Recurrence network analysis of EEG signals: A Geometric Approach

Understanding the neuronal dynamics of dynamical diseases like epilepsy is of fundamental importance. For instance, establishing the presence of deterministic chaos can open up possibilities that can lead to potential medical applications, including timely prevention of seizures. Additionally, understanding the dynamics of interictal activity can greatly aid the localization of epileptic foci without the need for recording seizures. Recurrences, a fundamental property of dynamical systems, are useful for characterizing nonlinear systems. Recurrence networks, which are obtained by reinterpreting the recurrence matrix as an adjacency matrix of a complex network, are useful in characterizing the structural or geometric properties of the underlying system. Recurrence network analysis has established itself as a versatile tool in the field of nonlinear time series analysis and its applicability in investigating neural dynamics remains unexplored. Certain recurrence network measures are particularly sensitive to the presence of unstable periodic orbits (UPOs), which are important for detecting determinism and are the backbone of chaotic attractors.In this thesis, we introduce recurrence network analysis as a tool for nonlinear time series analysis of epileptic electroencephalographic (EEG) signals. We present novel results based on the application of recurrence network analysis combined with surrogate testing to intracranial and extracranial epileptic EEG signals. In addition, using paradigmatic examples of dynamical systems, we present theoretical results exploring the effect of increasing noise levels on recurrence network measures.Using paradigmatic model systems, we first demonstrate that recurrence network measures can distinguish between deterministic (chaos) and stochastic processes, even at short data lengths (≈ 200 samples). In particular, our results from theoretical simulations show that recurrence network measures, particularly transitivity, local clustering coefficient, assortativity, and betweenness centrality can successfully distinguish between deterministic chaotic and stochastic processes (after additional embedding) due to their sensitivity to the presence of UPOs. Our results also show that recurrence network measures like transitivity and average path length are robust against noise and perform better than the Complexity-Entropy plane method at short data lengths. Furthermore, our results show that the effect of noise on the recurrence network measures can be minimized by increasing the recurrence rate.For the analysis of real-world data such as EEG signals, we combined the recurrence network approach with surrogate data to test for the structural complexity in healthy and epileptic EEG signals. Here our results point to an increasing complexity of EEG recordings when moving from healthy to epileptic conditions. Furthermore, we used both univariate network measure and bivariate cross-network measure to distinguish between the structural properties of interictal EEG signals recorded from epileptic and nonepileptic brain areas. Here, our results clearly demonstrated that interictal EEG signals recorded from epileptic areas are more deterministic and interdependent compared to interictal activity recorded from nonepileptic areas. Finally, we show that recurrence network analysis can be applied to uncover the dynamical transitions in neural signals using short segments of data (≈ 150 to 500 samples). To demonstrate this, we used two kinds of neural data - epileptic EEG data and local field potential (LFP) signals recorded during a visuomotor task. We observed that the temporal fluctuations observed in the recurrence network measures are consistent with the dynamical transitions underlying the epileptic and task-based LFP signals.To conclude, recurrence network analysis analysis can capture the complexity in the organization of EEG data in different dynamical states in a more elaborated fashion compared to other approaches such as nonlinear prediction error or correlation dimension. By means of the recurrence network measures, this difference can be assessed not only qualitatively (as when using as tests for nonlinearity), but also quantitatively. Thus, coupled with its ability to operate on short-window sizes and robustness to noise, recurrence network analysis can be a powerful tool to analyze the dynamics of multi-scale neural signals

FusionSense: Emotion Classification using Feature Fusion of Multimodal Data and Deep learning in a Brain-inspired Spiking Neural Network

Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing. Several studies have utilized state of the art deep learning methods and combined physiological signals, such as the electrocardiogram (EEG), electroencephalogram (ECG), skin temperature, along with facial expressions, voice, posture to name a few, in order to classify emotions. Spiking neural networks (SNNs) represent the third generation of neural networks and employ biologically plausible models of neurons. SNNs have been shown to handle Spatio-temporal data, which is essentially the nature of the data encountered in emotion recognition problem, in an efficient manner. In this work, for the first time, we propose the application of SNNs in order to solve the emotion recognition problem with the multimodal dataset. Specifically, we use the NeuCube framework, which employs an evolving SNN architecture to classify emotional valence and evaluate the performance of our approach on the MAHNOB-HCI dataset. The multimodal data used in our work consists of facial expressions along with physiological signals such as ECG, skin temperature, skin conductance, respiration signal, mouth length, and pupil size. We perform classification under the Leave-One-Subject-Out (LOSO) cross-validation mode. Our results show that the proposed approach achieves an accuracy of 73.15% for classifying binary valence when applying feature-level fusion, which is comparable to other deep learning methods. We achieve this accuracy even without using EEG, which other deep learning methods have relied on to achieve this level of accuracy. In conclusion, we have demonstrated that the SNN can be successfully used for solving the emotion recognition problem with multimodal data and also provide directions for future research utilizing SNN for Affective computing. In addition to the good accuracy, the SNN recognition system is requires incrementally trainable on new data in an adaptive way. It only one pass training, which makes it suitable for practical and on-line applications. These features are not manifested in other methods for this problem.Peer reviewe

Editorial : Data science in neuro- and onco-biology

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Causal coupling inference from multivariate time series based on ordinal partition transition networks

Identifying causal relationships is a challenging yet crucial problem in many fields of science like epidemiology, climatology, ecology, genomics, economics and neuroscience, to mention only a few. Recent studies have demonstrated that ordinal partition transition networks (OPTNs) allow inferring the coupling direction between two dynamical systems. In this work, we generalize this concept to the study of the interactions among multiple dynamical systems and we propose a new method to detect causality in multivariate observational data. By applying this method to numerical simulations of coupled linear stochastic processes as well as two examples of interacting nonlinear dynamical systems (coupled Lorenz systems and a network of neural mass models), we demonstrate that our approach can reliably identify the direction of interactions and the associated coupling delays. Finally, we study real-world observational microelectrode array electrophysiology data from rodent brain slices to identify the causal coupling structures underlying epileptiform activity. Our results, both from simulations and real-world data, suggest that OPTNs can provide a complementary and robust approach to infer causal effect networks from multivariate observational data

Non-linear dynamical analysis of resting tremor for demand-driven deep brain stimulation.

Parkinson's Disease (PD) is currently the second most common neurodegenerative disease. One of the most characteristic symptoms of PD is resting tremor. Local Field Potentials (LFPs) have been widely studied to investigate deviations from the typical patterns of healthy brain activity. However, the inherent dynamics of the Sub-Thalamic Nucleus (STN) LFPs and their spatiotemporal dynamics have not been well characterized. In this work, we study the non-linear dynamical behaviour of STN-LFPs of Parkinsonian patients using ε -recurrence networks. RNs are a non-linear analysis tool that encodes the geometric information of the underlying system, which can be characterised (for example, using graph theoretical measures) to extract information on the geometric properties of the attractor. Results show that the activity of the STN becomes more non-linear during the tremor episodes and that ε -recurrence network analysis is a suitable method to distinguish the transitions between movement conditions, anticipating the onset of the tremor, with the potential for application in a demand-driven deep brain stimulation system

Identification of Copy Number Gain and Overexpressed Genes on Chromosome Arm 20q by an Integrative Genomic Approach in Cervical Cancer: Potential Role in Progression

Recurrent karyotypic abnormalities are a characteristic feature of cervical cancer (CC) cells, which may result in deregulated expression of important genes that contribute to tumor initiation and progression. To examine the role of gain of the long arm of chromosome 20 (20q), one of the common chromosomal gains in CC, we evaluated CC at various stages of progression using single nucleotide polymorphism (SNP) array, gene expression profiling, and fluorescence in situ hybridization (FISH) anal- yses. This analysis revealed copy number increase (CNI) of 20q in >50% of invasive CC and identified two focal amplicons at 20q11.2 and 20q13.13 in a subset of tumors. We further demonstrate that the acquisition of 20q gain occurs at an early stage in CC development and the high-grade squamous intraepithelial lesions (HSIL) that exhibit 20q CNI are associated (P 5 0.05) with persistence or progression to invasive cancer. We identified a total of 26 overexpressed genes as consequence of 20q gain (N 5 14), as targets of amplicon 1 (N 5 9; two genes also commonly expressed with 20q gain) and amplicon 2 (N 5 6; one gene also commonly expressed with 20q gain). These include a number of functionally important genes in cell cycle regulation (E2F1, TPX2, KIF3B, PIGT, and B4GALT5), nuclear function (CSEL1), viral replication (PSMA7 and LAMA5), methylation and chromatin remodeling (ASXL1, AHCY, and C20orf20), and transcription regulation (TCEA2). Our findings implicate a role for these genes in CC tumorigenesis, represent an important step toward the development of clinically significant biomarkers, and form a framework for testing as molecular therapeutic targets

Integrative genomics analysis of chromosome 5p gain in cervical cancer reveals target over-expressed genes, including Drosha

Background: Copy number gains and amplifications are characteristic feature of cervical cancer (CC) genomes for which the underlying mechanisms are unclear. These changes may possess oncogenic properties by deregulating tumor-related genes. Gain of short arm of chromosome 5 (5p) is the most frequent karyotypic change in CC. Methods: To examine the role of 5p gain, we performed a combination of single nucleotide polymorphism (SNP) array, fluorescence in situ hybridization (FISH), and gene expression analyses on invasive cancer and in various stages of CC progression. Results: The SNP and FISH analyses revealed copy number increase (CNI) of 5p in 63% of invasive CC, which arises at later stages of precancerous lesions in CC development. We integrated chromosome 5 genomic copy number and gene expression data to identify key target over expressed genes as a consequence of 5p gain. One of the candidates identified was Drosha (RNASEN), a gene that is required in the first step of microRNA (miRNA) processing in the nucleus. Other 5p genes identified as targets of CNI play a role in DNA repair and cell cycle regulation (BASP1, TARS, PAIP1, BRD9, RAD1, SKP2, and POLS), signal transduction (OSMR), and mitochondrial oxidative phosphorylation (NNT, SDHA, and NDUFS6), suggesting that disruption of pathways involving these genes may contribute to CC progression. Conclusion: Taken together, we demonstrate the power of integrating genomics data with expression data in deciphering tumor-related targets of CNI. Identification of 5p gene targets in CC denotes an important step towards biomarker development and forms a framework for testing as molecular therapeutic targets

Promoter Methylation-Mediated Inactivation of PCDH10 in Acute Lymphoblastic Leukemia Contributes to Chemotherapy Resistance

PCDH10 has been implicated as a tumor suppressor, since epigenetic alterations of this gene have been noted in multiple tumor types. However, to date, studies regarding its role in acute and chronic leukemias are lacking. Here, we have investi- gated the presence of promoter hypermethylation of two CpG islands of the PCDH10 gene by methylation-specific PCR in 215 cases of various subsets of myeloid- and lymphoid-lineage leukemias. We found that PCDH10 promoter hypermethyl- ation was frequent in both B-cell (81.9%) and T-cell (80%) acute lymphoblastic leukemia (ALL), while it was present in low frequency in most subtypes of myeloid leukemias (25.9%) and rare in chronic myeloid leukemia (2.2%). PCDH10 expression was downregulated via promoter hypermethylation in primary ALL samples (N 1⁄4 4) and leukemia cell lines (N 1⁄4 11). The transcriptional repression caused by PCDH10 methylation could be restored by pharmacologic inhibition of DNA methyl- transferases. ALL cell lines harboring methylation-mediated inactivation of PCDH10 were less sensitive to commonly used leukemia-specific drugs suggesting that PCDH10 methylation might serve as a biomarker of chemotherapy response. Our results demonstrate that PCDH10 is a target of epigenetic silencing in ALL, a phenomenon that may impact lymphoid-line- age leukemogenesis, serve as an indicator of drug resistance and may also have potential implications for targeted epige- netic therapy

Tracking of dynamic functional connectivity from MEG data with Kalman filtering

Owing to their millisecond-scale temporal resolution, magnetoencephalography (MEG) and electroencephalography (EEG) are well-suited tools to study dynamic functional connectivity between regions in the human brain. However, current techniques to estimate functional connectivity from MEG/EEG are based on a two-step approach; first, the MEG/EEG inverse problem is solved to estimate the source activity, and second, connectivity is estimated between the sources. In this work, we propose a method for simultaneous estimation of source activities and their dynamic functional connectivity using a Kalman filter. Based on simulations, our approach can reliably estimate source activities and resolve their time-varying interactions even at low SNR (< 1). When applied on empirical MEG responses to simple visual stimuli, our approach could capture the dynamic patterns of the underlying functional connectivity changes between the lower (pericalcarine) and higher (fusiform and parahippocampal) visual areas. In conclusion, wedemonstrate that our approach is capable of tracking changes in functional connectivity at the millisecond resolution of MEG/EEG and thus making it suitable for real-time tracking of functional connectivity, which none of the current techniques are capable of.Peer reviewe