Mapping hybrid functional-structural connectivity traits in the human connectome

One of the crucial questions in neuroscience is how a rich functional repertoire of brain states relates to its underlying structural organization. How to study the associations between these structural and functional layers is an open problem that involves novel conceptual ways of tackling this question. We here propose an extension of the Connectivity Independent Component Analysis (connICA) framework, to identify joint structural-functional connectivity traits. Here, we extend connICA to integrate structural and functional connectomes by merging them into common hybrid connectivity patterns that represent the connectivity fingerprint of a subject. We test this extended approach on the 100 unrelated subjects from the Human Connectome Project. The method is able to extract main independent structural-functional connectivity patterns from the entire cohort that are sensitive to the realization of different tasks. The hybrid connICA extracted two main task-sensitive hybrid traits. The first, encompassing the within and between connections of dorsal attentional and visual areas, as well as fronto-parietal circuits. The second, mainly encompassing the connectivity between visual, attentional, DMN and subcortical networks. Overall, these findings confirms the potential ofthe hybrid connICA for the compression of structural/functional connectomes into integrated patterns from a set of individual brain networks.Comment: article: 34 pages, 4 figures; supplementary material: 5 pages, 5 figure

Dynamic Construction of Stimulus Values in the Ventromedial Prefrontal Cortex

Signals representing the value assigned to stimuli at the time of choice have been repeatedly observed in ventromedial prefrontal cortex (vmPFC). Yet it remains unknown how these value representations are computed from sensory and memory representations in more posterior brain regions. We used electroencephalography (EEG) while subjects evaluated appetitive and aversive food items to study how event-related responses modulated by stimulus value evolve over time. We found that value-related activity shifted from posterior to anterior, and from parietal to central to frontal sensors, across three major time windows after stimulus onset: 150–250 ms, 400–550 ms, and 700–800 ms. Exploratory localization of the EEG signal revealed a shifting network of activity moving from sensory and memory structures to areas associated with value coding, with stimulus value activity localized to vmPFC only from 400 ms onwards. Consistent with these results, functional connectivity analyses also showed a causal flow of information from temporal cortex to vmPFC. Thus, although value signals are present as early as 150 ms after stimulus onset, the value signals in vmPFC appear relatively late in the choice process, and seem to reflect the integration of incoming information from sensory and memory related regions

Persistent Homology in Sparse Regression and its Application to Brain Morphometry

Sparse systems are usually parameterized by a tuning parameter that determines the sparsity of the system. How to choose the right tuning parameter is a fundamental and difficult problem in learning the sparse system. In this paper, by treating the the tuning parameter as an additional dimension, persistent homological structures over the parameter space is introduced and explored. The structures are then further exploited in speeding up the computation using the proposed soft-thresholding technique. The topological structures are further used as multivariate features in the tensor-based morphometry (TBM) in characterizing white matter alterations in children who have experienced severe early life stress and maltreatment. These analyses reveal that stress-exposed children exhibit more diffuse anatomical organization across the whole white matter region.Comment: submitted to IEEE Transactions on Medical Imagin

Search for patterns of functional specificity in the brain: A nonparametric hierarchical Bayesian model for group fMRI data

Functional MRI studies have uncovered a number of brain areas that demonstrate highly specific functional patterns. In the case of visual object recognition, small, focal regions have been characterized with selectivity for visual categories such as human faces. In this paper, we develop an algorithm that automatically learns patterns of functional specificity from fMRI data in a group of subjects. The method does not require spatial alignment of functional images from different subjects. The algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary activation variable. At the next level, we define a prior over sets of activation variables in all subjects. We use a Hierarchical Dirichlet Process as the prior in order to learn the patterns of functional specificity shared across the group, which we call functional systems, and estimate the number of these systems. Inference based on our model enables automatic discovery and characterization of dominant and consistent functional systems. We apply the method to data from a visual fMRI study comprised of 69 distinct stimulus images. The discovered system activation profiles correspond to selectivity for a number of image categories such as faces, bodies, and scenes. Among systems found by our method, we identify new areas that are deactivated by face stimuli. In empirical comparisons with previously proposed exploratory methods, our results appear superior in capturing the structure in the space of visual categories of stimuli.McGovern Institute for Brain Research at MIT. Neurotechnology (MINT) ProgramNational Institutes of Health (U.S.) (Grant NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (Grant NCRR NAC P41-RR13218)National Eye Institute (Grant 13455)National Science Foundation (U.S.) (CAREER Grant 0642971)National Science Foundation (U.S.) (Grant IIS/CRCNS 0904625)Harvard University--MIT Division of Health Sciences and Technology (Catalyst Grant)American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowshi

Group-level impacts of within- and between-subject hemodynamic variability in fMRI

International audienceInter-subject fMRI analyses have specific issues regarding the reliability of the results concerning both the detection of brain activation patterns and the estimation of the underlying dynamics. Among these issues lies the variability of the hemodynamic response function (HRF), that is usually accounted for using functional basis sets in the general linear model context. Here, we use the joint detection-estimation approach (JDE) (Makni et al., 2008; Vincent et al., 2010) which combines regional nonparametric HRF inference with spatially adaptive regularization of activation clusters to avoid global smoothing of fMRI images. We show that the JDE-based inference brings a significant improvement in statistical sensitivity for detecting evoked activity in parietal regions. In contrast, the canonical HRF associated with spatially adaptive regularization is more sensitive in other regions, such as motor cortex. This different regional behavior is shown to reflect a larger discrepancy of HRF with the canonical model. By varying parallel imaging acceleration factor, SNR-specific region-based hemodynamic parameters (activation delay and duration) were extracted from the JDE inference. Complementary analyses highlighted their significant departure from the canonical parameters and the strongest between-subject variability that occurs in the parietal region, irrespective of the SNR value. Finally, statistical evidence that the fluctuation of the HRF shape is responsible for the significant change in activation detection performance is demonstrated using paired t-tests between hemodynamic parameters inferred by GLM and JDE

Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach.

Functional magnetic resonance imaging (fMRI) is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a general linear model (GLM) approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power, and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making

A New Approach to Spatial Covariance Modeling of Functional Brain Imaging Data: Ordinal Trend Analysis

In neuroimaging studies of human cognitive abilities, brain activation patterns that include regions that are strongly interactive in response to experimental task demands are of particular interest. Among the existing network analyses, partial least squares (PLS; McIntosh, 1999; McIntosh, Bookstein, Haxby, & Grady, 1996) has been highly successful, particularly in identifying group differences in regional functional connectivity, including differences as diverse as those associated with states of awareness and normal aging. However, we address the need for a within-group model that identifies patterns of regional functional connectivity that exhibit sustained activity across graduated changes in task parameters. For example, predictions of sustained connectivity are commonplace in studies of cognition that involve a series of tasks over which task difficulty increases (Baddeley, 2003). We designed ordinal trend analysis (OrT) to identify activation patterns that increase monotonically in their expression as the experimental task parameter increases, while the correlative relationships between brain regions remain constant. Of specific interest are patterns that express positive ordinal trends on a subject-by-subject basis. A unique feature of OrT is that it recovers information about functional connectivity based solely on experimental design variables. In particular, there is no requirement by OrT to provide either a quantitative model of the uncertain relationship between functional brain circuitry and subject variables (e.g., task performance and IQ) or partial information about the regions that are functionally connected. In this letter, we provide a step-by-step recipe of the computations performed in the new OrT analysis, including a description of the inferential statistical methods applied. Second, we describe applications of OrT to an event-related fMRI study of verbal working memory and H2 15 O-PET study of visuomotor learning. In sum, OrT has potential applications to not only studies of young adults and their cognitive abilities, but also studies of normal aging and neurological and psychiatric disease