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Supplemental material, sj-pdf-1-jcb-10.1177_0271678X221119760 for Transfer function analysis of dynamic cerebral autoregulation: a CARNet white paper 2022 update by Ronney B Panerai, Patrice Brassard, Joel S Burma, Pedro Castro, Jurgen AHR Claassen, Johannes J van Lieshout, Jia Liu, Samuel JE Lucas, Jatinder S Minhas, Georgios D Mitsis, Ricardo C Nogueira, Shigehiko Ogoh, Stephen J Payne, Caroline A Rickards, Andrew D Robertson, Gabriel D Rodrigues, Jonathan D Smirl David M Simpson in Journal of Cerebral Blood Flow & Metabolism

We have performed whole transcriptome sequencing of 5-FU resistant and 5-FU sensitive tumors generated in a mouse model of de novo carcinogenesis that closely recapitulates tumor initiation, progression and maintenance in vivo. Tumors were generated using the DMBA/TPA model of chemically induced carcinogenesis [1] , tumor-bearing mice were subsequently treated with 5-FU, and tumor growth as well as response to treatment was monitored by measuring tumor volume twice a week. Based on these measurements, we selected two 5-FU resistant and two 5-FU sensitive tumors and performed whole transcriptome sequencing and in order to identify differentially expressed transcripts between the two sets. Data obtained is deposited and available through NCBI SRA (reference number SRP155180 – https://www.ncbi.nlm.nih.gov/sra/?term=SRP155180 ).

This paper proposes an experimental design for the compositional classification of 177 ceramic samples deriving from domestic and tomb contexts in Cyprus dated to the Early and Middle Bronze Age. In this design, ceramic sample classification is achieved with three well-known methods, a standard statistical learning method termed k-Nearest Neighbours (k-NN), a method using Decision Trees (C4.5) and a more complex neural network based method known as Learning Vector Quantisation (LVQ). It is shown that the examination of classification patterns through confusion matrices allows the exploitation of inter-class relationships and the ability to provide extra information to the researcher about the compositional categorisation of samples; which could not be grouped (with certainty) into classes with the employment of ceramic petrography. Due to the compositional heterogeneity of ceramics, the effectiveness of classification using only chemical elements with mean concentrations lower than 0.1% is also evaluated to illustrate their potential significance. The developed design follows a systematic approach and well-established methods, such as bootstrapping with replacement and the 5 × 2 cross validation (paired t-test and F-test) tests, to ensure that the results are statistically significant.

It is well established that confounding factors related to head motion and physiological processes (e.g. cardiac and breathing activity) should be taken into consideration when analyzing and interpreting results in fMRI studies. However, even though recent studies aimed to evaluate the performance of different preprocessing pipelines there is still no consensus on the optimal strategy. This may be partly because the quality control (QC) metrics used to evaluate differences in performance across pipelines often yielded contradictory results. Importantly, noise correction techniques based on physiological recordings or expansions of tissue-based techniques such as aCompCor have not received enough attention. Here, to address the aforementioned issues, we evaluate the performance of a large range of pipelines by using previously proposed and novel quality control (QC) metrics. Specifically, we examine the effect of three commonly used practices: 1) Removal of nuisance regressors from fMRI data, 2) discarding motion-contaminated volumes (i.e., scrubbing) before regression, and 3) low-pass filtering the data and the nuisance regressors before their removal. To this end, we propose a framework that summarizes the scores from eight QC metrics to a reduced set of two QC metrics that reflect the signal-to-noise ratio (SNR) and the reduction in motion artifacts and biases in the preprocessed fMRI data. Using resting-state fMRI data from the Human Connectome Project, we show that the best data quality, is achieved when the global signal (GS) and about 17% of principal components from white matter (WM) are removed from the data. In addition, while scrubbing does not yield any further improvement, low-pass filtering at 0.20 Hz leads to a small improvement.

The recording of brain event-related potentials (ERPs) is a widely used technique to investigate the neural basis of sensory perception and cognitive processing in humans. Due to the low magnitude of ERPs, averaging of several consecutive stimuli is typically employed to enhance the signal to noise ratio (SNR) before subsequent analysis. However, when the temporal interval between two consecutive stimuli is smaller than the latency of the main ERP peaks, i.e., when the stimuli are presented at a fast rate, overlaps between the corresponding ERPs may occur. These overlaps are usually dealt with by assuming that there is a simple additive superposition between the elicited ERPs and consequently performing algebraic waveform subtractions. Here, we test this assumption rigorously by providing a statistical framework that examines the presence of nonlinear additive effects between overlapping ERPs elicited by successive stimuli with short interstimulus intervals (ISIs). The results suggest that there are no nonlinear additive effects due to the time overlap per se but that, for the range of ISIs examined, the second ERP is modulated by the presence of the first stimulus irrespective of whether there is time overlap or not. In other words, two ERPs that overlap in time can still be written as an addition of two ERPs but with the second ERP being different from the first. This difference is also present in the case of nonoverlapping ERPs with short ISIs. The modulation effect elicited on the second ERP by the first stimulus is dependent on the ISI value.

In this work, we investigate the linear dynamic interactions between fluctuations in arterial CO2 that occur during normal breathing, and the BOLD fMRI signal. We cast this problem within a systems-theoretic framework, where we employ functional expansions for the estimation of the impulse responses in large regions of interest, as well as in individual voxels. We also implement classification schemes in order to identify different brain regions with similar cerebrovascular reactivity characteristics. Our results reveal that it is feasible to obtain reliable estimates of cerebrovascular reactivity curves from resting-state data and that these curves exhibit considerable variability across different brain regions that may be related to the underlying anatomy.

The combination of mathematical modeling and optimal control techniques holds great potential for quantitatively describing tumor progression and optimal treatment planning. Hereby, we use a Gompertz-type growth law and a pharmacokinetic-pharmacodynamic approach for modeling the effects of drugs on tumor progression in tumor bearing mice, and we combine these in order to design optimal therapeutic patterns. Specifically, we describe colon cancer progression in both untreated mice as well as mice treated with widely used anticancer agents. We also present a pharmacokinetic model to describe the kinetics of drugs in the body as well as detailed toxicity models to describe the severity of side effects. Finally, we propose a promising methodology by which cancer progression in mice with drug resistance can be controlled. By using optimal control, we demonstrate that the optimal planning of the frequency and magnitude of treatment interruptions is key to the control of cancer progression in subjects with resistance and should be further investigated in an experimental setting, which is currently underway.

We propose a sequential probabilistic mixture model for individualized tumor growth forecasting. In contrast to conventional deterministic methods for estimation and prediction of tumor evolution, we utilize all available tumor-specific observations up to the present time to approximate the unknown multi-scale process of tumor growth over time, in a stochastic context. The suggested mixture model uses prior information obtained from the general population and becomes more individualized as more observations from the tumor are sequentially taken into account. Inference can be carried out using the full, possibly multimodal, posterior, and predictive distributions instead of point estimates. In our simulation study we illustrate the superiority of the suggested multi-process dynamic linear model compared to the single process alternative. The validation of our approach was performed with experimental data from mice. The methodology suggested in the present study may provide a starting point for personalized adaptive treatment strategies.

Objective This paper proposes supervised and unsupervised algorithms for automatic muscle artifact detection and removal from long-term EEG recordings, which combine canonical correlation analysis (CCA) and wavelets with random forests (RF). Methods The proposed algorithms first perform CCA and continuous wavelet transform of the canonical components to generate a number of features which include component autocorrelation values and wavelet coefficient magnitude values. A subset of the most important features is subsequently selected using RF and labelled observations (supervised case) or synthetic data constructed from the original observations (unsupervised case). The proposed algorithms are evaluated using realistic simulation data as well as 30 min epochs of non-invasive EEG recordings obtained from ten patients with epilepsy. Results We assessed the performance of the proposed algorithms using classification performance and goodness-of-fit values for noisy and noise-free signal windows. In the simulation study, where the ground truth was known, the proposed algorithms yielded almost perfect performance. In the case of experimental data, where expert marking was performed, the results suggest that both the supervised and unsupervised algorithm versions were able to remove artifacts without affecting noise-free channels considerably, outperforming standard CCA, independent component analysis (ICA) and Lagged Auto-Mutual Information Clustering (LAMIC). Conclusion The proposed algorithms achieved excellent performance for both simulation and experimental data. Importantly, for the first time to our knowledge, we were able to perform entirely unsupervised artifact removal, i.e. without using already marked noisy data segments, achieving performance that is comparable to the supervised case. Significance Overall, the results suggest that the proposed algorithms yield significant future potential for improving EEG signal quality in research or clinical settings without the need for marking by expert neurophysiologists, EMG signal recording and user visual inspection.

The emerging paradigm of Machine to Machine (M2M)-driven Internet of Things (IoT), where physical objects are not disconnected from the virtual world but aim at collectively provide contextual services, calls for enhanced and more energy-efficient resource management approaches. In this paper, the problem is addressed through a joint interest, physical and energy-aware clustering and resource management framework, capitalizing on the wireless powered communication (WPC) technique. Within the proposed framework the numerous M2M devices initially form different clusters based on the low complexity Chinese Restaurant Process (CRP), properly adapted to account for interest, physical and energy related factors. Following that, a cluster-head is selected among the members of each cluster. The proposed approach enables the devices of a cluster with the support of the cluster-head to harvest and store energy in a stable manner through Radio Frequency (RF) signals adopting the WPC paradigm, thus prolonging the operation of the overall M2M network. Each M2M device is associated with a generic utility function, which appropriately represents its degree of satisfaction in relation to the consumed transmission power. Based on the distributed nature of the M2M network, a maximization problem of each device's utility function is formulated as a non-cooperative game and its unique Nash equilibrium point is determined, in terms of devices’ optimal transmission powers. Considering the devices’ equilibrium transmission powers, the optimal charging transmission powers of the cluster-heads are derived. The performance of the proposed approach is evaluated via modeling and simulation and under various topologies and scenarios, and its operational efficiency and effectiveness is demonstrated.

We investigated the correlation of epileptic seizure onset times with long term EEG functional brain network properties. To do so, we constructed binary functional brain networks from long-term, multichannel electroencephalographic data recorded from nine patients with epilepsy. The corresponding network properties were quantified using the average network degree. It was found that the network degree (as well as other network properties such as the network efficiency and clustering coefficient) exhibited large fluctuations over time; however, it also exhibited specific periodic temporal structure over different time scales (1.5hr-24hr periods) that was consistent across subjects. We investigated the correlation of the phases of these network periodicities with the seizure onset by using circular statistics. The results showed that the instantaneous phases of the 3.5hr, 5.5hr, 12hr and 24hr network degree periodic components are not uniformly distributed, suggesting that functional network properties are related to seizure generation and occurrence.

Orthonormal function expansions have been used extensively in the context of linear and nonlinear systems identification, since they result in a significant reduction in the number of required free parameters. In particular, Laguerre basis expansions have been used in the context of biological/ physiological systems identification, due to the exponential decaying characteristics of the Laguerre orthonormal basis, the rate of which is determined by the Laguerre parameter α. A critical aspect of the Laguerre expansion technique is the selection of the model structural parameters, i.e., polynomial model order for nonlinear systems, number of Laguerre functions and value of the Laguerre parameter α. This selection is typically made by trial-and-error procedures on the basis of the model prediction error. In the present paper, we formulate the Laguerre expansion technique in a Bayesian framework. Based on this formulation, we derive analytically the posterior distribution of the α parameter and the model evidence, in order to perform model order selection. We also demonstrate the performance of the proposed method by simulated examples and compare it to alternative statistical criteria for model order selection. ©2010 IEEE.

We present a random forest (RF) classification and regression technique to predict, intraoperatively, the unified Parkinson's disease rating scale (UPDRS) improvement after deep brain stimulation (DBS). We hypothesized that a data-informed combination of features extracted from intraoperative microelectrode recordings (MERs) can predict the motor improvement of Parkinson's disease patients undergoing DBS surgery. We modified the employed RFs to account for unbalanced datasets and multiple observations per patient, and showed, for the first time, that only five neurophysiologically interpretable MER signal features are sufficient for predicting UPDRS improvement. This finding suggests that subthalamic nucleus (STN) electrophysiological signal characteristics are strongly correlated to the extent of motor behavior improvement observed in STN-DBS.

The cerebrovascular bed is very sensitive to CO 2 changes, particularly in respiratory-related areas, such as the brainstem. Therefore, the hemodynamic response to such changes is of interest as it quantifies this sensitivity. Here, we examine in detail the regional characteristics of the hemodynamic response to spontaneous and larger, externally induced step CO 2 changes CO 2 (end-tidal forcing) by utilizing BOLD functional magnetic resonance imaging (fMRI) measurements from healthy humans. We first obtain estimates of the impulse response between CO 2 and BOLD signal in several anatomically and functionally defined regions of interest, using function expansions with different basis sets. These include the Laguerre basis, which has been widely used in linear and nonlinear systems identification particularly for biological/physiological systems, as well as different variants of gamma functions, which have been widely used in functional neuroimaging due to physiological considerations with regards to the characteristics of the BOLD response to external (sensory or other) stimuli. Based on the aforementioned comparisons, we perform the same analysis in smaller anatomical areas, considering voxel neighborhoods that span the entire image, in order to map key features of the hemodynamic response function such as peak value, time-to-peak and area, in finer spatial resolution.

Functional magnetic resonance imaging (fMRI) is widely viewed as the gold standard for studying brain function due to its high spatial resolution and non-invasive nature. However, it is well established that changes in breathing patterns and heart rate strongly influence the blood oxygen-level dependent (BOLD) fMRI signal and this, in turn, can have considerable effects on fMRI studies, particularly resting-state studies. The dynamic effects of physiological processes are often quantified by using convolution models along with simultaneously recorded physiological data. In this context, physiological response function (PRF) curves (cardiac and respiratory response functions), which are convolved with the corresponding physiological fluctuations, are commonly employed. While it has often been suggested that the PRF curves may be region- or subject- specific, it is still an open question whether this is the case. In the present study, we propose a novel framework for the robust estimation of PRF curves and use this framework to rigorously examine the implications of using population-, subject-, session- and scan-specific PRF curves. The proposed framework was tested on resting-state fMRI and physiological data from the Human Connectome Project. Our results suggest that PRF curves vary significantly across subjects and, to a lesser extent, across sessions from the same subject. These differences can be partly attributed to physiological variables such as the mean and variance of the heart rate during the scan. The proposed methodological framework can be used to obtain robust scan-specific PRF curves from data records with duration longer than 5 minutes, exhibiting significantly improved performance compared to previously defined canonical cardiac and respiration response functions. Besides removing physiological confounds from the BOLD signal, accurate modeling of subject- (or session-/scan-) specific PRF curves is of importance in studies that involve populations with altered vascular responses, such as aging subjects.HighlightsPhysiological response functions (PRF) vary considerably across subjects/sessionsScan-specific PRF curves can be obtained from data records longer than 5 minutesThe shape of the cardiac response function is linked to the mean heart rate (HR)Brain regions affected by HR and breathing patterns exhibit substantial overlapHR and breathing patterns affect distinct regions as compared to cardiac pulsatility