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Over the past decade, machine learning techniques and in particular predictive modeling and pattern recognition in biomedical sciences, from drug delivery systems to medical imaging, have become one of the most important methods of assisting researchers in gaining a deeper understanding of issues in their entirety and solving complex medical problems. Deep learning is a powerful machine learning algorithm in classification that extracts low-to high-level features. In this paper, we employ a convolutional neural network to distinguish an Alzheimers brain from a normal, healthy brain. The importance of classifying this type of medical data lies in its potential to develop a predictive model or system in order to recognize the symptoms of Alzheimers disease when compared with normal subjects and to estimate the stages of the disease. Classification of clinical data for medical conditions such as Alzheimers disease has always been challenging, and the most problematic aspect has always been selecting the strongest discriminative features. Using the Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified functional MRI data of Alzheimers subjects from normal controls, where the accuracy of testing data reached 96.85%. This experiment suggests that the shift and scale invariant features extracted by CNN followed by deep learning classification represents the most powerful method of distinguishing clinical data from healthy data in fMRI. This approach also allows for expansion of the methodology to predict more complicated systems.

Recently, big data applications have been rapidly expanding into various industries. Healthcare is among those industries that are willing to use big data platforms, and as a result, some large data analytics tools have been adopted in this field. Medical imaging, which is a pillar in diagnostic healthcare, involves a high volume of data collection and processing. A massive number of 3D and 4D images are acquired in different forms and resolutions using a variety of medical imaging modalities. Preprocessing and analysis of imaging data is currently a costly and time-consuming process. However, few big data platforms have been created or modified for medical imaging purposes because of certain restrictions, such as data format. In this paper, we designed, developed and successfully tested a new pipeline for medical imaging data (in particular, functional magnetic resonance imaging — fMRI) using the Big Data Spark / PySpark platform on a single node, which allowed us to read and load imaging data, convert the data to Resilient Distributed Datasets in order to manipulate and perform in-memory data processing in parallel, and convert final results to an imaging format. Additionally, the pipeline provides an option to store the results in other formats, such as data frames. Using this new pipeline, we repeated our previous work, in which we extracted brain networks from fMRI data using template matching and the sum of squared differences (SSD) method. The final results revealed that our Spark (PySpark) based solution improved the performance (in terms of processing time) approximately fourfold when compared with the previous work developed in Python.

As the interest in functional connectivity continues to increase among neuroimaging researchers there becomes a greater need to develop an objective method of network identification. The current paper offers a solution to this problem by developing a robust decision making algorithm that can extract a target neural network from an array of spatial maps. We used a probabilistic independent component analysis to generate spatial maps of the Default Mode Network (DMN); however, this adaptive pipeline can be applied to any network of interest. Different template matching algorithms including: Normalized Cross-Correlation, Sum of Squared Differences and Dice Coefficient, were applied to the spatial and frequency domains of the dataset to identify the components that shared the greatest similarity to our DMN template. After identifying components within the resting state, the decision making pipeline selected the components within each method that had the highest matching scores to our DMN template. The final decision of selecting the most prototypical DMN components was made by a comparison between methods. This resulted in a DMN mask that was generated by the components chosen by our decision-making algorithm. To evaluate the accuracy of the decision-maker, a cross-correlation between each final mask and the template was measured. Results indicated that the Normalized Cross Correlation method, using both the spatial and frequency domain, and the Dice Coefficient method, generated the optimal DMN mask. This demonstrates the utility of our algorithm in providing an objective method for network extraction.

To evaluate discriminant models in fMRI data we introduce the pseudo-Receiver Operating Characteristic plot defined by subsampled, spit-half measures of prediction (P) versus spatial pattern reproducibility (R). We illustrate (P, R) plots using denoised fMRI data with 10%–100% of the components from a 1st-level principal component analysis (PCA). An LD model is then regularized in split-half subsamples with 2nd-level PCAs that retain Q PCs from the largest to smaller variance. We show that the resulting Z-scored, LD spatial maps with monotonically increasing P and Q reflect regionally-dependent hierarchies of underlying brain-networks adapted to meet particular task demands.

The human brain is a complicated network made-up of a large number of regions, which are structurally and/or functionally connected. Recently, neuroimaging studies using functional Magnetic Resonance Imaging have revealed that certain neural structures are highly active during periods of rest. Amongst several methods that have been developed to analyze resting-state fMRI data, Probabilistic Independent Component Analysis (PICA) is currently the most popular technique. The major challenge of using PICA is that resting-state networks are split into several components and visually extracting them can be difficult. In this paper, we propose a fast and precise algorithm based on advanced template matching in spatial domain such as Normalized Cross Correlation adapted to functional images in order to automatically extract the Default Mode Network (DMN) which is the task independent resting state network in the brain using PICA. We create a DMN template covering all reported regions in literature using two standard atlases. Ultimately, we reconstruct an image of the extracted DMN from PICA using an optimized decision making. Our approach was effective given that our algorithm results correlated highly with the DMN template.

This paper examines the application of mathematical modeling to a novel drug delivery system using artificial neural networks. For this purpose, a Feed-Forward back propagation network was trained by two different concepts and the behavior of this drug delivery system was analyzed based on the simulated results. The network also successfully determined the most accurate release profiles under specific formulation parameters. The simulated results showed a high correlation with the real data in this study. Furthermore, a new method was proposed in order to predict the burst release point in Poly Lactic-co-Glycolic Acid (PLGA) based drug delivery systems. This paper reveals that the mathematical modeling of novel drug delivery systems not only significantly decreases time and cost, but also facilitates the design of new pharmaceutical formulations.