Ústav biomedicínského inženýrství

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Shadows of very high-frequency oscillations can be detected in lower frequency bands of routine stereoelectroencephalography
(NATURE PORTFOLIO, 2024-01-19) Vašíčková, Zuzana; Klimeš, Petr; Cimbálník, Jan; Trávníček, Vojtěch; Pail, Martin; Halámek, Josef; Jurák, Pavel; Brázdil, Milan
Very high-frequency oscillations (VHFOs, > 500 Hz) are more specific in localizing the epileptogenic zone (EZ) than high-frequency oscillations (HFOs, < 500 Hz). Unfortunately, VHFOs are not visible in standard clinical stereo-EEG (SEEG) recordings with sampling rates of 1 kHz or lower. Here we show that "shadows" of VHFOs can be found in frequencies below 500 Hz and can help us to identify SEEG channels with a higher probability of increased VHFO rates. Subsequent analysis of Logistic regression models on 141 SEEG channels from thirteen patients shows that VHFO "shadows" provide additional information to gold standard HFO analysis and can potentially help in precise EZ delineation in standard clinical recordings.
Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time
(AME PUBLISHING COMPANY, 2024-04-11) Jurka, Martin; Macová, Iva; Wagnerová, Monika; Čapoun, Otakar; Jakubíček, Roman; Ouředníček, Petr; Lambert, Lukáš; Burgetová, Andrea
Background: Deep-learning-based reconstruction (DLR) improves the quality of magnetic resonance (MR) images which allows faster acquisitions. The aim of this study was to compare the image quality of standard and accelerated T2 weighted turbo-spin-echo (TSE) images of the prostate reconstructed with and without DLR and to find associations between perceived image quality and calculated image characteristics. Methods: In a cohort of 47 prospectively enrolled consecutive patients referred for bi-parametric prostate magnetic resonance imaging (MRI), two T2-TSE acquisitions in the transverse plane were acquired on a 3T scanner-a standard T2-TSE sequence and a short sequence accelerated by a factor of two using compressed sensing (CS). The images were reconstructed with and without DLR in super-resolution mode. The image quality was rated in six domains. Signal-to-noise ratio (SNR), and image sharpness were measured. Results: The mean acquisition time was 281 +/- 23 s for the standard and 140 +/- 12 s for the short acquisition (P<0.0001). DLR images had higher sharpness compared to non-DLR (P<0.001). Short and short-DLR had lower SNR than the standard and standard-DLR (P<0.001). The perceived image quality of short-DLR was rated better in all categories compared to the standard sequence (P<0.001 to P=0.004). All domains of subjective evaluation were correlated with measured image sharpness (P<0.001). Conclusions: T2-TSE acquisition of the prostate accelerated using CS combined with DLR reconstruction provides images with increased sharpness that have a superior quality as perceived by human readers compared to standard T2-TSE. The perceived image quality is correlated with measured image contrast.
Method for cycle detection in sparse, irregularly sampled, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term, inter-ictal epileptiform activity
(PUBLIC LIBRARY SCIENCE, 2024-04-25) Balzekas, Irena; Trzasko, Joshua; Yu, Grace; Richner, Thomas; Mívalt, Filip; Sladký, Vladimír; Gregg, Nicholas M.; Van Gompel, Jamie J.; Miller, Kai J.; Croarkin, Paul E.; Křemen, Václav; Worrell, Gregory
Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep, wakefulness, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals.Trial Registration: NCT03946618. Circadian and multiday cycles are an important part of many long-term neuro-behavioral phenomena such as pathological inter-ictal epileptiform discharges (IEDs) and seizures in epilepsy. Long-term, ambulatory, neuro-behavioral monitoring in human patients involves complex recording systems that can be subject to intermittent, irregular data loss and storage limitations, resulting in sparse, irregularly sampled data. Cycle identification in sparse data or irregular data using traditional frequency decomposition techniques typically requires interpolation to create a regular timeseries. Using unique, long-term recordings of pathological brain activity in people with epilepsy implanted with an investigational device, we developed a method to identify cycles in sparse, irregular neuro-behavioral data without interpolation. We anticipate this approach will enable retrospective cycle identification in sparse neuro-behavioral timeseries and support prospective sparse sampling in monitoring systems to enable long-term monitoring of patients and to extend storage capacity in a variety of ambulatory monitoring applications.
Chronic citalopram effects on the brain neurochemical profile and perfusion in a rat model of depression detected by the NMR techniques - spectroscopy and perfusion
(Elsevier, 2024-12-12) Haraštová-Pavlova, Iveta; Dražanová, Eva; Krátká, Lucie; Amchová, Petra; Hricková, Mária; Macíček, Ondřej; Vitouš, Jiří; Jiřík, Radovan; Ruda, Jana
Background: Major depressive disorder (MDD) is a mental illness with a high worldwide prevalence and suboptimal pharmacological treatment, which necessitates the development of novel, more efficacious MDD medication. Nuclear magnetic resonance (NMR) can non-invasively provide insight into the neurochemical state of the brain using proton magnetic resonance spectroscopy (1H MRS), and an assessment of regional cerebral blood flow (rCBF) by perfusion imaging. These methods may provide valuable in vivo markers of the pathological processes underlying MDD. Methods: This study examined the effects of the chronic antidepressant medication, citalopram, in a well-validated MDD model induced by bilateral olfactory bulbectomy (OB) in rats. 1H MRS was utilized to assess key metabolite ratios in the dorsal hippocampus and sensorimotor cortex bilaterally, and arterial spin labelling was employed to estimate rCBF in several additional brain regions. Results: The 1H MRS data results suggest lower hippocampal Cho/tCr and lower cortical NAA/tCr levels as a characteristic of the OB phenotype. Spectroscopy revealed lower hippocampal Tau/tCr in citalopram-treated rats, indicating a potentially deleterious effect of the drug. However, the significant OB model–citalopram treatment interaction was observed using 1H MRS in hippocampal mI/tCr, Glx/tCr and Gln/tCr, indicating differential treatment effects in the OB and control groups. The perfusion data revealed higher rCBF in the whole brain, hippocampus and thalamus in the OB rats, while citalopram appeared to normalise it without affecting the control group. Conclusion: Collectively, 1H MRS and rCBF approaches demonstrated their capacity to capture an OB-induced phenotype and chronic antidepressant treatment effect in multiple brain regions.
Analyzing the performance of biomedical time-series segmentation with electrophysiology data
(NATURE PORTFOLIO, 2025-04-06) Ředina, Richard; Hejč, Jakub; Filipenská, Marina; Stárek, Zdeněk
Accurate segmentation of biomedical time-series, such as intracardiac electrograms, is vital for understanding physiological states and supporting clinical interventions. Traditional rule-based and feature engineering approaches often struggle with complex clinical patterns and noise. Recent deep learning advancements offer solutions, showing various benefits and drawbacks in segmentation tasks. This study evaluates five segmentation algorithms, from traditional rule-based methods to advanced deep learning models, using a unique clinical dataset of intracardiac signals from 100 patients. We compared a rule-based method, a support vector machine (SVM), fully convolutional semantic neural network (UNet), region proposal network (Faster R-CNN), and recurrent neural network for electrocardiographic signals (DENS-ECG). Notably, Faster R-CNN has never been applied to 1D signals segmentation before. Each model underwent Bayesian optimization to minimize hyperparameter bias. Results indicated that deep learning models outperformed traditional methods, with UNet achieving the highest segmentation score of 88.9 % (root mean square errors for onset and offset of 8.43 ms and 7.49 ms), closely followed by DENS-ECG at 87.8 %. Faster R-CNN and SVM showed moderate performance, while the rule-based method had the lowest accuracy (77.7 %). UNet and DENS-ECG excelled in capturing detailed features and handling noise, highlighting their potential for clinical application. Despite greater computational demands, their superior performance and diagnostic potential support further exploration in biomedical time-series analysis.

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