A quantitative comparison of simultaneous BOLD fMRI and NIRS recordings during functional brain activation.

Neuroimage

Neural Systems Group, NMR Center, Massachusetts General Hospital-Harvard Medical School, Harvard-MIT Division of Health Sciences and Technology, Charlestown 02129, USA.

Published: October 2002

Near-infrared spectroscopy (NIRS) has been used to noninvasively monitor adult human brain function in a wide variety of tasks. While rough spatial correspondences with maps generated from functional magnetic resonance imaging (fMRI) have been found in such experiments, the amplitude correspondences between the two recording modalities have not been fully characterized. To do so, we simultaneously acquired NIRS and blood-oxygenation level-dependent (BOLD) fMRI data and compared Delta(1/BOLD) (approximately R(2)(*)) to changes in oxyhemoglobin, deoxyhemoglobin, and total hemoglobin concentrations derived from the NIRS data from subjects performing a simple motor task. We expected the correlation with deoxyhemoglobin to be strongest, due to the causal relation between changes in deoxyhemoglobin concentrations and BOLD signal. Instead we found highly variable correlations, suggesting the need to account for individual subject differences in our NIRS calculations. We argue that the variability resulted from systematic errors associated with each of the signals, including: (1) partial volume errors due to focal concentration changes, (2) wavelength dependence of this partial volume effect, (3) tissue model errors, and (4) possible spatial incongruence between oxy- and deoxyhemoglobin concentration changes. After such effects were accounted for, strong correlations were found between fMRI changes and all optical measures, with oxyhemoglobin providing the strongest correlation. Importantly, this finding held even when including scalp, skull, and inactive brain tissue in the average BOLD signal. This may reflect, at least in part, the superior contrast-to-noise ratio for oxyhemoglobin relative to deoxyhemoglobin (from optical measurements), rather than physiology related to BOLD signal interpretation.

Download full-text PDF

Source

Publication Analysis

Top Keywords

bold signal
12
bold fmri
8
partial volume
8
concentration changes
8
bold
5
nirs
5
changes
5
deoxyhemoglobin
5
quantitative comparison
4
comparison simultaneous
4

Similar Publications

Advances in the fMRI analysis of the default mode network: a review.

Brain Struct Funct

December 2024

Departamento de Psicobiología y Metodología en Ciencias del Comportamiento, Facultad de Psicología, Universidad Complutense de Madrid, Pozuelo de Alarcón, 28223, Madrid, Spain.

The default mode network (DMN) is a singular pattern of synchronization between brain regions, usually observed using resting-state functional magnetic resonance imaging (rs-fMRI) and functional connectivity analyses. In comparison to other brain networks that are primarily involved in attentional-demanding tasks (such as the frontoparietal network), the DMN is linked with self-referential activities, and alterations in its pattern of connectivity have been related to a wide range of disorders. Structural connectivity analyses have highlighted the vital role of the posterior cingulate cortex and the precuneus as integrative hubs, and advanced parcellation methods have further contributed to elucidate the DMN's regions, enriching its explanatory potential across cognitive functions and dysfunctions.

View Article and Find Full Text PDF

Concurrent optoacoustic tomography and magnetic resonance imaging of resting-state functional connectivity in the mouse brain.

Nat Commun

December 2024

Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, Zurich, Switzerland.

Resting-state functional connectivity (rsFC) has been essential to elucidate the intricacy of brain organization, further revealing clinical biomarkers of neurological disorders. Although functional magnetic resonance imaging (fMRI) remains a cornerstone in the field of rsFC recordings, its interpretation is often hindered by the convoluted physiological origin of the blood-oxygen-level-dependent (BOLD) contrast affected by multiple factors. Here, we capitalize on the unique concurrent multiparametric hemodynamic recordings of a hybrid magnetic resonance optoacoustic tomography platform to comprehensively characterize rsFC in female mice.

View Article and Find Full Text PDF

Introduction: Detrusor contractions can be classified as either volitional or involuntary. The latter are a hallmark of urge urinary incontinence. Understanding differences in neuroactivation associated with both types of contractions can help elucidate pathophysiology and therapeutic targets.

View Article and Find Full Text PDF

ACTION: Augmentation and computation toolbox for brain network analysis with functional MRI.

Neuroimage

December 2024

Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States. Electronic address:

Functional magnetic resonance imaging (fMRI) has been increasingly employed to investigate functional brain activity. Many fMRI-related software/toolboxes have been developed, providing specialized algorithms for fMRI analysis. However, existing toolboxes seldom consider fMRI data augmentation, which is quite useful, especially in studies with limited or imbalanced data.

View Article and Find Full Text PDF

Integration of partially observed multimodal and multiscale neural signals for estimating a neural circuit using dynamic causal modeling.

PLoS Comput Biol

December 2024

Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea.

Integrating multiscale, multimodal neuroimaging data is essential for a comprehensive understanding of neural circuits. However, this is challenging due to the inherent trade-offs between spatial coverage and resolution in each modality, necessitating a computational strategy that combines modality-specific information effectively. This study introduces a dynamic causal modeling (DCM) framework designed to address the challenge of combining partially observed, multiscale signals across a larger-scale neural circuit by employing a shared neural state model with modality-specific observation models.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!