Review of fMRI Data Analysis

Proceedings of the 20th international …, 2007

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.

One of the key challenges in cognitive neuroscience is determining the mapping between neural activities and mental representations. The functional magnetic resonance imaging (fMRI) provides measure of brain activity in response to cognitive tasks and proved as one of the most effective tool in brain imaging and studying the brain activities. The complexities involved in fMRI classification are: high dimensionality of fMRI data, smaller size of the dataset, interindividual differences, and dependence on data acquisition techniques. The state-of-the-art machine learning techniques popularly used by neuroimaging community for variety of fMRI data analysis has created exciting possibilities to understand deeply the functioning of inner structure of the human brain. In this paper, we present an overview of different stages involved in cognitive state classification and focuses on different machine learning approaches, their worthiness, and potentiality in identifying brain states into pre-specified classes. The machine learning techniques ranges from conventional to recent hybrid techniques which have shown promising result in fMRI classification are discussed here. Further, this paper suggests direction for further research in this area by synergizing with other related fields.

Functional magnetic resonance imaging (fMRI) makes it possible to detect brain activities in order to elucidate cognitive-states. The complex nature of fMRI data requires understanding of the analyses applied to produce possible avenues for developing models of cognitive state classi¯cation and improving brain activity prediction. While many models of classi¯cation task of fMRI data analysis have been developed, in this paper, we present a novel hybrid technique through combining the best attributes of genetic algorithms (GAs) and ensemble decision tree technique that consistently outperforms all other methods which are being used for cognitive-state classi¯cation. Speci¯cally, this paper illustrates the combined e®ort of decision-trees ensemble and GAs for feature selection through an extensive simulation study and discusses the classi¯cation performance with respect to fMRI data. We have shown that our proposed method exhibits signi¯cant reduction of the number of features with clear edge classi¯cation accuracy over ensemble of decision-trees.

The 16th Annual …

Is it feasible to train classifiers to decode the cognitive state of a human subject, based on single-episode fMRI data? If so, these trained classifiers could be used as virtual sensors to detect hidden cognitive states of a subject, providing a key tool for experimental research in cognitive science and in diagnosis of mental processes in patients with brain injuries. Whereas much work has been done on fMRI data analysis methods that average together data collected from repeated stimuli over multiple episodes, little is known about the feasibility of training classifiers to decode cognitive states from single episodes. This paper presents several case studies in which we have successfully trained such classifiers. We explore the technical issues involved in training such single-episode classifiers, and discuss areas for future research. These case studies include training a classifier to determine (1) which of twelve semantic categories of words is being read by a human subject (e.g., a word describing animals or one describing buildings), (2) whether or not a subject finds a sentence ambiguous, and (3) whether the subject is looking at a picture or at a sentence describing a picture.

2017

Classification of brain states obtained through functional magnetic resonance imaging (fMRI) poses a serious challenges for neuroimaging community to uncover discriminating patterns of brain state activity that define independent thought processes. This challenge came into existence because of the large number of voxels in a typical fMRI scan, the classifier is presented with a massive feature set coupled with a relatively small training samples. One of the most popular research topics in last few years is the application of machine learning algorithms for mental states classification, decoding brain activation, and finding the variable of interest from fMRI data. In classification scenario, different algorithms have different biases, in the sequel performances differs across datasets, and for a particular dataset the accuracy varies from classifier to classifier. To overcome the limitations of individual techniques, hybridization or fusion of these machine learning techniques emerg...

AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 2003

We consider the problem of detecting the instantaneous cognitive state of a human subject based on their observed functional Magnetic Resonance Imaging (fMRI) data. Whereas fMRI has been widely used to determine average activation in different brain regions, our problem of automatically decoding instantaneous cognitive states has received little attention. This problem is relevant to diagnosing cognitive processes in neurologically normal and abnormal subjects. We describe a machine learning approach to this problem, and report on its successful use for discriminating cognitive states such as observing a picture versus reading a sentence, and reading a word about people versus reading a word about buildings.

2014 International Conference on Signal Processing and Communications (SPCOM), 2014

One approach, for understanding human brain functioning, is to analyze the changes in the brain while performing cognitive tasks. Towards this, Functional Magnetic Resonance (fMR) images of subjects performing well-defined tasks are widely utilized for task-specific analyses. In this work, we propose a procedure to enable classification between two chosen cognitive tasks, using their respective fMR image sequences. The time series of expert-marked anatomically-mapped relevant voxels are processed and fed as input to the classical Naive Bayesian and SVM classifiers. The processing involves use of random sieve function, phase information in the data transformed using Fourier and Hilbert transformations. This processing results in improved classification, as against using the voxel intensities directly, as illustrated. The novelty of the proposed method lies in utilizing the phase information in the transformed domain, for classifying between the cognitive tasks along with random sieve function chosen with a particular probability distribution. The proposed classification procedure is applied on a publicly available dataset, StarPlus data, with 6 subjects performing the two distinct cognitive tasks of watching either a picture or a sentence. The classification accuracy stands at an average of 65.6%(using Naive Bayes classifier) and 76.4%(using SVM classifier) for raw data. The corresponding classification accuracy stands at 96.8% and 97.5% for Fourier transformed data. For Hilbert transformed data, it is 93.7% and 99%, for 6 subjects, on 2 cognitive tasks.

2007

Over the last few years, functional Magnetic Resonance Imaging (fMRI) has emerged as a new and powerful method to map the cognitive states of a human subject to specific functional areas of the subject brain. Although fMRI has been widely used to determine average activation in different brain regions, the problem of automatically decoding the cognitive state from instantaneous brain activations has received little attention. In this paper, we study this prediction problem on a complex time-series dataset that relates fMRI data (brain images) with the corresponding cognitive states of the subjects while watching three 20 minute movies. This work describes the process we used to reduce the extremely high-dimensional feature space and a comparison of the models used for prediction. To solve the prediction task we explored a standard linear model frequently used by neuroscientists, as well as a k-nearest neighbor model, that now are the state-of-art in this area. Finally, we provide experimental evidence that non-linear models such as multi-layer perceptron and especially recurrent neural networks are significantly better.

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