Gerald K. Cooray

Towards a Neuronal Gauge Theory

In a published paper [10], we have proposed that the brain (and other self-organized biological and artificial systems) can be characterized via the mathematical apparatus of a gauge theory. The picture that emerges from this approach suggests that any biological system (from a neuron to an organism) can be cast as resolving uncertainty about its external milieu, either by changing its internal states or its relationship to the environment. Using formal arguments, we have shown that a gauge theory for neuronal dynamics – based on approximate Bayesian inference – has the potential to shed new light on phenomena that have thus far eluded a formal description, such as attention and the link between action and perception. Here, we describe the technical apparatus that enables such a variational inference on manifolds.Given the amount of knowledge and data accruing in the neurosciences, is it time to formulate a general principle for neuronal dynamics that holds at evolutionary, developmental, and perceptual timescales? In this paper, we propose that the brain (and other self-organised biological systems) can be characterised via the mathematical apparatus of a gauge theory. The picture that emerges from this approach suggests that any biological system (from a neuron to an organism) can be cast as resolving uncertainty about its external milieu, either by changing its internal states or its relationship to the environment. Using formal arguments, we show that a gauge theory for neuronal dynamics—based on approximate Bayesian inference—has the potential to shed new light on phenomena that have thus far eluded a formal description, such as attention and the link between action and perception.

Viral triggering of anti-NMDA receptor encephalitis in a child - an important cause for disease relapse.

Herpes simplex encephalitis (HSE) in children is a potentially devastating condition which is occasionally complicated by a clinical relapse. An autoimmune component has long been suspected in these relapses and recent findings suggest that antibodies against N-methyl-D-aspartate receptors (NMDARs) may be part of this mechanism. We here report an 11 months old girl with acute HSE and with negative NMDAR antibody serology at presentation who after an initial response to antiviral treatment deteriorated with seizures, abnormal movements, focal neurologic deficits and psychiatric symptoms. We show that this relapse occurred as production of NMDAR antibodies developed and that clinical improvement followed immunotherapy with a concomitant decrease in NMDAR antibody titers in CSF. She also developed a characteristic 15-20 Hz activity over both hemispheres which has been previously described as an electroencephalographic presentation of anti-NMDAR encephalitis. We conclude that relapse or persisting symptoms in HSE in children may represent an immune-mediated mechanism rather than a viral reactivation and that NMDAR antibodies should be analyzed as this may be of importance for the choice of therapy.

Dynamic causal modelling of electrographic seizure activity using Bayesian belief updating.

Seizure activity in EEG recordings can persist for hours with seizure dynamics changing rapidly over time and space. To characterise the spatiotemporal evolution of seizure activity, large data sets often need to be analysed. Dynamic causal modelling (DCM) can be used to estimate the synaptic drivers of cortical dynamics during a seizure; however, the requisite (Bayesian) inversion procedure is computationally expensive. In this note, we describe a straightforward procedure, within the DCM framework, that provides efficient inversion of seizure activity measured with non-invasive and invasive physiological recordings; namely, EEG/ECoG. We describe the theoretical background behind a Bayesian belief updating scheme for DCM. The scheme is tested on simulated and empirical seizure activity (recorded both invasively and non-invasively) and compared with standard Bayesian inversion. We show that the Bayesian belief updating scheme provides similar estimates of time-varying synaptic parameters, compared to standard schemes, indicating no significant qualitative change in accuracy. The difference in variance explained was small (less than 5%). The updating method was substantially more efficient, taking approximately 5–10 min compared to approximately 1–2 h. Moreover, the setup of the model under the updating scheme allows for a clear specification of how neuronal variables fluctuate over separable timescales. This method now allows us to investigate the effect of fast (neuronal) activity on slow fluctuations in (synaptic) parameters, paving a way forward to understand how seizure activity is generated.

Characterising seizures in anti-NMDA-receptor encephalitis with dynamic causal modelling

We characterised the pathophysiology of seizure onset in terms of slow fluctuations in synaptic efficacy using EEG in patients with anti-N-methyl-d-aspartate receptor (NMDA-R) encephalitis. EEG recordings were obtained from two female patients with anti-NMDA-R encephalitis with recurrent partial seizures (ages 19 and 31). Focal electrographic seizure activity was localised using an empirical Bayes beamformer. The spectral density of reconstructed source activity was then characterised with dynamic causal modelling (DCM). Eight models were compared for each patient, to evaluate the relative contribution of changes in intrinsic (excitatory and inhibitory) connectivity and endogenous afferent input. Bayesian model comparison established a role for changes in both excitatory and inhibitory connectivity during seizure activity (in addition to changes in the exogenous input). Seizures in both patients were associated with a sequence of changes in inhibitory and excitatory connectivity; a transient increase in inhibitory connectivity followed by a transient increase in excitatory connectivity and a final peak of excitatory–inhibitory balance at seizure offset. These systematic fluctuations in excitatory and inhibitory gain may be characteristic of (anti NMDA-R encephalitis) seizures. We present these results as a case study and replication to motivate analyses of larger patient cohorts, to see whether our findings generalise and further characterise the mechanisms of seizure activity in anti-NMDA-R encephalitis.

Dynamic causal modelling of seizure activity in a rat model

Abstract This paper presents a physiological account of seizure activity and its evolution over time using a rat model of induced epilepsy. We analyse spectral activity recorded in the hippocampi of three rats who received kainic acid injections in the right hippocampus. We use dynamic causal modelling of seizure activity and Bayesian model reduction to identify the key synaptic and connectivity parameters that underlie seizure onset. Using recent advances in hierarchical modelling (parametric empirical Bayes), we characterise seizure onset in terms of slow fluctuations in synaptic excitability of specific neuronal populations. Our results suggest differences in the pathophysiology – of seizure activity in the lesioned versus the non‐lesioned hippocampus – with pronounced changes in excitation‐inhibition balance and temporal summation on the lesioned side. In particular, our analyses suggest that marked reductions in the synaptic time constant of the deep pyramidal cells and the self‐inhibition of inhibitory interneurons (in the lesioned hippocampus) are sufficient to explain changes in spectral activity. Although these synaptic changes are consistent over rats, the resulting electrophysiological phenotype can be quite diverse.

The maturation of mismatch negativity networks in normal adolescence

OBJECTIVEnWe investigated the neurophysiological mechanisms underpinning the generation of the mismatch negativity (MMN) and its development from adolescence to early adulthood.nnnMETHODSnWe used dynamic causal modelling (DCM) to study connectivity models for healthy adults and adolescents. MMN was elicited with an auditory oddball paradigm in two groups of healthy subjects with mean age 14 (n=52) and 26 (n=26). We tested models with different hierarchical complexities including up to five cortical nodes.nnnRESULTSnWe showed that the network generating MMN consisted of 5 nodes that could modulate all intra- and internodal connections. The inversion of this model showed that adolescents had reduced backward connection from rIFG to rSTG (p<0.04) together with increased excitatory activity in rSTG (p<0.02). There was a reduced modulation of excitability in rSTG (p<0.02) and of forward connectivity from lA1 to lSTG (p<0.03).nnnCONCLUSIONnThe cortical network generating MMN continues to develop in adolescence up to adulthood. Cortical regions in the temporal and frontal lobes, involved in auditory processing, mature with increasing fronto-temporal connectivity together with increased sensitivity in the temporal regions for changes in sound stimuli.nnnSIGNIFICANCEnThis study may offer an explanation for the neurobiological maturation of the MMN in adolescence.

The Cumulative Effects of Predictability on Synaptic Gain in the Auditory Processing Stream

Stimulus predictability can lead to substantial modulations of brain activity, such as shifts in sustained magnetic field amplitude, measured with magnetoencephalography (MEG). Here, we provide a mechanistic explanation of these effects using MEG data acquired from healthy human volunteers (N = 13, 7 female). In a source-level analysis of induced responses, we established the effects of orthogonal predictability manipulations of rapid tone-pip sequences (namely, sequence regularity and alphabet size) along the auditory processing stream. In auditory cortex, regular sequences with smaller alphabets induced greater gamma activity. Furthermore, sequence regularity shifted induced activity in frontal regions toward higher frequencies. To model these effects in terms of the underlying neurophysiology, we used dynamic causal modeling for cross-spectral density and estimated slow fluctuations in neural (postsynaptic) gain. Using the model-based parameters, we accurately explain the sensor-level sustained field amplitude, demonstrating that slow changes in synaptic efficacy, combined with sustained sensory input, can result in profound and sustained effects on neural responses to predictable sensory streams. SIGNIFICANCE STATEMENT Brain activity can be strongly modulated by the predictability of stimuli it is currently processing. An example of such a modulation is a shift in sustained magnetic field amplitude, measured with magnetoencephalography. Here, we provide a mechanistic explanation of these effects. First, we establish the oscillatory neural correlates of independent predictability manipulations in hierarchically distinct areas of the auditory processing stream. Next, we use a biophysically realistic computational model to explain these effects in terms of the underlying neurophysiology. Finally, using the model-based parameters describing neural gain modulation, we can explain the previously unexplained effects observed at the sensor level. This demonstrates that slow modulations of synaptic gain can result in profound and sustained effects on neural activity.

Hemispheric brain asymmetry differences in youths with attention-deficit/hyperactivity disorder

Introduction Attention-deficit hyperactive disorder (ADHD) is the most common neurodevelopmental disorder in children. Diagnosis is currently based on behavioral criteria, but magnetic resonance imaging (MRI) of the brain is increasingly used in ADHD research. To date however, MRI studies have provided mixed results in ADHD patients, particularly with respect to the laterality of findings. Methods We studied 849 children and adolescents (ages 6–21u202fy.o.) diagnosed with ADHD (nu202f=u202f341) and age-matched typically developing (TD) controls with structural brain MRI. We calculated volumetric measures from 34 cortical and 14 non-cortical brain regions per hemisphere, and detailed shape morphometry of subcortical nuclei. Diffusion tensor imaging (DTI) data were collected for a subset of 104 subjects; from these, we calculated mean diffusivity and fractional anisotropy of white matter tracts. Group comparisons were made for within-hemisphere (right/left) and between hemisphere asymmetry indices (AI) for each measure. Results DTI mean diffusivity AI group differences were significant in cingulum, inferior and superior longitudinal fasciculus, and cortico-spinal tracts (pu202f<u202f0.001) with the effect of stimulant treatment tending to reduce these patterns of asymmetry differences. Gray matter volumes were more asymmetric in medication free ADHD individuals compared to TD in twelve cortical regions and two non-cortical volumes studied (pu202f<u202f0.05). Morphometric analyses revealed that caudate, hippocampus, thalamus, and amygdala were more asymmetric (pu202f<u202f0.0001) in ADHD individuals compared to TD, and that asymmetry differences were more significant than lateralized comparisons. Conclusions Brain asymmetry measures allow each individual to serve as their own control, diminishing variability between individuals and when pooling data across sites. Asymmetry group differences were more significant than lateralized comparisons between ADHD and TD subjects across morphometric, volumetric, and DTI comparisons.

ID 202 – Characterising seizures in anti-NMDA-receptor encephalitis with Dynamic Causal Modelling

Objective To characterise the pathophysiology of seizures in terms of slow fluctuations in synaptic efficacy in patients with anti-N-methyl- d -aspartate Receptor (NMDA-R) encephalitis. Method Non-invasive clinical EEG recordings were obtained from two patients with anti-NMDA-R encephalitis with recurrent partial seizures (both females of age 19 and 33) using nine scalp electrodes positioned according to the 10–20 system. Electrographic seizure activity was localised using a Beamformer inversion. A virtual electrode at this position was used to estimate the underlying dynamics of the cortex using a Dynamic Causal Model (DCM). Results The DCM generated activity with a close fit to measured seizure activity which allowed us to confidently track changes in both excitatory and inhibitory connectivity. Seizure initiation was induced with a disruption in both excitatory and inhibitory connectivity resulting in excitatory–inhibitory imbalance, with an increasing weight on excitation as the seizure progressed. Conclusion Our findings suggest that seizures in anti-NMDA-R encephalitis are due to disruption in both inhibitory, GABA dependent, and excitatory, partly NMDA dependent, connectivity. Key message This study illustrates the inversion of dynamic causal models of seizure activity – recorded from the scalp in a clinical setting – to identify the underlying pathophysiological mechanisms.

P36: The maturation of mismatch negativity networks in late normal adolescence

R. Boegle1,2, C.A.M. Cyran3, T. Stephan1,2,3,4, M. Dieterich1,3,5, S. Glasauer1,4,6 1German Vertigo Center (IFB-LMU), Munich, Germany; 2Graduate School of Systemic Neuroscience (GSN-LMU), Munich, Germany; 3Department of Neurology (LMU), Munich, Germany; 4Institute for Clinical Neurosciences (LMU), Munich, Germany; 5Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; 6Center for Sensorimotor Research (LMU), Munich, Germany