Dataset Information

Stable between-subject statistical inference from unstable within-subject functional connectivity estimates.

ABSTRACT: Spatial or temporal aspects of neural organization are known to be important indices of how cognition is organized. However, measurements and estimations are often noisy and many of the algorithms used are probabilistic, which in combination have been argued to limit studies exploring the neural basis of specific aspects of cognition. Focusing on static and dynamic functional connectivity estimations, we propose to leverage this variability to improve statistical efficiency in relating these estimations to behavior. To achieve this goal, we use a procedure based on permutation testing that provides a way of combining the results from many individual tests that refer to the same hypothesis. This is needed when testing a measure whose value is obtained from a noisy process, which can be repeated multiple times, referred to as replications. Focusing on functional connectivity, this noisy process can be: (a) computational, for example, when using an approximate inference algorithm for which different runs can produce different results or (b) observational, if we have the capacity to acquire data multiple times, and the different acquired data sets can be considered noisy examples of some underlying truth. In both cases, we are not interested in the individual replications but on the unobserved process generating each replication. In this note, we show how results can be combined instead of choosing just one of the estimated models. Using both simulations and real data, we show the benefits of this approach in practice.

SUBMITTER: Vidaurre D

PROVIDER: S-EPMC6492297 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Bidirectional integration between sensory stimuli and contextual framing is fundamental to action control. Stimuli may entail context-dependent actions, while temporal or spatial characteristics of a stimulus train may establish a contextual framework for upcoming stimuli. Here we aimed at identifying core areas for stimulus-context integration and delineated their functional connectivity (FC) using meta-analytic connectivity modeling (MACM) and analysis of resting-state networks. In a multi-study conjunction, consistently increased activity under higher demands on stimulus-context integration was predominantly found in the right temporo-parietal junction (TPJ), which represented the largest cluster of overlap and was thus used as the seed for the FC analyses. The conjunction between task-dependent (MACM) and task-free (resting state) FC of the right TPJ revealed a shared network comprising bilaterally inferior parietal and frontal cortices, anterior insula, premotor cortex, putamen and cerebellum, i.e., a 'ventral' action/attention network. Stronger task-dependent (vs. task-free) connectivity was observed with the pre-SMA, dorsal premotor cortex, intraparietal sulcus, basal ganglia and primary sensori motor cortex, while stronger resting-state (vs. task-dependent) connectivity was found with the dorsolateral prefrontal and medial parietal cortex. Our data provide strong evidence that the right TPJ may represent a key region for the integration of sensory stimuli and contextual frames in action control. Task-dependent associations with regions related to stimulus processing and motor responses indicate that the right TPJ may integrate 'collaterals' of sensory processing and apply (ensuing) contextual frames, most likely via modulation of preparatory loops. Given the pattern of resting-state connectivity, internal states and goal representations may provide the substrates for the contextual integration within the TPJ in the absence of a specific task.

Project description:Functional magnetic resonance imaging (fMRI) is increasingly used to characterize functional connectivity between brain regions. Given the vast number of between-voxel interactions in high-dimensional fMRI data, it is an ongoing challenge to detect stable and generalizable functional connectivity in the brain among groups of subjects. Component models can be used to define subspace representations of functional connectivity that are more interpretable. It is, however, unclear which component model provides the optimal representation of functional networks for multi-subject fMRI datasets. A flexible cross-validation approach that assesses the ability of the models to predict voxel-wise covariance in new data, using three different measures of generalization was proposed. This framework is used to compare a range of component models with varying degrees of flexibility in their representation of functional connectivity, evaluated on both simulated and experimental resting-state fMRI data. It was demonstrated that highly flexible subject-specific component subspaces, as well as very constrained average models, are poor predictors of whole-brain functional connectivity, whereas the best-generalizing models account for subject variability within a common spatial subspace. Within this set of models, spatial Independent Component Analysis (sICA) on concatenated data provides more interpretable brain patterns, whereas a consistent-covariance model that accounts for subject-specific network scaling (PARAFAC2) provides greater stability in functional connectivity relationships between components and their spatial representations. The proposed evaluation framework is a promising quantitative approach to evaluating component models, and reveals important differences between subspace models in terms of predictability, robustness, characterization of subject variability, and interpretability of the model parameters. Hum Brain Mapp 38:882-899, 2017. © 2016 Wiley Periodicals, Inc.

Dataset Information

Stable between-subject statistical inference from unstable within-subject functional connectivity estimates.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets