- grid.13402.340000 0004 1759 700XMental Health Center, Zhejiang University School of Medicine, Hangzhou, China ,grid.28046.380000 0001 2182 2255Institute of Mental Health Research, University of Ottawa, Ottawa, Canada ,grid.410595.c0000 0001 2230 9154Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China
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7
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Scheel N, Keller JN, Binder EF, Vidoni ED, Burns JM, Thomas BP, Stowe AM, Hynan LS, Kerwin DR, Vongpatanasin W, Rossetti H, Cullum CM, Zhang R, Zhu DC. Evaluation of noise regression techniques in resting-state fMRI studies using data of 434 older adults. Front Neurosci 2022; 16:1006056. [PMID: 36340768 PMCID: PMC9626831 DOI: 10.3389/fnins.2022.1006056] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/27/2022] [Indexed: 01/24/2023] Open
Abstract
Subject motion is a well-known confound in resting-state functional MRI (rs-fMRI) and the analysis of functional connectivity. Consequently, several clean-up strategies have been established to minimize the impact of subject motion. Physiological signals in response to cardiac activity and respiration are also known to alter the apparent rs-fMRI connectivity. Comprehensive comparisons of common noise regression techniques showed that the "Independent Component Analysis based strategy for Automatic Removal of Motion Artifacts" (ICA-AROMA) was a preferred pre-processing technique for teenagers and adults. However, motion and physiological noise characteristics may differ substantially for older adults. Here, we present a comprehensive comparison of noise-regression techniques for older adults from a large multi-site clinical trial of exercise and intensive pharmacological vascular risk factor reduction. The Risk Reduction for Alzheimer's Disease (rrAD) trial included hypertensive older adults (60-84 years old) at elevated risk of developing Alzheimer's Disease (AD). We compared the performance of censoring, censoring combined with global signal regression, non-aggressive and aggressive ICA-AROMA, as well as the Spatially Organized Component Klassifikator (SOCK) on the rs-fMRI baseline scans from 434 rrAD subjects. All techniques were rated based on network reproducibility, network identifiability, edge activity, spatial smoothness, and loss of temporal degrees of freedom (tDOF). We found that non-aggressive ICA-AROMA did not perform as well as the other four techniques, which performed table with marginal differences, demonstrating the validity of these techniques. Considering reproducibility as the most important factor for longitudinal studies, given low false-positive rates and a better preserved, more cohesive temporal structure, currently aggressive ICA-AROMA is likely the most suitable noise regression technique for rs-fMRI studies of older adults.
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Affiliation(s)
- Norman Scheel
- Department of Radiology, Michigan State University, East Lansing, MI, United States
| | - Jeffrey N. Keller
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Ellen F. Binder
- Division of Geriatrics and Nutritional Science, Washington University School of Medicine, St. Louis, MO, United States
| | - Eric D. Vidoni
- Alzheimer’s Disease Center, University of Kansas, Fairway, KS, United States
| | - Jeffrey M. Burns
- Alzheimer’s Disease Center, University of Kansas, Fairway, KS, United States
| | - Binu P. Thomas
- UT Southwestern Medical Center, Dallas, TX, United States
| | - Ann M. Stowe
- Department of Neurology, University of Kentucky, Lexington, KY, United States
| | - Linda S. Hynan
- UT Southwestern Medical Center, Dallas, TX, United States
| | - Diana R. Kerwin
- Texas Health Presbyterian Hospital, Dallas, TX, United States
| | | | - Heidi Rossetti
- UT Southwestern Medical Center, Dallas, TX, United States
| | | | - Rong Zhang
- UT Southwestern Medical Center, Dallas, TX, United States,Texas Health Presbyterian Hospital, Dallas, TX, United States
| | - David C. Zhu
- Department of Radiology, Michigan State University, East Lansing, MI, United States,*Correspondence: David C. Zhu,
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8
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Colenbier N, Marino M, Arcara G, Frederick B, Pellegrino G, Marinazzo D, Ferrazzi G. WHOCARES: WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions. J Neural Eng 2022; 19:10.1088/1741-2552/ac8bff. [PMID: 35998568 PMCID: PMC9673276 DOI: 10.1088/1741-2552/ac8bff] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 08/23/2022] [Indexed: 11/12/2022]
Abstract
Objective. To spatio-temporally resolve cardiac signals in functional magnetic resonance imaging (fMRI) time-series of the human brain using neither external physiological measurements nor ad hoc modelling assumptions.Approach. Cardiac pulsation is a physiological confound of fMRI time-series that introduces spurious signal fluctuations in proximity to blood vessels. fMRI alone is not sufficiently fast to resolve cardiac pulsation. Depending on the ratio between the instantaneous heart-rate and the acquisition sampling frequency (1/TR, with TR being the repetition time), the cardiac signal may alias into the frequency band of neural activation so that its removal through spectral filtering techniques is generally not possible. In this paper, we show that it is feasible to temporally and spatially resolve cardiac signals throughout the brain even when cardiac aliasing occurs by combining fMRI hyper-sampling with simultaneous multislice (SMS) imaging. The technique, which we name WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions (WHOCARES), was developed on 695 healthy subjects selected from the Human Connectome Project and its performance validated against the RETROICOR, HAPPY and the pulse oxymeter signal regression methods.Main results.WHOCARES is capable of retrieving voxel-wise cardiac signal regressors. This is achieved without employing external physiological recordings nor through ad hoc modelling assumptions. The performance of WHOCARES was, on average, superior to RETROICOR, HAPPY and the pulse oxymeter regression methods.Significance.WHOCARES holds basis for the reliable mapping of cardiac activity in fMRI time-series. WHOCARES can be employed for the retrospective removal of cardiac noise in publicly available fMRI datasets where physiological recordings are not available. WHOCARES is freely available athttps://github.com/gferrazzi/WHOCARES.
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Affiliation(s)
- Nigel Colenbier
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
| | - Marco Marino
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, 3001, Belgium
| | - Giorgio Arcara
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
| | - Blaise Frederick
- Brain Imaging Center, McLean Hospital, 115 Mill St., Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard University Medical School, 25 Shattuck St., Boston, MA, 02115, USA
| | | | - Daniele Marinazzo
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
| | - Giulio Ferrazzi
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
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9
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Mann‐Krzisnik D, Mitsis GD. Extracting electrophysiological correlates of functional magnetic resonance imaging data using the canonical polyadic decomposition. Hum Brain Mapp 2022; 43:4045-4073. [PMID: 35567768 PMCID: PMC9374895 DOI: 10.1002/hbm.25902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 04/25/2022] [Accepted: 04/27/2022] [Indexed: 11/11/2022] Open
Abstract
The relation between electrophysiology and BOLD-fMRI requires further elucidation. One approach for studying this relation is to find time-frequency features from electrophysiology that explain the variance of BOLD time-series. Convolution of these features with a canonical hemodynamic response function (HRF) is often required to model neurovascular coupling mechanisms and thus account for time shifts between electrophysiological and BOLD-fMRI data. We propose a framework for extracting the spatial distribution of these time-frequency features while also estimating more flexible, region-specific HRFs. The core component of this method is the decomposition of a tensor containing impulse response functions using the Canonical Polyadic Decomposition. The outputs of this decomposition provide insight into the relation between electrophysiology and BOLD-fMRI and can be used to construct estimates of BOLD time-series. We demonstrated the performance of this method on simulated data while also examining the effects of simulated measurement noise and physiological confounds. Afterwards, we validated our method on publicly available task-based and resting-state EEG-fMRI data. We adjusted our method to accommodate the multisubject nature of these datasets, enabling the investigation of inter-subject variability with regards to EEG-to-BOLD neurovascular coupling mechanisms. We thus also demonstrate how EEG features for modelling the BOLD signal differ across subjects.
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Affiliation(s)
- Dylan Mann‐Krzisnik
- Graduate Program in Biological and Biomedical EngineeringMcGill UniversityMontréalQuebecCanada
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10
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Scholkmann F, Tachtsidis I, Wolf M, Wolf U. Systemic physiology augmented functional near-infrared spectroscopy: a powerful approach to study the embodied human brain. NEUROPHOTONICS 2022; 9:030801. [PMID: 35832785 PMCID: PMC9272976 DOI: 10.1117/1.nph.9.3.030801] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/07/2022] [Indexed: 05/15/2023]
Abstract
In this Outlook paper, we explain why an accurate physiological interpretation of functional near-infrared spectroscopy (fNIRS) neuroimaging signals is facilitated when systemic physiological activity (e.g., cardiorespiratory and autonomic activity) is measured simultaneously by employing systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS). The rationale for SPA-fNIRS is twofold: (i) SPA-fNIRS enables a more complete interpretation and understanding of the fNIRS signals measured at the head since they contain components originating from neurovascular coupling and from systemic physiological sources. The systemic physiology signals measured with SPA-fNIRS can be used for regressing out physiological confounding components in fNIRS signals. Misinterpretations can thus be minimized. (ii) SPA-fNIRS enables to study the embodied brain by linking the brain with the physiological state of the entire body, allowing novel insights into their complex interplay. We envisage the SPA-fNIRS approach will become increasingly important in the future.
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Affiliation(s)
- Felix Scholkmann
- University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
- University Hospital Zurich, University of Zurich, Biomedical Optics Research Laboratory, Neonatology Research, Department of Neonatology, Zurich, Switzerland
| | - Ilias Tachtsidis
- University College London, Biomedical Optics Research Laboratory, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Martin Wolf
- University Hospital Zurich, University of Zurich, Biomedical Optics Research Laboratory, Neonatology Research, Department of Neonatology, Zurich, Switzerland
| | - Ursula Wolf
- University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
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11
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Scheel N, Tarumi T, Tomoto T, Cullum CM, Zhang R, Zhu DC. Resting-state functional MRI signal fluctuation amplitudes are correlated with brain amyloid- β deposition in patients with mild cognitive impairment. J Cereb Blood Flow Metab 2022; 42:876-890. [PMID: 34861133 PMCID: PMC9254039 DOI: 10.1177/0271678x211064846] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Mounting evidence suggests that amyloid-β (Aβ) and vascular etiologies are intertwined in the pathogenesis of Alzheimer's disease (AD). Blood-oxygen-level-dependent (BOLD) signals, measured by resting-state functional MRI (rs-fMRI), are associated with neuronal activity and cerebrovascular hemodynamics. Nevertheless, it is unclear if BOLD fluctuations are associated with Aβ deposition in individuals at high risk of AD. Thirty-three patients with amnestic mild cognitive impairment underwent rs-fMRI and AV45 PET. The AV45 standardized uptake value ratio (AV45-SUVR) was calculated using cerebral white matter as reference, to assess Aβ deposition. The whole-brain normalized amplitudes of low-frequency fluctuations (sALFF) of local BOLD signals were calculated in the frequency band of 0.01-0.08 Hz. Stepwise increasing physiological/vascular signal regressions on the rs-fMRI data examined whether sALFF-AV45 correlations were driven by vascular hemodynamics, neuronal activities, or both. We found that sALFF and AV45-SUVR were negatively correlated in regions of default-mode and visual networks (precuneus, angular, lingual and fusiform gyri). Regions with higher sALFF had less Aβ accumulation. Correlated cluster sizes in MNI space (r ≈ -0.47) were reduced from 3018 mm3 to 1072 mm3 with stronger cardiovascular regression. These preliminary findings imply that local brain blood fluctuations due to vascular hemodynamics or neuronal activity can affect Aβ homeostasis.
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Affiliation(s)
- Norman Scheel
- Department of Radiology and Cognitive Imaging Research Center, Michigan State University, East Lansing, MI, USA
| | - Takashi Tarumi
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital, Dallas, TX, USA.,Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, Japan
| | - Tsubasa Tomoto
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital, Dallas, TX, USA.,Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - C Munro Cullum
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA.,Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA.,Department of Neurological Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Rong Zhang
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital, Dallas, TX, USA.,Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - David C Zhu
- Department of Radiology and Cognitive Imaging Research Center, Michigan State University, East Lansing, MI, USA
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12
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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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13
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A comprehensive investigation of physiologic noise modeling in resting state fMRI; time shifted cardiac noise in EPI and its removal without external physiologic signal measures. Neuroimage 2022; 254:119136. [PMID: 35346840 DOI: 10.1016/j.neuroimage.2022.119136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/18/2022] [Accepted: 03/22/2022] [Indexed: 11/23/2022] Open
Abstract
Hemodynamic cardiac and respiratory-cycle fluctuations are a source of unwanted non-neuronal signal components, often called physiologic noise, in resting state (rs-) fMRI studies. Here, we use image-based retrospective correction of physiological motion (RETROICOR) with externally measured physiologic signals to investigate cardiac and respiratory hemodynamic phase functions reflected in rs-fMRI data. We find that the cardiac phase function is time shifted locally, while the respiratory phase function is described as single, fixed phase form across the brain. In light of these findings, we propose an update to Physiologic EStimation by Temporal ICA (PESTICA), our publically available software package that estimates physiologic signals when external physiologic measures are not available. This update incorporates: 1) auto-selection of slicewise physiologic regressors and generation of physiologic fixed phase regressors with total slices/TR sampling rate, 2) Fourier series expansion of the cardiac fixed phase regressor to account for time delayed cardiac noise 3) removal of cardiac and respiratory noise in imaging data. We compare the efficacy of the updated method to RETROICOR.
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14
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Towards reliable spinal cord fMRI: assessment of common imaging protocols. Neuroimage 2022; 250:118964. [DOI: 10.1016/j.neuroimage.2022.118964] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 01/07/2022] [Accepted: 02/01/2022] [Indexed: 01/29/2023] Open
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15
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Williams JC, Tubiolo PN, Luceno JR, Van Snellenberg JX. Advancing motion denoising of multiband resting-state functional connectivity fMRI data. Neuroimage 2022; 249:118907. [PMID: 35033673 PMCID: PMC9057309 DOI: 10.1016/j.neuroimage.2022.118907] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 01/11/2022] [Accepted: 01/12/2022] [Indexed: 11/26/2022] Open
Abstract
Simultaneous multi-slice (multiband) accelerated functional magnetic resonance imaging (fMRI) provides dramatically improved temporal and spatial resolution for resting-state functional connectivity (RSFC) studies of the human brain in health and disease. However, multiband acceleration also poses unique challenges for denoising of subject motion induced data artifacts, the presence of which is a major confound in RSFC research that substantively diminishes reliability and reproducibility. We comprehensively evaluated existing and novel approaches to volume censoring-based motion denoising in the Human Connectome Project (HCP) dataset. We show that assumptions underlying common metrics for evaluating motion denoising pipelines, especially those based on quality control-functional connectivity (QC-FC) correlations and differences between high- and low-motion participants, are problematic, and appear to be inappropriate in their current widespread use as indicators of comparative pipeline performance and as targets for investigators to use when tuning pipelines for their own datasets. We further develop two new quantitative metrics that are instead agnostic to QC-FC correlations and other measures that rely upon the null assumption that no true relationships exist between trait measures of subject motion and functional connectivity, and demonstrate their use as benchmarks for comparing volume censoring methods. Finally, we develop and validate quantitative methods for determining dataset-specific optimal volume censoring parameters prior to the final analysis of a dataset, and provide straightforward recommendations and code for all investigators to apply this optimized approach to their own RSFC datasets.
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Affiliation(s)
- John C Williams
- Department of Psychiatry and Behavioral Health, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, 11794 USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794 USA
| | - Philip N Tubiolo
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794 USA
| | - Jacob R Luceno
- Department of Psychiatry and Behavioral Health, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, 11794 USA
| | - Jared X Van Snellenberg
- Department of Psychiatry and Behavioral Health, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, 11794 USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794 USA; Department of Psychology, Stony Brook University, Stony Brook, NY, 11794 USA; Division of Translational Imaging, New York State Psychiatric Institute, New York, NY, 10032 USA; Department of Psychiatry, Columbia University Medical Center, New York, NY, 10032 USA.
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16
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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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17
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Neudorf J, Kress S, Borowsky R. Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity. Brain Struct Funct 2021; 227:331-343. [PMID: 34633514 PMCID: PMC8741721 DOI: 10.1007/s00429-021-02403-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 09/30/2021] [Indexed: 02/07/2023]
Abstract
Although functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully understood. Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain inherent network structure. Here, we applied this model to predict functional connectivity from structural connectivity in a sample of 998 participants from the Human Connectome Project. Our results showed that the graph neural network accounted for 89% of the variance in mean functional connectivity, 56% of the variance in individual-level functional connectivity, 99% of the variance in mean functional centrality, and 81% of the variance in individual-level functional centrality. These results represent an important finding that functional centrality can be robustly predicted from structural connectivity. Regions of particular importance to the model's performance as determined through lesioning are discussed, whereby regions with higher centrality have a higher impact on model performance. Future research on models of patient, demographic, or behavioural data can also benefit from this graph neural network method as it is ideally-suited for depicting connectivity and centrality in brain networks. These results have set a new benchmark for prediction of functional connectivity from structural connectivity, and models like this may ultimately lead to a way to predict functional connectivity in individuals who are unable to do fMRI tasks (e.g., non-responsive patients).
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Affiliation(s)
- Josh Neudorf
- Cognitive Neuroscience Lab, Department of Psychology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Shaylyn Kress
- Cognitive Neuroscience Lab, Department of Psychology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ron Borowsky
- Cognitive Neuroscience Lab, Department of Psychology, University of Saskatchewan, Saskatoon, SK, Canada.
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18
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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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19
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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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20
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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] [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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21
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Kassinopoulos M, Harper RM, Guye M, Lemieux L, Diehl B. Altered Relationship Between Heart Rate Variability and fMRI-Based Functional Connectivity in People With Epilepsy. Front Neurol 2021; 12:671890. [PMID: 34177777 PMCID: PMC8223068 DOI: 10.3389/fneur.2021.671890] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 05/18/2021] [Indexed: 12/23/2022] Open
Abstract
Background: Disruptions in central autonomic processes in people with epilepsy have been studied through evaluation of heart rate variability (HRV). Decreased HRV appears in epilepsy compared to healthy controls, suggesting a shift in autonomic balance toward sympathetic dominance; recent studies have associated HRV changes with seizure severity and outcome of interventions. However, the processes underlying these autonomic changes remain unclear. We examined the nature of these changes by assessing alterations in whole-brain functional connectivity, and relating those alterations to HRV. Methods: We examined regional brain activity and functional organization in 28 drug-resistant epilepsy patients and 16 healthy controls using resting-state functional magnetic resonance imaging (fMRI). We employed an HRV state-dependent functional connectivity (FC) framework with low and high HRV states derived from the following four cardiac-related variables: 1. RR interval, 2. root mean square of successive differences (RMSSD), 4. low-frequency HRV (0.04-0.15 Hz; LF-HRV) and high-frequency HRV (0.15-0.40 Hz; HF-HRV). The effect of group (epilepsy vs. controls), HRV state (low vs. high) and the interactions of group and state were assessed using a mixed analysis of variance (ANOVA). We assessed FC within and between 7 large-scale functional networks consisting of cortical regions and 4 subcortical networks, the amygdala, hippocampus, basal ganglia and thalamus networks. Results: Consistent with previous studies, decreased RR interval (increased heart rate) and decreased HF-HRV appeared in people with epilepsy compared to healthy controls. For both groups, fluctuations in heart rate were positively correlated with BOLD activity in bilateral thalamus and regions of the cerebellum, and negatively correlated with BOLD activity in the insula, putamen, superior temporal gyrus and inferior frontal gyrus. Connectivity strength in patients between right thalamus and ventral attention network (mainly insula) increased in the high LF-HRV state compared to low LF-HRV; the opposite trend appeared in healthy controls. A similar pattern emerged for connectivity between the thalamus and basal ganglia. Conclusion: The findings suggest that resting connectivity patterns between the thalamus and other structures underlying HRV expression are modified in people with drug-resistant epilepsy compared to healthy controls.
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Affiliation(s)
- Michalis Kassinopoulos
- Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, United Kingdom
- Epilepsy Society, Buckinghamshire, United Kingdom
| | - Ronald M. Harper
- Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Maxime Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, United Kingdom
- Epilepsy Society, Buckinghamshire, United Kingdom
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, United Kingdom
- Epilepsy Society, Buckinghamshire, United Kingdom
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22
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Wang Z. The neurocognitive correlates of brain entropy estimated by resting state fMRI. Neuroimage 2021; 232:117893. [PMID: 33621695 PMCID: PMC8138544 DOI: 10.1016/j.neuroimage.2021.117893] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/02/2021] [Accepted: 02/16/2021] [Indexed: 11/17/2022] Open
Abstract
Resting state brain activity consumes most of brain energy, likely creating and maintaining a reserve of general brain functionality. The latent reserve if it exists may be reflected by the profound long-range fluctuations of resting brain activity. The long-range temporal coherence (LRTC) can be characterized by resting state fMRI (rsfMRI)-based brain entropy (BEN) mapping. While BEN mapping results have shown sensitivity to neuromodulations or disease conditions, the underlying neuromechanisms especially the associations of BEN or LRTC to neurocognition still remain unclear. To address this standing question and to test a novel hypothesis that resting BEN reflects a latent functional reserve through the link to general functionality, we mapped resting BEN of 862 young adults and comprehensively examined its associations to neurocognitions using data from the Human Connectome Project (HCP). Our results unanimously highlighted two brain circuits: the default mode network (DMN) and executive control network (ECN) through their negative associations of BEN to general functionality, which is independent of age and sex. While BEN in DMN/ECN increases with age, it decreases with education years. These results demonstrated the neurocognitive correlates of resting BEN in DMN/ECN and suggest resting BEN in DMN/ECN as a potential proxy of the latent functional reserve that facilitates general brain functionality and may be enhanced by education.
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Affiliation(s)
- Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670W. Baltimore St, Baltimore, MD 20201, United States.
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23
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Attarpour A, Ward J, Chen JJ. Vascular origins of low-frequency oscillations in the cerebrospinal fluid signal in resting-state fMRI: Interpretation using photoplethysmography. Hum Brain Mapp 2021; 42:2606-2622. [PMID: 33638224 PMCID: PMC8090775 DOI: 10.1002/hbm.25392] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 02/09/2021] [Accepted: 02/16/2021] [Indexed: 12/12/2022] Open
Abstract
In vivo mapping of cerebrovascular oscillations in the 0.05–0.15 Hz remains difficult. Oscillations in the cerebrospinal fluid (CSF) represent a possible avenue for noninvasively tracking these oscillations using resting‐state functional MRI (rs‐fMRI), and have been used to correct for vascular oscillations in rs‐fMRI functional connectivity. However, the relationship between low‐frequency CSF and vascular oscillations remains unclear. In this study, we investigate this relationship using fast simultaneous rs‐fMRI and photoplethysmogram (PPG), examining the 0.1 Hz PPG signal, heart‐rate variability (HRV), pulse‐intensity ratio (PIR), and the second derivative of the PPG (SDPPG). The main findings of this study are: (a) signals in different CSF regions are not equivalent in their associations with vascular and tissue rs‐fMRI signals; (b) the PPG signal is maximally coherent with the arterial and CSF signals at the cardiac frequency, but coherent with brain tissue at ~0.2 Hz; (c) PIR is maximally coherent with the CSF signal near 0.03 Hz; and (d) PPG‐related vascular oscillations only contribute to ~15% of the CSF (and arterial) signal in rs‐fMRI. These findings caution against averaging all CSF regions when extracting physiological nuisance regressors in rs‐fMRI applications, and indicate the drivers of the CSF signal are more than simply cardiac. Our study is an initial attempt at the refinement and standardization of how the CSF signal in rs‐fMRI can be used and interpreted. It also paves the way for using rs‐fMRI in the CSF as a potential tool for tracking cerebrovascular health through, for instance, the potential relationship between PIR and the CSF signal.
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Affiliation(s)
- Ahmadreza Attarpour
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - James Ward
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
| | - J Jean Chen
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
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24
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Harrison SJ, Bianchi S, Heinzle J, Stephan KE, Iglesias S, Kasper L. A Hilbert-based method for processing respiratory timeseries. Neuroimage 2021; 230:117787. [PMID: 33516897 DOI: 10.1016/j.neuroimage.2021.117787] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 01/09/2021] [Accepted: 01/14/2021] [Indexed: 01/22/2023] Open
Abstract
In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators. Finally, we demonstrate that this leads to an increase in the amount of respiration-related variance removed from fMRI data when used as part of a typical preprocessing pipeline. Our implementation is publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (https://translationalneuromodeling.org/tapas).
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Affiliation(s)
- Samuel J Harrison
- Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland; FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.
| | - Samuel Bianchi
- Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland; Institute for Biomedical Engineering, ETH Zurich & University of Zurich, Zurich, Switzerland
| | - Jakob Heinzle
- Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland
| | - Klaas Enno Stephan
- Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany; Techna Institute, University Health Network, Toronto, Canada
| | - Sandra Iglesias
- Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland
| | - Lars Kasper
- Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland; Institute for Biomedical Engineering, ETH Zurich & University of Zurich, Zurich, Switzerland; Techna Institute, University Health Network, Toronto, Canada
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Pinto J, Bright MG, Bulte DP, Figueiredo P. Cerebrovascular Reactivity Mapping Without Gas Challenges: A Methodological Guide. Front Physiol 2021; 11:608475. [PMID: 33536935 PMCID: PMC7848198 DOI: 10.3389/fphys.2020.608475] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 12/02/2020] [Indexed: 01/08/2023] Open
Abstract
Cerebrovascular reactivity (CVR) is defined as the ability of vessels to alter their caliber in response to vasoactive factors, by means of dilating or constricting, in order to increase or decrease regional cerebral blood flow (CBF). Importantly, CVR may provide a sensitive biomarker for pathologies where vasculature is compromised. Furthermore, the spatiotemporal dynamics of CVR observed in healthy subjects, reflecting regional differences in cerebral vascular tone and response, may also be important in functional MRI studies based on neurovascular coupling mechanisms. Assessment of CVR is usually based on the use of a vasoactive stimulus combined with a CBF measurement technique. Although transcranial Doppler ultrasound has been frequently used to obtain global flow velocity measurements, MRI techniques are being increasingly employed for obtaining CBF maps. For the vasoactive stimulus, vasodilatory hypercapnia is usually induced through the manipulation of respiratory gases, including the inhalation of increased concentrations of carbon dioxide. However, most of these methods require an additional apparatus and complex setups, which not only may not be well-tolerated by some populations but are also not widely available. For these reasons, strategies based on voluntary breathing fluctuations without the need for external gas challenges have been proposed. These include the task-based methodologies of breath holding and paced deep breathing, as well as a new generation of methods based on spontaneous breathing fluctuations during resting-state. Despite the multitude of alternatives to gas challenges, existing literature lacks definitive conclusions regarding the best practices for the vasoactive modulation and associated analysis protocols. In this work, we perform an extensive review of CVR mapping techniques based on MRI and CO2 variations without gas challenges, focusing on the methodological aspects of the breathing protocols and corresponding data analysis. Finally, we outline a set of practical guidelines based on generally accepted practices and available data, extending previous reports and encouraging the wider application of CVR mapping methodologies in both clinical and academic MRI settings.
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Affiliation(s)
- Joana Pinto
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Molly G. Bright
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States
| | - Daniel P. Bulte
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Patrícia Figueiredo
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
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Salas JA, Bayrak RG, Huo Y, Chang C. Reconstruction of respiratory variation signals from fMRI data. Neuroimage 2020; 225:117459. [PMID: 33129927 PMCID: PMC7868104 DOI: 10.1016/j.neuroimage.2020.117459] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 10/02/2020] [Accepted: 10/09/2020] [Indexed: 11/25/2022] Open
Abstract
Functional MRI signals can be heavily influenced by systemic physiological processes in addition to local neural activity. For example, widespread hemodynamic fluctuations across the brain have been found to correlate with natural, low-frequency variations in the depth and rate of breathing over time. Acquiring peripheral measures of respiration during fMRI scanning not only allows for modeling such effects in fMRI analysis, but also provides valuable information for interrogating brain-body physiology. However, physiological recordings are frequently unavailable or have insufficient quality. Here, we propose a computational technique for reconstructing continuous low-frequency respiration volume (RV) fluctuations from fMRI data alone. We evaluate the performance of this approach across different fMRI preprocessing strategies. Further, we demonstrate that the predicted RV signals can account for similar patterns of temporal variation in resting-state fMRI data compared to measured RV fluctuations. These findings indicate that fluctuations in respiration volume can be extracted from fMRI alone, in the common scenario of missing or corrupted respiration recordings. The results have implications for enriching a large volume of existing fMRI datasets through retrospective addition of respiratory variations information.
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Affiliation(s)
- Jorge A Salas
- Department of Electrical Engineering and Computer Science, Vanderbilt University, USA.
| | - Roza G Bayrak
- Department of Electrical Engineering and Computer Science, Vanderbilt University, USA
| | - Yuankai Huo
- Department of Electrical Engineering and Computer Science, Vanderbilt University, USA
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, USA; Department of Biomedical Engineering, Vanderbilt University, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, USA.
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Zhang J, Huang Z, Tumati S, Northoff G. Rest-task modulation of fMRI-derived global signal topography is mediated by transient coactivation patterns. PLoS Biol 2020; 18:e3000733. [PMID: 32649707 PMCID: PMC7375654 DOI: 10.1371/journal.pbio.3000733] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 07/22/2020] [Accepted: 06/23/2020] [Indexed: 12/26/2022] Open
Abstract
Recent resting-state functional MRI (fMRI) studies have revealed that the global signal (GS) exhibits a nonuniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of GS topography by analyzing static GS correlation and dynamic coactivation patterns in a large sample of an fMRI dataset (n = 837) from the Human Connectome Project. The GS topography in the resting state and in seven different tasks was first measured by correlating the GS with the local time series (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, whereas low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient coactivation patterns at the peak period of GS (GS-peak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of GS in the resting state, whereas both differed during the task states; because of such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of GS topography by showing its rest-task modulation, the underlying dynamic coactivation patterns, and its partial dissociation from respiration effects during task states. Recent resting-state fMRI studies have shown that the global signal exhibits a nonuniform spatial distribution across gray matter, but is this informative? This neuroimaging study reveals novel insights into the informative nature of global signal by rest-task modulation of the global signal topography.
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Affiliation(s)
- Jianfeng Zhang
- Mental Health Center, Zhejiang University School of Medicine, Hangzhou, China
- College of Biomedical Engineering and Instrument Sciences, Zhejiang University, Hangzhou, China
| | - Zirui Huang
- Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Shankar Tumati
- Institute of Mental Health Research, University of Ottawa, Ottawa, Canada
| | - Georg Northoff
- Mental Health Center, Zhejiang University School of Medicine, Hangzhou, China
- Institute of Mental Health Research, University of Ottawa, Ottawa, Canada
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China
- Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan
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