Dataset Information

Evaluation of the Contribution of Signals Originating from Large Blood Vessels to Signals of Functionally Specific Brain Areas.

ABSTRACT: The fusiform face area (FFA) is known to play a pivotal role in face processing. The FFA is located in the ventral region, at the base of the brain, through which large blood vessels run. The location of the FFA via functional MRI (fMRI) may be influenced by these large blood vessels. Responses of large blood vessels may not exactly correspond to neuronal activity in a target area, because they may be diluted and influenced by inflow effects. In this study, we investigated the effects of large blood vessels in the FFA, that is, whether the FFA includes large blood vessels and/or whether inflow signals contribute to fMRI signals of the FFA. For this purpose, we used susceptibility-weighted imaging (SWI) sequences to visualize large blood vessels and dual-echo gradient-echo echo-planar imaging (GE-EPI) to measure inflow effects. These results showed that the location and response signals of the FFA were not influenced by large blood vessels or inflow effects, although large blood vessels were located near the FFA. Therefore, the data from the FFA obtained by individual analysis were robust to large blood vessels but leaving a warning that the data obtained by group analysis may be prone to large blood vessels.

SUBMITTER: Chung JY

PROVIDER: S-EPMC4564580 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Evaluation of the Contribution of Signals Originating from Large Blood Vessels to Signals of Functionally Specific Brain Areas.

Chung Jun-Young JY Sung Yul-Wan YW Ogawa Seiji S

BioMed research international 20150827

The fusiform face area (FFA) is known to play a pivotal role in face processing. The FFA is located in the ventral region, at the base of the brain, through which large blood vessels run. The location of the FFA via functional MRI (fMRI) may be influenced by these large blood vessels. Responses of large blood vessels may not exactly correspond to neuronal activity in a target area, because they may be diluted and influenced by inflow effects. In this study, we investigated the effects of large b ...[more]

PMID: 26413511

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Project description:BACKGROUND:The systemic low-frequency oscillation (sLFO) functional (f)MRI signals extracted from the internal carotid artery (ICA) and the superior sagittal sinus (SSS) are found to have valuable physiological information. PURPOSE:1) To further develop and validate a method utilizing these signals to measure the delay times from the ICAs and the SSS. 2) To establish the delay time as an effective perfusion biomarker that associates with cerebral circulation time (CCT). 3) To explore within subject variations, and the effects of gender and age on the delay times. STUDY TYPE:Prospective. SUBJECTS:In all, 100 healthy adults (Human Connectome Project [HCP], age range 22-36 years, 54 females and 46 males), 56 healthy children (Adolescent Brain Cognitive Development project) were included. FIELD STRENGTH/SEQUENCE:Echo planar imaging (EPI) sequence at 3T. ASSESSMENT:The sLFO fMRI signals from the ICAs and the SSSs were extracted from the resting state fMRI data. The maximum cross-correlation coefficients and their corresponding delay times were calculated. The gender and age differences of delay times were assessed statistically. STATISTICAL TESTS:T-tests were conducted to measure the gender differences. The Kruskal-Wallis test was used to detect age differences. RESULTS:Consistent and robust results were found from 80% of the 400 HCP scans included. Negative correlations (-0.67) between the ICA and the SSS signals were found with the ICA signal leading the SSS signal by ?5 sec. Within subject variation was 2.23 sec at the 5% significance level. The delay times were not significantly different between genders (P = 0.9846, P = 0.2288 for the left and right ICA, respectively). Significantly shorter delay times (4.3 sec) were found in the children than in the adults (P < 0.01). DATA CONCLUSION:We have shown that meaningful perfusion information (ie, CCT) can be derived from the sLFO fMRI signals of the large blood vessels. LEVEL OF EVIDENCE:1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;50:1504-1513.

Dataset Information

Evaluation of the Contribution of Signals Originating from Large Blood Vessels to Signals of Functionally Specific Brain Areas.

Publications

Evaluation of the Contribution of Signals Originating from Large Blood Vessels to Signals of Functionally Specific Brain Areas.

Similar Datasets

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