Biology subjects

Olivetti, E.

Publications and source records attributed to Olivetti, E..

6 recordsLinked to original sources

bioRxiv2024

Surface-based probabilistic tractography uncovers segregated white matter pathways underlying spatial and non-spatial control

The frontal eye field (FEF) and the inferior frontal junction (IFJ) are prefrontal regions that mediate top-down functions, with mounting neuroimaging evidence suggesting that they specialize in controlling spatial versus non-spatial processing, respectively. We hypothesized that their unique patterns of structural connectivity underlie these specialized roles. To accurately infer the localization of FEF and IFJ in standard space, we performed an activation likelihood estimation meta-analysis of fMRI paradigms that reliably targeted these regions. Using surface-based probabilistic tractography methods at the individual subject level, we tracked streamlines ipsilaterally from the inferred FEF and IFJ activation peaks to the dorsal and ventral visual streams mapped on the native white matter surface parcellated using the multimodal Glasser atlas. By contrasting FEF and IFJ connectivity likelihoods, we found predominant structural connectivity from the FEF to regions of the dorsal visual stream compared to the IFJ (particularly in the left hemisphere), and conversely, predominant structural connectivity from the IFJ to regions of the ventral visual stream compared to the FEF bilaterally. Additionally, we analyzed the cortical terminations of the superior longitudinal fasciculus to the FEF and IFJ, implicating its first and third branches as segregated pathways mediating their communication to the posterior parietal cortex. The structural connectivity fingerprints of the FEF and IFJ support the view that the two visual stream architectures extend to the posterior lateral prefrontal cortex and provide converging anatomical evidence of their specialization in spatial versus non-spatial control.

neuroscience↗

bioRxiv2023

BundleWarp, streamline-based nonlinear registration of white matter tracts

Tractometry analysis represents a significant advancement in neuroimaging, offering a detailed examination of the brains white matter at a micro level. Unlike traditional ROI or voxel-based methods, tractometry precisely reconstructs and characterizes white matter tracts. Using advanced diffusion MRI and tractography algorithms, it maps the trajectory, shape, and connectivity patterns of individual white matter bundles. Accurate alignment of these tracts across different groups is crucial for reliable and reproducible results. Nonlinear registration techniques are essential for achieving this alignment, harmonizing bundle shapes, and improving sensitivity to disease-related changes. However, nonlinear registration is complex, especially with tractography data, which digitally represents the brains white matter anatomy. Potential structural changes in the bundles shape during registration can lead to artifacts that obscure critical anatomical details needed for disease identification. We introduce BundleWarp, a streamline-based nonlinear deformable registration method designed specifically for white matter tracts. BundleWarp employs a sophisticated approach to align two white matter bundles while preserving their topological and anatomical features. It is formulated as a probability density estimation problem with motion coherence penalties, ensuring coherent movement of points along streamlines and maintaining the anatomical integrity of tracts through displacement field regularization. Additionally, we introduce a tract morphometry framework utilizing the displacement field generated by BundleWarp to analyze white matter tract shape differences. Our results show that BundleWarp effectively quantifies bundle shape differences and enhances structural harmonization in tractometry analysis for diverse subjects, including those with Alzheimers and Parkinsons disease. Test-retest experiments further demonstrate that BundleWarp substantially improves subject finger-printing by increasing within-subject reproducibility of both bundle shape and microstructural profiles (FA, MD, RD, AD). It precisely maps the brains neuronal pathways, offering a robust tractometry framework with enhanced sensitivity for detecting disease-related structural and microstructural changes in white matter tracts associated with Mild Cognitive Impairment (MCI), dementia, and early-stage Alzheimers biomarkers, including amyloid-beta plaques and tau neurofibrillary tangles.

neuroscience↗

bioRxiv2022

How Does White Matter Registration Affect Tractography Alignment?

Tractography is a powerful method to represent the structural connectivity of the brain white matter. Nevertheless, the comparison of these data structures between two individuals is still an open challenge because of their complexity, e.g. digital representation of millions of fibers as polylines. The scientific community spent a meaningful effort to develop new methods of white matter registration aiming to take advantage of diffusion MRI models. Despite the effort to improve the registration of the white matter, little is known about the effect of the registration on tractogram alignment. The main issue for an empirical evaluation is the lack of ground truth, e.g. a sample of data where the correct alignment is validated by experts. This work aims to overcome this drawback by proposing an evaluation framework based on the matching of homologous fiber structures, e.g. known neuroanatomical bundles. The contribution is a quantitative comparison of how the most representative methods of white matter registration affect tractogram alignment.

neuroscience↗

bioRxiv2022

Covariance-based Decoding Reveals Content-specific Feature Integration and Top-down Processing for Imagined Faces versus Places

When we internally generate mental images, we need to combine multiple features into a whole. Direct evidence for such feature integration during visual imagery is still lacking. Moreover, cognitive control mechanisms, including memory and attention, exert top-down influences on the perceptual system during mental images generation. However, it is unclear whether such top-down processing is content-specific or not. Feature integration and top-down processing involve short-range connectivity within visual areas, and long-range connectivity between control and visual areas, respectively. Here, we used a minimally constrained experimental paradigm wherein imagery categories were prompted using visual word cues only, and we decoded face versus place imagery based on their underlying connectivity patterns. Our results show that face and place imagery can be decoded from both short-range and long-range connections. These findings suggest that feature integration does not require an external stimulus but occurs also for purely internally generated images. Furthermore, control and visual areas exchange information specifically tailored to imagery content. TeaserDecoding visual imagery from brain connectivity reveals a content-specific interconnected neural code for internal image generation.

neuroscience↗

bioRxiv2022

Accurate localization and coactivation profiles of the FEF and IFJ: an ALE and MACM fMRI meta-analysis

The frontal eye field (FEF) and the inferior frontal junction (IFJ) are prefrontal structures involved in mediating multiple aspects of goal-driven behavior. Despite being recognized as prominent nodes of the networks underlying spatial attention and oculomotor control, and working memory and cognitive control, respectively, the limited quantitative evidence on their precise localization has considerably impeded the detailed understanding of their structure and connectivity. In this study, we performed an activation likelihood estimation (ALE) fMRI meta-analysis by selecting studies that employed standard paradigms to accurately infer the localization of these regions in stereotaxic space. For the FEF, we found the highest spatial convergence of activations for prosaccades and antisaccades paradigms at the junction of the precentral sulcus and superior frontal sulcus. For the IFJ, we found consistent activations across oddball/attention, working memory, Stroop, and task-switching paradigms at the junction of the inferior precentral sulcus and inferior frontal sulcus. We related these clusters to previous meta-analyses, sulcal/gyral neuroanatomy, and a recent comprehensive brain parcellation, highlighting important differences compared to their results and taxonomy. Finally, we employed the ALE peak coordinates as seeds to perform a meta-analytic connectivity modeling (MACM) analysis, which revealed systematic coactivation patterns spanning the frontal, parietal and temporal cortices. We decoded the behavioral domains associated with these coactivations, suggesting that these may allow FEF and IFJ to support their specialized roles in flexible behavior. Our study provides meta-analytic groundwork for investigating the relationship between functional specialization and connectivity of two crucial control structures of the prefrontal cortex.

neuroscience↗

bioRxiv2020

Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation

Virtual delineation of white matter bundles in the human brain is of paramount importance for multiple applications, such as pre-surgical planning and connectomics. A substantial body of literature is related to methods that automatically segment bundles from diffusion Magnetic Resonance Imaging (dMRI) data indirectly, by exploiting either the idea of connectivity between regions or the geometry of fiber paths obtained with tractography techniques, or, directly, through the information in volumetric data. Despite the remarkable improvement in automatic segmentation methods over the years, their segmentation quality is not yet satisfactory, especially when dealing with datasets with very diverse characteristics, such as different tracking methods, bundle sizes or data quality. In this work, we propose a novel, supervised streamline-based segmentation method, called Classifyber, which combines information from atlases, connectivity patterns, and the geometry of fiber paths into a simple linear model. With a wide range of experiments on multiple datasets that span from research to clinical domains, we show that Classifyber substantially improves the quality of segmentation as compared to other state-of-the-art methods and, more importantly, that it is robust across very diverse settings. We provide an implementation of the proposed method as open source code, as well as web service.

neuroscience↗