Estimating the Memory Order of Electrocorticography Recordings.

OBJECTIVE: This paper presents a data-driven method for estimating the memory order (the average length of the statistical dependence of a given sample on previous samples) of a recorded electrocorticography (ECoG) sequence. METHODS: The proposed inference method is based on the relationship between the loss in predicting the next sample in a time-series and the dependence of this sample on the previous samples. Specifically, the memory order is estimated to be the number of past samples that minimize the least squares error (LSE) in predicting the next sample. To deal with the lack of an analytical model for ECoG recordings, the proposed method combines a collection of different predictors, thereby achieving LSE at least as low as the LSE achieved by each of the different predictors. RESULTS: ECoG recordings from six patients with epilepsy were analyzed, and the empirical cumulative density functions (ECDFs) of the memory orders estimated from these recordings were generated, for rest as well as pre-ictal time intervals. For pre-ictal time intervals, the electrodes corresponding to the seizure-onset-zone were separately analyzed. The estimated ECDFs were different between patients and between different types of blocks. For all the analyzed patients, the estimated memory orders were on the order of tens of milliseconds (up to 100 ms). SIGNIFICANCE: The proposed method facilitates the estimation of the causal associations between ECoG recordings, as these associations strongly depend on the recordings' memory. An improved estimation of causal associations can improve the performance of algorithms that use ECoG recordings to localize the epileptogenic zone. Such algorithms can aid doctors in their pre-surgical planning for the surgery of patients with epilepsy.

SozRank: A new approach for localizing the epileptic seizure onset zone.

Epilepsy is one of the most common neurological disorders affecting about 1% of the world population. For patients with focal seizures that cannot be treated with antiepileptic drugs, the common treatment is a surgical procedure for removal of the seizure onset zone (SOZ). In this work we introduce an algorithm for automatic localization of the seizure onset zone (SOZ) in epileptic patients based on electrocorticography (ECoG) recordings. The proposed algorithm builds upon the hypothesis that the abnormal excessive (or synchronous) neuronal activity in the brain leading to seizures starts in the SOZ and then spreads to other areas in the brain. Thus, when this abnormal activity starts, signals recorded at electrodes close to the SOZ should have a relatively large causal influence on the rest of the recorded signals. The SOZ localization is executed in two steps. First, the algorithm represents the set of electrodes using a directed graph in which nodes correspond to recording electrodes and the edges' weights quantify the pair-wise causal influence between the recorded signals. Then, the algorithm infers the SOZ from the estimated graph using a variant of the PageRank algorithm followed by a novel post-processing phase. Inference results for 19 patients show a close match between the SOZ inferred by the proposed approach and the SOZ estimated by expert neurologists (success rate of 17 out of 19).

CaMoDi: a new method for cancer module discovery.

BACKGROUND: Identification of genomic patterns in tumors is an important problem, which would enable the community to understand and extend effective therapies across the current tissue-based tumor boundaries. With this in mind, in this work we develop a robust and fast algorithm to discover cancer driver genes using an unsupervised clustering of similarly expressed genes across cancer patients. Specifically, we introduce CaMoDi, a new method for module discovery which demonstrates superior performance across a number of computational and statistical metrics. RESULTS: The proposed algorithm CaMoDi demonstrates effective statistical performance compared to the state of the art, and is algorithmically simple and scalable - which makes it suitable for tissue-independent genomic characterization of individual tumors as well as groups of tumors. We perform an extensive comparative study between CaMoDi and two previously developed methods (CONEXIC and AMARETTO), across 11 individual tumors and 8 combinations of tumors from The Cancer Genome Atlas. We demonstrate that CaMoDi is able to discover modules with better average consistency and homogeneity, with similar or better adjusted R2 performance compared to CONEXIC and AMARETTO. CONCLUSIONS: We present a novel method for Cancer Module Discovery, CaMoDi, and demonstrate through extensive simulations on the TCGA Pan-Cancer dataset that it achieves comparable or better performance than that of CONEXIC and AMARETTO, while achieving an order-of-magnitude improvement in computational run time compared to the other methods.

A self-directed method for cell-type identification and separation of gene expression microarrays.

Gene expression analysis is generally performed on heterogeneous tissue samples consisting of multiple cell types. Current methods developed to separate heterogeneous gene expression rely on prior knowledge of the cell-type composition and/or signatures--these are not available in most public datasets. We present a novel method to identify the cell-type composition, signatures and proportions per sample without need for a-priori information. The method was successfully tested on controlled and semi-controlled datasets and performed as accurately as current methods that do require additional information. As such, this method enables the analysis of cell-type specific gene expression using existing large pools of publically available microarray datasets.