Examining the performance of trend surface models for inference on Functional Magnetic Resonance Imaging (fMRI) data

The current predominant approach to neuroimaging data analysis is to use voxels as units of computation in a mass univariate approach which does not appropriately account for the existing spatial correlation and is plagued by problems of multiple comparisons. Therefore, there is a need to explore alternative approaches for inference on neuroimaging data that accurately model spatial autocorrelation, potentially providing better type I error control and more sensitive inference. In this project we examine the performance of a trend surface modeling (TSM) approach that is based on a biologically relevant parcellation of the brain. We present our results from applying the TSM to both task fMRI and resting-state fMRI and compare the latter to the results from the parametric software, FSL. We demonstrate that the TSM provides better Type I error control, as well as sensitive inference on task data.The current predominant approach to neuroimaging data analysis is to use voxels as units of computation in a mass univariate approach which does not appropriately account for the existing spatial correlation and is plagued by problems of multiple comparisons. Therefore, there is a need to explore alternative approaches for inference on neuroimaging data that accurately model spatial autocorrelation, potentially providing better type I error control and more sensitive inference. In this project we examine the performance of a trend surface modeling (TSM) approach that is based on a biologically relevant parcellation of the brain. We present our results from applying the TSM to both task fMRI and resting-state fMRI and compare the latter to the results from the parametric software, FSL. We demonstrate that the TSM provides better Type I error control, as well as sensitive inference on task data

Predictions of regional HCE: spatial and time patterns in an ageing population framework

This research aims at building reliable information about the drivers of expenditures at municipality level that could be used to guide policy makers in the public health care organization. In addition, the spatial temporal structure of the dataset (built using administrative data) is investigated considering a panel data spa- tial error model for per capita expenditures. The results of model estimation and the demographic projections are employed for the long-run predictions of regional health expenditures in the well-known aging population framework characterizing the Italian health system

Characterizing the dimensional structure of early-life adversity in the Adolescent Brain Cognitive Development (ABCD) Study

Early-life adversity has profound consequences for youth neurodevelopment and adjustment; however, experiences of adversity are heterogeneous and interrelated in complex ways that can be difficult to operationalize and organize in developmental research. We sought to characterize the underlying dimensional structure of co-occurring adverse experiences among a subset of youth (ages 9–10) from the Adolescent Brain Cognitive Development (ABCD) Study (N = 7115), a community sample of youth in the United States. We identified 60 environmental and experiential variables that reflect adverse experiences. Exploratory factor analysis identified 10 robust dimensions of early-life adversity co-occurrence, corresponding to conceptual domains such as caregiver substance use and biological caregiver separation, caregiver psychopathology, caregiver lack of support, and socioeconomic disadvantage / neighborhood lack of safety. These dimensions demonstrated distinct associations with internalizing problems, externalizing problems, cognitive flexibility, and inhibitory control. Non-metric multidimensional scaling characterized qualitative similarity among the 10 identified dimensions. Results supported a nonlinear three-dimensional structure representing early-life adversity, including continuous gradients of “perspective”, “environmental uncertainty”, and “acts of omission/commission”. Our findings suggest that there are distinct dimensions of early-life adversity co-occurrence in the ABCD sample at baseline, and the resulting dimensions may have unique implications for neurodevelopment and youth behavior