Detecting Cognitive States from fMRI Images by Machine Learning and Multivariante Classification

The major obstacle in building classifiers that robustly detect a particular cognitive state across different subjects using fMRI images has been the high inter-subject functional variability in brain activation patterns. To overcome this obstacle, firstly, the brain regions that are relevant to the problem under study are determined from the training data; then, statistical information of each brain region is extracted to form regional features, which are robust to inter-subject functional variations within the brain region; finally, the regional feature statistical variations across different samples are further alleviated by a PCA technique. To improve the generalization ability and efficiency of the classification, from the extracted regional features, a hybrid feature selection method is utilized to select the most discriminative features, which are used to train a SVM classifier for decoding brain states from fMRI images. The performance of this method is validated in a deception fMRI study. The proposed method yielded better results compared to other commonly used fMRI image classification methods

A supervised clustering approach for fMRI-based inference of brain states

We propose a method that combines signals from many brain regions observed in functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior during a scanning session. Such predictions suffer from the huge number of brain regions sampled on the voxel grid of standard fMRI data sets: the curse of dimensionality. Dimensionality reduction is thus needed, but it is often performed using a univariate feature selection procedure, that handles neither the spatial structure of the images, nor the multivariate nature of the signal. By introducing a hierarchical clustering of the brain volume that incorporates connectivity constraints, we reduce the span of the possible spatial configurations to a single tree of nested regions tailored to the signal. We then prune the tree in a supervised setting, hence the name supervised clustering, in order to extract a parcellation (division of the volume) such that parcel-based signal averages best predict the target information. Dimensionality reduction is thus achieved by feature agglomeration, and the constructed features now provide a multi-scale representation of the signal. Comparisons with reference methods on both simulated and real data show that our approach yields higher prediction accuracy than standard voxel-based approaches. Moreover, the method infers an explicit weighting of the regions involved in the regression or classification task

A Comparative Assessment of Statistical Approaches for fMRI Data to Obtain Activation Maps

Functional Magnetic Resonance Imaging (fMRI) lets us peek into the human mind and try to identify which brain areas are associated with certain tasks without the need for an invasive procedure. However, the data collected during fMRI sessions is complex; this 4 dimensional sequence of 3 dimensional volumes as images of the brain does not allow for straightforward inference. Multiple models have been developed to analyze this data and each comes with its intricacies and problems. Two of the most common ones are 2-step General Linear Model (GLM) and Independent Component Analysis (ICA). We compare these approaches empirically by fitting the models to real fMRI data using packages developed and readily available in R. The real data, obtained from an open source database openneuro.org, is named BOLD5000. The task of interest for this thesis is image viewing versus fixation cross (resting state). We found that both the first-level GLM and ICA revealed significant activation located in the occipital lobe which is consistent with the literature on visual tasks. The second-level GLM results were consistent with the first level and found activation located in the occipital lobe as well. The Group ICA results however found activation located mainly in the temporal lobe.No embargoAcademic Major: Statistic

Methods and Applications of Multivariate Pattern Analysis in Functional MRI Data Analysis.

In spite of the tremendous advances in science and technology, the human brain and its functions are still not completely understood. Functional magnetic resonance imaging (fMRI) is an imaging modality that allows for non-invasive study of brain function and physiology. Thus, fMRI has found many applications in various fields involved in the study of cognition, psychology, psychiatry, neuroscience, etc. Machine learning techniques have gained tremendous interest in recent times for fMRI data analysis. These methods involve learning from numerous examples and then making predictions for new unseen examples. This work addresses the use of machine learning techniques to find and study multivariate patterns in the fMRI brain data. The two main applications explored in this work include temporal brain-state prediction and subject categorization. The within-subject brain-state prediction setup has been used to compare and contrast three different acquisition techniques in a motor-visual activation study. It has also been implemented to highlight the differences in pain regulation networks in healthy controls and subjects with temporomandibular disorders. Lastly, regression has been used to predict graded fMRI activation on a continuous scale in a motor activation and craving study. The between-subject categorization setup has been used to distinguish between patients with Asperger's disorder and healthy controls. A major contribution of our work involves a novel multi-subject machine learning framework. This technique helps to learn a model which is based on information acquired from multiple other subjects' data in addition to the subject's own data. This has been used to classify the craving and non-craving brain states of nicotine-dependent subjects, allowing examination of both population-wide as well as subject-specific neural correlates of nicotine craving. A real-time neurofeedback setup was implemented to provide feedback to a subject using their own brain activation data. Subjects can then be trained to self-regulate their own brain activation.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111357/1/ysshah_1.pd

Detecting cognitive states from fmri images by machine learning and multivariate classification

The major obstacle in building classifiers that robustly detect a particular cognitive state across different subjects using fMRI images has been the high inter-subject functional variability in brain activation patterns. To overcome this obstacle, firstly, the brain regions that are relevant to the problem under study are determined from the training data; then, statistical information of each brain region is extracted to form regional features, which are robust to inter-subject functional variations within the brain region; finally, the regional feature statistical variations across different samples are further alleviated by a PCA technique. To improve the generalization ability and efficiency of the classification, from the extracted regional features, a hybrid feature selection method is utilized to select the most discriminative features, which are used to train a SVM classifier for decoding brain states from fMRI images. The performance of this method is validated in a deception fMRI study. The proposed method yielded better results compared to other commonly used fMRI image classification methods. 1

Normativités et usages judiciaires des technologies : l’exemple controversé de la neuroimagerie en France et au Canada

L’observation du système nerveux, de son métabolisme et de certaines de ses structures est possible grâce à la neuroimagerie. Une littérature importante issue du « neurodroit » véhicule des imaginaires et des fantasmes relatifs aux possibilités judiciaires qu’offriraient ces technologies. Qu’il s’agisse de détection du mensonge, d’identification cérébrale des individus dangereux ou encore de prédiction de comportements déviants, la neuroimagerie, en l’état actuel des technologies, ne peut pourtant être sérieusement conçue comme pouvant faire l’objet de telles applications. L’utilisation de la neuroimagerie dans le cadre d’expertises est néanmoins une réalité, dans les tribunaux canadiens comme dans la loi française. Cette thèse souligne que les conceptions des technologies dont témoignent les deux systèmes juridiques étudiés s’avèrent lacunaires, ce qui engendre des risques. Elle évoque les conditions du recours à une normativité extra-juridique, la normalisation technique, qui pourrait s’élaborer dans ce contexte controversé, et esquisse les traits d’un dialogue amélioré entre les normativités juridique et technologique.Neuroimaging allows the observation of the nervous system, of both its metabolism and some of its structures. An important literature in “neurolaw” conveys illusions and fantaisies about the judicial possibilities that imaging technologies would contain. Whether it is about lies detection, cerebral identifications of dangerous individuals through their neurobiology or predictions of criminal behaviors, neuroimaging, in the current state of technologies, can not be seriously conceived as being able to offer such applications. Judicial uses of neuroimaging through expertise are a reality nonetheless, in Canadian courts as in French law. This thesis emphasizes that the conceptions of imaging technologies integrated in the two legal systems studied are incomplete, which creates an important amount of risks. It discusses the conditions for the use of an extra-legal normativity, the international technical standardization, which could be elaborated in this particular and controversial context, and outlines several features of an increased dialogue between legal and technological norm