Alexithymia refers to deficiencies in identifying and expressing emotions. This might be related to changes in structural brain volumes, but its neuroanatomical basis remains uncertain as studies have shown heterogeneous findings. Therefore, we conducted a parametric coordinate-based meta-analysis. We identified seventeen structural neuroimaging studies (including a total of 2586 individuals with different levels of alexithymia) investigating the association between gray matter volume and alexithymia. Volumes of the left insula, left amygdala, orbital frontal cortex and striatum were consistently smaller in people with high levels of alexithymia. These areas are important for emotion perception and emotional experience. Smaller volumes in these areas might lead to deficiencies in appropriately identifying and expressing emotions. These findings provide the first quantitative integration of results pertaining to the structural neuroanatomical basis of alexithymia.
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http://dx.doi.org/10.1016/j.neubiorev.2018.01.004 | DOI Listing |
Sci Rep
October 2024
ISAE Supaero (DAEP), Toulouse, France.
This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains, based on Implicit Neural Representations (INRs). The proposed models can be applied directly to unstructured domains for different flow conditions, handle non-parametric 3D geometric variations, and generalize to unseen shapes at test time. The coordinate-based formulation naturally leads to robustness with respect to discretization, allowing an excellent trade-off between computational cost (memory footprint and training time) and accuracy.
View Article and Find Full Text PDFNeurosci Biobehav Rev
September 2020
Department of Rehabilitation, Lentis Mental Health Care, PO box 128, 9470 KA, Zuidlaren, the Netherlands; Department of Clinical and Developmental Neuropsychology, University of Groningen, Grote Kruisstraat 2/1, 9712 TS, Groningen, the Netherlands.
In the past years, ample interest in brain abnormalities related to clinical and cognitive insight in psychosis has contributed several neuroimaging studies to the literature. In the current study, published findings on the neural substrates of clinical and cognitive insight in psychosis are integrated by performing a systematic review and meta-analysis. Coordinate-based meta-analyses were performed with the parametric coordinate-based meta-analysis approach, non-coordinate based meta-analyses were conducted with the metafor package in R.
View Article and Find Full Text PDFFront Neurosci
October 2019
NeuroMI, Milan Centre for Neuroscience, Milan, Italy.
In this paper we describe and validate a new coordinate-based method for meta-analysis of neuroimaging data based on an optimized hierarchical clustering algorithm: CluB (Clustering the Brain). The CluB toolbox permits both to extract a set of spatially coherent clusters of activations from a database of stereotactic coordinates, and to explore each single cluster of activation for its composition according to the cognitive dimensions of interest. This last step, called "cluster composition analysis," permits to explore neurocognitive effects by adopting a factorial-design logic and by testing the working hypotheses using either asymptotic tests, or exact tests either in a classic inference, or in a Bayesian-like context.
View Article and Find Full Text PDFNeurosci Biobehav Rev
April 2018
Shenzhen Key Laboratory of Affective and Social Neuroscience, Shenzhen University, Shenzhen, China; Department of Neuroscience, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Psychology, University of Groningen, The Netherlands.
Hum Brain Mapp
April 2016
Division of Psychiatry, the University of Edinburgh, Edinburgh, United Kingdom.
Objective: Several neuroimaging meta-analyses have summarized structural brain changes in major depression using coordinate-based methods. These methods might be biased toward brain regions where significant differences were found in the original studies. In this study, a novel voxel-based technique is implemented that estimates and meta-analyses between-group differences in grey matter from individual MRI studies, which are then applied to the study of major depression.
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