Interval importance index to select relevant ATR-FTIR wavenumber Intervals for falsified drug classification.

The commerce of falsified drugs has substantially grown in recent years due to facilitated access to technologies needed for copying authentic pharmaceutical products. Attenuated Total Reflectance coupled with Fourier Transform Infrared (ATR-FTIR) spectroscopy has been successfully employed as an analytical tool to identify falsified products and support legal agents in interrupting illegal operations. ATR-FTIR spectroscopy typically yields datasets comprised of hundreds of highly correlated wavenumbers, which may compromise the performance of classical multivariate techniques used for sample classification. In this paper we propose a new wavenumber interval selection method aimed at selecting regions of spectra that best discriminate samples of seized drugs into two classes, authentic or falsified. The discriminative power of spectra regions is represented by an Interval Importance Index (III) based on the Two-Sample Kolmogorov-Smirnov test statistic, which is a novel proposition of this paper. The III guides an iterative forward approach for wavenumber selection; different data mining techniques are used for sample classification. In 100 replications using the best combination of classification technique and wavenumber intervals, we obtained average 99.87% accurate classifications on a Cialis® dataset, while retaining 12.5% of the authentic wavenumbers, and average 99.43% accurate classifications on a Viagra® dataset, while retaining 23.75% of the authentic wavenumbers. Our proposition was compared with alternative approaches for individual and interval wavenumber selection available in the literature, always leading to more consistent and easier to interpret results.

Local functional connectivity as a pre-surgical tool for seizure focus identification in non-lesion, focal epilepsy.

Successful resection of cortical tissue engendering seizure activity is efficacious for the treatment of refractory, focal epilepsy. The pre-operative localization of the seizure focus is therefore critical to yielding positive, post-operative outcomes. In a small proportion of focal epilepsy patients presenting with normal MRI, identification of the seizure focus is significantly more challenging. We examined the capacity of resting state functional MRI (rsfMRI) to identify the seizure focus in a group of four non-lesion, focal (NLF) epilepsy individuals. We predicted that computing patterns of local functional connectivity in and around the epileptogenic zone combined with a specific reference to the corresponding region within the contralateral hemisphere would reliably predict the location of the seizure focus. We first averaged voxel-wise regional homogeneity (ReHo) across regions of interest (ROIs) from a standardized, probabilistic atlas for each NLF subject as well as 16 age- and gender-matched controls. To examine contralateral effects, we computed a ratio of the mean pair-wise correlations of all voxels within a ROI with the corresponding contralateral region (IntraRegional Connectivity - IRC). For each subject, ROIs were ranked (from lowest to highest) on ReHo, IRC, and the mean of the two values. At the group level, we observed a significant decrease in the rank for ROI harboring the seizure focus for the ReHo rankings as well as for the mean rank. At the individual level, the seizure focus ReHo rank was within bottom 10% lowest ranked ROIs for all four NLF epilepsy patients and three out of the four for the IRC rankings. However, when the two ranks were combined (averaging across ReHo and IRC ranks and scalars), the seizure focus ROI was either the lowest or second lowest ranked ROI for three out of the four epilepsy subjects. This suggests that rsfMRI may serve as an adjunct pre-surgical tool, facilitating the identification of the seizure focus in focal epilepsy.