SpatialOne: end-to-end analysis of visium data at scale.

MOTIVATION: Spatial transcriptomics allow to quantify mRNA expression within the spatial context. Nonetheless, in-depth analysis of spatial transcriptomics data remains challenging and difficult to scale due to the number of methods and libraries required for that purpose. RESULTS: Here we present SpatialOne, an end-to-end pipeline designed to simplify the analysis of 10x Visium data by combining multiple state-of-the-art computational methods to segment, deconvolve, and quantify spatial information; this approach streamlines the analysis of reproducible spatial-data at scale. AVAILABILITY AND IMPLEMENTATION: SpatialOne source code and execution examples are available at https://github.com/Sanofi-Public/spatialone-pipeline, experimental data is available at https://zenodo.org/records/12605154. SpatialOne is distributed as a docker container image.

Synergistic interaction between amyloid and tau predicts the progression to dementia.

INTRODUCTION: Recent literature proposes that amyloid ß (Aß) and phosphorylated tau (p-tau) synergism accelerates biomarker abnormalities in controls. Yet, it remains to be answered whether this synergism is the driving force behind Alzheimer disease (AD) dementia. METHODS: We stratified 314 mild cognitive impairment individuals using [18F]florbetapir positron emission tomography Aß imaging and cerebrospinal fluid p-tau. Regression and voxel-based logistic regression models with interaction terms evaluated 2-year changes in cognition and clinical status as a function of baseline biomarkers. RESULTS: We found that the synergism between [18F]florbetapir and p-tau, rather than their additive effects, was associated with the cognitive decline and progression to AD. Furthermore, voxel-based analysis revealed that temporal and inferior parietal were the regions where the synergism determined an increased likelihood of developing AD. DISCUSSION: Together, the present results support that progression to AD dementia is driven by the synergistic rather than a mere additive effect between Aß and p-tau proteins.

VoxelStats: A MATLAB Package for Multi-Modal Voxel-Wise Brain Image Analysis.

In healthy individuals, behavioral outcomes are highly associated with the variability on brain regional structure or neurochemical phenotypes. Similarly, in the context of neurodegenerative conditions, neuroimaging reveals that cognitive decline is linked to the magnitude of atrophy, neurochemical declines, or concentrations of abnormal protein aggregates across brain regions. However, modeling the effects of multiple regional abnormalities as determinants of cognitive decline at the voxel level remains largely unexplored by multimodal imaging research, given the high computational cost of estimating regression models for every single voxel from various imaging modalities. VoxelStats is a voxel-wise computational framework to overcome these computational limitations and to perform statistical operations on multiple scalar variables and imaging modalities at the voxel level. VoxelStats package has been developed in Matlab(®) and supports imaging formats such as Nifti-1, ANALYZE, and MINC v2. Prebuilt functions in VoxelStats enable the user to perform voxel-wise general and generalized linear models and mixed effect models with multiple volumetric covariates. Importantly, VoxelStats can recognize scalar values or image volumes as response variables and can accommodate volumetric statistical covariates as well as their interaction effects with other variables. Furthermore, this package includes built-in functionality to perform voxel-wise receiver operating characteristic analysis and paired and unpaired group contrast analysis. Validation of VoxelStats was conducted by comparing the linear regression functionality with existing toolboxes such as glim_image and RMINC. The validation results were identical to existing methods and the additional functionality was demonstrated by generating feature case assessments (t-statistics, odds ratio, and true positive rate maps). In summary, VoxelStats expands the current methods for multimodal imaging analysis by allowing the estimation of advanced regional association metrics at the voxel level.

Double dissociation of error inhibition and correction deficits after basal ganglia or dorsomedial frontal damage in humans.

Effective self-control relies on the rapid adjustment of inappropriate responses. Understanding the brain basis of these processes has the potential to inform neurobiological models of the many neuropsychiatric disorders that are marked by maladaptive responding. Research on error processing in particular has implicated the dorsomedial frontal lobe (DMF) and basal ganglia (BG) in error detection, inhibition and correction. However there is controversy regarding the specific contributions of these regions to each of these component processes. Here we examined the effects of lesions affecting DMF or BG on these error-related processes. A flanker task was used to induce errors that in turn led to spontaneous, online corrections, while response kinematics were measured with high spatiotemporal resolution. The acceleration of errors was initially greater than that of correct responses. Errors then showed slower acceleration compared to correct responses, consistent with engagement of inhibition shortly after error response onset. BG damage disproportionately disrupted this early inhibitory phenomenon, above and beyond effects on baseline motor performance, but did not affect the kinematics of the corrective response. DMF damage showed the opposite pattern, with relatively delayed onset and weaker initial acceleration of the corrective response, but error suppression kinematics similar to that of the control group. This work clarifies the component processes and neural substrates of online post-error control, providing evidence for dissociable contributions of BG to error inhibition, but not correction, and DMF to rapid error correction, but not error suppression.

Common Effects of Amnestic Mild Cognitive Impairment on Resting-State Connectivity Across Four Independent Studies.

Resting-state functional connectivity is a promising biomarker for Alzheimer's disease. However, previous resting-state functional magnetic resonance imaging studies in Alzheimer's disease and amnestic mild cognitive impairment (aMCI) have shown limited reproducibility as they have had small sample sizes and substantial variation in study protocol. We sought to identify functional brain networks and connections that could consistently discriminate normal aging from aMCI despite variations in scanner manufacturer, imaging protocol, and diagnostic procedure. We therefore combined four datasets collected independently, including 112 healthy controls and 143 patients with aMCI. We systematically tested multiple brain connections for associations with aMCI using a weighted average routinely used in meta-analyses. The largest effects involved the superior medial frontal cortex (including the anterior cingulate), dorsomedial prefrontal cortex, striatum, and middle temporal lobe. Compared with controls, patients with aMCI exhibited significantly decreased connectivity between default mode network nodes and between regions of the cortico-striatal-thalamic loop. Despite the heterogeneity of methods among the four datasets, we identified common aMCI-related connectivity changes with small to medium effect sizes and sample size estimates recommending a minimum of 140 to upwards of 600 total subjects to achieve adequate statistical power in the context of a multisite study with 5-10 scanning sites and about 10 subjects per group and per site. If our findings can be replicated and associated with other established biomarkers of Alzheimer's disease (e.g., amyloid and tau quantification), then these functional connections may be promising candidate biomarkers for Alzheimer's disease.