scholarly journals A Bayesian method for inference of effective connectivity in brain networks for detecting the Mozart effect

2020 ◽  
Vol 127 ◽  
pp. 104055
Author(s):  
Rik J.C. van Esch ◽  
Shengling Shi ◽  
Antoine Bernas ◽  
Svitlana Zinger ◽  
Albert P. Aldenkamp ◽  
...  
Keyword(s):  
Bayesian Method ◽  
Effective Connectivity ◽  
Brain Networks ◽  
Mozart Effect

scholarly journals Parcels and particles: Markov blankets in the brain

2020 ◽  
pp. 1-76
Author(s):  
Karl J. Friston ◽  
Erik D. Fagerholm ◽  
Tahereh S. Zarghami ◽  
Thomas Parr ◽  
Inês Hipólito ◽  
...  
Keyword(s):  
Message Passing ◽  
Effective Connectivity ◽  
Directed Graphs ◽  
Functional Integration ◽  
Brain Networks ◽  
Functional Anatomy ◽  
Phenomenological Approach ◽  
Dynamical Instability ◽  
Self Organised Criticality ◽  
Markov Blankets

At the inception of human brain mapping, two principles of functional anatomy underwrote most conceptions – and analyses – of distributed brain responses: namely functional segregation and integration. There are currently two main approaches to characterising functional integration. The first is a mechanistic modelling of connectomics in terms of directed effective connectivity that mediates neuronal message passing and dynamics on neuronal circuits. The second phenomenological approach usually characterises undirected functional connectivity (i.e., measurable correlations), in terms of intrinsic brain networks, self-organised criticality, dynamical instability, etc. This paper describes a treatment of effective connectivity that speaks to the emergence of intrinsic brain networks and critical dynamics. It is predicated on the notion of Markov blankets that play a fundamental role in the self-organisation of far from equilibrium systems. Using the apparatus of the renormalisation group, we show that much of the phenomenology found in network neuroscience is an emergent property of a particular partition of neuronal states, over progressively coarser scales. As such, it offers a way of linking dynamics on directed graphs to the phenomenology of intrinsic brain networks.

scholarly journals Multivariate Granger Causality Analysis of Acupuncture Effects in Mild Cognitive Impairment Patients: An fMRI Study

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Shangjie Chen ◽  
Lijun Bai ◽  
Maosheng Xu ◽  
Fang Wang ◽  
Liang Yin ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Granger Causality ◽  
Effective Connectivity ◽  
Brain Networks ◽  
Brain Regions ◽  
Causality Analysis ◽  
Granger Causality Analysis ◽  
Cognitive States ◽  
Dorsolateral Prefrontal

Evidence from clinical reports has indicated that acupuncture has a promising effect on mild cognitive impairment (MCI). However, it is still unknown that by what way acupuncture can modulate brain networks involving the MCI. In the current study, multivariate Granger causality analysis (mGCA) was adopted to compare the interregional effective connectivity of brain networks by varying needling depths (deep acupuncture, DA; superficial acupuncture, SA) and at different cognitive states, which were the MCI and healthy control (HC). Results from DA at KI3 in MCI showed that the dorsolateral prefrontal cortex and hippocampus emerged as central hubs and had significant causal influences with each other, but significant in HC for DA. Moreover, only several brain regions had remarkable causal interactions following SA in MCI and even few brain regions following SA in HC. Our results indicated that acupuncture at KI3 at different cognitive states and with varying needling depths may induce distinct reorganizations of effective connectivities of brain networks, and DA at KI3 in MCI can induce the strongest and more extensive effective connectivities related to the therapeutic effect of acupuncture in MCI. The study demonstrated the relatively functional specificity of acupuncture at KI3 in MCI, and needling depths play an important role in acupuncture treatments.

Effective connectivity of brain networks during self-initiated movement in Parkinson's disease

NeuroImage ◽  
2011 ◽  
Vol 55 (1) ◽  
pp. 204-215 ◽  
Author(s):  
Tao Wu ◽  
Liang Wang ◽  
Mark Hallett ◽  
Yi Chen ◽  
Kuncheng Li ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Effective Connectivity ◽  
Brain Networks

Effective Connectivity Analysis in Brain Networks: A GPU-Accelerated Implementation of the Cox Method

2016 ◽  
Vol 10 (7) ◽  
pp. 1226-1237 ◽  
Author(s):  
Vafa Andalibi ◽  
Francois Christophe ◽  
Teemu Laukkarinen ◽  
Tommi Mikkonen
Keyword(s):  
Effective Connectivity ◽  
Brain Networks ◽  
Connectivity Analysis

scholarly journals Regression dynamic causal modeling for resting-state fMRI

2020 ◽  
Author(s):  
Stefan Frässle ◽  
Samuel J. Harrison ◽  
Jakob Heinzle ◽  
Brett A. Clementz ◽  
Carol A. Tamminga ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Causal Modeling ◽  
Computationally Efficient ◽  
Dynamic Causal Modeling ◽  
Whole Brain ◽  
Wide Range

Abstract“Resting-state” functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks.Here, we show that a method recently developed for task-fMRI – regression dynamic causal modeling (rDCM) – extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.

scholarly journals Variability and reliability of effective connectivity within the core default mode network: A longitudinal spectral DCM study

2018 ◽  
Author(s):  
Hannes Almgren ◽  
Frederik Van de Steen ◽  
Simone Kühn ◽  
Adeel Razi ◽  
Karl Friston ◽  
...  
Keyword(s):  
Resting State ◽  
Hemispheric Asymmetry ◽  
Large Scale ◽  
Effective Connectivity ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Global Signal ◽  
Cognitive Behavioural ◽  
Connectivity Patterns ◽  
Within Subjects

AbstractDynamic causal modelling (DCM) for resting state fMRI – namely spectral DCM – is a recently developed and widely adopted method for inferring effective connectivity in intrinsic brain networks. Most research applying spectral DCM has focused on group-averaged connectivity within large-scale intrinsic brain networks; however, the consistency of subject- and session-specific estimates of effective connectivity has not been evaluated. Establishing reliability (within subjects) is crucial for its clinical use; e.g., as a neurophysiological phenotype of disease progression. Effective connectivity during rest is likely to vary due to changes in cognitive, behavioural, and physical states. Determining the sources of fluctuations in effective connectivity may yield greater understanding of brain processes and inform clinical applications about potential confounds. In the present study, we investigated the consistency of effective connectivity within and between subjects, as well as potential sources of variability (e.g., hemispheric asymmetry). We further investigated how standard procedures for data processing and signal extraction affect this consistency. DCM analyses were applied to four longitudinal resting state fMRI datasets. Our sample consisted of 20 subjects with 653 resting state fMRI sessions in total. These data allowed to quantify the robustness of connectivity estimates for each subject, and to draw conclusions beyond specific data features. We found that subjects contributing to all datasets showed systematic and reliable patterns of hemispheric asymmetry. When asymmetry was taken into account, subjects showed very similar connectivity patterns. We also found that various processing procedures (e.g. global signal regression and ROI size) had little effect on inference and reliability of connectivity for the majority of subjects. Bayesian model reduction increased reliability (within-subjects) and stability (between-subjects) of connectivity patterns.

scholarly journals Transmission delays and frequency detuning can regulate information flow between brain regions

2020 ◽  
Author(s):  
Aref Pariz ◽  
Ingo Fischer ◽  
Alireza Valizadeh ◽  
Claudio Mirasso
Keyword(s):  
Functional Connectivity ◽  
Information Transfer ◽  
Effective Connectivity ◽  
Signal Transmission ◽  
Brain Networks ◽  
Frequency Detuning ◽  
Brain Oscillations ◽  
Brain Areas ◽  
Neural Populations

AbstractBrain networks exhibit very variable and dynamical functional connectivity and flexible configurations of information exchange despite their overall fixed structure (connectome). Brain oscillations are hypothesized to underlie time-dependent functional connectivity by periodically changing the excitability of neural populations. In this paper, we investigate the role that the connection delay and the frequency detuning between different neural populations play in the transmission of signals. Based on numerical simulations and analytical arguments, we show that the amount of information transfer between two oscillating neural populations can be determined solely by their connection delay and the mismatch in their oscillation frequencies. Our results highlight the role of the collective phase response curve of the oscillating neural populations for the efficacy of signal transmission and the quality of the information transfer in brain networks.Author summaryCollective dynamics in brain networks is characterized by a coordinated activity of their constituent neurons that lead to brain oscillations. Many evidences highlight the role that brain oscillations play in signal transmission, the control of the effective communication between brain areas and the integration of information processed by different specialized regions. Oscillations periodically modulate the excitability of neurons and determine the response those areas receiving the signals. Based on the communication trough coherence (CTC) theory, the adjustment of the phase difference between local oscillations of connected areas can specify the timing of exchanged signals and therefore, the efficacy of the communication channels. An important factor is the delay in the transmission of signals from one region to another that affects the phase difference and timing, and consequently the impact of the signals. Despite this delay plays an essential role in CTC theory, its role has been mostly overlooked in previous studies. In this manuscript, we concentrate on the role that the connection delay and the oscillation frequency of the populations play in the signal transmission, and consequently in the effective connectivity, between two brain areas. Through extensive numerical simulations, as well as analytical results with reduced models, we show that these parameters have two essential impacts on the effective connectivity of the neural networks: First, that the populations advancing in phase to others do not necessarily play the role of the information source; and second, that the amount and direction of information transfer dependents on the oscillation frequency of the populations.

scholarly journals Hypothalamic effective connectivity at rest is associated with body weight and energy homeostasis

2021 ◽  
Author(s):  
Katharina Voigt ◽  
Zane B Andrews ◽  
Ian H Harding ◽  
Adeel Razi ◽  
Antonio Verdejo-Garcia
Keyword(s):  
Information Flow ◽  
Weight Status ◽  
Energy Homeostasis ◽  
Effective Connectivity ◽  
Brain Networks ◽  
Substantia Nigra Pars Compacta ◽  
Central Component ◽  
Inhibitory Influence ◽  
Eating Behaviours ◽  
Top Down

Hunger and satiety states drive eating behaviours via changes in brain function. The hypothalamus is a central component of the brain networks that regulate food intake. Animal research parsed the roles of the lateral hypothalamus (LH) and the medial hypothalamus (MH) in hunger and satiety respectively. Here, we examined how hunger and satiety change information flow between human LH and MH brain networks, and how these interactions are influenced by body mass index. Forty participants (15 overweight/obese) underwent two resting-state functional MRI scans: after overnight fasting (fasted state) and following a standardised meal (sated state). The direction and valence (excitatory/inhibitory influence) of information flow between the MH and LH was modelled using spectral dynamic causal modelling. Our results revealed two core networks interacting across homeostatic state and weight status: subcortical bidirectional connections between the LH, MH and the substantia nigra pars compacta (prSN), and cortical top-down inhibition from frontoparietal and temporal areas. During fasting relative to satiety, we found higher inhibition between the LH and prSN, whereas the prSN received greater top-down inhibition from across the cortex. Individuals with higher BMI showed that these network dynamics occur irrespective of fasted or satiety states. Our findings reveal fasting affects brain dynamics over a distributed hypothalamic-midbrain-cortical network. This network is less sensitive to state-related fluctuations among people with obesity.

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