Utilizing anatomical information for signal detection in functional magnetic resonance imaging

We are considering the statistical analysis of functional magnetic resonance imaging (fMRI) data. As demonstrated in previous work, grouping voxels into regions (of interest) and carrying out a multiple test for signal detection on the basis of these regions typically leads to a higher sensitivity when compared with voxel-wise multiple testing approaches. In the case of a multi-subject study, we propose to define the regions for each subject separately based on their individual brain anatomy, represented, e.g., by so-called Aparc labels. The aggregation of the subject-specific evidence for the presence of signals in the different regions is then performed by means of a combination function for p-values. We apply the proposed methodology to real fMRI data and demonstrate that our approach can perform comparably to a two-stage approach for which two independent experiments are needed, one for defining the regions and one for actual signal detection

A Gaussian-mixed Fuzzy Clustering Model on Valence-Arousal-related fMRI Data-Set

Previous medical experiments illustrated that Valence and Arousal were high corresponded to brain response by amygdala and orbital frontal cortex through observation by functional magnetic resonance imaging (fMRI). In this paper, Valence-Arousal related fMRI data-set were acquired from the picture stimuli experiments, and finally the relative Valence -Arousal feature values for a given word that corresponding to a given picture stimuli were calculated. Gaussian bilateral filter and independent components analysis (ICA) based Gaussian component method were applied for image denosing and segmenting; to construct the timing signals of Valence and Arousal from fMRI data-set separately, expectation maximal of Gaussian mixed model was addressed to calculate the histogram, and furthermore, Otsu curve fitting algorithm was introduced to scale the computational complexity; time series based Valence -Arousal related curve were finally generated. In Valence-Arousal space, a fuzzy c-mean method was applied to get typical point that represented the word relative to the picture. Analyzed results showed the effectiveness of the proposed methods by comparing with other algorithms for feature extracting operations on fMRI data-set including power spectrum density (PSD), spline, shape-preserving and cubic fitting methods

Automated Knowledge Discovery from Functional Magnetic Resonance Images using Spatial Coherence

Functional Magnetic Resonance Imaging (fMRI) has the potential to unlock many of the mysteries of the brain. Although this imaging modality is popular for brain-mapping activities, clinical applications of this technique are relatively rare. For clinical applications, classification models are more useful than the current practice of reporting loci of neural activation associated with particular disorders. Also, since the methods used to account for anatomical variations between subjects are generally imprecise, the conventional voxel-by-voxel analysis limits the types of discoveries that are possible. This work presents a classification-based framework for knowledge discovery from fMRI data. Instead of voxel-centric knowledge discovery, this framework is segment-centric, where functional segments are clumps of voxels that represent a functional unit in the brain. With simulated activation images, it is shown that this segment-based approach can be more successful for knowledge discovery than conventional voxel-based approaches. The spatial coherence principle refers to the homogeneity of behavior of spatially contiguous voxels. Auto-threshold Contrast Enhancing Iterative Clustering (ACEIC) - a new algorithm based on the spatial coherence principle is presented here for functional segmentation. With benchmark data, it is shown that the ACEIC method can achieve higher segmentation accuracy than Probabilistic Independent Component Analysis - a popular method used for fMRI data analysis. The spatial coherence principle can also be exploited for voxel-centric image-classification problems. Spatially Coherent Voxels (SCV) is a new feature selection method that uses the spatial coherence principle to eliminate features that are unlikely to be useful for classification. For a Substance Use Disorder dataset, it is demonstrated that feature selection with SCV can achieve higher classification accuracies than conventional feature selection methods

Graph learning and its applications : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science, Massey University, Albany, Auckland, New Zealand

Since graph features consider the correlations between two data points to provide high-order information, i.e., more complex correlations than the low-order information which considers the correlations in the individual data, they have attracted much attention in real applications. The key of graph feature extraction is the graph construction. Previous study has demonstrated that the quality of the graph usually determines the effectiveness of the graph feature. However, the graph is usually constructed from the original data which often contain noise and redundancy. To address the above issue, graph learning is designed to iteratively adjust the graph and model parameters so that improving the quality of the graph and outputting optimal model parameters. As a result, graph learning has become a very popular research topic in traditional machine learning and deep learning. Although previous graph learning methods have been applied in many fields by adding a graph regularization to the objective function, they still have some issues to be addressed. This thesis focuses on the study of graph learning aiming to overcome the drawbacks in previous methods for different applications. We list the proposed methods as follows. • We propose a traditional graph learning method under supervised learning to consider the robustness and the interpretability of graph learning. Specifically, we propose utilizing self-paced learning to assign important samples with large weights, conducting feature selection to remove redundant features, and learning a graph matrix from the low dimensional data of the original data to preserve the local structure of the data. As a consequence, both important samples and useful features are used to select support vectors in the SVM framework. • We propose a traditional graph learning method under semi-supervised learning to explore parameter-free fusion of graph learning. Specifically, we first employ the discrete wavelet transform and Pearson correlation coefficient to obtain multiple fully connected Functional Connectivity brain Networks (FCNs) for every subject, and then learn a sparsely connected FCN for every subject. Finally, the ℓ1-SVM is employed to learn the important features and conduct disease diagnosis. • We propose a deep graph learning method to consider graph fusion of graph learning. Specifically, we first employ the Simple Linear Iterative Clustering (SLIC) method to obtain multi-scale features for every image, and then design a new graph fusion method to fine-tune features of every scale. As a result, the multi-scale feature fine-tuning, graph learning, and feature learning are embedded into a unified framework. All proposed methods are evaluated on real-world data sets, by comparing to state-of-the-art methods. Experimental results demonstrate that our methods outperformed all comparison methods

Generative models for group fMRI data

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 151-174).In this thesis, we develop an exploratory framework for design and analysis of fMRI studies. In our framework, the experimenter presents subjects with a broad set of stimuli/tasks relevant to the domain under study. The analysis method then automatically searches for likely patterns of functional specificity in the resulting data. This is in contrast to the traditional confirmatory approaches that require the experimenter to specify a narrow hypothesis a priori and aims to localize areas of the brain whose activation pattern agrees with the hypothesized response. To validate the hypothesis, it is usually assumed that detected areas should appear in consistent anatomical locations across subjects. Our approach relaxes the conventional anatomical consistency constraint to discover networks of functionally homogeneous but anatomically variable areas. Our analysis method relies on generative models that explain fMRI data across the group as collections of brain locations with similar profiles of functional specificity. We refer to each such collection as a functional system and model it as a component of a mixture model for the data. The search for patterns of specificity corresponds to inference on the hidden variables of the model based on the observed fMRI data. We also develop a nonparametric hierarchical Bayesian model for group fMRI data that integrates the mixture model prior over activations with a model for fMRI signals. We apply the algorithms in a study of high level vision where we consider a large space of patterns of category selectivity over 69 distinct images. The analysis successfully discovers previously characterized face, scene, and body selective areas, among a few others, as the most dominant patterns in the data. This finding suggests that our approach can be employed to search for novel patterns of functional specificity in high level perception and cognition.by Danial Lashkari.Ph.D

Darstellung des kortikalen motorischen Handareals mittels funktioneller Magnetresonanztomographie unter Anwendung eines künstlichen neuronalen Netzes

Ziel: In der vorliegenden Studie untersuchten wir fMRT-Datensätze des kortikalen motorischen Handareals gesunder Probanden mittels künstlicher neuronaler Netze. Ziel war es, eine feinere Unterscheidung der Signalzeitreihen zu erreichen, als dies mit derzeit etablierten Methoden möglich ist. Einleitung: In einem kurzen Überblick wurden die wichtigsten Methoden und Erkenntnisse der Erforschung funktioneller Gehirnzentren dargestellt. Daneben wurde auf die Mustererkennung mittels neuronaler Netze und deren Einsatzmöglichkeiten eingegangen. Methode: Zunächst wurden die methodischen Grundlagen, auf denen die funktionelle MRT beruht, mit ihren technisch-physikalischen und physiologischen Zusammenhängen vorgestellt. Im speziellen Methodikteil wurde das untersuchte Kollektiv (24 gesunde Personen), das Messprotokoll und der Versuchsaufbau beschrieben. Die funktionellen Messungen wurden an einem 1,5 Tesla-Magnetresonanz-tomographen mit einer T2*-gewichteten Echo-Planar-Sequenz durchgeführt. Als Paradigma wurden Fingerbewegungen jeweils der rechten oder linken Hand mit maximalem Druck bzw. mit maximaler Frequenz gewählt. Bei allen fMRT-Messungen wurden Kraft und Geschwindigkeit der Fingerbewegungen mit hydraulischen Druckaufnehmern aufgezeichnet. Die Auswertetechnik wurde in einem eigenen Kapitel beschrieben, das sich mit den Verfahren zur Nachverarbeitung der gewonnenen Schnittbildserien auseinander setzte: Hierbei wurde die Korrelationsanalyse als ein etabliertes Auswerteverfahren beschrieben und das Prinzip der Mustererkennung durch Neuronale Netze dargestellt. Als spezielles Verfahren zur Clusteranalyse wurde die minimal free energy Vektorquantisierung erklärt. Schließlich wurde der von uns verwendete kombinierte Ansatz einer Vorselektion der Zeitreihen durch Korrelationsanalyse, gefolgt von der Vektorquantisierung der über einem definierten Schwellwert liegenden Pixel vorgestellt. Ergebnisse: Zunächst wurden die Resultate der Korrelationsanalyse mit den Ergebnissen der Vektorquantisierung verglichen. Dabei zeigte sich, daß mittels Vektorquantisierung die zerebralen Aktivierungen des Motorkortex in kortikale und vaskuläre Anteile subdifferenziert werden konnten. Zudem ließen sich Artefakte, die durch Kopfbewegungen verursacht wurden, erkennen und eliminieren. Außerdem konnte nachgewiesen werden, daß durch die ausschließliche Verwendung der Korrelationsanalyse eine systematische Überbetonung der vaskulären Signalveränderungen erfolgt. Schließlich wurden die Probanden auf die kortikalen Aktivierungen in der Zentralregion, der Postzentralregion, der Präzentralregion und der supplementären Motorregion (SMA) hin untersucht. Dabei wurden Unterschiede hinsichtlich der Händigkeit und der motorischen Fertigkeiten der Probanden herausgearbeitet. Hier fand sich vor allem in der Präzentralregion ein Unterschied zwischen Rechts- und Linkshändern, wobei Rechtshänder nur bei Bewegung der linken Hand eine verstärkte Aktivierung der kontralateralen rechten Präzentralregion zeigten, während Linkshänder bei allen Aufgaben (mit rechter und linker Hand) eine verstärkte Aktivierung der rechten Präzentralregion aufwiesen. Für Aufgaben mit starkem Druck waren die aktivierten Areale in allen ausgewerteten Hirnregionen umso ausgedehnter, je geübter die Probanden waren. Bei Aufgaben mit hoher Frequenz fand sich nur in der supplementären Motorregion eine Ausweitung der Aktivierung mit zunehmenden motorischen Fertigkeiten der Probanden. Schlußfolgerung: Die Anwendung künstlicher Neuronaler Netze auf fMRT-Datensätze ist eine vielversprechende Methode, mit der die Aussagekraft bezüglich Lokalisation und Ausdehnung kortikaler Aktivierungen verbessert werden kann

Exploring regions of interest with cluster analysis (EROICA) using a spectral peak statistic for selecting and testing the significance of fMRI activation time-series

NRC publication: Ye

A Comparative Study of the Effects of Music on Emotional State in the Normal and High-functioning Autistic Population

It has been assumed that the social deficits inherent in autism imply that individuals with the condition will be unable fully to appreciate the emotional content of music. My aim was to test this assumption, and to explore more widely the similarities and differences between the experience of music in the normal population and those with autism. My first study used musically-induced mood changes and a behavioural measure to show that music does indeed have a more than superficial effect on cognitive processes in a control group. The second study focused on high-functioning adults on the autism spectrum, using semi-structured interviews to investigate the part that music played in their everyday lives, concluding that autism is no bar to full appreciation of the emotional uses of music, though suggesting a degree of impoverishment in the language they used to describe the emotions. The final set of experiments compared control and autism group directly, using physiological (GSR) measures of arousal together with self-report of the emotions evoked by a set of musical items. Standardised questionnaires were used to measure alexithymia (difficulty in identifying and describing feelings) in individuals. Although the autism group experienced comparable levels of physiological arousal to music, they used fewer words than the control group to describe their emotional responses, a difference which correlated strongly with their level of alexithymia. My results are consistent with the hypothesis that in autism, the basic physiological and emotional component of their reactivity to music is functioning normally, but that their ability to translate these reactions into conventional emotional language is reduced, precisely in line with the extent of their alexithymia. These results suggest that the preserved ability of music to generate emotional arousal in autism may lead to clinical applications for the treatment of alexithymia in autism and other conditions