scholarly journals Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

2020 ◽  
Vol 4 (3) ◽  
pp. 788-806 ◽  
Author(s):  
John Fallon ◽  
Phillip G. D. Ward ◽  
Linden Parkes ◽  
Stuart Oldham ◽  
Aurina Arnatkevičiūtė ◽  
...  
Keyword(s):  
Resting State ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Information Storage ◽  
Brain Organization ◽  
Temporal Properties ◽  
Temporal Features ◽  
Human Connectome Project ◽  
The Brain ◽  
Connectivity Strength

Intrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brain’s underlying structural connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale- related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6,000 time series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain region’s spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

scholarly journals Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

2019 ◽  
Author(s):  
John Fallon ◽  
Phil Ward ◽  
Linden Parkes ◽  
Stuart Oldham ◽  
Aurina Arnatkevic̆iūtė ◽  
...  
Keyword(s):  
Resting State ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Information Storage ◽  
Brain Organization ◽  
Temporal Properties ◽  
Temporal Features ◽  
Human Connectome Project ◽  
The Brain ◽  
Connectivity Strength

AbstractIntrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brain’s underlying structural-connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural-connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale-related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6000 time-series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain region’s spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

scholarly journals Large-Scale Intrinsic Functional Brain Organization Emerges from Three Canonical Spatiotemporal Patterns

2021 ◽  
Author(s):  
Taylor S Bolt ◽  
Jason Nomi ◽  
Danilo Bzdok ◽  
Catie Chang ◽  
B.T. Thomas Yeo ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Spatiotemporal Patterns ◽  
Brain Organization ◽  
Functional Brain ◽  
Temporal Properties ◽  
Human Connectome Project ◽  
Functional Brain Organization

The characterization of intrinsic functional brain organization has been approached from a multitude of analytic techniques and methods. We are still at a loss of a unifying conceptual framework for capturing common insights across this patchwork of empirical findings. By analyzing resting-state fMRI data from the Human Connectome Project using a large number of popular analytic techniques, we find that all results can be seamlessly reconciled by three fundamental low-frequency spatiotemporal patterns that we have identified via a novel time-varying complex pattern analysis. Overall, these three spatiotemporal patterns account for a wide variety of previously observed phenomena in the resting-state fMRI literature including the task-positive/task-negative anticorrelation, the global signal, the primary functional connectivity gradient and the network community structure of the functional connectome. The shared spatial and temporal properties of these three canonical patterns suggest that they arise from a single hemodynamic mechanism.

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.

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.

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 Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times

2021 ◽  
Author(s):  
Yusi Chen ◽  
Qasim Bukhari ◽  
Tiger Wutu Lin ◽  
Terrence J Sejnowski
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Resting State ◽  
Structural Connectivity ◽  
Reaction Times ◽  
Resonance Imaging ◽  
Brain Areas ◽  
Human Connectome Project ◽  
Wide Range

Recordings from resting state functional magnetic resonance imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of ''ground truth'' has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas. Differential covariance (dCov) is an algorithm for analyzing FC with directed graph edges. Applied to synthetic datasets, dCov-FC was more effective than covariance and partial correlation in reducing false positive connections and more accurately matching the underlying structural connectivity. When we applied dCov-FC to resting state fMRI recordings from the human connectome project (HCP) and anesthetized mice, dCov-FC accurately identified strong cortical connections from diffusion Magnetic Resonance Imaging (dMRI) in individual humans and viral tract tracing in mice. In addition, those HCP subjects whose rs-fMRI were more integrated, as assessed by a graph-theoretic measure, tended to have shorter reaction times in several behavioral tests. Thus, dCov-FC was able to identify anatomically verified connectivity that yielded measures of brain integration causally related to behavior.

scholarly journals Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

2018 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Temporal Structure ◽  
Structural Connectivity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Network ◽  
Dynamical Analysis ◽  
Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

scholarly journals Distinct structure-function relationships across cortical regions and connectivity scales in the rat brain

2019 ◽  
Author(s):  
Milou Straathof ◽  
Michel R.T. Sinke ◽  
Theresia J.M. Roelofs ◽  
Erwin L.A. Blezer ◽  
R. Angela Sarabdjitsingh ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Structure Function ◽  
Rat Brain ◽  
Resting State ◽  
Structural Connectivity ◽  
Distinct Structure ◽  
Macro Scale ◽  
Function Relationship ◽  
The Brain ◽  
Meso Scale

AbstractAn improved understanding of the structure-function relationship in the brain is necessary to know to what degree structural connectivity underpins abnormal functional connectivity seen in many disorders. We integrated high-field resting-state fMRI-based functional connectivity with high-resolution macro-scale diffusion-based and meso-scale neuronal tracer-based structural connectivity, to obtain an accurate depiction of the structure-function relationship in the rat brain. Our main goal was to identify to what extent structural and functional connectivity strengths are correlated, macro- and meso-scopically, across the cortex. Correlation analyses revealed a positive correspondence between functional connectivity and macro-scale diffusion-based structural connectivity, but no correspondence between functional connectivity and meso-scale neuronal tracer-based structural connectivity. Locally, strong functional connectivity was found in two well-known resting-state networks: the sensorimotor and default mode network. Strong functional connectivity within these networks coincided with strong short-range intrahemispheric structural connectivity, but with weak heterotopic interhemispheric and long-range intrahemispheric structural connectivity. Our study indicates the importance of combining measures of connectivity at distinct hierarchical levels to accurately determine connectivity across networks in the healthy and diseased brain. Distinct structure-function relationships across the brain can explain the organization of networks and may underlie variations in the impact of structural damage on functional networks and behavior.

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 Local and Distant responses to single pulse electrical stimulation reflect different forms of connectivity

2020 ◽  
Author(s):  
Britni Crocker ◽  
Lauren Ostrowski ◽  
Ziv M. Williams ◽  
Darin D. Dougherty ◽  
Emad N. Eskandar ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Resting State ◽  
Diffusion Tensor ◽  
Effective Connectivity ◽  
Clinical Care ◽  
Structural Connectivity ◽  
Intractable Epilepsy ◽  
Single Pulse ◽  
Intracranial Electrode ◽  
The Brain

AbstractBackgroundMeasuring connectivity in the human brain can involve innumerable approaches using both noninvasive (fMRI, EEG) and invasive (intracranial EEG or iEEG) recording modalities, including the use of external probing stimuli, such as direct electrical stimulation.Objective/HypothesisTo examine how different measures of connectivity correlate with one another, we compared ‘passive’ measures of connectivity during resting state conditions map to the more ‘active’ probing measures of connectivity with single pulse electrical stimulation (SPES).MethodsWe measured the network engagement and spread of the cortico-cortico evoked potential (CCEP) induced by SPES at 53 total sites across the brain, including cortical and subcortical regions, in patients with intractable epilepsy (N=11) who were undergoing intracranial recordings as a part of their clinical care for identifying seizure onset zones. We compared the CCEP network to functional, effective, and structural measures of connectivity during a resting state in each patient. Functional and effective connectivity measures included correlation or Granger causality measures applied to stereoEEG (sEEGs) recordings. Structural connectivity was derived from diffusion tensor imaging (DTI) acquired before intracranial electrode implant and monitoring (N=8).ResultsThe CCEP network was most similar to the resting state voltage correlation network in channels near to the stimulation location. In contrast, the distant CCEP network was most similar to the DTI network. Other connectivity measures were not as similar to the CCEP network.ConclusionsThese results demonstrate that different connectivity measures, including those derived from active stimulation-based probing, measure different, complementary aspects of regional interrelationships in the brain.

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