Detection and statistical evaluation of spike patterns in parallel electrophysiological recordings

Quaglio, Pietro; Grün, Sonja Annemarie (Thesis advisor); Kampa, Björn Michael (Thesis advisor)

Jülich : Forschungszentrum Jülich GmbH (2020)
Book, Dissertation / PhD Thesis

In: Schriften des Forschungszentrums Jülich. Reihe Schlüsseltechnologien/ key technologies 217
Page(s)/Article-Nr.: 1 Online-Ressource (128 Seiten) : Illustrationen, Diagramme

Dissertation, RWTH Aachen University, 2019


The computational processes deployed by the brain to represent, process and transmit information are largely unknown. Cell assemblies (highly inter-connected groups of neurons) have been hypothesized to be the building block of the computational processes in the cerebral network. The coordination of spikes emission among neurons at millisecondtemporal scale is one of the possible mechanisms of information coding and a signature of assembly activation. In particular, specific temporally precise spike sequences in the input can reliably cause a spike emission in a post-synaptic neuron. Evidences of coordination of the spiking activity at milliseconds precision have been collected in the past, yet such studies present two main limitations: in most cases they consider few neurons recorded in parallel and the correlation analysis are limited to spike synchronicity. Recent developments of the recording devices overcome the first limitation. Modern electrophysiological technologies enable to obtain the spiking activity of hundreds of neurons in parallel, a number which is destined to grow. The size of the current available data requires optimized computational analysis technique and sophisticated statistical approaches. In this work we address the second limitation, developing a method to detect spatiotemporal patterns of spikes in large parallel recordings. In particular we extend the Spike Pattern Detection and Evaluation (SPADE) method, originally limited to synchronous patterns detection, to search for any repeated sequence of spikes. SPADE can be summarized in two steps: a) extraction of all the repeated spike sequences using the frequent item-set mining framework, b) statistical evaluation of the significance of the mined sequences in respect to the null hypothesis of independent spike emissions in time. We extensively refined and validated the method using ground-truth artificial data designed to resemble experimental data to test the statistical performances of the method. We then made the python implementation of SPADE publicly available online as a submodule of the Electrophysiological Analysis Toolkit (Elephant).We applied SPADE to in-vivo parallel recordings of neuronal activity in the motor area of two macaque monkeys performing a reach-to-grasp task, finding a large number of significant spike patterns. We then investigated the statistical features of the detected patterns in terms of neuronal composition, temporal occurrences and relation to behavior. Most of the patterns occur during the reach movement of the task and they are formed by two to four different neurons. Furthermore the neurons forming the patterns differ for different grip types, hinting to a high specificity of the patterns to the different behavioural contexts. In the last part of this work we compare SPADE to other existing methods in the context of a more general review of methods for the analysis of correlations in parallel spike trains. In particular we argue for the importance of a thorough comparison of the different methods and for the integration different methodologies that highlight different aspects of the correlation structure of the data. In summary we show that SPADE robustly detects and selects significant precise spike sequences and that multiple significant patterns repeat during the execution of a reach to grasp task. Nevertheless the spatiotemporal patterns alone do not guarantee a complete description of the correlation structure of the data, hence we present and compare alternative correlation analysis methods for parallel spike trains.