scholarly journals Identification of physiological response functions to correct for fluctuations in resting-state fMRI related to heart rate and respiration

NeuroImage ◽  
2019 ◽  
Vol 202 ◽  
pp. 116150 ◽  
Author(s):  
Michalis Kassinopoulos ◽  
Georgios D. Mitsis
Keyword(s):  
Heart Rate ◽  
Resting State ◽  
Physiological Response ◽  
Resting State Fmri ◽  
Response Functions

scholarly journals Identification of Physiological Response Functions to Correct for Fluctuations in Resting-State fMRI related to Heart Rate and Respiration

2019 ◽  
Author(s):  
Michalis Kassinopoulos ◽  
Georgios D. Mitsis
Keyword(s):  
Heart Rate ◽  
Response Function ◽  
Resting State ◽  
Physiological Response ◽  
Resting State Fmri ◽  
Cardiac Response ◽  
Physiological Data ◽  
Response Functions ◽  
Breathing Patterns ◽  
The Mean

AbstractFunctional magnetic resonance imaging (fMRI) is widely viewed as the gold standard for studying brain function due to its high spatial resolution and non-invasive nature. However, it is well established that changes in breathing patterns and heart rate strongly influence the blood oxygen-level dependent (BOLD) fMRI signal and this, in turn, can have considerable effects on fMRI studies, particularly resting-state studies. The dynamic effects of physiological processes are often quantified by using convolution models along with simultaneously recorded physiological data. In this context, physiological response function (PRF) curves (cardiac and respiratory response functions), which are convolved with the corresponding physiological fluctuations, are commonly employed. While it has often been suggested that the PRF curves may be region- or subject- specific, it is still an open question whether this is the case. In the present study, we propose a novel framework for the robust estimation of PRF curves and use this framework to rigorously examine the implications of using population-, subject-, session- and scan-specific PRF curves. The proposed framework was tested on resting-state fMRI and physiological data from the Human Connectome Project. Our results suggest that PRF curves vary significantly across subjects and, to a lesser extent, across sessions from the same subject. These differences can be partly attributed to physiological variables such as the mean and variance of the heart rate during the scan. The proposed methodological framework can be used to obtain robust scan-specific PRF curves from data records with duration longer than 5 minutes, exhibiting significantly improved performance compared to previously defined canonical cardiac and respiration response functions. Besides removing physiological confounds from the BOLD signal, accurate modeling of subject- (or session-/scan-) specific PRF curves is of importance in studies that involve populations with altered vascular responses, such as aging subjects.HighlightsPhysiological response functions (PRF) vary considerably across subjects/sessionsScan-specific PRF curves can be obtained from data records longer than 5 minutesThe shape of the cardiac response function is linked to the mean heart rate (HR)Brain regions affected by HR and breathing patterns exhibit substantial overlapHR and breathing patterns affect distinct regions as compared to cardiac pulsatility

Heart rate variability interacts with recovery of resting-state connectivity in amygdala circuitry and the persistence of symptoms after sport-related concussion

Neurology ◽  
2020 ◽  
Vol 95 (20 Supplement 1) ◽  
pp. S15.2-S16
Author(s):  
Kevin Bickart ◽  
Christopher Andrew Sheridan ◽  
Corey M. Thibeault ◽  
Robert Hamilton ◽  
James LeVangie ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Resting State ◽  
Autonomic Function ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Network Recovery ◽  
Symptom Persistence ◽  
Resting State Connectivity ◽  
Over Time

ObjectiveWe investigated longitudinal trajectories of resting-state fMRI (rsfMRI), autonomic function, and graded symptoms after sport-related concussion (SRC).BackgroundLimbic circuitry may be particularly vulnerable to traumatic brain injury, which could explain the affective and autonomic dysfunction that some patients develop. Relatively few studies have performed longitudinal rsfMRI analyses in concussion and fewer have combined imaging with autonomic and symptom data. We leveraged published limbic rsfMRI networks centered on the amygdala that include core affective and autonomic structures to test whether athletes with SRC would have altered connectivity, and that network recovery would be related to measures of autonomic function and symptom persistence.Design/MethodsWe compared rsfMRI connectivity of amygdala networks in college athletes with SRC (N = 31, female = 14) at three time points after concussion (T1 = 4 days, T2 = 10–14 days, T3 = 2–3 months) and matched controls with no concussion (in-sport control [ISC] N = 36, female = 17).ResultsSRCs show greater amygdala network connectivity as compared to ISCs (T1 p = 0.003, T2 p = 0.014) that normalizes over time (T3 p = 0.182). However, SRCs with higher versus lower heart rate variability (HRV), as measured by pNN50 at T1, have opposing trajectories of connectivity. That is, SRCs with higher HRV have connectivity that starts high and normalizes over time (T1 p = 0.001, T2 p = 0.055, T3 p = 0.576) whereas SRCs with lower HRV have connectivity that increases over time (T1 p = 0.429, T2 p = 0.050, T3 p = 0.002). Furthermore, SRCs with greatest connectivity at T3, presumably the least recovered, have the most symptoms on the Graded Symptom Checklist at ∼3 months (r = 0.635, p = 0.001).ConclusionsHeightened connectivity of amygdala circuitry acutely after a concussion and its normalization over time may be protective, and with HRV, may be a biomarker of symptom persistence.

scholarly journals Improved 7 Tesla resting-state fMRI connectivity measurements by cluster-based modeling of respiratory volume and heart rate effects

NeuroImage ◽  
2017 ◽  
Vol 153 ◽  
pp. 262-272 ◽  
Author(s):  
Joana Pinto ◽  
Sandro Nunes ◽  
Marta Bianciardi ◽  
Afonso Dias ◽  
L. Miguel Silveira ◽  
...  
Keyword(s):  
Heart Rate ◽  
Resting State ◽  
Resting State Fmri ◽  
7 Tesla ◽  
Respiratory Volume ◽  
Rate Effects

scholarly journals Characterization of brain-wide somatosensory BOLD fMRI in mice under dexmedetomidine/isoflurane and ketamine/xylazine

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Taeyi You ◽  
Geun Ho Im ◽  
Seong-Gi Kim
Keyword(s):  
Heart Rate ◽  
Resting State ◽  
Intravenous Infusion ◽  
Resting State Fmri ◽  
Bold Response ◽  
Bold Fmri ◽  
Whisker Pad ◽  
The Brain ◽  
Higher Sensitivity

AbstractMouse fMRI under anesthesia has become increasingly popular due to improvement in obtaining brain-wide BOLD response. Medetomidine with isoflurane has become well-accepted for resting-state fMRI, but whether this combination allows for stable, expected, and robust brain-wide evoked response in mice has yet to be validated. We thus utilized intravenous infusion of dexmedetomidine with inhaled isoflurane and intravenous infusion of ketamine/xylazine to elucidate whether stable mouse physiology and BOLD response are obtainable in response to simultaneous forepaw and whisker-pad stimulation throughout 8 h. We found both anesthetics result in hypercapnia with depressed heart rate and respiration due to self-breathing, but these values were stable throughout 8 h. Regardless of the mouse condition, brain-wide, robust, and stable BOLD response throughout the somatosensory axis was observed with differences in sensitivity and dynamics. Dexmedetomidine/isoflurane resulted in fast, boxcar-like, BOLD response with consistent hemodynamic shapes throughout the brain. Ketamine/xylazine response showed higher sensitivity, prolonged BOLD response, and evidence for cortical disinhibition as significant bilateral cortical response was observed. In addition, differing hemodynamic shapes were observed between cortical and subcortical areas. Overall, we found both anesthetics are applicable for evoked mouse fMRI studies.

070 Respiratory, cardiac, EEG, BOLD signals and functional connectivity over multiple microsleep episodes

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A29-A29
Author(s):  
Chun Siong Soon ◽  
Ksenia Vinogradova ◽  
Ju Lynn Ong ◽  
Vince Calhoun ◽  
Thomas Liu ◽  
...  
Keyword(s):  
Heart Rate ◽  
Functional Connectivity ◽  
Resting State ◽  
Sleep Onset ◽  
Resting State Fmri ◽  
Bold Signal ◽  
Falling Asleep ◽  
Eye Opening ◽  
Signal Changes ◽  
Eye Closure

Abstract Introduction Brief intrusions of unintended sleep can occur in various contexts, for example during resting-state fMRI scans. In addition to changes in neural activity, such microsleep episodes are also associated with shifts in respiration and heartrate. Here we investigated how these concurrent changes alter the dynamics of the BOLD signal in the brain and estimates of functional connectivity. Methods Ten participants underwent 6 runs of 20 minute resting-state fMRI scans with concurrent respiration, PPG and EEG recording. Realtime eye-closure monitoring combined with post eye-opening self-reports were used to identify microsleep episodes of different durations. Results During microsleep, sustained reductions were observed in arousal as assessed by EEG (ratio of alpha to delta and theta bands), as expected. In comparison, cortical BOLD signal exhibited more complex, temporally multiphasic changes which were consistent across different microsleep durations from 4 to 44s: (i) an initial sleep-onset dip reaching a nadir after ~6s, followed by (ii) an increase above wake baseline that plateaued till awakening. On awakening, (iii) a transient positive bump occurred up to 6s, followed by (iv) an undershoot below baseline lasting ~30s. While seen across the whole brain, these changes showed regional variations, e.g., the signal plateau in the thalamus remained below wake baseline. Sleep onset and awakening were also associated with respective reductions and increases in respiration and heart rate, which affect blood oxygen levels. Brain functional connectivity estimates were altered by the frequency of falling asleep, and this was not resolved by global signal regression. Conclusion Falling asleep and awakening are shown here to be associated with large, widespread BOLD signal changes consistent across varied durations of microsleep. These signal changes are intimately intertwined with shifts in respiration and heart rate, which are influenced by common brainstem nuclei controlling sleep. These autonomic contributions to ‘brain signal’ changes at microsleep onset and awakening are integral to sleep, and urge the integration of autonomic and central nervous system contributions to BOLD signal into frameworks for understanding brain function using fMRI. In addition, the correlation between frequency of microsleep and extent of altered functional connectivity highlight the need to minimize sleep during resting state scans. Support (if any) NMRC/STaR/015/2013

Resting-state fMRI used for intraoperative neuronavigation in children

2013 ◽  
Vol 44 (S 01) ◽  
Author(s):  
C Dorfer ◽  
T Czech ◽  
G Kasprian ◽  
A Azizi ◽  
J Furtner ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri
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