A single-task functional magnetic resonance imaging (fMRI) experiment may only partially highlight alterations to functional brain networks affected by a particular disorder. Multivariate analysis across multiple fMRI tasks may increase the sensitivity of fMRI-based diagnosis. Prior research using multi-task analysis in fMRI, such as those that use joint independent component analysis (jICA), has mainly assumed that brain activity patterns evoked by different tasks are independent. This may not be valid in practice. Here, we use sparsity, which is a natural characteristic of fMRI data in the spatial domain, and propose a joint sparse representation analysis (jSRA) method to identify common information across different functional subtraction (contrast) images in data from a multi-task fMRI experiment. Sparse representation methods do not require independence, or that the brain activity patterns be nonoverlapping. We use functional subtraction images within the joint sparse representation analysis to generate joint activation sources and their corresponding sparse modulation profiles. We evaluate the use of sparse representation analysis to capture individual differences with simulated fMRI data and with experimental fMRI data. The experimental fMRI data was acquired from 16 young (age: 19-26) and 16 older (age: 57-73) adults obtained from multiple speech comprehension tasks within subjects, where an independent measure (namely, age in years) can be used to differentiate between groups. Simulation results show that this method yields greater sensitivity, precision, and higher Jaccard indexes (which measures similarity and diversity of the true and estimated brain activation sources) than does the jICA method. Moreover, superiority of the jSRA method in capturing individual differences was successfully demonstrated using experimental fMRI data.

Classification of individuals based on patterns of brain activity observed in functional MRI contrasts may be helpful for diagnosis of neurological disorders. Prior work for classification based on these patterns have primarily focused on using a single contrast, which does not take advantage of complementary information that may be available in multiple contrasts. Where multiple contrasts are used, the objective has been only to identify the joint, distinct brain activity patterns that differ between groups of subjects; not to use the information to classify individuals. Here, we use joint Independent Component Analysis (jICA) within a Support Vector Machine (SVM) classification method, and take advantage of the relative contribution of activation patterns generated from multiple fMRI contrasts to improve classification accuracy. Young (age: 19-26) and older (age: 57-73) adults (16 each) were scanned while listening to noise alone and to speech degraded with noise, half of which contained meaningful context that could be used to enhance intelligibility. Functional contrasts based on these conditions (and a silent baseline condition) were used within jICA to generate spatially independent joint activation sources and their corresponding modulation profiles. Modulation profiles were used within a non-linear SVM framework to classify individuals as young or older. Results demonstrate that a combination of activation maps across the multiple contrasts yielded an area under ROC curve of 0.86, superior to classification resulting from individual contrasts. Moreover, class separability, measured by a divergence criterion, was substantially higher when using the combination of activation maps.

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Many neurological disorders can change patterns of brain activity observed in functional imaging studies. These functional differences may be useful for classification of individuals into diagnostic categories. However, due to the high dimensionality of the input feature space and small set of subjects that are usually available, classification based on fMRI data is not trivial. Here, we evaluate the use of a Sparse Representation Analysis method within a Fisher Linear Discriminant (FLD) classification method, taking functional patterns characteristic of different cognitive tasks as the data input. As a test dataset, with a clear 'gold-standard' classification, we attempt to classify individuals as young, or older, based only on functional activation patterns in a speech listening task. Thirty two young (age: 19-26) and older (age: 57-73) adults (16 each) were scanned while listening to noise and to sentences degraded with noise, half of which contained meaningful context that could be used to enhance intelligibility. Different functional contrast images were used within K-SVD to generate basis activation sources and their corresponding sparse modulation profiles. Sparse modulation profiles were used in a FLD framework to classify individuals into the young and older categories. The results demonstrate the feasibility of the general approach, and confirm the potential applicability of the proposed method for real-world diagnostic problems.

The Magnetic Flux Leakage (MFL) technique is sensitive both to pipe wall geometry and pipe wall strain, therefore MFL inspection tools have the potential to locate and characterize mechanical damage in pipelines. However, the combined influence of strain and geometry makes MFL signals from dents and gouges difficult to interpret for a number of reasons: 1) the MFL signal from mechanical damage is a superposition of geometrical and strain effects, 2) the strain distribution around a mechanically damaged region can be very complex, often consisting of plastic deformation and residual (elastic) strain, 3) the effect of strain on magnetic behaviour is not well understood. Accurate magnetic models that can incorporate both strain and geometry effects are essential in order to understand MFL signals from mechanical damage. This paper reviews work conducted over the past few years involving magnetic finite element analysis (FEA) modeling of MFL dent signals and comparison with experimental results obtained both from laboratory-dented samples and dented pipe sections.