doc. Ing. Daniel Novák, Ph.D.

The current research projects in neuroscience include the analysis of complex data sets incorporating multiple modalities, such as demography and clinical data, structural or functional MRI, genomic data, and other. High dimensionality and strong multicollinearity of input data, together with typically small sample sizes pose a challenge for modeling tasks, such as differential diagnostics or functional outcome prediction. The aim of the project is the study, to evaluate and to develop appropriate methods for aggregation of input data for supervised, as well as unsupervised machine learning tasks.

Complexity measures can describe dynamic of physiological process such as heart beat or level of glycemia in blood. After publication of seminal paper in Nature predicting heart stoke, this theory found its way into common signal processing methods in clinical practice. The main aim of this topic is processing of neurological and electrocardiogram data to distinguished between different nuclei in basal ganglia or assess level of fragmentation of internal ECG signal [1,2,3]. [1] Eva M. Cirugeda–Roldan, D. Novak, V. Kremen, D. Cuesta–Frau, M.W. Keller, C. Schilling, O. Doessel, C. Schmitt, A. Luik, Characterization of Complex Fractionated Atrial Electrograms by Sample Entropy: An International Multi–Center Study, Entropy, 17(11), p.7493-7509,2015, IF 1.579 http://www.mdpi.com/1099-4300/17/11/7493/htm [2] Andres Orozco-Duque, Daniel Novak, Vaclav Kremen and John Bustamante, Multifractal analysis for grading complex fractionated electrograms in atrial fibrillation, Physiological Measurement, Physiological Measurement, IF 1.808, 36(11), p. 2269-84,2015 http://www.ncbi.nlm.nih.gov/pubmed/26450345 [3] Juan Pablo Ugarte Macías; Andrés Orozco-Duque; Catalina Tobón; Vaclav Kremen; Daniel Novak; Javier Saiz; Tobias Oesterlein; Clauss Schmitt; Armin Luik; John Bustamante , Dynamic approximate entropy electroanatomic maps detect rotors in a simulated atrial fibrillation model. PLOS One, 9(12), 2014, IF 3.79 http://journals.plos.org/plosone/article?id 10.1371/journal.pone.0114577

Computation neuroscience are one of the most developing areas in the field of physiological modeling being backed up nowadays by two big EU and USA initiatives. The goal of this topic is processing of neurological data, most of them having origin in human basal ganglia. Several sub-topics are addressed as correlation between objective neural recordings and so called UPDRS score, or modeling electrophysiology of basal ganglia [1,2,3]. More information can be found on neuro.felk.cvut.cz [1] Tomas Sieger , Tereza Serranova, Pavel Vostatek, Jiri Wild, Daniela Stastna, Cecilia Bonnet, Daniel Novak, Evzen Ruzicka, Dusan Urgosik,Robert Jech, Distinct Populations of Neurons Respond to Emotional Valence and Arousal in the Human Subthalamic Nucleus, Proceedings of the National Academy of Sciences (PNAS), 112(10), p.3116-3121, 2015 [2] Sieger T, Bonnet C, Serranová T, Wild J, Novák D, Růžička F, Urgošík D, Růžička E, Gaymard B, Jech R., Basal Ganglia Neuronal Activity during Scanning Eye Movements in Parkinson’s Disease, PlosOne, 8(11), 2013, IF 3.79 http://journals.plos.org/plosone/article?id 10.1371/journal.pone.0078581 [3] J.Wild, Z.Prekopcsak, T.Sieger, D.Novak, ,R.Jech, Performance comparison of spike sorting algorithms for single-channel recordings, Journal of Neuroscience Methods, 203(2), p.369-76, 2012 , IF 2.114 http://www.ncbi.nlm.nih.gov/pubmed/22037595