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Aeby, A.

Publications and source records attributed to Aeby, A..

2 recordsLinked to original sources

The infant brain rapidly entrains to visual statistical regularities during stimulus exposure

Statistical learning (SL) has been studied quite extensively in infancy. Still, most evidence relies on post-exposure behavioural tasks whose directionality (familiarity vs. novel effects) may not be straightforward to predict nor to interpret. In addition, these tasks do not tell anything about the online learning dynamics and may be influenced by memory effects. In this work, we investigated online SL mechanisms by tracking neural entrainment to visual regularities in a group of 4- to 6-month-old infants exposed to a stream of shapes presented at 6 Hz. Shapes were either organized in doublets or presented randomly. Results revealed that entrainment at the doublet frequency of 3 Hz and harmonics varied across conditions and trials. Infants showed greater occipital entrainment to the doublet frequency in the deterministic condition than in the random one, especially over the first trials of exposure. This suggests that the brain can detect visual regularities from early infancy. Further, this sensitivity emerged early over the exposure period and did not show a learning curve when the evolution of the doublet-level SNR was assessed in relation with the base-level SNR over time. Hence, considering its time course and the brain regions involved, neural entrainment at the doublet frequency seems to reflect a bottom-up detection mechanism rather than a learning process. These findings are crucial to better understand how infants extract regularities during stimulus exposure and what neural entrainment can reveal in a visual SL task.

neuroscience↗

Towards the automated detection of interictal epileptiform discharges with magnetoencephalography

The analysis of clinical magnetoencephalography (MEG) in patients with epilepsy traditionally relies on the visual identification of interictal epileptiform discharges (IEDs), which is time consuming and dependent on (subjective) human criteria. Data-driven approaches enabling both spatial and temporal localization of epileptic spikes would represent a major leap forward in clinical MEG practice. Here, we explore the ability of Independent Components Analysis (ICA) and Hidden Markov Modeling (HMM) to automatically detect and localize IEDs. Combined with kurtosis mapping, we developed a fully automated identification of epileptiform independent components (ICs) or HMM states. We tested our pipeline on MEG recordings at rest from 10 school-age children with either focal or multifocal epilepsy and compared results with the traditional MEG analysis performed by an experienced clinical magnetoencephalographer. In patients with focal epilepsy, both ICA- and HMM-based pipelines successfully detected visually identified IEDs with high sensitivity, but also revealed low-amplitude IEDs unidentified by the visual detection. Success was more mitigated in patients with multifocal epilepsy, as our automated pipeline missed IED activity associated with some foci--an issue that could be alleviated by post-hoc manual selection of epileptiform ICs or HMM states. Therefore, IED detection based on ICA or HMM represents an efficient way to identify spike localization and timing, with heightened sensitivity to IEDs compared to visual MEG signal inspection and requiring minimal input from clinical practitioners.

neuroscience↗