Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data

Clustering is a fundamental data processing technique. While clustering of static (vector based) data and of fixed window size time series have been well explored, dynamic clustering of spatiotemporal data has been little researched if at all. Especially when patterns of changes (events) in the data across space and time have to be captured and understood. The paper presents novel methods for clustering of spatiotemporal data using the NeuCube spiking neural network (SNN) architecture. Clusters of spatiotemporal data were created and modified on-line in a continuous, incremental way, where spatiotemporal relationships of changes in variables are incrementally learned in a 3D SNN model and the model connectivity and spiking activity are incrementally clustered. Two clustering methods were proposed for SNN, one performed during unsupervised and one—during supervised learning models. Before submitted to the models, the data is encoded as spike trains, a spike representing a change in the variable value (an event). During the unsupervised learning, the cluster centres were predefined by the spatial locations of the input data variables in a 3D SNN model. Then clusters are evolving during the learning, i.e. they are adapted continuously over time reflecting the dynamics of the changes in the data. In the supervised learning, clusters represent the dynamic sequence of neuron spiking activities in a trained SNN model, specific for a particular class of data or for an individual instance. We illustrate the proposed clustering method on a real case study of spatiotemporal EEG data, recorded from three groups of subjects during a cognitive task. The clusters were referred back to the brain data for a better understanding of the data and the processes that generated it. The cluster analysis allowed to discover and understand differences on temporal sequences and spatial involvement of brain regions in response to a cognitive task

Limitations of rapid myelin water quantification using 3D bSSFP

Imaging of the myelin water fraction (MWF) is conventionally performed using a multi-echo spin-echo sequence. This technique requires long acquisition times and therefore often suffers from a lack of volume coverage. In this work, the application of 3D balanced steady-state free precession (bSSFP) sequences to extract high-resolution myelin water maps is discussed

Recent developments in MEG network analysis

In this chapter we will describe recent developments in magnetoencephalography (MEG) network analysis, where we will focus on the rationale behind, and application in clinical cohorts, of an atlas-based beamforming approach. This approach contains three main components, namely, (i) the reconstruction of time series of neuronal activation through beamforming; (ii) the use of a standard atlas, which enables comparisons across studies and modalities; and (iii) the estimation of functional connectivity using the phase lag index (PLI), a measure that is insensitive to the effects of field spread/volume conduction. Moreover, we will discuss the use of the minimum spanning tree (MST), which allows for a biasfree characterization of the topology of the reconstructed functional networks. Application of this approach will be illustrated through examples from recent studies in patients with gliomas, Parkinson's disease, and multiple sclerosis

Disturbance and the role of refuges in mediterranean climate streams

Refuges protect plant and animal populations from disturbance. Knowledge of refuges from disturbance in mediterranean climate rivers (med-rivers) has increased the last decade. We review disturbance processes and their relationship to refuges in streams in mediterranean climate regions (med-regions). Med-river fauna show high endemicity and their populations are often exposed to disturbance; hence the critical importance of refuges during (both seasonal and supraseasonal) disturbances. Disturbance pressures are increasing in med-regions, in particular from climatic change, salinisation, sedimentation, water extraction, hydropower generation, supraseasonal drought, and wildfire. Med-rivers show annual cycles of constrained precipitation and predictable seasonal drying, causing the biota to depend on seasonal refuges, in particular, those that are spatially predictable. This creates a spatial and temporal mosaic of inundation that determines habitat extent and refuge function. Refuges of sufficient size and duration to maintain populations, such as perennially flowing reaches, sustain biodiversity and may harbour relict populations, particularly during increasing aridification, where little other suitable habitat remains in landscapes. Therefore, disturbances that threaten perennial flows potentially cascade disproportionately to reduce regional scale biodiversity in med-regions. Conservation approaches for med-river systems need to conserve both refuges and refuge connectivity, reduce the impact of anthropogenic disturbances and sustain predictable, seasonal flow patterns