scholarly journals Resting-state fMRI correlations: from link-wise unreliability to whole brain stability

2016 ◽  
Author(s):  
Mario Pannunzi ◽  
Rikkert Hindriks ◽  
Ruggero G. Bettinardi ◽  
Elisabeth Wenger ◽  
Nina Lisofsky ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Systematic Investigation ◽  
Resting State Functional Connectivity ◽  
Data Set ◽  
Whole Brain ◽  
Statistical Framework ◽  
Subject Variability ◽  
Test Retest Reliability ◽  
Different Sources

AbstractThe functional architecture of spontaneous BOLD fluctuations has been characterized in detail by numerous studies, demonstrating its potential relevance as a biomarker. However, the systematic investigation of its consistency is still in its infancy. Here, we analyze both the within- and between-subject variability as well as the test-retest reliability of resting-state functional connectivity (FC) estimates in a unique data set comprising multiple fMRI scans (42) from 5 subjects, and 50 single scans from 50 subjects. To this aim we adopted a statistical framework enabling us to disentangle the contribution of different sources of variability and their dependence on scan duration, and showed that the low reliability of single links can be largely improved using multiple scans per subject. Moreover, we show that practically all observed inter-region variability (at the link-level) is not significant and due to the statistical uncertainty of the estimator itself rather than to genuine variability among areas. Finally, we use the proposed statistical framework to demonstrate that, despite the poor consistency of single links, the information carried by the whole-brain spontaneous correlation structure is indeed robust, and can in fact be used as a functional fingerprint.

scholarly journals Heritability analysis with repeat measurements and its application to resting-state functional connectivity

2017 ◽  
Vol 114 (21) ◽  
pp. 5521-5526 ◽  
Author(s):  
Tian Ge ◽  
Avram J. Holmes ◽  
Randy L. Buckner ◽  
Jordan W. Smoller ◽  
Mert R. Sabuncu
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Resting State Fmri ◽  
Transient Effects ◽  
Resting State Functional Connectivity ◽  
Linear Mixed Effects ◽  
Heritability Analysis ◽  
Test Retest Reliability ◽  
Different Levels

Heritability, defined as the proportion of phenotypic variation attributable to genetic variation, provides important information about the genetic basis of a trait. Existing heritability analysis methods do not discriminate between stable effects (e.g., due to the subject’s unique environment) and transient effects, such as measurement error. This can lead to misleading assessments, particularly when comparing the heritability of traits that exhibit different levels of reliability. Here, we present a linear mixed effects model to conduct heritability analyses that explicitly accounts for intrasubject fluctuations (e.g., due to measurement noise or biological transients) using repeat measurements. We apply the proposed strategy to the analysis of resting-state fMRI measurements—a prototypic data modality that exhibits variable levels of test–retest reliability across space. Our results reveal that the stable components of functional connectivity within and across well-established large-scale brain networks can be considerably heritable. Furthermore, we demonstrate that dissociating intra- and intersubject variation can reveal genetic influence on a phenotype that is not fully captured by conventional heritability analyses.

scholarly journals Altered resting state functional connectivity in a thalamo-cortico-cerebellar network in patients with schizophrenia

2018 ◽  
Author(s):  
Caroline Garcia Forlim ◽  
Leonie Klock ◽  
Johanna Baechle ◽  
Laura Stoll ◽  
Patrick Giemsa ◽  
...  
Keyword(s):  
Resting State ◽  
Local Level ◽  
Resting State Fmri ◽  
Brain Network ◽  
Graph Analysis ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Schizophrenic Patients ◽  
Brain Circuitry ◽  
Behavioral Impairments

Schizophrenia is described as a disease in which complex psychopathology together with cognitive and behavioral impairments are related to widely disrupted brain circuitry causing a failure in coordinating information across multiple brain sites. This led to the hypothesis of schizophrenia as a network disease e.g. in the cognitive dysmetria model and the dysconnectivity theory. Nevertheless, there is no consensus regarding localized mechanisms, namely dysfunction of certain networks underlying the multifaceted symptomatology. In this study, we investigated potential functional disruptions in 35 schizophrenic patients and 41 controls using complex cerebral network analysis, namely network-based statistic (NBS) and graph theory in resting state fMRI. NBS can reveal locally impaired subnetworks whereas graph analysis characterizes whole brain network topology. Using NBS we observed a local hyperconnected thalamo-cortico-cerebellar subnetwork in the schizophrenia group. Furthermore, nodal graph measures retrieved from the thalamo-cortico-cerebellar subnetwork revealed that the total number of connections from/to (degree) of the thalamus is higher in patients with schizophrenia. Interestingly, graph analysis on the whole brain functional networks did not reveal group differences. Together, our results suggest that disruptions in the brain networks of schizophrenia patients are situated at the local level of the hyperconnected thalamo-cortico-cerebellar rather than globally spread in brain. Our results provide further evidence for the importance of the thalamus and cerebellum in schizophrenia and to the notion that schizophrenia is a network disease in line with the dysconnectivity theory and cognitive dysmetria model.

scholarly journals A Whole-Brain Regression Method to Identify Individual and Group Variations in Functional Connectivity

2020 ◽  
Author(s):  
Yi Zhao ◽  
Brian S. Caffo ◽  
Bingkai Wang ◽  
Chiang-shan R. Li ◽  
Xi Luo
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Statistical Power ◽  
Resting State Fmri ◽  
Regression Method ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Human Connectome Project ◽  
Group Ica ◽  
Brain Functional Connectivity

AbstractResting-state functional connectivity is an important and widely used measure of individual and group differences. These differences are typically attributed to various demographic and/or clinical factors. Yet, extant statistical methods are limited to linking covariates with variations in functional connectivity across subjects, especially at the voxel-wise level of the whole brain. This paper introduces a generalized linear model method that regresses whole-brain functional connectivity on covariates. Our approach builds on two methodological components. We first employ whole-brain group ICA to reduce the dimensionality of functional connectivity matrices, and then search for matrix variations associated with covariates using covariate assisted principal regression, a recently introduced covariance matrix regression method. We demonstrate the efficacy of this approach using a resting-state fMRI dataset of a medium-sized cohort of subjects obtained from the Human Connectome Project. The results show that the approach enjoys improved statistical power in detecting interaction effects of sex and alcohol on whole-brain functional connectivity, and in identifying the brain areas contributing significantly to the covariate-related differences in functional connectivity.

scholarly journals Flexible annotation atlas of the mouse brain: combining and dividing brain structures of the Allen Brain Atlas while maintaining anatomical hierarchy

2020 ◽  
Author(s):  
Norio Takata ◽  
Nobuhiko Sato ◽  
Yuji Komaki ◽  
Hideyuki Okano ◽  
Kenji F. Tanaka
Keyword(s):  
Functional Connectivity ◽  
Mouse Brain ◽  
Resting State ◽  
Brain Structures ◽  
Resting State Fmri ◽  
Brain Atlas ◽  
List Type ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Link Type

AbstractA brain atlas is necessary for analyzing structure and function in neuroimaging research. Although various annotation volumes (AVs) for the mouse brain have been proposed, it is common in magnetic resonance imaging (MRI) of the mouse brain that regions-of-interest (ROIs) for brain structures (nodes) are created arbitrarily according to each researcher’s necessity, leading to inconsistent ROIs among studies. One reason for such a situation is the fact that earlier AVs were fixed, i.e. combination and division of nodes were not implemented. This report presents a pipeline for constructing a flexible annotation atlas (FAA) of the mouse brain by leveraging public resources of the Allen Institute for Brain Science on brain structure, gene expression, and axonal projection. A mere two-step procedure with user-specified, text-based information and Python codes constructs FAA with nodes which can be combined or divided objectively while maintaining anatomical hierarchy of brain structures. Four FAAs with total node count of 4, 101, 866, and 1,381 were demonstrated. Unique characteristics of FAA realized analysis of resting-state functional connectivity (FC) across the anatomical hierarchy and among cortical layers, which were thin but large brain structures. FAA can improve the consistency of whole brain ROI definition among laboratories by fulfilling various requests from researchers with its flexibility and reproducibility.Highlights–A flexible annotation atlas (FAA) for the mouse brain is proposed.–FAA is expected to improve whole brain ROI-definition consistency among laboratories.–The ROI can be combined or divided objectively while maintaining anatomical hierarchy.–FAA realizes functional connectivity analysis across the anatomical hierarchy.–Codes for FAA reconstruction is available at https://github.com/ntakata/flexible-annotation-atlas–Datasets for resting-state fMRI in awake mice are available at https://openneuro.org/datasets/ds002551

Tracking whole-brain connectivity dynamics in the resting-state fMRI with post-facial paralysis synkinesis

2021 ◽  
Author(s):  
Zhen-Zhen Ma ◽  
Jia-Jia Wu ◽  
Xu-Yun Hua ◽  
Mou-Xiong Zheng ◽  
Xiang-Xin Xing ◽  
...  
Keyword(s):  
Facial Paralysis ◽  
Resting State ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Whole Brain

scholarly journals Whole-brain modelling of resting state fMRI differentiates ADHD subtypes and facilitates stratified neuro-stimulation therapy

NeuroImage ◽  
2021 ◽  
Vol 231 ◽  
pp. 117844
Author(s):  
Behzad Iravani ◽  
Artin Arshamian ◽  
Peter Fransson ◽  
Neda Kaboodvand
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Adhd Subtypes ◽  
Whole Brain

scholarly journals Corrigendum to “Resting-state fMRI correlations: From link-wise unreliability to whole brain stability” [NeuroImage 157 (2017 Aug 15) 250–262]

NeuroImage ◽  
2018 ◽  
Vol 174 ◽  
pp. 599-604 ◽  
Author(s):  
M. Pannunzi ◽  
R. Hindriks ◽  
R.G. Bettinardi ◽  
E. Wenger ◽  
N. Lisofsky ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Whole Brain

scholarly journals Disrupted Topological Organization in Whole-Brain Functional Networks of Heroin-Dependent Individuals: A Resting-State fMRI Study

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e82715 ◽  
Author(s):  
Guihua Jiang ◽  
Xue Wen ◽  
Yingwei Qiu ◽  
Ruibin Zhang ◽  
Junjing Wang ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Functional Networks ◽  
Whole Brain ◽  
Brain Functional Networks ◽  
Fmri Study ◽  
Topological Organization

The Effect of General Anesthesia On Test-Retest Reliability of Resting-State FMRI Metrics And Optimization of Scan Length

2021 ◽  
Author(s):  
Faezeh Vedaei ◽  
Mahdi Alizadeh ◽  
Victor M Romo ◽  
Feroze B. Mohamed ◽  
Chengyuan Wu
Keyword(s):  
Resting State ◽  
Intraclass Correlation ◽  
Resting State Fmri ◽  
Frequency Fluctuation ◽  
Retest Reliability ◽  
Factors Affecting ◽  
Fractional Frequency ◽  
Drug Resistant Epilepsy ◽  
Test Retest Reliability ◽  
Scan Length

Abstract Resting-state functional magnetic resonance imaging (rs-fMRI) has been known as a powerful tool in neuroscience. However, exploring the test-retest reliability of the metrics derived from rs-fMRI BOLD signal is essential particularly in the studies of patients with neurological development. Two factors affecting reliability of rs-fMRI measurements including the effect of anesthesia and scan length have been estimated in this study. A total of 9 patients with drug-resistant epilepsy (DRE) of requiring interstitial thermal therapy (LITT) were scanned in two states of awake and under anesthesia. At each state, two rs-fMRI sessions were obtained that each one lasted 15 minutes, and the effect of scan length was evaluated. Voxel-wise rs-fMRI metrics including amplitude of low fractional frequency fluctuation (ALFF), amplitude of low fractional frequency fluctuation (fALFF), functional connectivity (FC), and regional homogeneity (ReHo) were measured. Intraclass correlation coefficient (ICC) was calculated to estimate the reliability between two sessions of scanning for both states. Overall, our finding revealed that reliability improves under anesthesia as well as by increasing the scanning length of the scanning sessions. Furthermore, we showed that the optimal scan length to achieve reliable rs-fMRI measurements was 3.1 – 7.5 minutes shorter in an anesthetized, compared to wakeful state.

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