2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI)

This work presents two novel species-specific adaptations of a MRI based biomarker that indicates individual deviations from normal brain aging trajectories for rodents and non-human primates. By employing automatic, species-specific preprocessing of anatomical brain MRI as well as high-dimensional pattern recognition methods, this approach uses the distribution of healthy brain-aging patterns to ... View full abstract»

In this paper, we propose a fixed low-rank spatial filter estimation for brain computer interface (BCI) systems with an application that recognizes emotions elicited by movies. The proposed approach unifies such tasks as feature extraction, feature selection, and classification, which are often independently tackled in a "bottom-up" manner, under a regularized loss minimization problem. We explici... View full abstract»

We investigate the choice of tuning parameters for a Bayesian multi-level group lasso model developed for the joint analysis of neuroimaging and genetic data. The regression model we consider relates multivariate phenotypes consisting of brain summary measures (volumetric and cortical thickness values) to single nucleotide polymorphism (SNPs) data and imposes penalization at two nested levels, the... View full abstract»

Large-scale automated meta-analysis of neuroimaging data has recently established itself as an important tool in advancing our understanding of human brain function. This research has been pioneered by NeuroSynth, a database collecting both brain activation coordinates and associated text across a large cohort of neuroimaging research papers. One of the fundamental aspects of such meta-analysis is... View full abstract»

The most widely used classification techniques for whole brain image classification rely on kernel machines such as support vector machines and Gaussian processes, due to their computational efficiency, accurate prediction and suitability to tackle the combination of small sample sizes and high dimensionality that make neuroimaging data a challenging problem. Such methods generally make use of lin... View full abstract»

Causal inference concerns the identification of cause-effect relationships between variables. However, often only linear combinations of variables constitute meaningful causal variables. For example, recovering the signal of a cortical source from electroencephalography requires a well-tuned combination of signals recorded at multiple electrodes. We recently introduced the MERLiN (Mixture Effect R... View full abstract»

The author-topic (AT) model has been recently used to discover the relationships between brain regions, cognitive components and behavioral tasks in the human brain. In this work, we propose a novel Collapsed Variational Bayesian (CVB) inference algorithm for the AT model. The proposed algorithm is compared with the Expectation-Maximization (EM) algorithm on the large-scale BrainMap database of br... View full abstract»

This paper investigates a new measure of voxel importance based on analysing the sign consistency of voxels in an ensemble of linear SVM classifiers. The ensemble is endowed with a significant degree of diversity since the training set for each individual classifier is a random subsample of the initial training set. The importance of a voxel is proportional to the number of times that the voxel's ... View full abstract»

Network analysis based on anatomical covariance (cortical thickness) has been gaining increasing popularity in the last decade. However, there has not been a systematic study of the impact of nodal sizes and edge definitions on predictive performance among various network studies. In order to obtain a clear understanding of relative performance, there is a need for systematic comparison. In this s... View full abstract»

In neuroimaging data analysis, classification algorithms are frequently used to discriminate between two populations of interest, like patients and healthy controls, or between stimuli presented to the subject, like face and house. Usually, the ability of the classifier to discriminate populations is used within a statistical test, in order to evaluate scientific hypotheses. In the literature, dif... View full abstract»

We present an automated solution for detecting bad trials in magneto-/electroencephalography (M/EEG). Bad trials are commonly identified using peak-to-peak rejection thresholds that are set manually. This work proposes a solution to determine them automatically using cross-validation. We show that automatically selected rejection thresholds perform at par with manual thresholds, which can save hou... View full abstract»

Magnetoencephalography (MEG) and electroencephalography (EEG) source localization is a challenging illposed problem. To identify an appropriate solution out of an infinite set of possible candidates, the problem requires setting certain constraints depending on the assumptions or a priori knowledge about the source distribution. Different constraints have been proposed so far, including those that... View full abstract»

Non-invasive brain stimulation, such as transcranial alternating current stimulation (tACS) provides a powerful tool to directly modulate brain oscillations that mediate complex cognitive processes. While the body of evidence about the effect of tACS on behavioral and cognitive performance is constantly growing, those studies fail to address the importance of subjectspecific stimulation protocols.... View full abstract»

Network theory provides a principled abstraction of the human brain: reducing a complex system into a simpler representation from which to investigate brain organisation. Recent advancement in the neuroimaging field are towards representing brain connectivity as a dynamic process in order to gain a deeper understanding of the interplay between functional modules for efficient information transport... View full abstract»

Men and women differ in specific cognitive abilities and in the expression of several neuropsychiatric conditions. Such findings could be attributed to sex hormones, brain differences, as well as a number of environmental variables. Existing research on identifying sex-related differences in brain structure have predominantly used cross-sectional studies to investigate, for instance, differences i... View full abstract»

Visual processing of actions is supported by a network of brain regions in occipito-temporal, parietal, and premotor cortex in the primate brain, known as the Action Observation Network (AON). What remain unclear are the representational properties of each node of this network. In this study, we investigated the representational content of brain areas in AON using fMRI, representational similarity... View full abstract»

Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) have similar symptomatology and therefore it is difficult to distinguish among them, especially at early stage. Clinicians normally use different neuroimaging modalities to assist the diagnosis of these disorders, however obtaining an accurate diagnosis is still a challenge. In this work we analyzed a ... View full abstract»

Recent methods for diffusion weighted magnetic resonance convey information about tissue microstructure. In the last years, many models have been proposed for recovering the diffusion signal and extracting information to constitute new families of microstructural indices. Here we focus on three leading diffusion MRI models: NODDI (Neurite Orientation Dispersion and Density Imaging), 3D-SHORE (3D S... View full abstract»

Voxel-based lesion symptom mapping (VLSM) allows studying the relationship between stroke location and clinical outcome. The core idea of VLSM is to map all patient cases into a common atlas space and then apply statistical tests on a voxel level comparing outcome measures of patients with a lesion in the voxel to those without lesion. A major limitation of VLSM is that it requires a previous lesi... View full abstract»

Is there a common neural code underlying the representation of different quantity dimensions? In a high resolution fMRI protocol, we compared the activation evoked by the presentation of lines with different lengths, and sets of different numbers. We contrasted the results obtained with standard univariate analyses with a multivariate approach comparing the representational similarities within and... View full abstract»

Describing the changes of modularity in different clinical states can help to understand the alterations of the global processing mechanisms. In the current study we applied the modularity in a local scale. The method is based on measuring the decrease of the modularity caused by shifting a node from its own module to another module. On one hand local modularity measures the influence of a node in... View full abstract»

Connectomics is gaining increasing interest in the scientific and clinical communities. It consists in deriving models of structural or functional brain connections based on some local measures. Here we focus on structural connectivity as detected by diffusion MRI. Connectivity matrices are derived from microstructural indices obtained by the 3D-SHORE. Typically, graphs are derived from connectivi... View full abstract»