Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models

Pattern recognition models have been increasingly applied to neuroimaging data over the last two decades. These applications have ranged from cognitive neuroscience to clinical problems. A common limitation of these approaches is that they do not incorporate previous knowledge about the brain structure and function into the models. Previous knowledge can be embedded into pattern recognition models by imposing a grouping structure based on anatomically or functionally defined brain regions. In this work, we present a novel approach that uses group sparsity to model the whole brain multivariate pattern as a combination of regional patterns. More specifically, we use a sparse version of Multiple Kernel Learning (MKL) to simultaneously learn the contribution of each brain region, previously defined by an atlas, to the decision function. Our application of MKL provides two beneficial features: (1) it can lead to improved overall generalisation performance when the grouping structure imposed by the atlas is consistent with the data; (2) it can identify a subset of relevant brain regions for the predictive model. In order to investigate the effect of the grouping in the proposed MKL approach we compared the results of three different atlases using three different datasets. The method has been implemented in the new version of the open-source Pattern Recognition for Neuroimaging Toolbox (PRoNTo).

Enhanced Interictal Responsiveness of the Migraineous Visual Cortex to Incongruent Bar Stimulation: A Functional MRI Visual Activation Study

Since visual aura is usually described as expanding zigzag lines, neurones involved with the perception of line orientation may initiate this phenomenon. A visual incongruent line stimulation protocol was developed to obtain functional magnetic resonance images (fMRI) interictally in 5 female migraine patients with typical fortification spectra and in 5 normal matched controls. Activation in the visual cortex was present contralateral to the side of stimulation in 4 of 5 patients, notably in the extrastriate visual cortex. In 4 of 5 controls activation was observed in the medial and anterior orbitofrontal cortex. In one of them additional activation at the right nucleus accumbens/ventral striatum and right ventral pallidum was present. In the remaining control subject activation was present in the left primary visual cortex. The enhanced interictal reactivity of the visual cortex in migraineurs supports the hypothesis of abnormal cortical excitability as an important pathophysiological mechanism in migraine aura, though the role of specific regions of the visual cortex remains to be explored.

PRoNTo: pattern recognition for neuroimaging toolbox

In the past years, mass univariate statistical analyses of neuroimaging data have been complemented by the use of multivariate pattern analyses, especially based on machine learning models. While these allow an increased sensitivity for the detection of spatially distributed effects compared to univariate techniques, they lack an established and accessible software framework. The goal of this work was to build a toolbox comprising all the necessary functionalities for multivariate analyses of neuroimaging data, based on machine learning models. The “Pattern Recognition for Neuroimaging Toolbox” (PRoNTo) is open-source, cross-platform, MATLAB-based and SPM compatible, therefore being suitable for both cognitive and clinical neuroscience research. In addition, it is designed to facilitate novel contributions from developers, aiming to improve the interaction between the neuroimaging and machine learning communities. Here, we introduce PRoNTo by presenting examples of possible research questions that can be addressed with the machine learning framework implemented in PRoNTo, and cannot be easily investigated with mass univariate statistical analysis.

The pathways for intelligible speech: multivariate and univariate perspectives

An anterior pathway, concerned with extracting meaning from sound, has been identified in nonhuman primates. An analogous pathway has been suggested in humans, but controversy exists concerning the degree of lateralization and the precise location where responses to intelligible speech emerge. We have demonstrated that the left anterior superior temporal sulcus (STS) responds preferentially to intelligible speech (Scott SK, Blank CC, Rosen S, Wise RJS. 2000. Identification of a pathway for intelligible speech in the left temporal lobe. Brain. 123:2400–2406.). A functional magnetic resonance imaging study in Cerebral Cortex used equivalent stimuli and univariate and multivariate analyses to argue for the greater importance of bilateral posterior when compared with the left anterior STS in responding to intelligible speech (Okada K, Rong F, Venezia J, Matchin W, Hsieh IH, Saberi K, Serences JT,Hickok G. 2010. Hierarchical organization of human auditory cortex: evidence from acoustic invariance in the response to intelligible speech. 20: 2486–2495.). Here, we also replicate our original study, demonstrating that the left anterior STS exhibits the strongest univariate response and, in decoding using the bilateral temporal cortex, contains the most informative voxels showing an increased response to intelligible speech. In contrast, in classifications using local “searchlights” and a whole brain analysis, we find greater classification accuracy in posterior rather than anterior temporal regions. Thus, we show that the precise nature of the multivariate analysis used will emphasize different response profiles associated with complex sound to speech processing.

PROBABILISTIC PREDICTION OF NEUROLOGICAL DISORDERS WITH A STATISTICAL ASSESSMENT OF NEUROIMAGING DATA MODALITIES

For many neurological disorders, prediction of disease state is an important clinical aim. Neuroimaging provides detailed information about brain structure and function from which such predictions may be statistically derived. A multinomial logit model with Gaussian process priors is proposed to: (i) predict disease state based on whole-brain neuroimaging data and (ii) analyze the relative informativeness of different image modalities and brain regions. Advanced Markov chain Monte Carlo methods are employed to perform posterior inference over the model. This paper reports a statistical assessment of multiple neuroimaging modalities applied to the discrimination of three Parkinsonian neurological disorders from one another and healthy controls, showing promising predictive performance of disease states when compared to nonprobabilistic classifiers based on multiple modalities. The statistical analysis also quantifies the relative importance of different neuroimaging measures and brain regions in discriminating between these diseases and suggests that for prediction there is little benefit in acquiring multiple neuroimaging sequences. Finally, the predictive capability of different brain regions is found to be in accordance with the regional pathology of the diseases as reported in the clinical literature.

Contributions of stimulus valence and arousal to visual activation during emotional perception

Neuroimaging experiments have revealed that the visual cortex is involved in the processing of affective stimuli: seeing emotional pictures leads to greater activation than seeing neutral ones. It is unclear, however, whether such differential activation is due to stimulus valence or whether the results are confounded by arousal level. In order to investigate the contributions of valence and arousal to visual activation, we created a new category of "interesting" stimuli designed to have high arousal, but neutral valence, and employed standard neutral, unpleasant, and pleasant picture categories. Arousal ratings for pleasant and neutral pictures were equivalent, as were valence ratings for interesting and neutral pictures. Differential activation for conditions matched for arousal (pleasant vs neutral) as well as matched for valence (interesting vs neutral) indicated that both stimulus valence and arousal contributed to visual activation.