scholarly journals The resting-state fMRI arterial signal predicts differential blood transit time through the brain

2018 ◽  
Vol 39 (6) ◽  
pp. 1148-1160 ◽  
Author(s):  
Yunjie Tong ◽  
Jinxia (Fiona) Yao ◽  
J Jean Chen ◽  
Blaise deB Frederick
Keyword(s):  
Transit Time ◽  
Resting State ◽  
Time Delays ◽  
Carotid Arteries ◽  
Near Infrared ◽  
Low Frequency ◽  
Hemoglobin Concentration ◽  
Resting State Fmri ◽  
Single Subject ◽  
The Brain

Previous studies have found that aperiodic, systemic low-frequency oscillations (sLFOs) are present in blood-oxygen-level-dependent (BOLD) data. These signals are in the same low frequency band as the “resting state” signal; however, they are distinct signals which represent non-neuronal, physiological oscillations. The same sLFOs are found in the periphery (i.e. finger tips) as changes in oxy/deoxy-hemoglobin concentration using concurrent near-infrared spectroscopy. Together, this evidence points toward an extra-cerebral origin of these sLFOs. If this is the case, it is expected that these sLFO signals would be found in the carotid arteries with time delays that precede the signals found in the brain. To test this hypothesis, we employed the publicly available MyConnectome dataset (a two-year longitudinal study of a single subject) to extract the sLFOs in the internal carotid arteries (ICAs) with the help of the T1/T2-weighted images. Significant, but negative, correlations were found between the LFO BOLD signals from the ICAs and (1) the global signal (GS), (2) the superior sagittal sinus, and (3) the jugulars. We found the consistent time delays between the sLFO signals from ICAs, GS and veins which coincide with the blood transit time through the cerebral vascular tree.

Coupling between cerebrovascular oscillations and CSF flow fluctuations during wakefulness: An fMRI study

2022 ◽  
pp. 0271678X2210746
Author(s):  
Ho-Ching (Shawn) Yang ◽  
Ben Inglis ◽  
Thomas M Talavage ◽  
Vidhya Vijayakrishnan Nair ◽  
Jinxia (Fiona) Yao ◽  
...  
Keyword(s):  
Time Delays ◽  
Cerebral Blood Volume ◽  
Low Frequency ◽  
Nrem Sleep ◽  
Resting State Fmri ◽  
Csf Flow ◽  
Flow Fluctuations ◽  
Fmri Study ◽  
The Individual ◽  
The Brain

It is commonly believed that cerebrospinal fluid (CSF) movement is facilitated by blood vessel wall movements (i.e., hemodynamic oscillations) in the brain. A coherent pattern of low frequency hemodynamic oscillations and CSF movement was recently found during non-rapid eye movement (NREM) sleep via functional MRI. This finding raises other fundamental questions: 1) the explanation of coupling between hemodynamic oscillations and CSF movement from fMRI signals; 2) the existence of the coupling during wakefulness; 3) the direction of CSF movement. In this resting state fMRI study, we proposed a mechanical model to explain the coupling between hemodynamics and CSF movement through the lens of fMRI. Time delays between CSF movement and global hemodynamics were calculated. The observed delays between hemodynamics and CSF movement match those predicted by the model. Moreover, by conducting separate fMRI scans of the brain and neck, we confirmed the low frequency CSF movement at the fourth ventricle is bidirectional. Our finding also demonstrates that CSF movement is facilitated by changes in cerebral blood volume mainly in the low frequency range, even when the individual is awake.

scholarly journals Abnormal Baseline Brain Activity in Patients with Pulsatile Tinnitus: A Resting-State fMRI Study

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Lv Han ◽  
Liu Zhaohui ◽  
Yan Fei ◽  
Li Ting ◽  
Zhao Pengfei ◽  
...  
Keyword(s):  
Resting State ◽  
Structural Changes ◽  
Brain Activity ◽  
Inferior Frontal Gyrus ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Pulsatile Tinnitus ◽  
Spontaneous Brain Activity ◽  
Fmri Study ◽  
The Brain

Numerous investigations studying the brain functional activity of the tinnitus patients have indicated that neurological changes are important findings of this kind of disease. However, the pulsatile tinnitus (PT) patients were excluded in previous studies because of the totally different mechanisms of the two subtype tinnitus. The aim of this study is to investigate whether altered baseline brain activity presents in patients with PT using resting-state functional magnetic resonance imaging (rs-fMRI) technique. The present study used unilateral PT patients (n=42) and age-, sex-, and education-matched normal control subjects (n=42) to investigate the changes in structural and amplitude of low-frequency (ALFF) of the brain. Also, we analyzed the relationships between these changes with clinical data of the PT patients. Compared with normal controls, PT patients did not show any structural changes. PT patients showed significant increased ALFF in the bilateral precuneus, and bilateral inferior frontal gyrus (IFG) and decreased ALFF in multiple occipital areas. Moreover, the increased THI score and PT duration was correlated with increased ALFF in precuneus and bilateral IFG. The abnormalities of spontaneous brain activity reflected by ALFF measurements in the absence of structural changes may provide insights into the neural reorganization in PT patients.

Exploring the brain contour of implicit infra-low frequency EEG neurofeedback: a resting state fMRI study

2018 ◽  
Vol 131 ◽  
pp. S76
Author(s):  
O.R. Dobrushina ◽  
E.V. Pechenkova ◽  
R.M. Vlasova ◽  
A.D. Rumshiskaya ◽  
L.D. Litvinova ◽  
...  
Keyword(s):  
Resting State ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Fmri Study ◽  
Eeg Neurofeedback ◽  
The Brain

Functional MRI

2018 ◽  
Keyword(s):  
Functional Mri ◽  
Resting State ◽  
Brain Activity ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Blood Oxygen Level Dependent ◽  
Brain Regions ◽  
Functional Brain Imaging ◽  
Fmri Signal ◽  
The Brain

Functional MRI with BOLD (Blood Oxygen Level Dependent) imaging is one of the commonly used modalities for studying brain function in neuroscience. The underlying source of the BOLD fMRI signal is the variation in oxyhemoglobin to deoxyhemoglobin ratio at the site of neuronal activity in the brain. fMRI is mostly used to map out the location and intensity of brain activity that correlate with mental activities. In recent years, a new approach to fMRI was developed that is called resting-state fMRI. The fMRI signal from this method does not require the brain to perform any goal-directed task; it is acquired with the subject at rest. It was discovered that there are low-frequency fluctuations in the fMRI signal in the brain at rest. The signals originate from spatially distinct functionally related brain regions but exhibit coherent time-synchronous fluctuations. Several of the networks have been identified and are called resting-state networks. These networks represent the strength of the functional connectivity between distinct functionally related brain regions and have been used as imaging markers of various neurological and psychiatric diseases. Resting-state fMRI is also ideally suited for functional brain imaging in disorders of consciousness and in subjects under anesthesia. This book provides a review of the basic principles of fMRI (signal sources, acquisition methods, and data analysis) and its potential clinical applications.

Basic Principles of Functional MRI

2018 ◽  
pp. 20-29
Author(s):  
Cheuk Ying Tang
Keyword(s):  
Functional Mri ◽  
Resting State ◽  
Brain Activity ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Functional Brain Imaging ◽  
Basic Principles ◽  
Fmri Signal ◽  
The Brain

Blood oxygen level dependent (BOLD) MRI, also called functional MRI (fMRI), is one of the most widely used modalities for studying brain function. The underlying source of the fMRI signal is blood flow and the oxygenation state of hemoglobin. fMRI is mostly used to map out the location and intensity of brain activity that correlate with mental activities. In recent years, a new approach to fMRI has been developed that is called resting-state fMRI. The fMRI signal from this method does not require the brain to perform a goal-directed task; it is acquired with the subject at rest. It was discovered that there are low-frequency fluctuations in the fMRI signal in the brain at rest. These signals come from spatially distinct brain regions but exhibit coherent, time-synchronous fluctuations. Several of the networks have been identified and are called resting-state networks. The networks represent the strength of the functional connectivity between distinct brain regions and have been used as imaging biomarkers for various neurological and psychiatric diseases. Resting-state fMRI is also ideally suited for functional brain imaging in disorders of consciousness and in subjects under anesthesia. In this chapter, we provide an introductory review of the basic principles of fMRI: signal sources, acquisition methods, and data analysis.

scholarly journals Predicting Sex from Resting-State fMRI Across Multiple Independent Acquired Datasets

2020 ◽  
Author(s):  
Obada Al Zoubi ◽  
Masaya Misaki ◽  
Aki Tsuchiyagaito ◽  
Vadim Zotev ◽  
Evan White ◽  
...  
Keyword(s):  
Sex Differences ◽  
Resting State ◽  
Functional Organization ◽  
Intraclass Correlation ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Bold Signal ◽  
Brain Response ◽  
Human Connectome Project ◽  
The Brain

AbstractSex is an important biological variable often used in analyzing and describing the functional organization of the brain during cognitive and behavioral tasks. Several prior studies have shown that blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) functional connectivity (FC) can be used to differentiate sex among individuals. Herein, we demonstrate that sex can be further classified with high accuracy using the intrinsic BOLD signal fluctuations from resting-state fMRI (rs-fMRI). We adopted the amplitude of low-frequency fluctuation (ALFF), and the fraction of ALFF (fALFF) features from the automated anatomical atlas (AAL) and Power’s functional atlas as an input to different machine learning (ML) methods. Using datasets from five independently acquired subject cohorts and with eight fMRI scanning sessions, we comprehensively assessed unbiased performance using nested-cross validation for within-sample and across sample accuracies. The results demonstrated high prediction accuracies for the Human Connectome Project (HCP) dataset (area under cure (AUC) > 0.89). The yielded accuracies suggest that sex difference is embodied and well-pronounced in the low-frequency BOLD signal fluctuation. The performance degrades with the heterogeneity of the cohort and suggests that other factors,.e.g. psychiatric disorders and demographics influences the BOLD signal and may interact with the classification of sex. In addition, the results revealed high learning generalizability with the HCP scan, but not across different datasets. The intraclass correlation coefficient (ICC) across HCP scans showed moderate-to-good reliability based on atlas selection (ICC = 0.65 [0.63-0.67] and ICC= 0.78 [0.76-0.80].). We also assessed the effect of scan duration on the predictability of sex and showed that sex differences could be detected even with a short rs-fMRI scan (e.g., 2 minutes). Moreover, we provided statistical maps of the brain regions differentially recruited by or predicting sex using Shapely values and determined an overlap with previous reports of brain response due to sex differences. Altogether, our analysis suggests that sex differences are well-pronounced in rs-fMRI and should be considered seriously in any study design, analysis, or interpretation.

Functional abnormalities in Broca’s area in adolescents with ADHD: A resting-state fMRI study

2015 ◽  
Vol 51 (1) ◽  
Author(s):  
Michał Pikusa ◽  
Rafał Jończyk
Keyword(s):  
Resting State ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Broca’S Area ◽  
Broca's Area ◽  
Hyperactivity Disorder ◽  
Spontaneous Neural Activity ◽  
Fmri Study ◽  
Brodmann Areas ◽  
Underlying Causes

AbstractThere is evidence that attention-deficit/hyperactivity disorder (ADHD) is associated with linguistic difficulties. However, the pathophysiology underlying these difficulties is yet to be determined. This study investigates functional abnormalities in Broca’s area, which is associated with speech production and processing, in adolescents with ADHD by means of resting-state fMRI. Data for the study was taken from the ADHD-200 project and included 267 ADHD patients (109 with combined inattentive/hyperactive subtype and 158 with inattentive subtype) and 478 typically-developing control (TDC) subjects. An analysis of fractional amplitude of low-frequency fluctuations (fALFF), which reflects spontaneous neural activity, in Broca’s area (Brodmann Areas 44/45) was performed on the data and the results were compared statistically across the participant groups. fALFF was found to be significantly lower in the ADHD inattentive group as compared to TDC in BA 44, and in the ADHD combined group as compared to TDC in BA 45. The results suggest that there are functional abnormalities in Broca’s area with people suffering from ADHD, and that the localization of these abnormalities might be connected to particular language deficits associated with ADHD subtypes, which we discuss in the article. The findings might help explore the underlying causes of specific language difficulties in ADHD.

scholarly journals Wrist and finger motor representations embedded in the cerebral and cerebellar resting-state activation

2021 ◽  
Author(s):  
Toshiki Kusano ◽  
Hiroki Kurashige ◽  
Isao Nambu ◽  
Yoshiya Moriguchi ◽  
Takashi Hanakawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Body Part ◽  
Resting State Fmri ◽  
Body Parts ◽  
Finger Movements ◽  
Multiple Components ◽  
Brain Activity Pattern ◽  
Motor Representations ◽  
The Brain

AbstractSeveral functional magnetic resonance imaging (fMRI) studies have demonstrated that resting-state brain activity consists of multiple components, each corresponding to the spatial pattern of brain activity induced by performing a task. Especially in a movement task, such components have been shown to correspond to the brain activity pattern of the relevant anatomical region, meaning that the voxels of pattern that are cooperatively activated while using a body part (e.g., foot, hand, and tongue) also behave cooperatively in the resting state. However, it is unclear whether the components involved in resting-state brain activity correspond to those induced by the movement of discrete body parts. To address this issue, in the present study, we focused on wrist and finger movements in the hand, and a cross-decoding technique trained to discriminate between the multi-voxel patterns induced by wrist and finger movement was applied to the resting-state fMRI. We found that the multi-voxel pattern in resting-state brain activity corresponds to either wrist or finger movements in the motor-related areas of each hemisphere of the cerebrum and cerebellum. These results suggest that resting-state brain activity in the motor-related areas consists of the components corresponding to the elementary movements of individual body parts. Therefore, the resting-state brain activity possibly has a finer structure than considered previously.

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