A statistical method for analyzing and comparing spatiotemporal cortical activation patterns

Abstract

Information in the cortex is encoded in spatiotemporal patterns of neuronal activity, but the exact nature of that code still remains elusive. While onset responses to simple stimuli are associated with specific loci in cortical sensory maps, it is completely unclear how the information about a sustained stimulus is encoded that is perceived for minutes or even longer, when discharge rates have decayed back to spontaneous levels. Using a newly developed statistical approach (multidimensional cluster statistics (MCS)) that allows for a comparison of clusters of data points in n-dimensional space, we here demonstrate that the information about long-lasting stimuli is encoded in the ongoing spatiotemporal activity patterns in sensory cortex. We successfully apply MCS to multichannel local field potential recordings in different rodent models and sensory modalities, as well as to human MEG and EEG data, demonstrating its universal applicability. MCS thus indicates novel ways for the development of powerful read-out algorithms of spatiotemporal brain activity that may be implemented in innovative brain-computer interfaces (BCI).

Analysis and Visualization of Sleep Stages based on Deep Neural Networks

“…We visualize the sleep stage embeddings by a dimensionality reduction into 2D using multidimensional scaling (MDS) [14] ( Figure 2) and evaluate the generalized discrimination value (GDV) which quantifies separability of data classes in high-dimensional state spaces [15]. The MDS plots show that the convolutional layer lead to better separability (Figure 2 b-g) compared to z-scored raw EEG data (network input, Figure 2 a).…”

“…In previous studies, we developed several approaches to statistically analyze and visualize highdimensional neural data [14,15]. We developed a statistical method for analyzing and comparing high-dimensional spatiotemporal cortical activation patterns for different auditory and somatosensory stimulus conditions in both, rodents and humans [14]. The cortical activity patters were represented by amplitude vectors calculated via a sliding window method (for the exact procedure see [14]).…”

“…We developed a statistical method for analyzing and comparing high-dimensional spatiotemporal cortical activation patterns for different auditory and somatosensory stimulus conditions in both, rodents and humans [14]. The cortical activity patters were represented by amplitude vectors calculated via a sliding window method (for the exact procedure see [14]). We could already demonstrate that this method enables to discriminate different sleep stages in human EEG recordings [16].…”

Spatial Vector-based Approach to the ERP Analysis as Applied to an EEG-based Discrimination of Traffic Light Signals

“…On the other hand, addressing the outlined shortcoming, Krauss et al (2018) have recently introduced the quantity referred to as discrimination value as means to analyze spatiotemporal cortical activations; more specifically, to assess their clusterability. In present study, EEG recordings were partitioned into epochs synchronized with stimuli presentation allowing pre-and poststimulation buffers of 100 ms and 200 ms, respectively.…”

“…Utilizing the proposed approach and assuming that the data can be partitioned into three clusters A, B, and C (that are related to the color of traffic light perceived), the discrimination value of these data can be estimated as (Krauss et al, 2018): Krauss et al (2018), the more negative IDV is, the more distinct the assessed clusters are. Perhaps, we may argue that IDV can be used to predict the classification success if the underlying vectors are used as the classification features.…”