scholarly journals Imaging and dynamic causal modelling reveal brain-wide changes in effective connectivity and synaptic dynamics during epileptic seizures

2017 ◽  
Author(s):  
RE Rosch ◽  
PR Hunter ◽  
T Baldeweg ◽  
KJ Friston ◽  
MP Meyer
Keyword(s):  
Network Architecture ◽  
Empirical Bayes ◽  
Temporal Dynamics ◽  
Spatial Scales ◽  
Field Potential ◽  
Synaptic Coupling ◽  
Zebrafish Brain ◽  
Causal Modelling ◽  
Synaptic Dynamics ◽  
Dynamic Causal Modelling

SummaryPathophysiological explanations of epilepsy typically focus on either the micro/mesoscale (e.g. excitation-inhibition imbalance), or on the macroscale (e.g. network architecture). Linking abnormalities across spatial scales remains difficult, partly because of technical limitations in measuring neuronal signatures concurrently at the scales involved. Here we use light sheet imaging of the larval zebrafish brain during acute epileptic seizure induced with pentylenetetrazole. Empirically measured spectral changes of spontaneous neuronal activity during the seizure are then modelled using neural mass models, allowing Bayesian inference on changes in effective network connectivity and their underlying synaptic dynamics. This dynamic causal modelling of seizures in the zebrafish brain reveals concurrent changes in synaptic coupling at macro- and mesoscale. Fluctuations of synaptic connection strength and their temporal dynamics are both required to explain observed seizure patterns. These findings challenge a simple excitation-inhibition account of seizures, and highlight changes in synaptic transmission dynamics as a possible seizure generation pathomechanism.AbbreviationsLFPlocal field potentialPTZpentylenetetrazoleDCMdynamic causal modellingCSDcross spectral densitiesPEBParametric Empirical Bayes

scholarly journals The neurophysiological architecture of semantic dementia: spectral dynamic causal modelling of a neurodegenerative proteinopathy

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Elia Benhamou ◽  
Charles R. Marshall ◽  
Lucy L. Russell ◽  
Chris J. D. Hardy ◽  
Rebecca L. Bond ◽  
...  
Keyword(s):  
Network Architecture ◽  
Large Scale ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Semantic Dementia ◽  
Older Individuals ◽  
World Knowledge ◽  
Causal Modelling ◽  
Dynamic Causal Modelling ◽  
Semantic Impairment

Abstract The selective destruction of large-scale brain networks by pathogenic protein spread is a ubiquitous theme in neurodegenerative disease. Characterising the circuit architecture of these diseases could illuminate both their pathophysiology and the computational architecture of the cognitive processes they target. However, this is challenging using standard neuroimaging techniques. Here we addressed this issue using a novel technique—spectral dynamic causal modelling—that estimates the effective connectivity between brain regions from resting-state fMRI data. We studied patients with semantic dementia—the paradigmatic disorder of the brain system mediating world knowledge—relative to healthy older individuals. We assessed how the effective connectivity of the semantic appraisal network targeted by this disease was modulated by pathogenic protein deposition and by two key phenotypic factors, semantic impairment and behavioural disinhibition. The presence of pathogenic protein in SD weakened the normal inhibitory self-coupling of network hubs in both antero-mesial temporal lobes, with development of an abnormal excitatory fronto-temporal projection in the left cerebral hemisphere. Semantic impairment and social disinhibition were linked to a similar but more extensive profile of abnormally attenuated inhibitory self-coupling within temporal lobe regions and excitatory projections between temporal and inferior frontal regions. Our findings demonstrate that population-level dynamic causal modelling can disclose a core pathophysiological feature of proteinopathic network architecture—attenuation of inhibitory connectivity—and the key elements of distributed neuronal processing that underwrite semantic memory.

scholarly journals Structure learning in coupled dynamical systems and dynamic causal modelling

2019 ◽  
Vol 377 (2160) ◽  
pp. 20190048 ◽  
Author(s):  
Amirhossein Jafarian ◽  
Peter Zeidman ◽  
Vladimir Litvak ◽  
Karl Friston
Keyword(s):  
Dynamical Systems ◽  
Network Architecture ◽  
Bayesian Model ◽  
Structure Learning ◽  
Dynamical Interaction ◽  
Causal Modelling ◽  
Ill Posed ◽  
Competing Models ◽  
Neuronal Systems ◽  
Dynamic Causal Modelling

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill-posed problem that commonly arises when modelling real-world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of network architectures—and implicit coupling functions—in terms of their Bayesian model evidence. These methods are collectively referred to as dynamic causal modelling. We focus on a relatively new approach that is proving remarkably useful, namely Bayesian model reduction, which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems. This article is part of the theme issue ‘Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences'.

scholarly journals GABAergic cortical network physiology in frontotemporal lobar degeneration

Brain ◽  
2021 ◽  
Author(s):  
Natalie E Adams ◽  
Laura E Hughes ◽  
Matthew A Rouse ◽  
Holly N Phillips ◽  
Alexander D Shaw ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Progressive Supranuclear Palsy ◽  
Frontotemporal Lobar Degeneration ◽  
Empirical Bayes ◽  
Oddball Paradigm ◽  
Resonance Spectroscopy ◽  
Gaba Concentration ◽  
Causal Modelling ◽  
Local Circuits ◽  
Dynamic Causal Modelling

Abstract The clinical syndromes caused by frontotemporal lobar degeneration are heterogeneous, including the behavioural variant frontotemporal dementia (bvFTD) and progressive supranuclear palsy (PSP). Although pathologically distinct, they share many behavioural, cognitive and physiological features, which may in part arise from common deficits of major neurotransmitters such as γ-aminobutyric acid (GABA). Here, we quantify the GABA-ergic impairment and its restoration with dynamic causal modelling of a double-blind placebo-controlled crossover pharmaco-magnetoencephalography study. We analysed 17 people with bvFTD, 15 people with progressive supranuclear palsy, and 20 healthy age- and gender-matched controls. In addition to neuropsychological assessment and structural magnetic resonance imaging, participants undertook two magnetoencephalography sessions using a roving auditory oddball paradigm: Once on placebo and once on 10 mg of the oral GABA reuptake inhibitor tiagabine. A subgroup underwent ultrahigh-field magnetic resonance spectroscopy measurement of GABA concentration, which was reduced among patients. We identified deficits in frontotemporal processing using conductance-based biophysical models of local and global neuronal networks. The clinical relevance of this physiological deficit is indicated by the correlation between top-down connectivity from frontal to temporal cortex and clinical measures of cognitive and behavioural change. A critical validation of the biophysical modelling approach was evidence from Parametric Empirical Bayes analysis that GABA levels in patients, measured by spectroscopy, were related to posterior estimates of patients’ GABA-ergic synaptic connectivity. Further evidence for the role of GABA in frontotemporal lobar degeneration came from confirmation that the effects of tiagabine on local circuits depended not only on participant group, but also on individual baseline GABA levels. Specifically, the phasic inhibition of deep cortico-cortical pyramidal neurons following Tiagabine, but not placebo, was a function of GABA concentration. The study provides proof-of-concept for the potential of dynamic causal modelling to elucidate mechanisms of human neurodegenerative disease, and explain the variation in response to candidate therapies among patients. The laminar- and neurotransmitter-specific features of the modelling framework, can be used to study other treatment approaches and disorders. In the context of frontotemporal lobar degeneration, we suggest that neurophysiological restoration in selected patients, by targeting neurotransmitter deficits, could be used to bridge between clinical and preclinical models of disease, and inform the personalised selection of drugs and stratification of patients for future clinical trials.

scholarly journals Predicting ovulation from brain connectivity: Dynamic causal modelling of the menstrual cycle

2020 ◽  
Author(s):  
Esmeralda Hidalgo-Lopez ◽  
Peter Zeidman ◽  
TiAnni Harris ◽  
Adeel Razi ◽  
Belinda Pletzer
Keyword(s):  
Menstrual Cycle ◽  
Luteal Phase ◽  
Empirical Bayes ◽  
Angular Gyrus ◽  
Cycle Phase ◽  
Brain Organization ◽  
Cognitive Changes ◽  
Causal Modelling ◽  
Anterior Posterior ◽  
Dynamic Causal Modelling

AbstractLongitudinal menstrual cycle research allows the assessment of sex hormones effects on brain organization in a natural framework. Here, we used spectral dynamic causal modelling (spDCM) in a triple network model consisting of the default mode, salience and executive central networks (DMN, SN, and ECN), in order to address the changes in effective connectivity across the menstrual cycle. Sixty healthy young women were scanned three times (menses, pre-ovulatory and luteal phase) and spDCM was estimated for a total of 174 scans. Group level analysis using Parametric empirical Bayes showed lateralized and anterior-posterior changes in connectivity patterns depending on the cycle phase and related to the endogenous hormonal milieu. Right before ovulation the left insula recruited the frontoparietal network, while the right middle frontal gyrus decreased its connectivity to the precuneus. In exchange, the precuneus engaged bilateral angular gyrus, decoupling the DMN into anterior/posterior parts. During the luteal phase, bilateral insula engaged to each other decreasing the connectivity to parietal ECN, which in turn engaged the posterior DMN. Remarkably, the specific cycle phase in which a woman was in could be predicted by the connections that showed the strongest changes. These findings further corroborate the plasticity of the female brain in response to acute hormone fluctuations and have important implications for understanding the neuroendocrine interactions underlying cognitive changes along the menstrual cycle.

scholarly journals Brain pathology recapitulates physiology: A network meta-analysis

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Thomas J. Vanasse ◽  
Peter T. Fox ◽  
P. Mickle Fox ◽  
Franco Cauda ◽  
Tommaso Costa ◽  
...  
Keyword(s):  
Network Architecture ◽  
Spatial Scales ◽  
Meta Analysis ◽  
Metabolic Cost ◽  
Common Mechanism ◽  
Brain Disorders ◽  
Brain Pathology ◽  
Structural Network ◽  
Meta Analyses ◽  
Function Network

AbstractNetwork architecture is a brain-organizational motif present across spatial scales from cell assemblies to distributed systems. Structural pathology in some neurodegenerative disorders selectively afflicts a subset of functional networks, motivating the network degeneration hypothesis (NDH). Recent evidence suggests that structural pathology recapitulating physiology may be a general property of neuropsychiatric disorders. To test this possibility, we compared functional and structural network meta-analyses drawing upon the BrainMap database. The functional meta-analysis included results from >7,000 experiments of subjects performing >100 task paradigms; the structural meta-analysis included >2,000 experiments of patients with >40 brain disorders. Structure-function network concordance was high: 68% of networks matched (pFWE < 0.01), confirming the broader scope of NDH. This correspondence persisted across higher model orders. A positive linear association between disease and behavioral entropy (p = 0.0006;R2 = 0.53) suggests nodal stress as a common mechanism. Corroborating this interpretation with independent data, we show that metabolic ‘cost’ significantly differs along this transdiagnostic/multimodal gradient.

Propagating Neuronal Discharges in Neocortical Slices: Computational and Experimental Study

1997 ◽  
Vol 78 (3) ◽  
pp. 1199-1211 ◽  
Author(s):  
David Golomb ◽  
Yael Amitai
Keyword(s):  
Experimental Study ◽  
Ampa Receptors ◽  
Potassium Current ◽  
Initial Conditions ◽  
Field Potential ◽  
Integration Time ◽  
Synaptic Coupling ◽  
Discharge Velocity ◽  
Slow Potassium ◽  
Typical Cell

Golomb, David and Yael Amitai. Propagating neuronal discharges in neocortical slices: computational and experimental study. J. Neurophysiol. 78: 1199–1211, 1997. We studied the propagation of paroxysmal discharges in disinhibited neocortical slices by developing and analyzing a model of excitatory regular-spiking neocortical cells with spatially decaying synaptic efficacies and by field potential recording in rat slices. Evoked discharges may propagate both in the model and in the experiment. The model discharge propagates as a traveling pulse with constant velocity and shape. The discharge shape is determined by an interplay between the synaptic driving force and the neuron's intrinsic currents, in particular the slow potassium current. In the model, N-methyl-d-aspartate (NMDA) conductance contributes much less to the discharge velocity than amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) conductance. Blocking NMDA receptors experimentally with 2-amino-5-phosphonovaleric acid (APV) has no significant effect on the discharge velocity. In both model and experiments, propagation occurs for AMPA synaptic coupling g AMPA above a certain threshold, at which the velocity is finite (non-zero). The discharge velocity grows linearly with the g AMPA for g AMPA much above the threshold. In the experiments, blocking AMPA receptors gradually by increasing concentrations of 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX) in the perfusing solution results in a gradual reduction of the discharge velocity until propagation stops altogether, thus confirming the model prediction. When discharges are terminated in the model by the slow potassium current, a network with the same parameter set may display discharges with several forms, which have different velocities and numbers of spikes; initial conditions select the exhibited pattern. When the discharge is also terminated by strong synaptic depression, there is only one discharge form for a particular parameter set; the velocity grows continuously with increased synaptic conductances. No indication for more than one discharge velocity was observed experimentally. If the AMPA decay rate increases while the maximal excitatory postsynaptic conductance (EPSC) a cell receives is kept fixed, the velocity increases by ∼20% until it reaches a saturated value. Therefore the discharge velocity is determined mainly by the cells' integration time of input EPSCs. We conclude, on the basis of both the experiments and the model, that the total amount of excitatory conductance a typical cell receives in a control slice exhibiting paroxysmal discharges is only ∼5 times larger than the excitatory conductance needed for raising the potential of a resting cell above its action potential threshold.

scholarly journals Population dynamics of the modified theta model: macroscopic phase reduction and bifurcation analysis link microscopic neuronal interactions to macroscopic gamma oscillation

2014 ◽  
Vol 11 (95) ◽  
pp. 20140058 ◽  
Author(s):  
Kiyoshi Kotani ◽  
Ikuhiro Yamaguchi ◽  
Lui Yoshida ◽  
Yasuhiko Jimbo ◽  
G. Bard Ermentrout
Keyword(s):  
Bifurcation Analysis ◽  
Field Potential ◽  
Reversal Potential ◽  
Planck Equation ◽  
Gamma Oscillations ◽  
Gamma Oscillation ◽  
Synaptic Coupling ◽  
Time Constants ◽  
Shunting Inhibition ◽  
Synaptic Interactions

Gamma oscillations of the local field potential are organized by collective dynamics of numerous neurons and have many functional roles in cognition and/or attention. To mathematically and physiologically analyse relationships between individual inhibitory neurons and macroscopic oscillations, we derive a modification of the theta model, which possesses voltage-dependent dynamics with appropriate synaptic interactions. Bifurcation analysis of the corresponding Fokker–Planck equation (FPE) enables us to consider how synaptic interactions organize collective oscillations. We also develop the adjoint method (infinitesimal phase resetting curve) for simultaneous equations consisting of ordinary differential equations representing synaptic dynamics and a partial differential equation for determining the probability distribution of the membrane potential. This method provides a macroscopic phase response function (PRF), which gives insights into how it is modulated by external perturbation or internal changes of parameters. We investigate the effects of synaptic time constants and shunting inhibition on these gamma oscillations. The sensitivity of rising and decaying time constants is analysed in the oscillatory parameter regions; we find that these sensitivities are not largely dependent on rate of synaptic coupling but, rather, on current and noise intensity. Analyses of shunting inhibition reveal that it can affect both promotion and elimination of gamma oscillations. When the macroscopic oscillation is far from the bifurcation, shunting promotes the gamma oscillations and the PRF becomes flatter as the reversal potential of the synapse increases, indicating the insensitivity of gamma oscillations to perturbations. By contrast, when the macroscopic oscillation is near the bifurcation, shunting eliminates gamma oscillations and a stable firing state appears. More interestingly, under appropriate balance of parameters, two branches of bifurcation are found in our analysis of the FPE. In this case, shunting inhibition can effect both promotion and elimination of the gamma oscillation depending only on the reversal potential.

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