A practical modification to a resting state fMRI protocol for improved characterization of cerebrovascular function

ABSTRACTCerebrovascular 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 grey 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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AUTOMATED RECOGNITION OF DEPRESSION FROM FEWER-SHOT LEANING IN RESTING-STATE fMRI WITH ReHo USING DEEP CONVOLUTIONAL NEURAL NETWORK

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519421400686 ◽

2021 ◽

Author(s):

MI LI ◽

JINYU ZHANG ◽

QIAN ZHAI ◽

JIAMING KANG ◽

SHENGFU LU ◽

...

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Brain Regions ◽

Automated Recognition ◽

Convolutional Networks ◽

Behavioral Abnormalities ◽

Kernel Extreme Learning Machine ◽

Learning Machine ◽

Functional Areas

Up to now, there is still the absence of research about depression recognition using resting-state functional magnetic resonance imaging (rest_fMRI) and deep learning. Previous studies have shown that regional homogeneity (ReHo) of rest_fMRI (rest_ReHo_fMRI) is a characterization of the functional synchronization of adjacent voxels in brain regions, and the mental and behavioral abnormalities in depression are due to an imbalance of ReHo synchronization in some brain functional areas. Accordingly, this paper presents a method for depression recognition using rest_ReHo_fMRI. First, the rest_ReHo_fMRI is extracted from the preprocessed rest-fMRI by calculation. Then, deep convolutional networks (such as VGG16) pretrained on ImageNet are used to automatically complete extracting the classification features from rest_ReHo_fMRI. Finally, the Kernel Extreme Learning Machine (KELM) was used to classify the depression. The results of the test set show that the proposed method achieves 89.07% in sensitivity and 89.74% in specificity. This study suggests that features of rest_ReHo_fMRI can be used as biomarkers to distinguish depression from normal people.

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Characterization of resting-state fMRI-derived functional connectivity in patients with deficiency versus excess patterns of major depression

Complementary Therapies in Medicine ◽

10.1016/j.ctim.2014.12.010 ◽

2015 ◽

Vol 23 (1) ◽

pp. 7-13 ◽

Cited By ~ 7

Author(s):

Yin-feng Zhang ◽

Yu Han ◽

Yong-zhi Wang ◽

Yi-fan Zhang ◽

Hong-xiao Jia ◽

...

Keyword(s):

Major Depression ◽

Functional Connectivity ◽

Resting State ◽

Resting State Fmri

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

Scientific Reports ◽

10.1038/s41598-021-92582-5 ◽

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.

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