Dataset Information

The neuroimaging signal is a linear sum of neurally distinct stimulus- and task-related components.

ABSTRACT: Neuroimaging (for example, functional magnetic resonance imaging) signals are taken as a uniform proxy for local neural activity. By simultaneously recording electrode and neuroimaging (intrinsic optical imaging) signals in alert, task-engaged macaque visual cortex, we recently observed a large anticipatory trial-related neuroimaging signal that was poorly related to local spiking or field potentials. We used these same techniques to study the interactions of this trial-related signal with stimulus-evoked responses over the full range of stimulus intensities, including total darkness. We found that the two signals could be separated, and added linearly over this full range. The stimulus-evoked component was related linearly to local spiking and, consequently, could be used to obtain precise and reliable estimates of local neural activity. The trial-related signal likely has a distinct neural mechanism, however, and failure to account for it properly could lead to substantial errors when estimating local neural spiking from the neuroimaging signal.

SUBMITTER: Cardoso MM

PROVIDER: S-EPMC3690535 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The neuroimaging signal is a linear sum of neurally distinct stimulus- and task-related components.

Cardoso Mariana M B MM Sirotin Yevgeniy B YB Lima Bruss B Glushenkova Elena E Das Aniruddha A

Nature neuroscience 20120729 9

Neuroimaging (for example, functional magnetic resonance imaging) signals are taken as a uniform proxy for local neural activity. By simultaneously recording electrode and neuroimaging (intrinsic optical imaging) signals in alert, task-engaged macaque visual cortex, we recently observed a large anticipatory trial-related neuroimaging signal that was poorly related to local spiking or field potentials. We used these same techniques to study the interactions of this trial-related signal with stimu ...[more]

PMID: 22842146

Similar Datasets

Project description:Most connectivity metrics in neuroimaging research reduce multivariate activity patterns in regions-of-interests (ROIs) to one dimension, which leads to a loss of information. Importantly, it prevents us from investigating the transformations between patterns in different ROIs. Here, we applied linear estimation theory in order to robustly estimate the linear transformations between multivariate fMRI patterns with a cross-validated ridge regression approach. We used three functional connectivity metrics that describe different features of these voxel-by-voxel mappings: goodness-of-fit, sparsity and pattern deformation. The goodness-of-fit describes the degree to which the patterns in an input region can be described as a linear transformation of patterns in an output region. The sparsity metric, which relies on a Monte Carlo procedure, was introduced in order to test whether the transformation mostly consists of one-to-one mappings between voxels in different regions. Furthermore, we defined a metric for pattern deformation, i.e. the degree to which the transformation rotates or rescales the input patterns. As a proof of concept, we applied these metrics to an event-related fMRI data set consisting of four subjects that has been used in previous studies. We focused on the transformations from early visual cortex (EVC) to inferior temporal cortex (ITC), fusiform face area (FFA) and parahippocampal place area (PPA). Our results suggest that the estimated linear mappings explain a significant amount of response variance in the three output ROIs. The transformation from EVC to ITC shows the highest goodness-of-fit, and those from EVC to FFA and PPA show the expected preference for faces and places as well as animate and inanimate objects, respectively. The pattern transformations are sparse, but sparsity is lower than would have been expected for one-to-one mappings, thus suggesting the presence of one-to-few voxel mappings. The mappings are also characterised by different levels of pattern deformations, thus indicating that the transformations differentially amplify or dampen certain dimensions of the input patterns. While our results are only based on a small number of subjects, they show that our pattern transformation metrics can describe novel aspects of multivariate functional connectivity in neuroimaging data.

Dataset Information

The neuroimaging signal is a linear sum of neurally distinct stimulus- and task-related components.

Publications

The neuroimaging signal is a linear sum of neurally distinct stimulus- and task-related components.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets