Biology subjects

Maximov, I.

Publications and source records attributed to Maximov, I..

4 recordsLinked to original sources

bioRxiv2025

Seizure-induced Transient Disruptive Changes in Brain Microstructure

A leading hypothesis for the mechanisms of electroconvulsive therapy (ECT) posits an initial disruptive effect followed by enhanced neuroplasticity. However, direct evidence of the presumed early disruptive changes, potentially driven by seizures, remains limited. This study examined longitudinal changes in multishell diffusion MRI-derived metrics in 25 individuals with depression, with scans acquired two hours before and after their first ECT session. Follow-up scans were collected within 14 days and 6 months after the last ECT session. To control for potential confounding effects of anesthesia and repeated measurements, two additional groups were included: 16 individuals undergoing short-acting anesthesia and 16 healthy controls without interventions. A multicompartment model was applied to explore extracellular free water and microstructures in the intracellular/extracellular compartments. Whole-brain voxel-wise analyses identified a pattern consistent with vasogenic edema (e.g., increased extracellular free water) in widespread bilateral brain regions following a single ECT session. Increases in brain tissue free water were significantly correlated with electrical stimulus charge (r=0.51, p=0.01) but not with post-ictal reorientation time (r=0.11, p=0.92). These changes were not observed in either control group. Follow-up assessments confirmed that the alterations in tissue free water resolved within 14 days. A single ECT-induced seizure induces transient, reversible changes consistent with vasogenic edema, rather than irreversible cellular injury typically associated with cytotoxic edema. These reversible changes may represent an initial disruptive phase that facilitates subsequent adaptive brain responses, including neuroplasticity and network reorganization underlying the therapeutic effects of ECT.

neuroscience↗

bioRxiv2022

Free-water volume fraction increases non-linearly with age in the white matter of the healthy human brain

The term free-water volume fraction (FWVF) refers to the cerebrospinal and interstitial fluids in the extracellular space of the white matter (WM) of the brain, which has been demonstrated as a sensitive biomarker that correlates with the cognitive performance and the neuropathological processes modifying the interstitial extracellular spaces. It can be quantified by properly fitting the isotropic compartment of the magnetic resonance (MR) signal in diffusion-sensitized sequences. Using N = 287 healthy subjects aged 25-94, this study examines in detail the evolution of the FWVF in the human brain WM across the adult lifespan, which has been previously reported to exhibit a positive trend. We found evidence of a noticeably non-linear gain after the sixth decade of life, with a region-specific variate and varying change rate of the FWVF parameter with age, at the same time a heteroskedastic pattern across the adult lifespan is suggested. On the other hand, the FW-compensated MR signal leads to a region-dependent flattened age-related evolution of the mean diffusivity (MD) and fractional anisotropy (FA), along with a considerable reduction in their variability, as compared to standard studies conducted over the raw MR signal. This way, our study provides a new perspective on the trajectory-based assessment of the brain and explains the source of the variations observed in FA and MD parameters across the lifespan with previous studies with the standard diffusion tensor imaging.

neuroscience↗

bioRxiv2020

Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM) in big data analysis: UK Biobank 18608 example

Deriving reliable information about the structural and functional architecture of the brain in vivo is critical for the clinical and basic neurosciences. In the new era of large population-based datasets, when multiple brain imaging modalities and contrasts are combined in order to reveal latent brain structural patterns and associations with genetic, demographic and clinical information, automated and stringent quality control (QC) procedures are important. Diffusion magnetic resonance imaging (dMRI) is a fertile imaging technique for probing and visualising brain tissue microstructure in vivo, and has been included in most standard imaging protocols in large-scale studies. Due to its sensitivity to subject motion and technical artefacts, automated QC procedures prior to statistical analyses of dMRI data are required to minimise the influence of noise and artefacts. Here, we introduce Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM), a computationally efficient QC method utilising structural similarity to evaluate image quality and mean diffusion metrics. As an example, we applied YTTRIUM in the context of tract-based spatial statistics to assess associations between age and kurtosis imaging and white matter tract integrity in UK Biobank data (n = 18,608). In order to assess the influence of outliers on results obtained using machine learning approaches, we tested the effects of applying YTTRIUM on brain age prediction. We demonstrated that the proposed QC pipeline represents an efficient approach for identifying poor quality datasets and artifacts and increase the accuracy of machine learning based brain age prediction.

neuroscience↗

bioRxiv2019

Cumulative estrogen exposure, APOE genotype, and women’s brain aging - a population-based neuroimaging study

Sex hormones such as estrogen fluctuate across the female lifespan, with high levels during reproductive years and natural decline during the transition to menopause. Womens exposure to estrogen may influence their heightened risk of Alzheimers disease (AD) relative to men, but little is known about how it affects normal brain aging. Recent findings from the UK Biobank demonstrate less apparent brain aging in women with a history of multiple childbirths, highlighting a potential link between sex-hormone exposure and brain aging. We investigated endogenous and exogenous sex-hormone exposure, genetic risk for AD, and neuroimaging-derived biomarkers for brain aging in 16,854 middle to older-aged women. The results showed that as opposed to parity, higher cumulative sex-hormone exposure was associated with more evident brain aging, indicating that i) high levels of cumulative exposure to sex-hormones may have adverse effects on the brain, and ii) beneficial effects of pregnancies on the female brain are not solely attributable to modulations in sex-hormone exposure. In addition, for women using hormonal replacement therapy (HRT), starting treatment earlier was associated with less evident brain aging, but only in women with a genetic risk for AD. Genetic factors may thus contribute to how timing of HRT initiation influences womens brain aging trajectories.

neuroscience↗