Biology subjects

Baldeweg, T.

Publications and source records attributed to Baldeweg, T..

5 recordsLinked to original sources

bioRxiv2018

Disentangling the Role of Face Typicality and Affect in Emotional Face Processing: Self-reported and Electrophysiological Evidence

Typicality, or averageness, is one of the key features that influences face evaluation, but the role of this property in the perception of facial expressions of emotions is still not fully understood. Typical faces are usually considered more pleasant and trustworthy, and neuroimaging results suggest typicality modulates amygdala and fusiform activation, influencing face perception. At the same time, there is evidence that arousal is a key affective feature that modulates neural reactivity to emotional expressions. In this sense, it remains unclear whether the neural effects of typicality depend on altered perceptions of affect from facial expressions or if the effects of typicality and affect independently modulate face processing. The goal of this work was to dissociate the effects of typicality and affective properties, namely valence and arousal, in electrophysiological responses and self-reported ratings across several facial expressions of emotion. Two ERP components relevant for face processing were measured, the N170 and Vertex Positive Potential (VPP), complemented by subjective ratings of typicality, valence, and arousal, in a sample of 30 healthy young adults (21 female). The results point out to a modulation of the electrophysiological responses by arousal, regardless of the typicality or valence properties of the face. These findings suggest that previous findings of neural responses to typicality may be better explained by accounting for the subjective perception of arousal in facial expressions.

neuroscience↗

bioRxiv2017

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

Pathophysiological 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.\n\nAbbreviations

neuroscience↗

bioRxiv2017

NMDA-receptor antibodies alter cortical microcircuit dynamics

NMDA-receptor antibodies (NMDAR-Ab) cause an autoimmune encephalitis with a diverse range of electroencephalographic (EEG) abnormalities. NMDAR-Ab are believed to disrupt receptor function, but how blocking this excitatory neurotransmitter can lead to paroxysmal EEG abnormalities - or even seizures - is poorly understood. Here, we show that NMDAR-Ab change intrinsic cortical connections and neuronal population dynamics to alter the spectral composition of spontaneous EEG activity, and predispose to paroxysmal EEG abnormalities. Based on local field potential recordings in a mouse model, we first validate a dynamic causal model of NMDAR-Ab effects on cortical microcircuitry. Using this model, we then identify the key synaptic parameters that best explain EEG paroxysms in paediatric patients with NMDAR-Ab encephalitis. Finally, we use the mouse model to show that NMDAR-Ab- related changes render microcircuitry critically susceptible to overt EEG paroxysms, when these key parameters are changed. These findings offer mechanistic insights into circuit-level dysfunction induced by NMDAR-Ab.

neuroscience↗

bioRxiv2017

NMDA receptor blockade causes selective prefrontal disinhibition in a roving auditory oddball paradigm

N-methyl-D-aspartate receptors (NMDARs) are expressed widely throughout the human cortex. Yet disturbances in NMDAR transmission - as implicated in patients with schizophrenia or pharmacologically induced - can cause a regionally specific set of electrophysiological effects. Here, we present a double-blind placebo-controlled study of the effects of the NMDAR blocker ketamine in human volunteers. We employ a marker of auditory learning and putative synaptic plasticity - the mismatch negativity - in a roving auditory oddball paradigm. Using recent advances in Bayesian modelling of group effects in dynamic causal modelling, we fit biophysically plausible network models of the auditory processing hierarchy to whole-scalp evoked response potential recordings. This allowed us to identify the regionally specific effects of ketamine in a distributed network of interacting cortical sources. Under placebo, our analysis replicated previous findings regarding the effects of stimulus repetition and deviance on connectivity within the auditory hierarchy. Crucially, we show that the effect of ketamine is best explained as a selective change in intrinsic inhibition, with a pronounced ketamine-induced reduction of inhibitory interneuron connectivity in frontal sources. These results are consistent with findings from invasive recordings in animal models exposed to NMDAR blockers, and provide evidence that inhibitory-interneuron specific NMDAR dysfunction may be sufficient to explain electrophysiological abnormalities of sensory learning induced by ketamine in human subjects.\n\nO_TEXTBOXSignificance StatementDysfunction of N-methyl-D-aspartate receptors (NMDARs) has been implicated in a range of psychopathologies, yet mechanisms translating receptor-level abnormalities to whole-brain pathology remain unclear. We use computational modelling to infer microcircuit mechanisms by which ketamine, an NMDAR-blocker, alters brain responses to changing sequences of sounds that rely on sensory learning. This dynamic causal modelling (DCM) approach shows that ketamine-effects can be explained with brain region specific changes in inhibitory interneuron coupling alone, with a striking reduction in inhibition in the prefrontal cortex. This suggests that NMDAR-effects on excitation-inhibition balance differ between brain regions, and provides evidence from healthy human subjects that NMDAR blockade may cause prefrontal cortex disinhibition, one of the mechanisms hypothesised to underlie psychopathology in schizophrenia.\n\nC_TEXTBOX

neuroscience↗

bioRxiv2017

Network Dynamics In The Healthy And Epileptic Developing Brain

Electroencephalography (EEG) allows recording of cortical activity at high temporal resolution. EEG recordings can be summarised along different dimensions using network-level quantitative measures, e.g. channel-to-channel correlation, or band power distributions across channels. These reveal network patterns that unfold over a range of different time scales and can be tracked dynamically.\n\nHere we describe the dynamics of network-state transitions in EEG recordings of spontaneous brain activity in normally developing infants and infants with severe early infantile epileptic encephalopathies (n=8, age: 1-8 months). We describe differences in measures of EEG dynamics derived from band power, and correlation-based summaries of network-wide brain activity.\n\nWe further show that EEGs from different patient groups and controls can be distinguished based on a small set of the novel quantitative measures introduced here, which describe dynamic network state switching. Quantitative measures related to the smoothness of switching from one correlation pattern to another show the largest differences between groups.\n\nThese findings reveal that the early epileptic encephalopathies are associated with characteristic dynamic features at the network level. Quantitative network-based analyses like the one presented here may in future inform the clinical use of quantitative EEG for diagnosis.

neuroscience↗