Dataset Information

Static and dynamic functional connectivity analysis of cerebrovascular reactivity: An fMRI study.

ABSTRACT:

Background

Cerebrovascular reactivity (CVR) is an important aspect of brain function, and as such it is important to understand relationship between CVR and functional connectivity.

Methods

This research studied the role of CVR, or the brain's ability to react to vasoactive stimuli on brain functional connectivity by scanning subjects with blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) while they periodically inhale room air and a CO 2-enriched gas mixture. We developed a new metric to measure the effect of CVR on each intrinsic connectivity network (ICN), which contrasts to voxel-wise CVR. We also studied the changes in whole-brain connectivity patterns using both static functional network connectivity (sFNC) and dynamic FNC (dFNC).

Results

We found that network connectivity is generally weaker during vascular dilation, which is supported by previous research. The dFNC analysis revealed that participants did not return to the pre-CO 2 inhalation state, suggesting that one-minute periods of room-air inhalation is not enough for the CO 2 effect to fully dissipate.

Conclusions

Cerebrovascular reactivity is one tool that the cerebrovascular system uses to ensure the constant, finely-tuned flow of oxygen to function properly. Understanding the relationship between CVR and brain dynamism can provide unique information about cerebrovascular diseases and general brain function. We observed that CVR has a wide, but consistent relationship to connectivity patterns between functional networks.

SUBMITTER: Lewis N

PROVIDER: S-EPMC7303385 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Static and dynamic functional connectivity analysis of cerebrovascular reactivity: An fMRI study.

Lewis Noah N Lu Hanzhang H Liu Peiying P Hou Xirui X Damaraju Eswar E Iraji Armin A Calhoun Vince V

Brain and behavior 20200427 6

<h4>Background</h4>Cerebrovascular reactivity (CVR) is an important aspect of brain function, and as such it is important to understand relationship between CVR and functional connectivity.<h4>Methods</h4>This research studied the role of CVR, or the brain's ability to react to vasoactive stimuli on brain functional connectivity by scanning subjects with blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) while they periodically inhale room air and a CO 2-enrich ...[more]

PMID: 32342644

Similar Datasets

Project description:Background: Advanced imaging techniques such as diffusion and functional MRI can be used to identify pathology-related changes to the brain's structural and functional connectivity (SC and FC) networks and mapping of these changes to disability and compensatory mechanisms in people with multiple sclerosis (pwMS). No study to date performed a comparison study to investigate which connectivity type (SC, static or dynamic FC) better distinguishes healthy controls (HC) from pwMS and/or classifies pwMS by disability status. Aims: We aim to compare the performance of SC, static FC, and dynamic FC (dFC) in classifying (a) HC vs. pwMS and (b) pwMS who have no disability vs. with disability. The secondary objective of the study is to identify which brain regions' connectome measures contribute most to the classification tasks. Materials and Methods: One hundred pwMS and 19 HC were included. Expanded Disability Status Scale (EDSS) was used to assess disability, where 67 pwMS who had EDSS<2 were considered as not having disability. Diffusion and resting-state functional MRI were used to compute the SC and FC matrices, respectively. Logistic regression with ridge regularization was performed, where the models included demographics/clinical information and either pairwise entries or regional summaries from one of the following matrices: SC, FC, and dFC. The performance of the models was assessed using the area under the receiver operating curve (AUC). Results: In classifying HC vs. pwMS, the regional SC model significantly outperformed others with a median AUC of 0.89 (p <0.05). In classifying pwMS by disability status, the regional dFC and dFC metrics models significantly outperformed others with a median AUC of 0.65 and 0.61 (p < 0.05). Regional SC in the dorsal attention, subcortical and cerebellar networks were the most important variables in the HC vs. pwMS classification task. Increased regional dFC in dorsal attention and visual networks and decreased regional dFC in frontoparietal and cerebellar networks in certain dFC states was associated with being in the group of pwMS with evidence of disability. Discussion: Damage to SCs is a hallmark of MS and, unsurprisingly, the most accurate connectomic measure in classifying patients and controls. On the other hand, dynamic FC metrics were most important for determining disability level in pwMS, and could represent functional compensation in response to white matter pathology in pwMS.

Dataset Information

Static and dynamic functional connectivity analysis of cerebrovascular reactivity: An fMRI study.

Background

Methods

Results

Conclusions

Publications

Static and dynamic functional connectivity analysis of cerebrovascular reactivity: An fMRI study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets