Superheat: An R package for creating beautiful and extendable heatmaps for visualizing complex data

The technological advancements of the modern era have enabled the collection of huge amounts of data in science and beyond. Extracting useful information from such massive datasets is an ongoing challenge as traditional data visualization tools typically do not scale well in high-dimensional settings. An existing visualization technique that is particularly well suited to visualizing large datasets is the heatmap. Although heatmaps are extremely popular in fields such as bioinformatics for visualizing large gene expression datasets, they remain a severely underutilized visualization tool in modern data analysis. In this paper we introduce superheat, a new R package that provides an extremely flexible and customizable platform for visualizing large datasets using extendable heatmaps. Superheat enhances the traditional heatmap by providing a platform to visualize a wide range of data types simultaneously, adding to the heatmap a response variable as a scatterplot, model results as boxplots, correlation information as barplots, text information, and more. Superheat allows the user to explore their data to greater depths and to take advantage of the heterogeneity present in the data to inform analysis decisions. The goal of this paper is two-fold: (1) to demonstrate the potential of the heatmap as a default visualization method for a wide range of data types using reproducible examples, and (2) to highlight the customizability and ease of implementation of the superheat package in R for creating beautiful and extendable heatmaps. The capabilities and fundamental applicability of the superheat package will be explored via three case studies, each based on publicly available data sources and accompanied by a file outlining the step-by-step analytic pipeline (with code).Comment: 26 pages, 10 figure

Topological inference for EEG and MEG

Neuroimaging produces data that are continuous in one or more dimensions. This calls for an inference framework that can handle data that approximate functions of space, for example, anatomical images, time--frequency maps and distributed source reconstructions of electromagnetic recordings over time. Statistical parametric mapping (SPM) is the standard framework for whole-brain inference in neuroimaging: SPM uses random field theory to furnish $p$-values that are adjusted to control family-wise error or false discovery rates, when making topological inferences over large volumes of space. Random field theory regards data as realizations of a continuous process in one or more dimensions. This contrasts with classical approaches like the Bonferroni correction, which consider images as collections of discrete samples with no continuity properties (i.e., the probabilistic behavior at one point in the image does not depend on other points). Here, we illustrate how random field theory can be applied to data that vary as a function of time, space or frequency. We emphasize how topological inference of this sort is invariant to the geometry of the manifolds on which data are sampled. This is particularly useful in electromagnetic studies that often deal with very smooth data on scalp or cortical meshes. This application illustrates the versatility and simplicity of random field theory and the seminal contributions of Keith Worsley (1951--2009), a key architect of topological inference.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS337 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

Data-driven HRF estimation for encoding and decoding models

International audienceDespite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI data. However, unconstrained estimation of the HRF can yield highly unstable results when the number of free parameters is large. We develop a method for the joint estimation of activation and HRF using a rank constraint causing the estimated HRF to be equal across events/conditions, yet permitting it to be different across voxels. Model estimation leads to an optimization problem that we propose to solve with an efficient quasi-Newton method exploiting fast gradient computations. This model, called GLM with Rank-1 constraint (R1-GLM), can be extended to the setting of GLM with separate designs which has been shown to improve decoding accuracy in brain activity decoding experiments. We compare 10 different HRF modeling methods in terms of encoding and decoding score in two different datasets. Our results show that the R1-GLM model significantly outperforms competing methods in both encoding and decoding settings, positioning it as an attractive method both from the points of view of accuracy and computational efficiency

Low-level spatiochromatic grouping for saliency estimation

We propose a saliency model termed SIM (saliency by induction mechanisms), which is based on a low-level spatiochromatic model that has successfully predicted chromatic induction phenomena. In so doing, we hypothesize that the low-level visual mechanisms that enhance or suppress image detail are also responsible for making some image regions more salient. Moreover, SIM adds geometrical grouplets to enhance complex low-level features such as corners, and suppress relatively simpler features such as edges. Since our model has been fitted on psychophysical chromatic induction data, it is largely nonparametric. SIM outperforms state-of-the-art methods in predicting eye fixations on two datasets and using two metrics

Distinct Cortical Pathways for Music and Speech Revealed by Hypothesis-Free Voxel Decomposition

The organization of human auditory cortex remains unresolved, due in part to the small stimulus sets common to fMRI studies and the overlap of neural populations within voxels. To address these challenges, we measured fMRI responses to 165 natural sounds and inferred canonical response profiles ("components") whose weighted combinations explained voxel responses throughout auditory cortex. This analysis revealed six components, each with interpretable response characteristics despite being unconstrained by prior functional hypotheses. Four components embodied selectivity for particular acoustic features (frequency, spectrotemporal modulation, pitch). Two others exhibited pronounced selectivity for music and speech, respectively, and were not explainable by standard acoustic features. Anatomically, music and speech selectivity concentrated in distinct regions of non-primary auditory cortex. However, music selectivity was weak in raw voxel responses, and its detection required a decomposition method. Voxel decomposition identifies primary dimensions of response variation across natural sounds, revealing distinct cortical pathways for music and speech.National Eye Institute (Grant EY13455

Population based spatio-temporal probabilistic modelling of fMRI data

High-dimensional functional magnetic resonance imaging (fMRI) data is characterized by complex spatial and temporal patterns related to neural activation. Mixture based Bayesian spatio-temporal modelling is able to extract spatiotemporal components representing distinct haemodyamic response and activation patterns. A recent development of such approach to fMRI data analysis is so-called spatially regularized mixture model of hidden process models (SMM-HPM). SMM-HPM can be used to reduce the four-dimensional fMRI data of a pre-determined region of interest (ROI) to a small number of spatio-temporal prototypes, sufficiently representing the spatio-temporal features of the underlying neural activation. Summary statistics derived from these features can be interpreted as quantification of (1) the spatial extent of sub-ROI activation patterns, (2) how fast the brain respond to external stimuli; and (3) the heterogeneity in single ROIs. This thesis aims to extend the single-subject SMM-HPM to a multi-subject SMM-HPM so that such features can be extracted at group-level, which would enable more robust conclusion to be drawn

Abstracts of the 2014 Brains, Minds, and Machines Summer School

A compilation of abstracts from the student projects of the 2014 Brains, Minds, and Machines Summer School, held at Woods Hole Marine Biological Lab, May 29 - June 12, 2014.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216