Two dimensional histograms of gain and correlation of the reconstruction compared with the embedded signal at each location in the brain volume for one 3-d filter.

<p>The upper, middle and lower rows represent sources <i>seeded</i> in , and respectively; whilst the left, middle and right hand columns show <i>reconstructions</i> in , and . The diagonal of these figures shows the same data as that in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022251#pone-0022251-g005" target="_blank">Figure 5</a>.</p

Two dimensional histogram of gain and correlation of the reconstruction compared with the embedded signal at each location in the brain volume for one 3-dimensional filter.

<p>The left, middle and right columns show the results for sources both embedded and reconstructed in the , and directions respectively.</p

Two dimensional histograms of gain and correlation of the reconstruction compared with the embedded signal at each location in the brain volume for three 1-d filters.

<p>The upper, middle and lower rows represent sources <i>seeded</i> in , and respectively; whilst the left, middle and right hand columns show <i>reconstructions</i> in , and . The diagonal of these figures shows the same data as that in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022251#pone-0022251-g001" target="_blank">Figure 1</a>.</p

Volumetric image showing correlations between sources embedded in the direction and the reconstructed signals when using three 1-dimensional filters.

<p>The image is thresholded at 0.7.</p

Definitions of mathematical terms used in the text.

<p>Definitions of mathematical terms used in the text.</p

Comparison of the two filter implementations and their ability to localise a single embedded source.

<p>The open circles represent the seeded location, the filled circles represent the average localisation across 30 experiments (with 1 standard deviation shown as cross-hairs). Subfigure (a) shows three 1-dimensional filters. Subfigure (b) shows one 3-dimensional filter.</p

Bayesian inference general procedures for a single-subject test study

Abnormality detection in identifying a single-subject which deviates from the majority of a control group dataset is a fundamental problem. Typically, the control group is characterised using standard Normal statistics, and the detection of a single abnormal subject is in that context. However, in many situations, the control group cannot be described by Normal statistics, making standard statistical methods inappropriate. This paper presents a Bayesian Inference General Procedures for A Single-Subject Test (BIGPAST) designed to mitigate the effects of skewness under the assumption that the dataset of the control group comes from the skewed Student t distribution. BIGPAST operates under the null hypothesis that the single-subject follows the same distribution as the control group. We assess BIGPAST’s performance against other methods through simulation studies. The results demonstrate that BIGPAST is robust against deviations from normality and outperforms the existing approaches in accuracy. BIGPAST can reduce model misspecification errors under the skewed Student t assumption. We apply BIGPAST to a Magnetoencephalography (MEG) dataset consisting of an individual with mild traumatic brain injury and an age and gender-matched control group, demonstrating its effectiveness in detecting abnormalities in a single-subject.</p

Virtual electrode responses in the right fusiform gyrus (R-FG; 32, −57, −3) for ASD (a) and TD (b) participants.

<p>Evoked responses are displayed in the upper row of each figure; induced responses are displayed in the lower row. All responses indicate within subjects changes from baseline; changes significant at p<.05 level are indicated within dotted lines.</p

The lower band response (3–30 Hz), in TD (left) and ASD (right) participants.

<p>Red  =  maxima, blue  =  minima p<.05 for all highlighted voxels. Slice shown MNI: 32, −72, −2.</p

Response times for identifying specific emotions in ASD (red) and control (green) participants.

<p>Response times for identifying specific emotions in ASD (red) and control (green) participants.</p