scholarly journals PM299. Cortical thickness of resting state networks in the brain of male patients with alcohol dependence

2016 ◽  
Vol 19 (Suppl_1) ◽  
pp. 7-7
Keyword(s):  
Alcohol Dependence ◽  
Cortical Thickness ◽  
Resting State ◽  
Resting State Networks ◽  
Male Patients ◽  
The Brain

scholarly journals Test–Retest Reliability of Synchrony and Metastability in Resting State fMRI

2021 ◽  
Vol 12 (1) ◽  
pp. 66
Author(s):  
Lan Yang ◽  
Jing Wei ◽  
Ying Li ◽  
Bin Wang ◽  
Hao Guo ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Resting State Networks ◽  
Functional Magnetic Resonance ◽  
Intra Class Correlation ◽  
Retest Reliability ◽  
Brain Signals ◽  
Human Connectome Project ◽  
Test Retest Reliability ◽  
The Brain

In recent years, interest has been growing in dynamic characteristic of brain signals from resting-state functional magnetic resonance imaging (rs-fMRI). Synchrony and metastability, as neurodynamic indexes, are considered as one of methods for analyzing dynamic characteristics. Although much research has studied the analysis of neurodynamic indices, few have investigated its reliability. In this paper, the datasets from the Human Connectome Project have been used to explore the test–retest reliabilities of synchrony and metastability from multiple angles through intra-class correlation (ICC). The results showed that both of these indexes had fair test–retest reliability, but they are strongly affected by the field strength, the spatial resolution, and scanning interval, less affected by the temporal resolution. Denoising processing can help improve their ICC values. In addition, the reliability of neurodynamic indexes was affected by the node definition strategy, but these effects were not apparent. In particular, by comparing the test–retest reliability of different resting-state networks, we found that synchrony of different networks was basically stable, but the metastability varied considerably. Among these, DMN and LIM had a relatively higher test–retest reliability of metastability than other networks. This paper provides a methodological reference for exploring the brain dynamic neural activity by using synchrony and metastability in fMRI signals.

Exploring the Brain Lateralization in ADHD Based on Variability of Resting-State fMRI Signal

2018 ◽  
Vol 25 (2) ◽  
pp. 258-264
Author(s):  
Hongliang Zou ◽  
Jian Yang
Keyword(s):  
Resting State ◽  
Inferior Frontal Gyrus ◽  
Resting State Fmri ◽  
Cerebellar Hemisphere ◽  
Brain Lateralization ◽  
Female Patients ◽  
Fmri Signal ◽  
Male Patients ◽  
The Difference ◽  
The Brain

Objective: In this study, we investigate the brain lateralization in ADHD patients. Furthermore, we also explore the difference between male and female patients, and the difference among distinct ADHD subtypes, that is, ADHD–inattentive (ADHD-IA) and ADHD–combined (ADHD-C). Method: We employed the standard deviation to quantify the variability of resting-state functional magnetic resonance imaging (fMRI) signal and measure the lateralization index (LI). Results: ADHD patients showed significantly increased rightward lateralization in the inferior frontal gyrus (opercular), precuneus, and paracentral lobule, and decreased rightward lateralization in the insula. Compared with male patients, female patients showed significantly rightward lateralization in the putamen and lobule VII of cerebellar hemisphere. ADHD-C patients exhibited increased rightward lateralization in the inferior frontal gyrus (opercular), and decreased rightward lateralization in the inferior temporal gyrus, as compared with ADHD-IA. The LI was also found to be related to inattentive and hyper/impulsive scores. Conclusion: These key findings may aid in understanding the pathology of ADHD.

Identification of Brain Functional Networks Using a Model-Based Approach

2019 ◽  
Vol 34 (08) ◽  
pp. 2057004
Author(s):  
Vangelis P. Oikonomou ◽  
Konstantinos Blekas ◽  
Loukas Astrakas
Keyword(s):  
Time Series ◽  
Resting State ◽  
Probabilistic Method ◽  
Resting State Fmri ◽  
Model Parameters ◽  
Resting State Networks ◽  
Model Based ◽  
Functional Brain Networks ◽  
Regression Mixture ◽  
The Brain

Functional MRI (fMRI) is a valuable brain imaging technique. A significant problem, when analyzing fMRI time series, is the finding of functional brain networks when the brain is at rest, i.e. no external stimulus is applied to the subject. In this work, we present a probabilistic method to estimate the Resting State Networks (RSNs) using a model-based approach. More specifically, RSNs are assumed to be the result of a clustering procedure. In order to perform the clustering, a mixture of regression models are used. Furthermore, special care has been given in order to incorporate important functionalities, such as spatial and embedded sparsity enforcing properties, through the use of informative priors over the model parameters. Another interesting feature of the proposed scheme is the flexibility to handle all the brain time series at once, allowing more robust solutions. We provide comparative experimental results, using an artificial fMRI dataset and two real resting state fMRI datasets, that empirically illustrate the efficiency of the proposed regression mixture model.

scholarly journals Lag threads organize the brain’s intrinsic activity

2015 ◽  
Vol 112 (17) ◽  
pp. E2235-E2244 ◽  
Author(s):  
Anish Mitra ◽  
Abraham Z. Snyder ◽  
Tyler Blazey ◽  
Marcus E. Raichle
Keyword(s):  
Spontaneous Activity ◽  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Intrinsic Activity ◽  
Resting State Networks ◽  
Slow Activity ◽  
Intrinsic Brain Activity ◽  
Temporal Sequences ◽  
The Brain

It has been widely reported that intrinsic brain activity, in a variety of animals including humans, is spatiotemporally structured. Specifically, propagated slow activity has been repeatedly demonstrated in animals. In human resting-state fMRI, spontaneous activity has been understood predominantly in terms of zero-lag temporal synchrony within widely distributed functional systems (resting-state networks). Here, we use resting-state fMRI from 1,376 normal, young adults to demonstrate that multiple, highly reproducible, temporal sequences of propagated activity, which we term “lag threads,” are present in the brain. Moreover, this propagated activity is largely unidirectional within conventionally understood resting-state networks. Modeling experiments show that resting-state networks naturally emerge as a consequence of shared patterns of propagation. An implication of these results is that common physiologic mechanisms may underlie spontaneous activity as imaged with fMRI in humans and slowly propagated activity as studied in animals.

scholarly journals Mapping of Whole-Brain Resting-State Networks with Half-Millimetre Resolution

2021 ◽  
Author(s):  
Seong Dae Yun ◽  
Patricia Pais-Roldán ◽  
Nicola Palomero-Gallagher ◽  
N. Jon Shah
Keyword(s):  
Spatial Resolution ◽  
Resting State ◽  
Granular Layer ◽  
Resting State Fmri ◽  
Resting State Networks ◽  
Whole Brain ◽  
Anatomical Structures ◽  
Functional Image ◽  
Fmri Session ◽  
The Brain

AbstractResting-state fMRI has been used in numerous studies to map networks in the brain that employ spatially disparate regions. However, attempts to map networks with high spatial resolution have been hampered by conflicting technical demands and associated problems. Results from recent fMRI studies have shown that spatial resolution remains around 0.7 × 0.7 × 0.7 mm3, with only partial brain coverage. This work presents a novel fMRI method, TR-external EPI with keyhole (TR-external EPIK), which can provide a nominal spatial resolution of 0.51 × 0.51 × 1.00 mm3 (0.26 mm3 voxel) with whole-brain coverage. TR-external EPIK enabled the identification of various resting-state networks distributed throughout the brain from a single fMRI session, with mapping fidelity onto the grey matter at 7T. The high-resolution functional image further revealed mesoscale anatomical structures, such as small cerebral vessels and the internal granular layer of the cortex within the postcentral gyrus.

Spatiotemporal dynamics of the brain at rest — Exploring EEG microstates as electrophysiological signatures of BOLD resting state networks

NeuroImage ◽  
2012 ◽  
Vol 60 (4) ◽  
pp. 2062-2072 ◽  
Author(s):  
Han Yuan ◽  
Vadim Zotev ◽  
Raquel Phillips ◽  
Wayne C. Drevets ◽  
Jerzy Bodurka
Keyword(s):  
Resting State ◽  
Spatiotemporal Dynamics ◽  
Resting State Networks ◽  
The Brain

scholarly journals Comparing functional connectivity via thresholding correlations and singular value decomposition

2005 ◽  
Vol 360 (1457) ◽  
pp. 913-920 ◽  
Author(s):  
Keith J Worsley ◽  
Jen-I Chen ◽  
Jason Lerch ◽  
Alan C Evans
Keyword(s):  
Functional Connectivity ◽  
Singular Value Decomposition ◽  
Cortical Thickness ◽  
Resting State ◽  
Singular Value ◽  
Resting State Networks ◽  
Value Decomposition

We compare two common methods for detecting functional connectivity: thresholding correlations and singular value decomposition (SVD). We find that thresholding correlations are better at detecting focal regions of correlated voxels, whereas SVD is better at detecting extensive regions of correlated voxels. We apply these results to resting state networks in an fMRI dataset to look for connectivity in cortical thickness.

scholarly journals Cortical Thickness, Surface Area, and Volume of the Brain Reward System in Alcohol Dependence: Relationships to Relapse and Extended Abstinence

2011 ◽  
Vol 35 (6) ◽  
pp. 1187-1200 ◽  
Author(s):  
Timothy C. Durazzo ◽  
Duygu Tosun ◽  
Shannon Buckley ◽  
Stefan Gazdzinski ◽  
Anderson Mon ◽  
...  
Keyword(s):  
Alcohol Dependence ◽  
Surface Area ◽  
Cortical Thickness ◽  
Reward System ◽  
Brain Reward ◽  
The Brain ◽  
Area And Volume ◽  
Brain Reward System

scholarly journals Stomach-brain synchrony binds neural representations of the body in a novel, delayed-connectivity resting-state network

2017 ◽  
Author(s):  
Ignacio Rebollo ◽  
Anne-Dominique Devauchelle ◽  
Benoît Béranger ◽  
Catherine Tallon-Baudry
Keyword(s):  
Resting State ◽  
Temporal Dynamics ◽  
The Body ◽  
Resting State Networks ◽  
Body Area ◽  
Functional Architecture ◽  
Resting State Network ◽  
Neural Representations ◽  
Extrastriate Body Area ◽  
The Brain

AbstractResting-state networks offer a unique window into the brain’s functional architecture, but their characterization remains limited to instantaneous connectivity thus far. Here, we describe a novel resting-state network based on the delayed connectivity between the brain and the slow electrical rhythm (0.05 Hz) generated in the stomach. The gastric network cuts across classical resting-state networks with little overlap with autonomic regulation areas. This network is composed of regions with convergent functional properties involved in mapping bodily space through touch, action or vision, as well as mapping external space in bodily coordinates. The network is characterized by a precise temporal sequence of activations within a gastric cycle, beginning with somato-motor cortices and ending with the extrastriate body area and dorsal precuneus. Our results demonstrate that canonical resting-state networks based on instantaneous connectivity represent only one of the possible partitions of the brain into coherent networks based on temporal dynamics.

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