A versatile software package for inter-subject correlation based analyses of fMRI

In the inter-subject correlation (ISC) based analysis of the functional magnetic resonance imaging (fMRI) data, the extent of shared processing across subjects during the experiment is determined by calculating correlation coefficients between the fMRI time series of the subjects in the corresponding brain locations. This implies that ISC can be used to analyze fMRI data without explicitly modeling the stimulus and thus ISC is a potential method to analyze fMRI data acquired under complex naturalistic stimuli. Despite of the suitability of ISC based approach to analyze complex fMRI data, no generic software tools have been made available for this purpose, limiting a widespread use of ISC based analysis techniques among neuroimaging community. In this paper, we present a graphical user interface (GUI) based software package, ISC Toolbox, implemented in Matlab for computing various ISC based analyses. Many advanced computations such as comparison of ISCs between different stimuli, time window ISC, and inter-subject phase synchronization are supported by the toolbox. The analyses are coupled with resampling based statistical inference. The ISC based analyses are data and computation intensive and the ISC toolbox is equipped with mechanisms to execute the parallel computations in a cluster environment automatically and with an automatic detection of the cluster environment in use. Currently, SGE-based (Oracle Grid Engine, Son of a Grid Engine, or Open Grid Scheduler) and Slurm environments are supported. In this paper, we present a detailed account on the methods behind the ISC Toolbox, the implementation of the toolbox and demonstrate the possible use of the toolbox by summarizing selected example applications. We also report the computation time experiments both using a single desktop computer and two grid environments demonstrating that parallelization effectively reduces the computing time.Peer reviewe

Differences in fMRI intersubject correlation while viewing unedited and edited videos of dance performance

Intersubject Correlation (ISC) analysis of fMRI data provides insight into how continuous streams of sensory stimulation are processed by groups of observers. Although edited movies are frequently used as stimuli in ISC studies, there has been little direct examination of the effect of edits on the resulting ISC maps. In this study we showed 16 observers two audiovisual movie versions of the same dance. In one experimental condition there was a continuous view from a single camera (Unedited condition) and in the other condition there were views from different cameras (Edited condition) that provided close up views of the feet or face and upper body. We computed ISC maps for each condition, as well as created a map that showed the difference between the conditions. The results from the Unedited and Edited maps largely overlapped in the occipital and temporal cortices, although more voxels were found for the Edited map. The difference map revealed greater ISC for the Edited condition in the Postcentral Gyrus, Lingual Gyrus, Precentral Gyrus and Medial Frontal Gyrus, while the Unedited condition showed greater ISC in only the Superior Temporal Gyrus. These findings suggest that the visual changes associated with editing provide a source of correlation in maps obtained from edited film, and highlight the utility of using maps to evaluate the difference in ISC between conditions

Outcome contingency selectively affects the neural coding of outcomes but not of tasks

Value-based decision-making is ubiquitous in every-day life, and critically depends on the contingency between choices and their outcomes. Only if outcomes are contingent on our choices can we make meaningful value-based decisions. Here, we investigate the effect of outcome contingency on the neural coding of rewards and tasks. Participants performed a reversal-learning paradigm in which reward outcomes were contingent on trial-by-trial choices, and performed a ‘free choice’ paradigm in which rewards were random and not contingent on choices. We hypothesized that contingent outcomes enhance the neural coding of rewards and tasks, which was tested using multivariate pattern analysis of fMRI data. Reward outcomes were encoded in a large network including the striatum, dmPFC and parietal cortex, and these representations were indeed amplified for contingent rewards. Tasks were encoded in the dmPFC at the time of decision-making, and in parietal cortex in a subsequent maintenance phase. We found no evidence for contingency-dependent modulations of task signals, demonstrating highly similar coding across contingency conditions. Our findings suggest selective effects of contingency on reward coding only, and further highlight the role of dmPFC and parietal cortex in value-based decision-making, as these were the only regions strongly involved in both reward and task coding

Hierarchical Multi-resolution Mesh Networks for Brain Decoding

We propose a new framework, called Hierarchical Multi-resolution Mesh Networks (HMMNs), which establishes a set of brain networks at multiple time resolutions of fMRI signal to represent the underlying cognitive process. The suggested framework, first, decomposes the fMRI signal into various frequency subbands using wavelet transforms. Then, a brain network, called mesh network, is formed at each subband by ensembling a set of local meshes. The locality around each anatomic region is defined with respect to a neighborhood system based on functional connectivity. The arc weights of a mesh are estimated by ridge regression formed among the average region time series. In the final step, the adjacency matrices of mesh networks obtained at different subbands are ensembled for brain decoding under a hierarchical learning architecture, called, fuzzy stacked generalization (FSG). Our results on Human Connectome Project task-fMRI dataset reflect that the suggested HMMN model can successfully discriminate tasks by extracting complementary information obtained from mesh arc weights of multiple subbands. We study the topological properties of the mesh networks at different resolutions using the network measures, namely, node degree, node strength, betweenness centrality and global efficiency; and investigate the connectivity of anatomic regions, during a cognitive task. We observe significant variations among the network topologies obtained for different subbands. We, also, analyze the diversity properties of classifier ensemble, trained by the mesh networks in multiple subbands and observe that the classifiers in the ensemble collaborate with each other to fuse the complementary information freed at each subband. We conclude that the fMRI data, recorded during a cognitive task, embed diverse information across the anatomic regions at each resolution.Comment: 18 page

Integrated Real-Time Control And Processing Systems For Multi-Channel Near-Infrared Spectroscopy Based Brain Computer Interfaces

This thesis outlines approaches to improve the signal processing and anal- ysis of Near-infrared spectroscopy (NIRS) based brain-computer interfaces (BCI). These approaches were developed in conjunction with the implemen- tation of a new customized exible multi-channel NIRS based BCI hardware system (Soraghan, 2010). Using a comparable functional imaging modality the assumptions on which NIRS-BCI have been reassessed, with regard to cognitive task selection, active area locations and lateralized motor cortex activation separability. This dissertation will also present methods that have been implemented to allow reduced hardware requirements in future NIRS-BCI development. We will also examine the sources of homeostatic physiological interference and present new approaches for analysis and at- tenuation within a real-time NIRS-BCI paradigm

Flexible multivariate hemodynamics fMRI data analyses and simulations with PyHRF

International audienceAs part of fMRI data analysis, the pyhrf package provides a set of tools for addressing the two 3 main issues involved in intra-subject fMRI data analysis: (i) the localization of cerebral regions 4 that elicit evoked activity and (ii) the estimation of the activation dynamics also referenced to 5 as the recovery of the Hemodynamic Response Function (HRF). To tackle these two problems, 6 pyhrf implements the Joint Detection-Estimation framework (JDE) which recovers parcel-level 7 HRFs and embeds an adaptive spatio-temporal regularization scheme of activation maps. With 8 respect to the sole detection issue (i), the classical voxelwise GLM procedure is also available 9 through nipy, whereas Finite Impulse Response (FIR) and temporally regularized FIR models 10 are implemented to deal with HRF estimation concerns (ii). Several parcellation tools are also 11 integrated such as spatial and functional clustering. Parcellations may be used for spatial 12 averaging prior to FIR/RFIR analysis or to specify the spatial support of the HRF estimates 13 in the JDE approach. These analysis procedures can be applied either to volumic data sets or 14 to data projected onto the cortical surface. For validation purpose, this package is shipped with 15 artificial and real fMRI data sets, which are used in this paper to compare the outcome of the 16 different available approaches. The artificial fMRI data generator is also described to illustrate 17 how to simulate different activation configurations, HRF shapes or nuisance components. To 18 cope with the high computational needs for inference, pyhrf handles distributing computing 19 by exploiting cluster units as well as multiple cores computers. Finally, a dedicated viewer is 20 presented, which handles n-dimensional images and provides suitable features to explore whole 21 brain hemodynamics (time series, maps, ROI mask overlay)