scholarly journals Acute Effects of Alcohol on the Human Brain: A Resting-State fMRI Study

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Hongyi Zheng ◽  
Lingmei Kong ◽  
Lanmei Chen ◽  
Haidu Zhang ◽  
Wenbin Zheng
Keyword(s):  
Mr Imaging ◽  
Resting State ◽  
Internal Capsule ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Acute Effects ◽  
Neural Basis ◽  
Healthy Human ◽  
Alcohol Administration

The aim of this study is to assess the value of resting-state fMRI in detecting the acute effects of alcohol on healthy human brains. Thirty-two healthy volunteers were studied by conventional MR imaging and resting-state fMRI prior to and 0.5 hours after initiation of acute alcohol administration. The fMRI data, acquired during the resting state, were correlated with different breath alcohol concentrations (BrAC). We use the posterior cingulate cortex/precuneus as a seed for the default mode network (DMN) analysis. ALFF and ReHo were also used to investigate spontaneous neural activity in the resting state. Conventional MR imaging showed no abnormalities on all subjects. Compared with the prior alcohol administration, the ALFF and ReHo also indicated some specific brain regions which are affected by alcohol, including the superior frontal gyrus, cerebellum, hippocampal gyrus, left basal ganglia, and right internal capsule. Functional connectivity of the DMN was affected by alcohol. This resting-state fMRI indicates that brain regions implicated are affected by alcohol and might provide a neural basis for alcohol’s effects on behavioral performance.

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.

scholarly journals Parietal and hippocampal hyper-connectivity is associated with low math achievement in adolescence

2021 ◽  
Author(s):  
Roberto A. Abreu-Mendoza ◽  
Melanie Pincus ◽  
Yaira Chamorro ◽  
Dietsje Jolles ◽  
Esmeralda Matute ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Math Achievement ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Math Performance ◽  
Resting State Functional Connectivity ◽  
Neural Basis ◽  
Math Ability ◽  
Control Seed

Mathematical cognition requires coordinated activity across multiple brain regions, leading to the emergence of resting-state functional connectivity as a method for studying the neural basis of differences in mathematical achievement. Hyper-connectivity of the intraparietal sulcus (IPS), a key locus of mathematical and numerical processing, has been associated with poor mathematical skills in childhood, whereas greater connectivity has been related to better performance in adulthood. No studies to date have considered its role in adolescence. Further, hippocampal connectivity can predict mathematical learning, yet no studies have considered its contributions to contemporaneous measures of math achievement. Here, we used seed-based resting-state fMRI analyses to examine IPS and hippocampal intrinsic functional connectivity relations to math achievement in a group of 31 adolescents (mean age=16.42 years, range 15-17), whose math performance spanned the 1% to 99% percentile. After controlling for IQ, IPS connectivity was negatively related to math achievement, akin to findings in children. However, the specific temporooccipital regions, were more akin to the posterior loci implicated in adults. Hippocampal connectivity with frontal regions was also negatively correlated with concurrent math measures, which contrasts with results from learning studies. Finally, hyper-connectivity was not a global feature of low math performance, as connectivity of Heschl’s gyrus, a control seed not involved in math cognition, was not modulated by math performance. Together, our results point to adolescence as a transitional stage in which patterns found in childhood and adulthood can be observed; most notably, hyper-connectivity continues to be related to low math ability in this period.

scholarly journals High-throughput preprocessing pipeline for resting-state fMRI

2018 ◽  
Author(s):  
Evangeline Yee
Keyword(s):  
Functional Connectivity ◽  
High Throughput ◽  
Resting State ◽  
Cluster Computing ◽  
Medical Image Analysis ◽  
Building Blocks ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Resting State Functional Connectivity

Resting-state functional magnetic resonance imaging (fMRI) examines functional connectivity between brain regions by measuring spontaneous fluctuation of blood-oxygen-level dependent (BOLD) signal while subjects are at rest. Recent studies have revealed that neurodegenerative diseases are associated with abnormalities in resting-state functional connectivity. This thesis work aimed to develop a high-throughput preprocessing pipeline for minimizing spatial and temporal artifacts in resting-state fMRI data. The building blocks and cluster computing capabilities of the pipeline are discussed in detail. In an effort to validate this pipeline, a seed-based analysis was performed on preprocessed data using 3 seeds placed in the posterior cingulate cortex (PCC), supplementary motor area (SMA) and inferior parietal sulcus (IPS). These 3 seeds represent the core components of the default mode network (DMN) and task-positive network (TPN), two of the most commonly examined resting-state networks. Consistent with literature, results indicate that activity in the PCC is functionally correlated to regions of the DMN and anti-correlated to regions of the TPN, whereas activities in the SMA and IPS are functionally correlated to regions of the TPN and anti-correlated to regions of the DMN. These functional connectivity patterns were consistent across scans on both group-level and subject-level. This preprocessing pipeline will support researchers and clinical collaborators of Medical Image Analysis Lab (MIAL) in their analysis of resting-state functional connectivity.

Distinguishing hypochondriasis and schizophrenia using regional homogeneity: a resting-state fMRI study and support vector machine analysis

2021 ◽  
pp. 1-29
Author(s):  
Kangyu Jin ◽  
Zhe Shen ◽  
Guoxun Feng ◽  
Zhiyong Zhao ◽  
Jing Lu ◽  
...  
Keyword(s):  
Cognitive Function ◽  
Resting State ◽  
Parietal Lobe ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Regional Homogeneity ◽  
Middle Temporal Gyrus ◽  
Support Vector ◽  
Fmri Study

Abstract Objective: A few former studies suggested there are partial overlaps in abnormal brain structure and cognitive function between Hypochondriasis (HS) and schizophrenia (SZ). But their differences in brain activity and cognitive function were unclear. Methods: 21 HS patients, 23 SZ patients, and 24 healthy controls (HC) underwent Resting-state functional magnetic resonance imaging (rs-fMRI) with the regional homogeneity analysis (ReHo), subsequently exploring the relationship between ReHo value and cognitive functions. The support vector machines (SVM) were used on effectiveness evaluation of ReHo for differentiating HS from SZ. Results: Compared with HC, HS showed significantly increased ReHo values in right middle temporal gyrus (MTG), left inferior parietal lobe (IPL) and right fusiform gyrus (FG), while SZ showed increased ReHo in left insula, decreased ReHo values in right paracentral lobule. Additionally, HS showed significantly higher ReHo values in FG, MTG and left paracentral lobule but lower in insula than SZ. The higher ReHo values in insula were associated with worse performance in MCCB in HS group. SVM analysis showed a combination of the ReHo values in insula and FG was able to satisfactorily distinguish the HS and SZ patients. Conclusion: our results suggested the altered default mode network (DMN), of which abnormal spontaneous neural activity occurs in multiple brain regions, might play a key role in the pathogenesis of HS, and the resting-state alterations of insula closely related to cognitive dysfunction in HS. Furthermore, the combination of the ReHo in FG and insula was a relatively ideal indicator to distinguish HS from SZ.

scholarly journals Neural basis of global resting-state fMRI activity

2010 ◽  
Vol 107 (22) ◽  
pp. 10238-10243 ◽  
Author(s):  
M. L. Scholvinck ◽  
A. Maier ◽  
F. Q. Ye ◽  
J. H. Duyn ◽  
D. A. Leopold
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Neural Basis

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 Effects of long-term cocaine self-administration on brain resting-state functional connectivity in nonhuman primates

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Stephen J. Kohut ◽  
Dionyssios Mintzopoulos ◽  
Brian D. Kangas ◽  
Hannah Shields ◽  
Kelly Brown ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Processing ◽  
Resting State ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Resting State Functional Connectivity ◽  
Self Administration ◽  
Chronic Cocaine

AbstractLong-term cocaine use is associated with a variety of neural and behavioral deficits that impact daily function. This study was conducted to examine the effects of chronic cocaine self-administration on resting-state functional connectivity of the dorsal anterior cingulate (dACC) and putamen—two brain regions involved in cognitive function and motoric behavior—identified in a whole brain analysis. Six adult male squirrel monkeys self-administered cocaine (0.32 mg/kg/inj) over 140 sessions. Six additional monkeys that had not received any drug treatment for ~1.5 years served as drug-free controls. Resting-state fMRI imaging sessions at 9.4 Tesla were conducted under isoflurane anesthesia. Functional connectivity maps were derived using seed regions placed in the left dACC or putamen. Results show that cocaine maintained robust self-administration with an average total intake of 367 mg/kg (range: 299–424 mg/kg). In the cocaine group, functional connectivity between the dACC seed and regions primarily involved in motoric behavior was weaker, whereas connectivity between the dACC seed and areas implicated in reward and cognitive processing was stronger. In the putamen seed, weaker widespread connectivity was found between the putamen and other motor regions as well as with prefrontal areas that regulate higher-order executive function; stronger connectivity was found with reward-related regions. dACC connectivity was associated with total cocaine intake. These data indicate that functional connectivity between regions involved in motor, reward, and cognitive processing differed between subjects with recent histories of cocaine self-administration and controls; in dACC, connectivity appears to be related to cumulative cocaine dosage during chronic exposure.

scholarly journals Resting-State Isolated Effective Connectivity of the Cingulate Cortex as a Neurophysiological Biomarker in Patients with Severe Treatment-Resistant Schizophrenia

2020 ◽  
Vol 10 (3) ◽  
pp. 89
Author(s):  
Masataka Wada ◽  
Shinichiro Nakajima ◽  
Ryosuke Tarumi ◽  
Fumi Masuda ◽  
Takahiro Miyazaki ◽  
...  
Keyword(s):  
Resting State ◽  
Effective Connectivity ◽  
Treatment Resistance ◽  
Cingulate Cortex ◽  
Posterior Cingulate Cortex ◽  
Anterior Cingulate ◽  
Treatment Resistant ◽  
Neural Basis ◽  
Subject Factor ◽  
Post Hoc

Background: The neural basis of treatment-resistant schizophrenia (TRS) remains unclear. Previous neuroimaging studies suggest that aberrant connectivity between the anterior cingulate cortex (ACC) and default mode network (DMN) may play a key role in the pathophysiology of TRS. Thus, we aimed to examine the connectivity between the ACC and posterior cingulate cortex (PCC), a hub of the DMN, computing isolated effective coherence (iCoh), which represents causal effective connectivity. Methods: Resting-state electroencephalogram with 19 channels was acquired from seventeen patients with TRS and thirty patients with non-TRS (nTRS). The iCoh values between the PCC and ACC were calculated using sLORETA software. We conducted four-way analyses of variance (ANOVAs) for iCoh values with group as a between-subject factor and frequency, directionality, and laterality as within-subject factors and post-hoc independent t-tests. Results: The ANOVA and post-hoc t-tests for the iCoh ratio of directionality from PCC to ACC showed significant findings in delta (t45 = 7.659, p = 0.008) and theta (t45 = 8.066, p = 0.007) bands in the left side (TRS < nTRS). Conclusion: Left delta and theta PCC and ACC iCoh ratio may represent a neurophysiological basis of TRS. Given the preliminary nature of this study, these results warrant further study to confirm the importance of iCoh as a clinical indicator for treatment-resistance.

scholarly journals Organization of Propagated Intrinsic Brain Activity in Individual Humans

2019 ◽  
Vol 30 (3) ◽  
pp. 1716-1734 ◽  
Author(s):  
Ryan V Raut ◽  
Anish Mitra ◽  
Scott Marek ◽  
Mario Ortega ◽  
Abraham Z Snyder ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Cortical Excitability ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Organization ◽  
Individual Level ◽  
Slow Activity ◽  
Lag Structure

Abstract Spontaneous infra-slow (&lt;0.1 Hz) fluctuations in functional magnetic resonance imaging (fMRI) signals are temporally correlated within large-scale functional brain networks, motivating their use for mapping systems-level brain organization. However, recent electrophysiological and hemodynamic evidence suggest state-dependent propagation of infra-slow fluctuations, implying a functional role for ongoing infra-slow activity. Crucially, the study of infra-slow temporal lag structure has thus far been limited to large groups, as analyzing propagation delays requires extensive data averaging to overcome sampling variability. Here, we use resting-state fMRI data from 11 extensively-sampled individuals to characterize lag structure at the individual level. In addition to stable individual-specific features, we find spatiotemporal topographies in each subject similar to the group average. Notably, we find a set of early regions that are common to all individuals, are preferentially positioned proximal to multiple functional networks, and overlap with brain regions known to respond to diverse behavioral tasks—altogether consistent with a hypothesized ability to broadly influence cortical excitability. Our findings suggest that, like correlation structure, temporal lag structure is a fundamental organizational property of resting-state infra-slow activity.

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