Biology subjects

Brzezinski-Rittner, A.

Publications and source records attributed to Brzezinski-Rittner, A..

3 recordsLinked to original sources

bioRxiv2025

Cocaine use disorder is associated with lower brain state transition energy particularly in higher order and excitatory networks

Cocaine use disorder (CUD) detrimentally impacts personal health, social relationships, and economic opportunity. Here, we assess CUD-associated shifts in brain dynamics using Network Control Theory and examine how they align with previously identified changes in neurological systems and behavioral profiles of people with CUD. The SUDMEX CONN dataset consists of multi-modal MRI, cocaine use metrics, behavioral measures, and demographics of individuals with CUD (N=132, 71 CUD). We identified recurring brain activity states and used NCT to calculate the transition energy (TE) between pairs of states. ANCOVAs examined global and regional TE associations with drug use group (CUD vs controls (NC)), years of CUD, and risk-taking behaviors. We identified potential mechanisms driving the differences by correlating CUD-related regional TE effects with neurotransmitter/receptor systems. People with CUD had significantly lower global TE and default mode, dorsal attention and limbic network TE compared to non-user controls, particularly in regions enriched for noradrenaline and mu opioid receptors. Longer duration of CUD was associated with more decreased global TE, top-down TE, default mode, control and ventral attention network TE, and regional TE enriched for excitatory neurotransmitters and receptors. People with CUD needed to expend more global and top-down TE to perform better on a risk-taking task (the Iowa Gambling task), an effect which was not found in NCs. Our analysis of whole-brain activity dynamics provides a link between the effects of upstream glutamatergic excitotoxicity and/or opioid receptor dysfunction, and downstream weakening of inhibitory control that is central to CUD.

neuroscience↗

bioRxiv2024

Beyond brain size: disentangling the effect of sex and brain size on brain morphometry and cognitive functioning

It is imperative to study sex differences in brain morphology and function. However, there are major observable and unobservable confounding factors that can contribute to the estimated differences. Males have larger head sizes than females. Head size differences not only act as a confounding factor in studying sex differences in the brain, but also impact its anatomy and functioning. In this work, we seek to disentangle the effect of head size from sex in studying sex differentiated aging trajectories, its relation to canonical functional networks and cytoarchitectural classes, brain allometry, cognition. Using the UK Biobank (UKBB) neuroimaging data (N = 35,732 participants, 19,281 females, 44-82 years of age), we created a subsample (N = 11,294) where females (N = 5,657) and males were matched by their total intracranial volume (TIV) and age, a subsample that maintains the UKBB sample distribution, one matched only by age, and one that exaggerated the TIV difference between sexes. We then modeled the aging trajectories at both regional and vertex-wise levels in the four subsamples, and compared the estimations of the models. Our results show that when females and males have the same head size, the overall sex estimations tend towards zero, suggesting that most of the variability results from head size differences. Our approach also revealed bidirectional sex differences in brain neuroanatomy previously masked by the effect of head size. Further, the scaling relationship between regional and total brain volume remains fairly consistent across the lifespan and is not sex differentiated overall. We evaluated how the results of cognitive tests with perceived sex differences are influenced and explained by head size and found that although the correlation between TIV and cognitive scores is low, the matching process changes the direction of the effect sizes of differences between sexes in "verbal and numerical reasoning" and "working memory" cognitive domains. Taken together, employing a matching approach that is widely used in causal modeling studies, we provide new evidence for disentanglement of sex differences in the brain from head size as a biological confound.

neuroscience↗

bioRxiv2023

FONDUE: Robust resolution-invariant denoising of MR Images using Nested UNets

Recent human neuroimaging studies tend to have increased magnetic resonance image (MRI) acquisition resolutions, seeking finer levels of detail and more accurate brain morphometry. However, higher-resolution images inherently contain greater amounts of noise contamination, leading to poorer quality brain morphometry if not addressed adequately. This study proposes a novel, robust, resolution-invariant deep learning method to denoise structural human brain MRIs. We explore denoising of T1-weighted (T1w) brain images from varying field strengths (1.5T to 7T), voxel sizes (1.2mm to 250{micro}m), scanner vendors (Siemens, GE, and Phillips), and diseased and healthy participants from a wide age range (young adults to aging individuals). Our proposed Fast-Optimized Network for Denoising through residual Unified Ensembles (FONDUE) method demonstrated stable denoising capabilities across multiple resolutions with performance comparable to the state-of-the-art methods. FONDUE was capable of denoising 0.5mm3 isotropic T1w images in under 3 minutes on an NVIDIA RTX 3090 GPU using less than 8GB of video memory. We have also made the repository of FONDUE as well as its trained weights publicly available on: https://github.com/waadgo/FONDUE.

neuroscience↗