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Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark is implemented in a fully reproducible framework, where the provided research objects enable readers to reproduce or modify core computations, as well as the figures of the article using the Jupyter Book project and the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep software package. The majority of benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing however disrupts the continuous sampling of brain images and is incompatible with some statistical analyses, e.g. auto-regressive modeling. In this case, a simple strategy using motion parameters, average activity in select brain compartments, and global signal regression should be preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods. Our reproducible benchmark infrastructure will facilitate such continuous evaluation in the future, and may also be applied broadly to different tools or even research fields.

Item does not contain fulltext OBJECTIVE: The heterogeneous human frontal pole has been identified as a node in the dysfunctional network of major depressive disorder. The contribution of the medial (socio-affective) versus lateral (cognitive) frontal pole to major depression pathogenesis is currently unclear. The authors performed morphometric comparison of the microstructurally informed subdivisions of human frontal pole between depressed patients and comparison subjects using both uni- and multivariate statistics. METHOD: Multisite voxel- and region-based morphometric MRI analysis was conducted in 73 depressed patients and 73 matched comparison subjects without psychiatric history. Frontal pole volume was first compared between depressed patients and comparison subjects by subdivision-wise classical morphometric analysis. In a second approach, frontal pole volume was compared by subdivision-naive multivariate searchlight analysis based on support vector machines. RESULTS: Subdivision-wise morphometric analysis found a significantly smaller medial frontal pole in depressed patients, with a negative correlation of disease severity and duration. Histologically uninformed multivariate voxel-wise statistics provided converging evidence for structural aberrations specific to the microstructurally defined medial area of the frontal pole in depressed patients. CONCLUSIONS: Across disparate methods, subregion specificity in the left medial frontal pole volume in depressed patients was demonstrated. Indeed, the frontal pole was shown to structurally and functionally connect to other key regions in major depression pathology, such as the anterior cingulate cortex and the amygdala via the uncinate fasciculus. Present and previous findings consolidate the left medial portion of the frontal pole as particularly altered in major depression.

Item does not contain fulltext Social cognition and language are two core features of the human species. Despite distributed recruitment of brain regions in each mental capacity, the left parietal lobe (LPL) represents a zone of topographical convergence. This study quantitatively summarizes previous neuroimaging studies on social cognition and language. Using connectivity-based parcellation on a meta-analytically defined volume of interest (VOI), regional coactivation patterns within this VOI allowed for the identiHcation of functionally distinct subregions. Across parcellation solutions, two clusters emerged consistently in rostro-ventral and caudo-ventral aspects of the parietal VOI. Both clusters were functionally signiHcantly associated with social-cognitive and language processing. In particular, the rostra-ventral cluster was associated with lower-level processing facets, while the caudo-ventral cluster was associated with higher-level processes across both functions. Contrarily, in the (less stable) dorsal parietal VOI, all clusters reKected computation of general-purpose processes, including working memory and matching tasks, that are frequently co-recruited by social or language processes. Our results hence favor a rostro-caudal distinction of low- vs. high-level processes underlying social cognition and language in the left inferior parietal lobe. 16 p.