Detection of Cognitive States from fMRI data using Machine Learning Techniques

Over the past decade functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful technique to locate activity of human brain while engaged in a particular task or cognitive state. We consider the inverse problem of detecting the cognitive state of a human subject based on the fMRI data. We have explored classification techniques such as Gaussian Naive Bayes, k-Nearest Neighbour and Support Vector Machines. In order to reduce the very high dimensional fMRI data, we have used three feature selection strategies. Discriminating features and activity based features were used to select features for the problem of identifying the instantaneous cognitive state given a single fMRI scan and correlation based features were used when fMRI data from a single time interval was given. A case study of visuo-motor sequence learning is presented. The set of cognitive states we are interested in detecting are whether the subject has learnt a sequence, and if the subject is paying attention only towards the position or towards both the color and position of the visual stimuli. We have successfully used correlation based features to detect position-color related cognitive states with 80% accuracy and the cognitive states related to learning with 62.5% accuracy

MACHINE LEARNING CLASSIFICATION OF HEMODYNAMICS TO PREDICT SCIENCE STUDENT LEARNING OUTCOMES IN REAL-TIME DURING VIRTUAL REALITY EAND ONLINE LEARNING SESSIONS

Students' learning results in science content and practices are expected to be improved through automated interactive learning management systems and linked online video-based learning environments. The goal of this study is to see how hemodynamic response data may be used to build student-level answer predictions using machine learning algorithms in a science classroom while students are using an online learning management system. A charter school in the northeastern United States was used to recruit 40 participants (n=40), 21 females and 19 males. Students viewed a recorded film that included a 20-minute instruction and explanation of the DNA replication process. A female educator on a computer screen presented an overview of the DNA replication process during class. The findings illustrate those hemodynamic responses seen during topic presentations accurately predict student replies to subject-related questions. The results imply that hemodynamic response can be used to gauge degrees of student involvement in video-based tasks, with error rates in the predictive models below 30%. This could lead to the development of unique visual media assessment methodologies, allowing educators to assess whether students can comprehend the material

Brain connectivity analysis: a short survey

This short survey the reviews recent literature on brain connectivity studies. It encompasses all forms of static and dynamic connectivity whether anatomical, functional, or effective. The last decade has seen an ever increasing number of studies devoted to deduce functional or effective connectivity, mostly from functional neuroimaging experiments. Resting state conditions have become a dominant experimental paradigm, and a number of resting state networks, among them the prominent default mode network, have been identified. Graphical models represent a convenient vehicle to formalize experimental findings and to closely and quantitatively characterize the various networks identified. Underlying these abstract concepts are anatomical networks, the so-called connectome, which can be investigated by functional imaging techniques as well. Future studies have to bridge the gap between anatomical neuronal connections and related functional or effective connectivities

Použití hlubokých neuronových sítí pro analýzu biomedicínských obrazů

The use of machine learning is very prevalent now-days and more new applications are continuously discovered. One of the prominent paradigms in computer vision are Convolutional Neural Networks (CNN). The purpose of this thesis is to introduce the topics of machine learning, convolutional neural networks and to test and evaluate experimental deep neural network architectures on functional Magnetic Resonance Images (fMRI). A series of various multi-layer CNN architectures with alternating hyper-parameters was tested against two well-known benchmark problems in the area of image classification: MNIST, CIFAR-10. The models' capacity was also evaluated against a real-world dataset of fMRI images. The network's model was rebuilt with each test run, rotating between the possible configurations. The proposed models, while performing relatively well on benchmark problems, were not able to surpass the current state of the art in brain image classification. To achieve possibly better results, they would need to be expanded to allow a broader set of features to be absorbed and classified. Also the limitations of the used hardware and the resulting impact were established. Based on the empirical results, it can be concluded that CNN are a viable tool for image pattern recognition.Využívanie strojového učenia je v dnešních dňoch veľmi rozšírené a stále nové využitia sú postupne objavované. Jedno z prominentných paradigmat v strojovom videní sú konvolučné neurónové siete (CNN). Účelom tejto práce je priblížiť témy strojového učenia, konvolučných neurónových sietí a otestovať a vyhodnotiť experimentálne architektúry konvolučných neurónových sietí na obrazoch funkčnej magnetickej rezonancie (fMRI). Séria rôznych viac-vrstvových CNN architektúr s alternujúcimi hyper-parametrami bola testovaná na dvoch všeobecne známych vzorových úlohách z oblasti klasifikácie obrazov: MNIST, CIFAR-10. Schopnosti modelov bol vyhodnotené na skutočnných fMRI obrazoch. Model siete bol znovu vytvorený s každým testom, obmieňajúc možné konfigurácie. Navrhnuté modely vykazovali relatívne dobé výsledky na vzorových úlohách, ale neboli schopné prekonať súčasný stav vedy v klasifikácií obrazov mozgu. K získaniu možných lepších výsledkov, by bolo nutné ich rozšíriť, aby boli schopné absorbovať a rozlišovať medzi väčšie množstvo atribútov. Takisto bolo zistené limity použitého technického vybavenia a obmedzenia z nich vyplývajúce. Vychádzajúc z emprických výsledkov je možné vyhodnotiť CNN ako vhodný nástroj pre nachádzanie vzorov v obrazových dátach.460 - Katedra informatikyvelmi dobř