Single-Subject Network Analysis of FDOPA PET in Parkinson's Disease and Psychosis Spectrum.

Greater understanding of individual biological differences is essential for developing more targeted treatment approaches to complex brain disorders. Traditional analysis methods in molecular imaging studies have primarily focused on quantifying tracer binding in specific brain regions, often neglecting inter-regional functional relationships. In this study, we propose a statistical framework that combines molecular imaging data with perturbation covariance analysis to construct single-subject networks and investigate individual patterns of molecular alterations.

Radiomic Analysis of Striatal [F]FDOPA PET Imaging in Patients with Psychosis for the Identification of Antipsychotic Response.

Purpose: Schizophrenia (SCZ) is a severe psychiatric disorder marked by abnormal dopamine synthesis, measurable through [F]FDOPA PET imaging. This imaging technique has been proposed as a biomarker for treatment stratification in SCZ, where one-third of patients respond poorly to standard antipsychotics. This study explores the use of radiomics on [F]FDOPA PET data to examine dopamine synthesis in SCZ and predict antipsychotic response.

Investigating dopaminergic abnormalities in schizophrenia and first-episode psychosis with normative modelling and multisite molecular neuroimaging.

Molecular neuroimaging techniques, like PET and SPECT, offer invaluable insights into the brain's in-vivo biology and its dysfunction in neuropsychiatric patients. However, the transition of molecular neuroimaging into diagnostics and precision medicine has been limited to a few clinical applications, hindered by issues like practical feasibility, high costs, and high between-subject heterogeneity of neuroimaging measures. In this study, we explore the use of normative modelling (NM) to identify individual patient alterations by describing the physiological variability of molecular functions.

An automatic analysis framework for FDOPA PET neuroimaging.

In this study we evaluate the performance of a fully automated analytical framework for FDOPA PET neuroimaging data, and its sensitivity to demographic and experimental variables and processing parameters. An instance of XNAT imaging platform was used to store the King's College London institutional brain FDOPA PET imaging archive, alongside individual demographics and clinical information. By re-engineering the historical Matlab-based scripts for FDOPA PET analysis, a fully automated analysis pipeline for imaging processing and data quantification was implemented in Python and integrated in XNAT.

Bitbox: A Cloud-based data sharing solution for medical images.

With the modernization and digitisation of the healthcare system, the need for exchanging medical data has become increasingly compelling. Biomedical imaging has been no exception, where the gathering of medical imaging acquisitions from multi-site collaborations have enabled to reach data sizes never imaginable until few years ago. Usually, medical imaging data have very large volume and diverse complexity, requiring bespoken transfer systems that protect personal information as well as data integrity.

Integrating neuroimaging and gene expression data using the imaging transcriptomics toolbox.

The integration of neuroimaging and transcriptomics data, , is becoming increasingly popular but standardized workflows for its implementation are still lacking. We describe the Imaging Transcriptomics toolbox, a new package that implements a full imaging transcriptomics pipeline using a user-friendly, command line interface. This toolbox allows the user to identify patterns of gene expression which correlates with a specific neuroimaging phenotype and perform gene set enrichment analyses to inform the biological interpretation of the findings using up-to-date methods.

PET-BIDS, an extension to the brain imaging data structure for positron emission tomography.

The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets, serving not only to facilitate the process of data sharing and aggregation, but also to simplify the application and development of new methods and software for working with neuroimaging data. Here, we present an extension of BIDS to include positron emission tomography (PET) data, also known as PET-BIDS, and share several open-access datasets curated following PET-BIDS along with tools for conversion, validation and analysis of PET-BIDS datasets.

Imaging transcriptomics: Convergent cellular, transcriptomic, and molecular neuroimaging signatures in the healthy adult human brain.

The integration of transcriptomic and neuroimaging data, "imaging transcriptomics," has recently emerged to generate hypotheses about potential biological pathways underlying regional variability in neuroimaging features. However, the validity of this approach is yet to be examined in depth. Here, we sought to bridge this gap by performing transcriptomic decoding of the regional distribution of well-known molecular markers spanning different elements of the biology of the healthy human brain.

Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning.

Introduction: With biomedical imaging research increasingly using large datasets, it becomes critical to find operator-free methods to quality control the data collected and the associated analysis. Attempts to use artificial intelligence (AI) to perform automated quality control (QC) for both single-site and multi-site datasets have been explored in some neuroimaging techniques (e.g.