Biology subjects

Scarpazza, C.

Publications and source records attributed to Scarpazza, C..

4 recordsLinked to original sources

bioRxiv2022

Cortical areas involved in grasping and reaching actions with and without visual information: an ALE meta-analysis of neuroimaging studies

The functional specialization of the ventral stream in Perception and the dorsal stream in Action is the cornerstone of the leading model proposed by Goodale and Milner in 1982. This model is based on neuropsychological evidence and has been a matter of debate for almost three decades, during which the dual-visual stream hypothesis has received much attention, including support and criticisms. The advent of functional magnetic resonance imaging (fMRI) has allowed investigating the brain areas involved in Perception and Action and provided useful data on the functional specialization of the two streams. Research on this topic has been quite prolific, yet very little attempt has been made so far to identify consistent neuroimaging results across the available literature. In particular, no meta-analysis has explored the spatial convergence in the involvement of the two streams in Action. The present meta-analysis (N=53) was designed to reveal the specific neural activations associated with Action (i.e., grasping and reaching movements), and the extent to which visual information affects the involvement of the two streams during motor control. Our results provide a comprehensive view of the consistent and spatially convergent neural correlates of Action based on neuroimaging studies conducted over the past two decades. We discuss our results in light of the well-established dual-visual stream model and frame these findings in the context of recent discoveries obtained with advanced fMRI methods, such as multivoxel pattern analysis.

neuroscience↗

bioRxiv2020

From ATOM to GradiATOM: Cortical gradients support time and space processing as revealed by a meta-analysis of neuroimaging studies

According to the ATOM (A Theory Of Magnitude), formulated by Walsh more than fifteen years ago, there is a general system of magnitude in the brain that comprises regions, such as the parietal cortex, shared by space, time and other magnitudes (Walsh, 2003). The present meta-analysis of neuroimaging studies used the Activation Likelihood Estimation (ALE) method in order to determine the set of regions commonly activated in space and time processing and to establish the neural activations specific to each magnitude domain. Following PRISMA guidelines, we included in the analysis a total of 112 and 114 experiments, exploring space and time processing, respectively. We clearly identified the presence of a system of brain regions commonly recruited in both space and time and that includes: bilateral insula, the pre-supplementary motor area (SMA), the right frontal operculum and the intraparietal sulci. These regions might be the best candidates to form the core magnitude neural system. Surprisingly, along each of these regions but the insula, ALE values progressed in a cortical gradient from time to space. The SMA exhibited an anterior-posterior gradient, with space activating more-anterior regions (i.e., pre-SMA) and time activating more-posterior regions (i.e., SMA-proper). Frontal and parietal regions showed a dorsal-ventral gradient: space is mediated by dorsal frontal and parietal regions, and time recruits ventral frontal and parietal regions. Our study supports but also expands the ATOM theory. Therefore, we here re-named it the GradiATOM theory (Gradient Theory of Magnitude), proposing that gradient organization can facilitate the transformations and integrations of magnitude representations by allowing space- and time-related neural populations to interact with each other over minimal distances.

neuroscience↗

bioRxiv2020

Normative modelling using deep autoencoders: a multi-cohort study on mild cognitive impairment and Alzheimer's disease

Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including regression, support vector machines and Gaussian process models. With the advance of deep learning technology, the use of deep neural networks has also been proposed. In this study, we assessed normative models based on deep autoencoders using structural neuroimaging data from patients with Alzheimers disease (n=206) and mild cognitive impairment (n=354). We first trained the autoencoder on an independent dataset (UK Biobank dataset) with 11,034 healthy controls. Then, we estimated how each patient deviated from this norm and established which brain regions were associated to this deviation. Finally, we compared the performance of our normative model against traditional classifiers. As expected, we found that patients exhibited deviations according to the severity of their clinical condition. The model identified medial temporal regions, including the hippocampus, and the ventricular system as critical regions for the calculation of the deviation score. Overall, the normative model had comparable cross-cohort generalizability to traditional classifiers. In order to promote open science, we are making all scripts and the trained models available to the wider research community.

neuroscience↗

bioRxiv2018

Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia related genes

Schizophrenia has been conceived as a disorder of brain connectivity but it is unclear how this network phenotype is related to the emerging genetics. We used morphometric similarity analysis of magnetic resonance imaging (MRI) data as a marker of inter-areal cortical connectivity in three prior case-control studies of psychosis: in total, N=185 cases and N=227 controls. Psychosis was associated with globally reduced morphometric similarity (MS) in all 3 studies. There was also a replicable pattern of case-control differences in regional MS which was significantly reduced in patients in frontal and temporal cortical areas, but increased in parietal cortex. Using prior brain-wide gene expression data, we found that the cortical map of case-control differences in MS was spatially correlated with cortical expression of a weighted combination of genes enriched for neu-robiologically relevant ontology terms and pathways. In addition, genes that were normally over-expressed in cortical areas with reduced MS were significantly up-regulated in a prior post mortem study of schizophrenia. We propose that this combination of neuroimaging and transcriptional data provides new insight into how previously implicated genes and proteins, as well as a number of unreported proteins in their vicinity on the protein interaction network, may interact to drive structural brain network changes in schizophrenia.

neuroscience↗