Bayesian modelling of time series data (BayModTS)-a FAIR workflow to process sparse and highly variable data.

MOTIVATION: Systems biology aims to better understand living systems through mathematical modelling of experimental and clinical data. A pervasive challenge in quantitative dynamical modelling is the integration of time series measurements, which often have high variability and low sampling resolution. Approaches are required to utilize such information while consistently handling uncertainties. RESULTS: We present BayModTS (Bayesian modelling of time series data), a new FAIR (findable, accessible, interoperable, and reusable) workflow for processing and analysing sparse and highly variable time series data. BayModTS consistently transfers uncertainties from data to model predictions, including process knowledge via parameterized models. Further, credible differences in the dynamics of different conditions can be identified by filtering noise. To demonstrate the power and versatility of BayModTS, we applied it to three hepatic datasets gathered from three different species and with different measurement techniques: (i) blood perfusion measurements by magnetic resonance imaging in rat livers after portal vein ligation, (ii) pharmacokinetic time series of different drugs in normal and steatotic mice, and (iii) CT-based volumetric assessment of human liver remnants after clinical liver resection. AVAILABILITY AND IMPLEMENTATION: The BayModTS codebase is available on GitHub at https://github.com/Systems-Theory-in-Systems-Biology/BayModTS. The repository contains a Python script for the executable BayModTS workflow and a widely applicable SBML (systems biology markup language) model for retarded transient functions. In addition, all examples from the paper are included in the repository. Data and code of the application examples are stored on DaRUS: https://doi.org/10.18419/darus-3876. The raw MRI ROI voxel data were uploaded to DaRUS: https://doi.org/10.18419/darus-3878. The steatosis metabolite data are published on FairdomHub: 10.15490/fairdomhub.1.study.1070.1.

Perfusion and T2 Relaxation Time as Predictors of Severity and Outcome in Sepsis-Associated Acute Kidney Injury: A Preclinical MRI Study.

BACKGROUND: Preventing sepsis-associated acute kidney injury (S-AKI) can be challenging because it develops rapidly and is often asymptomatic. Probability assessment of disease progression for therapeutic follow-up and outcome are important to intervene and prevent further damage. PURPOSE: To establish a noninvasive multiparametric MRI (mpMRI) tool, including T1 , T2 , and perfusion mapping, for probability assessment of the outcome of S-AKI. STUDY TYPE: Preclinical randomized prospective study. ANIMAL MODEL: One hundred and forty adult female SD rats (65 control and 75 sepsis). FIELD STRENGTH/SEQUENCE: 9.4T; T1 and perfusion map (FAIR-EPI) and T2 map (multiecho RARE). ASSESSMENT: Experiment 1: To identify renal injury in relation to sepsis severity, serum creatinine levels were determined (31 control and 35 sepsis). Experiment 2: Animals underwent mpMRI (T1 , T2 , perfusion) 18 hours postsepsis. A subgroup of animals was immediately sacrificed for histology examination (nine control and seven sepsis). Result of mpMRI in follow-up subgroup (25 control and 33 sepsis) was used to predict survival outcomes at 96 hours. STATISTICAL TESTS: Mann-Whitney U test, Spearman/Pearson correlation (r), P < 0.05 was considered statistically significant. RESULTS: Severely ill septic animals exhibited significantly increased serum creatinine levels compared to controls (70 ± 30 vs. 34 ± 9 µmol/L, P < 0.0001). Cortical perfusion (480 ± 80 vs. 330 ± 140 mL/100 g tissue/min, P < 0.005), and cortical and medullary T2 relaxation time constants were significantly reduced compared to controls (41 ± 4 vs. 37 ± 5 msec in cortex, P < 0.05, 52 ± 7 vs. 45 ± 6 msec in medulla, P < 0.05). The combination of cortical T2 relaxation time constants and perfusion results at 18 hours could predict survival outcomes at 96 hours with high sensitivity (80%) and specificity (73%) (area under curve of ROC = 0.8, Jmax = 0.52). DATA CONCLUSION: This preclinical study suggests combined T2 relaxation time and perfusion mapping as first line diagnostic tool for treatment planning. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

Investigation of the cerebral hemodynamic response function in single blood vessels by functional photoacoustic microscopy.

The specificity of the hemodynamic response function (HRF) is determined spatially by the vascular architecture and temporally by the evolution of hemodynamic changes. Here, we used functional photoacoustic microscopy (fPAM) to investigate single cerebral blood vessels of rats after left forepaw stimulation. In this system, we analyzed the spatiotemporal evolution of the HRFs of the total hemoglobin concentration (HbT), cerebral blood volume (CBV), and hemoglobin oxygen saturation (SO(2)). Changes in specific cerebral vessels corresponding to various electrical stimulation intensities and durations were bilaterally imaged with 36 × 65-µm(2) spatial resolution. Stimulation intensities of 1, 2, 6, and 10 mA were applied for periods of 5 or 15 s. Our results show that the relative functional changes in HbT, CBV, and SO(2) are highly dependent not only on the intensity of the stimulation, but also on its duration. Additionally, the duration of the stimulation has a strong influence on the spatiotemporal characteristics of the HRF as shorter stimuli elicit responses only in the local vasculature (smaller arterioles), whereas longer stimuli lead to greater vascular supply and drainage. This study suggests that the current fPAM system is reliable for studying relative cerebral hemodynamic changes, as well as for offering new insights into the dynamics of functional cerebral hemodynamic changes in small animals.

Automatic spike sorting for extracellular electrophysiological recording using unsupervised single linkage clustering based on grey relational analysis.

Automatic spike sorting is a prerequisite for neuroscience research on multichannel extracellular recordings of neuronal activity. A novel spike sorting framework, combining efficient feature extraction and an unsupervised clustering method, is described here. Wavelet transform (WT) is adopted to extract features from each detected spike, and the Kolmogorov-Smirnov test (KS test) is utilized to select discriminative wavelet coefficients from the extracted features. Next, an unsupervised single linkage clustering method based on grey relational analysis (GSLC) is applied for spike clustering. The GSLC uses the grey relational grade as the similarity measure, instead of the Euclidean distance for distance calculation; the number of clusters is automatically determined by the elbow criterion in the threshold-cumulative distribution. Four simulated data sets with four noise levels and electrophysiological data recorded from the subthalamic nucleus of eight patients with Parkinson's disease during deep brain stimulation surgery are used to evaluate the performance of GSLC. Feature extraction results from the use of WT with the KS test indicate a reduced number of feature coefficients, as well as good noise rejection, despite similar spike waveforms. Accordingly, the use of GSLC for spike sorting achieves high classification accuracy in all simulated data sets. Moreover, J-measure results in the electrophysiological data indicating that the quality of spike sorting is adequate with the use of GSLC.

Nonlinear analysis of movement-related changes in human subthalamic local field potentials.

Movement-related changes such as event-related desynchronizationcan (ERD) and event-related synchronization (ERS) can be found in human subthalamic nucleus (STN) with analysis on local field potentials (LFP) recorded from Parkinson's disease (PD) patients. Besides traditional time-frequency (TF) analysis, we introduced nonlinear analysis, bispectral and approximate entropy (ApEn), to measure the signal nonlinear correlation and regularity in neural activities. Movement-related changes were found in the beta band, bicoherence and ApEn, and variation during stationary movement is more available by nonlinear methods. Therefore, we suggest nonlinear analysis for further related studies.