Brain dynamics: Synchronous peaks, functional connectivity, and its temporal variability

Rolls, Edmund T.; Cheng, Wei; Feng, Jianfeng

doi:10.1002/hbm.25404

Cited by 20 publications

(16 citation statements)

References 23 publications

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“…The human brain is a complex dynamic system (Fox et al, 2005). Recently, a dynamic FC (dFC) method was developed by measuring the variability in the strength or spatial dynamic organization of brain connectivity (Preti et al, 2017;Rolls et al, 2021). Li et al 10.3389/fnins.2022.953356 Previous studies in neurological disorders have proved that this approach can sensitively capture the time-varying changes of the ongoing activity over the whole scan time (Liao et al, 2018;Kottaram et al, 2019;Guo et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Reconfiguration of static and dynamic thalamo-cortical network functional connectivity of epileptic children with generalized tonic-clonic seizures

Wang

et al. 2022

Front. Neurosci.

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ObjectiveA number of studies in adults and children with generalized tonic-clonic seizure (GTCS) have reported the alterations in morphometry, functional activity, and functional connectivity (FC) in the thalamus. However, the neural mechanisms underlying the alterations in the thalamus of patients with GTCS are not well understood, particularly in children. The aim of the current study was to explore the temporal properties of functional pathways connecting thalamus in children with GTCS.MethodsHere, we recruited 24 children with GTCS and 36 age-matched healthy controls. Static and dynamic FC approaches were used to evaluate alterations in the temporal variability of thalamo-cortical networks in children with GTCS. The dynamic effective connectivity (dEC) method was also used to evaluate the directions of the fluctuations in effective connectivity. In addition, the relationships between the dynamic properties and clinical features were assessed.ResultsThe static FC analysis presented significantly decreased connectivity patterns between the bilateral thalamus and between the thalamus and right inferior temporal gyrus. The dynamic connectivity analysis found decreased FC variability in the thalamo-cortical network of children with epilepsy. Dynamic EC analyses identified increased connectivity variability from the frontal gyrus to the bilateral thalamus, and decreased connectivity variability from the right thalamus to the left thalamus and from the right thalamus to the right superior parietal lobe. In addition, correlation analysis revealed that both static FC and connectivity temporal variability in the thalamo-cortical network related to the clinical features (epilepsy duration and epilepsy onset time).SignificanceOur findings of both increased and decreased connectivity variability in the thalamo-cortical network imply a dynamic restructuring of the functional pathways connecting the thalamus in children with GTCS. These alterations in static and temporal dynamic pathways connecting the bilateral thalamus may extend our understanding of the neural mechanisms underlying the GTCS in children.

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Section: Introductionmentioning

confidence: 99%

Reconfiguration of static and dynamic thalamo-cortical network functional connectivity of epileptic children with generalized tonic-clonic seizures

Wang

et al. 2022

Front. Neurosci.

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“…In addition, deconvolution approaches hold a close parallelism to recent methodologies aiming to understand the dynamics of neuronal activations and interactions at short temporal resolution and that focus on extreme events of the fMRI signal (Lindquist et al 2007). As an illustration, Figure 6 shows that the innovation-or activity-inducing CAPs computed from deconvolved events in a single restingstate fMRI dataset closely resemble the conventional CAPs computed directly from extreme events of the fMRI signal Liu et al , 2018Cifre et al 2020a,b;Zhang et al 2020;Tagliazucchi et al 2011Tagliazucchi et al , 2012Tagliazucchi et al , 2016Rolls et al 2021). Similarly, we hypothesize that these extreme events will also show a close resemblance to intrinsic ignition events .…”

Section: Discussionmentioning

confidence: 74%

Hemodynamic Deconvolution Demystified: Sparsity-Driven Regularization at Work

Uruñuela¹,

Bolton²,

Ville³

et al. 2021

Preprint

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Deconvolution of the hemodynamic response is an important step to access short timescales of brain activity recorded by functional magnetic resonance imaging (fMRI). Albeit conventional deconvolution algorithms have been around for a long time (e.g., Wiener deconvolution), recent stateof-the-art methods based on sparsity-pursuing regularization are attracting increasing interest to investigate brain dynamics and connectivity. This technical note revisits the main concepts underlying two main methods, Paradigm Free Mapping and Total Activation, in the most accessible way. Despite their apparent differences, these methods are theoretically equivalent as they represent the synthesis and analysis sides of the same problem. We demonstrate this equivalence in practice with their best-available implementations using both simulations, with different signal-to-noise ratios, and experimental data of motor task and resting-state fMRI. We evaluate the parameter settings that lead to equivalent results, and showcase the potential of these algorithms compared to other widely-used approaches. This note is useful for practitioners interested in gaining a better understanding of state-of-the-art hemodynamic deconvolution, and who want to make use of them with the most efficient implementation.

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“…This similarity was already noted in Tagliazucchi et al ( 2012 ) by comparing the de-convolved BOLD signal using either a canonical HRF or the source event extracted by our approach (see Figure 1D in Tagliazucchi et al, 2012 ). The most recent work of Urunuela et al ( 2021 ) summarizes this point very well: “deconvolution approaches hold a close parallelism to recent methodologies aiming to understand the dynamics of neuronal activations and interactions at short temporal resolution and that focus on extreme events of the fMRI signal (Lindquist et al, 2007 ).” In that work, the authors provide a very persuading evidence of such parallelism: “ Figure 6 shows that the innovation- or activity-inducing CAPs computed from deconvolved events in a single resting-state fMRI dataset closely resemble the conventional CAPs computed directly from extreme events of the fMRI signal (Tagliazucchi et al, 2011 , 2012 , 2016 ; Liu and Duyn, 2013 ; Liu et al, 2013 , 2021 ; Cifre et al, 2020 ; Zhang et al, 2020 ; Rolls et al, 2021 ).”…”

Section: Discussion: Features Advantages and Limitations Of The Proposed Strategymentioning

confidence: 97%

Revisiting Nonlinear Functional Brain Co-activations: Directed, Dynamic, and Delayed

et al. 2021

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The center stage of neuro-imaging is currently occupied by studies of functional correlations between brain regions. These correlations define the brain functional networks, which are the most frequently used framework to represent and interpret a variety of experimental findings. In the previous study, we first demonstrated that the relatively stronger blood oxygenated level dependent (BOLD) activations contain most of the information relevant to understand functional connectivity, and subsequent work confirmed that a large compression of the original signals can be obtained without significant loss of information. In this study, we revisit the correlation properties of these epochs to define a measure of nonlinear dynamic directed functional connectivity (nldFC) across regions of interest. We show that the proposed metric provides at once, without extensive numerical complications, directed information of the functional correlations, as well as a measure of temporal lags across regions, overall offering a different and complementary perspective in the analysis of brain co-activation patterns. In this study, we provide further details for the computations of these measures and for a proof of concept based on replicating existing results from an Autistic Syndrome database, and discuss the main features and advantages of the proposed strategy for the study of brain functional correlations.

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Brain dynamics: Synchronous peaks, functional connectivity, and its temporal variability

Cited by 20 publications

References 23 publications

Reconfiguration of static and dynamic thalamo-cortical network functional connectivity of epileptic children with generalized tonic-clonic seizures

Reconfiguration of static and dynamic thalamo-cortical network functional connectivity of epileptic children with generalized tonic-clonic seizures

Hemodynamic Deconvolution Demystified: Sparsity-Driven Regularization at Work

Revisiting Nonlinear Functional Brain Co-activations: Directed, Dynamic, and Delayed

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