Noise induced quiescence of epileptic spike generation in patients with epilepsy.

Clinical scalp electroencephalographic recordings from patients with epilepsy are distinguished by the presence of epileptic discharges i.e. spikes or sharp waves. These often occur randomly on a background of fluctuating potentials. The spike rate varies between different brain states (sleep and awake) and patients. Epileptogenic tissue and regions near these often show increased spike rates in comparison to other cortical regions. Several studies have shown a relation between spike rate and background activity although the underlying reason for this is still poorly understood. Both these processes, spike occurrence and background activity show evidence of being at least partly stochastic processes. In this study we show that epileptic discharges seen on scalp electroencephalographic recordings and background activity are driven at least partly by a common biological noise. Furthermore, our results indicate noise induced quiescence of spike generation which, in analogy with computational models of spiking, indicate spikes to be generated by transitions between semi-stable states of the brain, similar to the generation of epileptic seizure activity. The deepened physiological understanding of spike generation in epilepsy that this study provides could be useful in the electrophysiological assessment of different therapies for epilepsy including the effect of different drugs or electrical stimulation.

Mechanism of visual network dysfunction in relapsing-remitting multiple sclerosis and its relation to cognition.

OBJECTIVE: To investigate if changes in brain network function and connectivity contribute to the abnormalities in visual event related potentials (ERP) in relapsing-remitting multiple sclerosis (RRMS), and explore their relation to a decrease in cognitive performance. METHODS: We evaluated 72 patients with RRMS and 89 healthy control subjects in a cross-sectional study. Visual ERP were generated using illusory and non-illusory stimuli and recorded using 21 EEG scalp electrodes. The measured activity was modelled using Dynamic Causal Modelling. The model network consisted of 4 symmetric nodes including the primary visual cortex (V1/V2) and the Lateral Occipital Complex. Patients and controls were tested with a neuropsychological test battery consisting of 18 cognitive tests covering six cognitive domains. RESULTS: We found reduced cortical connectivity in bottom-up and interhemispheric connections to the right lateral occipital complex in patients (p < 0.001). Furthermore, interhemispherical connections were related to cognitive dysfunction in several domains (attention, executive function, visual perception and organization, processing speed and global cognition) for patients (p < 0.05). No relation was seen between cortical network connectivity and cognitive function in the healthy control subjects. CONCLUSION: Changes in the functional connectivity to higher cortical regions provide a neurobiological explanation for the changes of the visual ERP in RRMS. SIGNIFICANCE: This study suggests that changes in connectivity to higher cortical regions partly explain visual network dysfunction in RRMS where a lower interhemispheric connectivity may contribute to impaired cognitive function.

Towards a Neuronal Gauge Theory.

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.

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 5-10min compared to approximately 1-2h. 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.

Decreased cortical connectivity and information flow in type 1 diabetes.

OBJECTIVE: To investigate the effect of type 1 diabetes on EEG connectivity and information flow and study the relationship between these parameters and electrophysiological, neuropsychological and clinical variables. METHODS: Connectivity was assessed using several measures (phase coherence, phase lag index, synchronization likelihood and phase slope index) on 119 patients and 61 healthy controls over several frequency bands (between 0.5 and 45 Hz). Data was further correlated to EEG power, event related potentials, neuropsychological function and demographic variables. RESULTS: Multivariate test on the connectivity data showed a difference between patients and controls both with mastoid reference (p<0.01) and current source density estimates (p<0.04). Connectivity and information flow correlated with EEG power but not with event related potentials or neuropsychological function. CONCLUSIONS: Connectivity and information flow are decreased in diabetes. These variables assess other functions of the brain than captured by the present cognitive tests. Several tests need to be performed in order to monitor the effect of diabetes on brain function. SIGNIFICANCE: The decrease in connectivity and cortical information flow are EEG abnormalities that add to the previously described EEG and ERP abnormalities described for type 1 diabetes.

Cognitive impairment correlates to low auditory event-related potential amplitudes in type 1 diabetes.

Type 1 diabetes may be associated with a mild decline in cognitive function and mostly in mental speed. In order to study the pathophysiology of this, we have investigated auditory event-related potentials (AERP) and their relation to cognitive function in diabetes patients. AERP was recorded in patients with type 1 diabetes (n=119) and in a healthy control group (n=61). AERP was obtained with an odd-ball and a two-stimulus paradigm. Cognitive function was evaluated in 10 domains in the patients. Patients had normal N100 latency, but a highly significant decrease in auditory N100 amplitude (p<10(-6)), which correlated with a decrease in psychomotor speed but not with function in other domains. Psychomotor speed also correlated with P300 amplitude, although P300 amplitude was only slightly decreased in the patients. Even stronger correlations were found with the parietal N100-P300 peak-to-peak amplitude, which correlated both to psychomotor speed (rho=0.61, p<10(-7)) and processing speed (p<0.005). P300 latency was increased in patients, and this correlated to low global cognitive score and older age. We conclude that the decline in psychomotor speed in type 1 diabetes is associated with a highly significant decrease in the auditory N100 peak amplitude. This association and the relatively small abnormality in P300 latency is quite different from those generally found in dementia, and suggest that the underlying defect is located in the brain stem or the white matter. Presumably small conduction defects in ascending fibers can distort the firing synchrony necessary for signal generation in the cortex.