Information Flow Between Resting-State Networks

Diez, Ibai; ErramuzpeAsier,; EscuderoIñaki,; Goñi, Beatriz Mateos; CabreraAlberto,; Marinazzo, Daniele; Sanz-Arigita, Ernesto; Stramaglia, Sebastiano; Cortes, Jesús M.

doi:10.1089/brain.2014.0337

Cited by 20 publications

(15 citation statements)

References 69 publications

(113 reference statements)

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“…Increased FC in the pathological brain has been observed before in different studies on traumatic brain injury or Alzheimer's Disease (Diez, Erramuzpe, et al, 2015). It is however not possible to establish a direct association between increased FC and behavioral or cognitive improvements.…”

Section: Network Homeostasis: Increased Fc In Combination With Decrmentioning

confidence: 87%

“…We applied resting fMRI preprocessing similar to previous work (Alonso-Montes et al, 2015;Amor et al, 2015;Diez, Erramuzpe, et al, 2015;Diez, Bonifazi, et al, 2015;Diez et al, 2017;Mäki-Marttunen, Diez, Cortes, Chialvo, & Villarreal, 2013;Marinazzo et al, 2014;Rasero, Pellicoro, et al, 2017;Stramaglia et al, 2016Stramaglia et al, , 2017Stramaglia, Angelini, Cortes, & Marinazzo, 2015) using FSL and AFNI (http://afni.nimh.nih.gov/afni/). First, slice-time correction was applied to the fMRI data set.…”

Section: Functional Mrimentioning

confidence: 99%

“…It is however not possible to establish a direct association between increased FC and behavioral or cognitive improvements. Indeed, studies have shown that increased FC can be associated with behavioral improvement (Grady, 2012;Hernandez-Castillo et al, 2017) but also with behavioral impairment (Diez, Erramuzpe, et al, 2015;Diez et al, 2017 4.5 | A general and novel methodology to quantify the multi-scale structure-function brain connectivity impact of therapies, diseases, and lifestyles…”

Section: Network Homeostasis: Increased Fc In Combination With Decrmentioning

confidence: 99%

See 2 more Smart Citations

Structure–function multi‐scale connectomics reveals a major role of the fronto‐striato‐thalamic circuit in brain aging

Bonifazi

Erramuzpe

Diez

et al. 2018

Human Brain Mapping

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Physiological aging affects brain structure and function impacting morphology, connectivity, and performance. However, whether some brain connectivity metrics might reflect the age of an individual is still unclear. Here, we collected brain images from healthy participants (N = 155) ranging from 10 to 80 years to build functional (resting state) and structural (tractography) connectivity matrices, both data sets combined to obtain different connectivity features. We then calculated the brain connectome age—an age estimator resulting from a multi‐scale methodology applied to the structure–function connectome, and compared it to the chronological age (ChA). Our results were twofold. First, we found that aging widely affects the connectivity of multiple structures, such as anterior cingulate and medial prefrontal cortices, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus, and temporal pole. Second, we found that the connectivity between basal ganglia and thalamus to frontal areas, also known as the fronto‐striato‐thalamic (FST) circuit, makes the major contribution to age estimation. In conclusion, our results highlight the key role played by the FST circuit in the process of healthy aging. Notably, the same methodology can be generally applied to identify the structural–functional connectivity patterns correlating to other biomarkers than ChA.

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Section: Network Homeostasis: Increased Fc In Combination With Decrmentioning

confidence: 87%

Section: Functional Mrimentioning

confidence: 99%

Section: Network Homeostasis: Increased Fc In Combination With Decrmentioning

confidence: 99%

See 1 more Smart Citation

Structure–function multi‐scale connectomics reveals a major role of the fronto‐striato‐thalamic circuit in brain aging

Bonifazi

Erramuzpe

Diez

et al. 2018

Human Brain Mapping

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“…Indeed, transfer entropy has proven particularly popular in computational neuroscience for characterizing neural information flows, with applications such as inferring effective neural information networks underpinning cognitive tasks and their variation [10][11][12][13][14], across data modalities including magnetoencephalography (MEG) [15,16], electroencephalography (EEG) [17][18][19], and functional magnetic resonance imaging (fMRI) [20,21]. Applications to spike train data have been less abundant, however.…”

Section: Introductionmentioning

confidence: 99%

Transfer entropy in continuous time, with applications to jump and neural spiking processes

2017

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Transfer entropy has been used to quantify the directed flow of information between source and target variables in many complex systems. While transfer entropy was originally formulated in discrete time, in this paper we provide a framework for considering transfer entropy in continuous time systems, based on Radon-Nikodym derivatives between measures of complete path realizations. To describe the information dynamics of individual path realizations, we introduce the pathwise transfer entropy, the expectation of which is the transfer entropy accumulated over a finite time interval. We demonstrate that this formalism permits an instantaneous transfer entropy rate. These properties are analogous to the behavior of physical quantities defined along paths such as work and heat. We use this approach to produce an explicit form for the transfer entropy for pure jump processes, and highlight the simplified form in the specific case of point processes (frequently used in neuroscience to model neural spike trains). Finally, we present two synthetic spiking neuron model examples to exhibit the pertinent features of our formalism, namely, that the information flow for point processes consists of discontinuous jump contributions (at spikes in the target) interrupting a continuously varying contribution (relating to waiting times between target spikes). Numerical schemes based on our formalism promise significant benefits over existing strategies based on discrete time formalisms.

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“…We applied resting fMRI preprocessing similar to previous work ( [23,24,25,26,27,28]) using FSL and AFNI (http://afni.nimh.nih.gov/afni/). First, slice-time was applied to the fMRI data set.…”

Section: Imaging Preprocessingmentioning

confidence: 99%

Interaction information along lifespan of the resting brain dynamics reveals a major redundant role of the default mode network

Camino-Pontes

Diez

Jiménez-Marín

et al. 2018

Preprint

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Interaction Information generalizes the univariate Shannon Entropy to triplets of variables, allowing the detection of redundant or synergetic interactions in dynamical networks. Here, we calculated interaction information from functional magnetic resonance imaging and asked whether redundancy or synergy vary across brain regions and along lifespan. We found high overlapping between the pattern of high redundancy and the default mode network, and this occurred along lifespan. The pattern of high values of synergy, more heterogeneous and variable along lifespan, was overlapping with different cognitive domains, such as spatial and temporal memory, emotion processing and motor skills. Moreover, the amount of redundancy and synergy seem to be balanced along lifespan, suggesting informational compensatory mechanisms in brain networks.

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Information Flow Between Resting-State Networks

Abstract: The spatial localization of the k=2 component, within RSNs, allows the characterization of IF differences between AD and controls.

Cited by 20 publications

References 69 publications

Structure–function multi‐scale connectomics reveals a major role of the fronto‐striato‐thalamic circuit in brain aging

Structure–function multi‐scale connectomics reveals a major role of the fronto‐striato‐thalamic circuit in brain aging

Transfer entropy in continuous time, with applications to jump and neural spiking processes

Interaction information along lifespan of the resting brain dynamics reveals a major redundant role of the default mode network

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