Spatial and Temporal Feature Extraction for Brain Decoding using CUDA

Brain decoding is the process of predicting cognitive states from medical data which consists of thousands of voxels and hundreds of samples. Features representing spatial and temporal relationships among neighboring voxels are discriminative and these relationships are estimated by solving regression for all samples of all voxels. Finding the nearest neighbors of all voxels and computing regression that includes matrix multiplication, addition and inverse with GPU implementation has a high speedup over CPU implementation.


Functional Mesh Learning for Pattern Analysis of Cognitive Processes
Firat, Orhan; Ozay, Mete; Onal, Itir; GİLLAM, İLKE; Yarman Vural, Fatoş Tunay (2013-07-18)
We propose a statistical learning model for classifying cognitive processes based on distributed patterns of neural activation in the brain, acquired via functional magnetic resonance imaging (fMRI). In the proposed learning machine, local meshes are formed around each voxel. The distance between voxels in the mesh is determined by using functional neighborhood concept. In order to define functional neighborhood, the similarities between the time series recorded for voxels are measured and functional connec...
Learning transferability of cognitive tasks by graph generation for brain decoding
Coşkun, Bilgin; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2021-12-10)
Brain decoding involves analyzing the cognitive states of human brain by using some statistical techniques in order to understand the relations among the cognitive states, based on neuroimaging data. A very powerful tool to acquire the brain data is functional magnetic resonance images (fMRI), which generates three-dimensional brain volume at each time instant, while a subject performs a cognitive task involving social activities, emotion processing, game playing, memory etc. However, it is very difficult a...
Solid Phase Microextraction-Based Miniaturized Probe and Protocol for Extraction of Neurotransmitters from Brains in Vivo
Lendor, Sofia; Hassani, Seyed-Alireza; Boyacı, Ezel; Singh, Varoon; Womelsdorf, Thilo; Pawliszyn, Janusz (American Chemical Society (ACS), 2019-04-02)
Despite the importance of monitoring and correlating neurotransmitter concentrations in the brain with observable behavior and brain areas in which they act, in vivo measurement of multiple neurochemicals in the brain remains a challenge. Here, we propose an alternative solid phase microextraction-based (SPME) chemical biopsy approach as a viable method for acquirement of quantitative information on multiple neurotransmitters by one device within a single sampling event, with multisite measurement capabilit...
Disease signature extraction for obsessive compulsive disorder using effective connectivity analysis based on dynamic causal modelling
Yüksel, Alican; Halıcı, Uğur; Çiçek, Metehan; Department of Electrical and Electronics Engineering (2016)
In neuroscience, there exist some studies on activations of human brain used to detect mental disorders and to extract their signatures. Obsessive Compulsive Disorder (OCD) is one of the most common mental disorder that is encountered. Although there are many studies concern about this disorder by using functional Magnetic Resonance Imagining (fMRI), there exist very limited studies for extracting OCD signature that is extracting features from brain activity data to discriminate successfully OCD and healthy...
Encoding Multi-Resolution Brain Networks Using Unsupervised Deep Learning
Rahnama, Arash; Alchihabi, Abdullah; Gupta, Vijay; Antsaklis, Panos J.; Yarman Vural, Fatoş Tunay (2017-10-25)
The main goal of this study is to extract a set of brain networks in multiple time-resolutions to analyze the connectivity patterns among the anatomic regions for a given cognitive task. We suggest a deep architecture which learns the natural groupings of the connectivity patterns of human brain in multiple time-resolutions. The suggested architecture is tested on task data set of Human Connectome Project (HCP) where we extract multi-resolution networks, each of which corresponds to a cognitive task. At the...
Citation Formats
I. Önal, A. Temizel, and F. T. Yarman Vural, “Spatial and Temporal Feature Extraction for Brain Decoding using CUDA,” 2015, Accessed: 00, 2021. [Online]. Available: