Edvall, N. K. | bioRxiv Science

bioRxiv2026

A Comparison and Validation of BIDS Apps for Preprocessing of UCB-J PET data

Background Preprocessing of Positron Emission Tomography (PET) data can be performed in many different ways and often involves manual steps, resulting in reduced comparability across analyses and pipelines. Apps working on PET data organized according to the Brain Imaging Data Structure (BIDS) seek to automate preprocessing, enabling the application of standardized pipelines that increase comparability and reproducibility. In this study, two of these apps were validated against in-house software. Methods Data were acquired in-house from 14 subjects who received placebo in a randomized controlled clinical trial and underwent two PET measurements using the radioligand [11C]UCB-J. Preprocessing was performed with two BIDS Apps, PETPrep and PICNIC, as well as an in-house software. Arterial blood data were processed using Bloodstream, and [11C]UCB-J total distribution volume (VT) values were calculated using a 1-tissue compartment model using kinfitr. Results VT values for all 9 regions of interest were highly consistent across tools with estimates of Intraclass Correlation Coefficients for consistency ranging between 0.94 and >0.99, although bias between tools measured as signed Percentage Difference was present, ranging from 0.1% - 10.2% depending on the region. Tools performed similarly on test-retest metrics. We observed a negative test-retest bias in [11C]UCB-J VT of unknown origin. Conclusion BIDS Apps produce reliable and consistent results when compared both with each other and with an in-house software optimized for processing and analysis of our in-house data. Region-dependant bias between tools warrants caution for direct comparison of VT between tools. The usage of these apps could enable better comparability across publications

neuroscience↗

bioRxiv2025

Frequency-Specific Resting-State MEG Network Characteristics of Tinnitus Patients Revealed by Graph Learning

Tinnitus, the perception of sound without an external source, affects a significant portion of the population, yet its impact on brain communication diagram known as the functional connectome, remains limited. Traditional functional connectivity (FC) methods, such as Pearson correlation, phase lag index and coherence rely on pairwise comparisons and are therefore limited in providing a holistic encoding of FC. Here, we employ an alternative approach to estimate the entire connectivity structure by analyzing all time-courses simultaneously. This approach is robust even for short-duration recordings, facilitating faster functional connectome identification and real-time applications. Using resting-state MEG recordings from controls and individuals with tinnitus, we demonstrated that the learned connectomes outperform correlation-based connectomes in fingerprinting, that is, identifying an individual from test-retest acquisitions. Group-level analysis revealed distinct altered FC in tinnitus across multiple frequency bands, affecting the default mode, auditory, visual, and salience networks, suggesting a reorganization of these large-scale networks beyond auditory areas. Our study reveals that tinnitus presents highly individualized and heterogeneous whole-brain connectome profiles, highlighting the need to focus on individual variability rather than group-level differences to gain a more nuanced understanding of tinnitus. Personalized FC could enable patient-specific tinnitus models, optimizing treatment strategies for individualized care.

neuroscience↗