Biology subjects

Fekete, T.

Publications and source records attributed to Fekete, T..

3 recordsLinked to original sources

bioRxiv2019

Multiscale criticality measures as general-purpose gauges of proper brain function

The brain is universally regarded as a system for processing information. If so, any behavioral or cognitive dysfunction should lend itself to depiction in terms of information processing deficiencies. Information is characterized by recursive, hierarchical complexity. The brain accommodates this complexity by a hierarchy of large/slow and small/fast spatiotemporal loops of activity. Thus, successful information processing hinges upon tightly regulating the spatiotemporal makeup of activity, to optimally match the underlying multiscale delay structure of such hierarchical networks. Reduced capacity for information processing will then be expressed as deviance from this requisite multiscale character of spatiotemporal activity. This deviance is captured by a general family of multiscale criticality measures (MsCr). We applied MsCr to MEG and EEG data in four telling degraded information processing scenarios: disorders of consciousness, mild cognitive impairment, schizophrenia and preictal activity. Consistently with our previous modeling work, MsCr measures systematically varied with information processing capacity. MsCr measures might thus be able to serve as general gauges of information processing capacity and, therefore, as normative measures of brain health.

neuroscience↗

bioRxiv2019

Local network determinants of spontaneously emerging cortical maps

Spontaneously emerging cortical maps related to the functional architecture of visual cortex have been observed initially in anesthetized cats and, subsequently, in monkey, albeit only under certain anesthetic regimes, and not in the awake state. Here we propose a network model that can accommodate these diverse findings. The model identifies two crucial determinants for the emergence of spontaneous map-like activity - local balance between excitatory and inhibitory activity, and the strength of feature-specific synaptic connections (e.g. orientation, ocularity). Our model further shows that dynamically, map-like activity patterns could be triggered either by standing or travelling waves, a mode of operation which is determined by the spatial extent of lateral connections within a given network. Our results suggest that careful pharmacological intervention can unveil the prevalence of maps - recurring spatial patterns of inhomogeneous lateral connectivity - in cortex without the need to explicitly identify area specific optimal features.

neuroscience↗

bioRxiv2019

EEG-based Prediction of Cognitive Load in Intelligence Tests

Measuring and assessing the cognitive load associated with different tasks is crucial for many applications, from the design of instructional materials to monitoring the mental wellbeing of aircraft pilots. The goal of this paper is to utilize EEG to infer the cognitive workload of subjects during intelligence tests. We chose the well established advanced progressive matrices test, an ideal work-frame because it presents problems at increasing levels of difficulty, and has been rigorously validated in past experiments. We train classic machine learning models using basic EEG measures as well as measures of network connectivity and signal complexity. Our findings demonstrate that cognitive load can be well predicted using these features, even for a low number of channels. We show that by creating an individually tuned neural network for each subject, we can improve prediction compared to a general model and that such models are robust to decreasing the number of available channels as well.

neuroscience↗