Biology subjects

Finos, L.

Publications and source records attributed to Finos, L..

3 recordsLinked to original sources

bioRxiv2023

lmeEEG: Mass linear mixed-effects modeling of EEG data with crossed random effects

BackgroundMixed-effects models are the current standard for the analysis of behavioral studies in psycholinguistics and related fields, given their ability to simultaneously model crossed random effects for subjects and items. However, they are hardly applied in neuroimaging and psychophysiology, where the use of mass univariate analyses in combination with permutation testing would be too computationally demanding to be practicable with mixed models. New methodHere, we propose and validate an analytical strategy that enables the use of linear mixed models (LMM) with crossed random intercepts in mass univariate analyses of EEG data (lmeEEG). It avoids the unfeasible computational costs that would arise from massive permutation testing with LMM using a simple solution: removing random-effects contributions from EEG data and performing mass univariate linear analysis and permutations on the obtained marginal EEG. ResultslmeEEG showed excellent performance properties in terms of power and false positive rate. Comparison with existing methodslmeEEG overcomes the computational costs of standard available approaches (our method was indeed more than 300 times faster). ConclusionslmeEEG allows researchers to use mixed models with EEG mass univariate analyses. Thanks to the possibility offered by the method described here, we anticipate that LMM will become increasingly important in neuroscience. Data and codes are available at osf.io/kw87a. The codes and a tutorial are also available at github.com/antovis86/lmeEEG.

neuroscience↗

bioRxiv2021

Body representation plasticity is altered in Developmental Coordination Disorder

Developmental Coordination Disorder (DCD) is a pathological condition characterized by impaired motor skills. Current theories advance that a deficit of the internal models is mainly responsible for DCD childrens altered behavior. Yet, accurate movement execution requires not only correct movement planning, but also integration of sensory feedback into body representation for action (Body Schema) to update the state of the body. Here we advance and test the hypothesis that the plasticity of this body representation is altered in DCD. To probe Body Schema (BS) plasticity, we submitted a well-established tool-use paradigm to seventeen DCD children, required to reach for an object with their hand before and after tool use, and compared their movement kinematics to that of a control group of Typically Developing (TD) peers. We also asked both groups to provide explicit estimates of their arm length to probe plasticity of their Body Image (BI). Results revealed that DCD children explicitly judged their arm shorter after tool use, showing changes in their BI comparable to their TD peers. Unlike them, though, DCD did not update their implicit BS estimate: kinematics showed that tool use affected their peak amplitudes, but not their latencies. Remarkably, the kinematics of tool use showed that the motor control of the tool was comparable between groups, both improving with practice, confirming that motor learning abilities are preserved in DCD. This study thus brings evidence in favor of an alternative theoretical account of the DCD etiology. Our findings point to a deficit in the plasticity of the body representation used to plan and execute movements. Though not mutually exclusive, this widens the theoretical perspective under which DCD should be considered: DCD may not be limited to a problem affecting the internal models and their motor functions, but may concern the state of the effector they have to use.

neuroscience↗

bioRxiv2017

All-Resolutions Inference for Brain Imaging

The most prevalent approach to activation localization in neuroimaging is to identify brain regions as contiguous supra-threshold clusters, check their significance using random field theory, and correct for the multiple clusters being tested. Besides recent criticism on the validity of the random field assumption, a spatial specificity paradox remains: the larger the detected cluster, the less we know about the location of activation within that cluster. This is because cluster inference implies \"there exists at least one voxel with an evoked response in the cluster\", and not that \"all the voxels in the cluster have an evoked response\". Inference on voxels within selected clusters is considered bad practice, due to the voxel-wise false positive rate inflation associated with this circular inference. Here, we propose a remedy to the spatial specificity paradox. By applying recent results from the multiple testing statistical literature, we are able to quantify the proportion of truly active voxels within selected clusters, an approach we call All-Resolutions Inference (ARI). If this proportion is high, the paradox vanishes. If it is low, we can further \"drill down\" from the cluster level to sub-regions, and even to individual voxels, in order to pinpoint the origin of the activation. In fact, ARI allows inference on the proportion of activation in all voxel sets, no matter how large or small, however these have been selected, all from the same data. We use two fMRI datasets to demonstrate the non-triviality of the spatial specificity paradox, and its resolution using ARI. One of these datasets is large enough for us to split it and validate the ARI estimates. The conservatism of ARI inference permits circularity without losing error guarantees, while still returning informative estimates.

neuroscience↗