scholarly journals Resting state fMRI in pre-surgical brain mapping. Literature review

2018 ◽  
pp. 6-13 ◽  
Author(s):  
A. S. Smirnov ◽  
M. G. Sharaev ◽  
T. V. Melnikova-Pitskhelauri ◽  
V. Yu. Zhukov ◽  
A. E. Bikanov ◽  
...  
Keyword(s):  
Brain Mapping ◽  
Resting State ◽  
Preoperative Planning ◽  
Clinical Information ◽  
Complete Information ◽  
Resting State Fmri ◽  
Blood Oxygen Level Dependent ◽  
Blood Oxygen Level ◽  
Spontaneous Fluctuations ◽  
The Brain

Today, functional magnetic resonance imaging (fMRI) allows to plan surgery based on the topography of functionally important areas of the human brain cortex and tumor. This method can complement the surgical strategy with significant clinical information. The stimulus-dependent fMRI with motor and language paradigms is generally used for preoperative planning. The study outcome depends on the patient's ability to perform tasks paradigm, which is broken in brain tumors. In an attempt to overcome this problem, resting-state fMRI (rs-fMRI) is used for brain mapping. Rs-fMRI is based on the measurement of spontaneous fluctuations of the BOLD signal (blood oxygen level-dependent), representing the functional structure of the brain. In contrast to stimulus-dependent fMRI, rs-fMRI provides more complete information about functional architecture of the brain. rs-fMRI is used in conditions where the results of stimulusdependent fMRI may be falsely positive or in the absence of the possibility of its implementation. In aggregate, both methods significantly expand the efficiency and specificity of preoperative planning.

scholarly journals Resting-State Blood Oxygen Level-Dependent Functional MRI: A Paradigm Shift in Preoperative Brain Mapping

2015 ◽  
Vol 93 (6) ◽  
pp. 427-439 ◽  
Author(s):  
Eric C. Leuthardt ◽  
Monica Allen ◽  
Mudassar Kamran ◽  
Ammar H. Hawasli ◽  
Abraham Z. Snyder ◽  
...  
Keyword(s):  
Functional Mri ◽  
Brain Mapping ◽  
Resting State ◽  
Paradigm Shift ◽  
Blood Oxygen Level Dependent ◽  
Blood Oxygen ◽  
Oxygen Level ◽  
Blood Oxygen Level

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.

scholarly journals Propagated infra-slow intrinsic brain activity reorganizes across wake and slow wave sleep

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Anish Mitra ◽  
Abraham Z Snyder ◽  
Enzo Tagliazucchi ◽  
Helmut Laufs ◽  
Marcus E Raichle
Keyword(s):  
Slow Wave ◽  
Resting State ◽  
Slow Wave Sleep ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Blood Oxygen Level Dependent ◽  
Intrinsic Activity ◽  
Subcortical Structures ◽  
Blood Oxygen Level ◽  
Intrinsic Brain Activity

Propagation of slow intrinsic brain activity has been widely observed in electrophysiogical studies of slow wave sleep (SWS). However, in human resting state fMRI (rs-fMRI), intrinsic activity has been understood predominantly in terms of zero-lag temporal synchrony (functional connectivity) within systems known as resting state networks (RSNs). Prior rs-fMRI studies have found that RSNs are generally preserved across wake and sleep. Here, we use a recently developed analysis technique to study propagation of infra-slow intrinsic blood oxygen level dependent (BOLD) signals in normal adults during wake and SWS. This analysis reveals marked changes in propagation patterns in SWS vs. wake. Broadly, ordered propagation is preserved within traditionally defined RSNs but lost between RSNs. Additionally, propagation between cerebral cortex and subcortical structures reverses directions, and intra-cortical propagation becomes reorganized, especially in visual and sensorimotor cortices. These findings show that propagated rs-fMRI activity informs theoretical accounts of the neural functions of sleep.

scholarly journals Resting-State Functional Connectivity in Mathematical Expertise

2021 ◽  
Vol 11 (4) ◽  
pp. 430
Author(s):  
Miseon Shim ◽  
Han-Jeong Hwang ◽  
Ulrike Kuhl ◽  
Hyeon-Ae Jeon
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
Blood Oxygen Level Dependent ◽  
Brain Regions ◽  
Resting State Functional Connectivity ◽  
Blood Oxygen Level ◽  
Orbital Gyrus ◽  
Neural Efficiency ◽  
Mathematical Expertise

To what extent are different levels of expertise reflected in the functional connectivity of the brain? We addressed this question by using resting-state functional magnetic resonance imaging (fMRI) in mathematicians versus non-mathematicians. To this end, we investigated how the two groups of participants differ in the correlation of their spontaneous blood oxygen level-dependent fluctuations across the whole brain regions during resting state. Moreover, by using the classification algorithm in machine learning, we investigated whether the resting-state fMRI networks between mathematicians and non-mathematicians were distinguished depending on features of functional connectivity. We showed diverging involvement of the frontal–thalamic–temporal connections for mathematicians and the medial–frontal areas to precuneus and the lateral orbital gyrus to thalamus connections for non-mathematicians. Moreover, mathematicians who had higher scores in mathematical knowledge showed a weaker connection strength between the left and right caudate nucleus, demonstrating the connections’ characteristics related to mathematical expertise. Separate functional networks between the two groups were validated with a maximum classification accuracy of 91.19% using the distinct resting-state fMRI-based functional connectivity features. We suggest the advantageous role of preconfigured resting-state functional connectivity, as well as the neural efficiency for experts’ successful performance.

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.

scholarly journals Neuronal variability of Resting State activity in Eating Disorders: increase and decoupling in Ventral Attention Network and relation with clinical symptoms

2019 ◽  
Vol 55 ◽  
pp. 10-17 ◽  
Author(s):  
Angela V. Spalatro ◽  
Federico Amianto ◽  
Zirui Huang ◽  
Federico D’Agata ◽  
Mauro Bergui ◽  
...  
Keyword(s):  
Eating Disorders ◽  
Resting State ◽  
Clinical Symptoms ◽  
Blood Oxygen Level Dependent ◽  
Healthy Controls ◽  
Resting State Functional Connectivity ◽  
Blood Oxygen Level ◽  
Attention Network ◽  
State Activity ◽  
New Perspective

AbstractBackground:Despite the great number of resting state functional connectivity studies on Eating Disorders (ED), no biomarkers could be detected yet. Therefore, we here focus on a different measure of resting state activity that is neuronal variability. The objective of this study was to investigate neuronal variability in the resting state of women with ED and to correlate possible differences with clinical and psychopathological indices.Methods:58 women respectively 25 with Anorexia Nervosa (AN), 16 with Bulimia Nervosa (BN) and 17 matched healthy controls (CN) were enrolled for the study. All participants were tested with a battery of psychometric tests and underwent a functional Magnetic Resonance Imaging (fMRI) resting state scanning. We investigated topographical patterns of variability measured by the Standard Deviation (SD) of the Blood-Oxygen-Level-Dependent (BOLD) signal (as a measure of neuronal variability) in the resting-state and their relationship to clinical and psychopathological indices.Results:Neuronal variability was increased in both anorectic and bulimic subjects specifically in the Ventral Attention Network (VAN) compared to healthy controls. No significant differences were found in the other networks. Significant correlations were found between neuronal variability of VAN and various clinical and psychopathological indices.Conclusions:We here show increased neuronal variability of VAN in ED patients. As the VAN is relevant for switching between endogenous and exogenous stimuli, our results showing increased neuronal variability suggest unstable balance between body attention and attention to external world. These results offer new perspective on the neurobiological basis of ED. Clinical and therapeutic implication will be discussed.

scholarly journals Graph Ricci Curvatures Reveal Disease-related Changes in Autism Spectrum Disorder

2021 ◽  
Author(s):  
Pavithra Elumalai ◽  
Yasharth Yadav ◽  
Nitin Williams ◽  
Emil Saucan ◽  
Jürgen Jost ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
Neurodevelopmental Disorders ◽  
Data Exchange ◽  
Meta Analysis ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
The Brain

Autism Spectrum Disorder (ASD) is a set of neurodevelopmental disorders that pose a significant global health burden. Measures from graph theory have been used to characterise ASD-related changes in resting-state fMRI functional connectivity networks (FCNs), but recently developed geometry-inspired measures have not been applied so far. In this study, we applied geometry-inspired graph Ricci curvatures to investigate ASD-related changes in resting-state fMRI FCNs. To do this, we applied Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and healthy controls (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We performed these comparisons at the brain-wide level as well as at the level of individual brain regions, and further, determined the behavioral relevance of region-specific differences with Neurosynth meta-analysis decoding. We found brain-wide ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures. For Forman-Ricci curvature, these differences were distributed across 83 of the 200 brain regions studied, and concentrated within the Default Mode, Somatomotor and Ventral Attention Network. Meta-analysis decoding identified the brain regions showing curvature differences as involved in social cognition, memory, language and movement. Notably, comparison with results from previous non-invasive stimulation (TMS/tDCS) experiments revealed that the set of brain regions showing curvature differences overlapped with the set of brain regions whose stimulation resulted in positive cognitive or behavioural outcomes in ASD patients. These results underscore the utility of geometry-inspired graph Ricci curvatures in characterising disease-related changes in ASD, and possibly, other neurodevelopmental disorders.

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

Inter-Hemispherical Investigations on the Functional Connectivity in Controls and Autism Spectrum Using Resting State fMRI

2020 ◽  
pp. 169-186
Author(s):  
S. Vidhusha ◽  
A. Kavitha
Keyword(s):  
Functional Connectivity ◽  
Granger Causality ◽  
Resting State ◽  
Cognitive Task ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Control Group ◽  
Neural Connectivity ◽  
Activation Patterns ◽  
The Brain

Autism spectrum disorders are connected with disturbances of neural connectivity. Functional connectivity is typically examined during a cognitive task, but also exists in the absence of a task. While a number of studies have performed functional connectivity analysis to differentiate controls and autism individuals, this work focuses on analyzing the brain activation patterns not only between controls and autistic subjects, but also analyses the brain behaviour present within autism spectrum. This can bring out more intuitive ways to understand that autism individuals differ individually. This has been performed between autism group relative to the control group using inter-hemispherical analysis. Indications of under connectivity were exhibited by the Granger Causality (GC) and Conditional Granger Causality (CGC) in autistic group. Results show that as connectivity decreases, the GC and CGC values also get decreased. Further, to demark the differences present within the spectrum of autistic individuals, GC and CGC values have been calculated.

scholarly journals Measurement of CMRO2 and its relationship with CBF in hypoxia with an extended calibrated BOLD method

2019 ◽  
Vol 40 (10) ◽  
pp. 2066-2080
Author(s):  
Yaoyu Zhang ◽  
Yayan Yin ◽  
Huanjie Li ◽  
Jia-Hong Gao
Keyword(s):  
Cerebral Blood Volume ◽  
Pulse Sequence ◽  
Blood Oxygen Level Dependent ◽  
Extended Model ◽  
Brain Health ◽  
Blood Oxygen Level ◽  
Oxygen Conditions ◽  
Health And Disease ◽  
The Brain ◽  
Mild Hypoxia

Cerebral blood flow (CBF) and cerebral metabolic rate of oxygen (CMRO2) are physiological parameters that not only reflect brain health and disease but also jointly contribute to blood oxygen level-dependent (BOLD) signals. Nevertheless, unsolved issues remain concerning the CBF–CMRO2 relationship in the working brain under various oxygen conditions. In particular, the CMRO2 responses to functional tasks in hypoxia are less studied. We extended the calibrated BOLD model to incorporate CMRO2 measurements in hypoxia. The extended model, which was cross-validated with a multicompartment BOLD model, considers the influences of the reduced arterial saturation level and increased baseline cerebral blood volume (CBV) and deoxyhemoglobin concentration on the changes of BOLD signals in hypoxia. By implementing a pulse sequence to simultaneously acquire the CBV-, CBF- and BOLD-weighted signals, we investigated the effects of mild hypoxia on the CBF and CMRO2 responses to graded visual stimuli. Compared with normoxia, mild hypoxia caused significant alterations in both the amplitude and the trend of the CMRO2 responses but did not impact the corresponding CBF responses. Our observations suggested that the flow-metabolism coupling strategies in the brain during mild hypoxia were different from those during normoxia.

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