Cancer cell sedimentation in 3D cultures reveals active migration regulated by self-generated gradients and adhesion sites.

Cell sedimentation in 3D hydrogel cultures refers to the vertical migration of cells towards the bottom of the space. Understanding this poorly examined phenomenon may allow us to design better protocols to prevent it, as well as provide insights into the mechanobiology of cancer development. We conducted a multiscale experimental and mathematical examination of 3D cancer growth in triple negative breast cancer cells. Migration was examined in the presence and absence of Paclitaxel, in high and low adhesion environments and in the presence of fibroblasts. The observed behaviour was modeled by hypothesizing active migration due to self-generated chemotactic gradients. Our results did not reject this hypothesis, whereby migration was likely to be regulated by the MAPK and TGF-ß pathways. The mathematical model enabled us to describe the experimental data in absence (normalized error<40%) and presence of Paclitaxel (normalized error<10%), suggesting inhibition of random motion and advection in the latter case. Inhibition of sedimentation in low adhesion and co-culture experiments further supported the conclusion that cells actively migrated downwards due to the presence of signals produced by cells already attached to the adhesive glass surface.

Modeling the dynamics of cerebrovascular reactivity to carbon dioxide in fMRI under task and resting-state conditions.

Conventionally, cerebrovascular reactivity (CVR) is estimated as the amplitude of the hemodynamic response to vascular stimuli, most commonly carbon dioxide (CO2). While the CVR amplitude has established clinical utility, the temporal characteristics of CVR (dCVR) have been increasingly explored and may yield even more pathology-sensitive parameters. This work is motivated by the current need to evaluate the feasibility of dCVR modeling in various experimental conditions. In this work, we present a comparison of several recently published/utilized model-based deconvolution (response estimation) approaches for estimating the CO2 response function h(t), including maximum a posteriori likelihood (MAP), inverse logit (IL), canonical correlation analysis (CCA), and basis expansion (using Gamma and Laguerre basis sets). To aid the comparison, we devised a novel simulation framework that incorporates a wide range of SNRs, ranging from 10 to -7 dB, representative of both task and resting-state CO2 changes. In addition, we built ground-truth h(t) into our simulation framework, overcoming the conventional limitation that the true h(t) is unknown. Moreover, to best represent realistic noise found in fMRI scans, we extracted noise from in-vivo resting-state scans. Furthermore, we introduce a simple optimization of the CCA method (CCAopt) and compare its performance to these existing methods. Our findings suggest that model-based methods can accurately estimate dCVR even amidst high noise (i.e. resting-state), and in a manner that is largely independent of the underlying model assumptions for each method. We also provide a quantitative basis for making methodological choices, based on the desired dCVR parameters, the estimation accuracy and computation time. The BEL method provided the highest accuracy and robustness, followed by the CCAopt and IL methods. Of the three, the CCAopt method has the lowest computational requirements. These findings lay the foundation for wider adoption of dCVR estimation in CVR mapping.

Deep learning prediction of motor performance in stroke individuals using neuroimaging data.

The degree of motor impairment and profile of recovery after stroke are difficult to predict for each individual. Measures obtained from clinical assessments, as well as neurophysiological and neuroimaging techniques have been used as potential biomarkers of motor recovery, with limited accuracy up to date. To address this, the present study aimed to develop a deep learning model based on structural brain images obtained from stroke participants and healthy volunteers. The following inputs were used in a multi-channel 3D convolutional neural network (CNN) model: fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity maps obtained from Diffusion Tensor Imaging (DTI) images, white and gray matter intensity values obtained from Magnetic Resonance Imaging, as well as demographic data (e.g., age, gender). Upper limb motor function was classified into "Poor" and "Good" categories. To assess the performance of the DL model, we compared it to more standard machine learning (ML) classifiers including k-nearest neighbor, support vector machines (SVM), Decision Trees, Random Forests, Ada Boosting, and Naïve Bayes, whereby the inputs of these classifiers were the features taken from the fully connected layer of the CNN model. The highest accuracy and area under the curve values were 0.92 and 0.92 for the 3D-CNN and 0.91 and 0.91 for the SVM, respectively. The multi-channel 3D-CNN with residual blocks and SVM supported by DL was more accurate than traditional ML methods to classify upper limb motor impairment in the stroke population. These results suggest that combining volumetric DTI maps and measures of white and gray matter integrity can improve the prediction of the degree of motor impairment after stroke. Identifying the potential of recovery early on after a stroke could promote the allocation of resources to optimize the functional independence of these individuals and their quality of life.

Extracting electrophysiological correlates of functional magnetic resonance imaging data using the canonical polyadic decomposition.

The relation between electrophysiology and BOLD-fMRI requires further elucidation. One approach for studying this relation is to find time-frequency features from electrophysiology that explain the variance of BOLD time-series. Convolution of these features with a canonical hemodynamic response function (HRF) is often required to model neurovascular coupling mechanisms and thus account for time shifts between electrophysiological and BOLD-fMRI data. We propose a framework for extracting the spatial distribution of these time-frequency features while also estimating more flexible, region-specific HRFs. The core component of this method is the decomposition of a tensor containing impulse response functions using the Canonical Polyadic Decomposition. The outputs of this decomposition provide insight into the relation between electrophysiology and BOLD-fMRI and can be used to construct estimates of BOLD time-series. We demonstrated the performance of this method on simulated data while also examining the effects of simulated measurement noise and physiological confounds. Afterwards, we validated our method on publicly available task-based and resting-state EEG-fMRI data. We adjusted our method to accommodate the multisubject nature of these datasets, enabling the investigation of inter-subject variability with regards to EEG-to-BOLD neurovascular coupling mechanisms. We thus also demonstrate how EEG features for modelling the BOLD signal differ across subjects.

Modeling the Hemodynamic Response Function Using EEG-fMRI Data During Eyes-Open Resting-State Conditions and Motor Task Execution.

Being able to accurately quantify the hemodynamic response function (HRF) that links the blood oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI) signal to the underlying neural activity is important both for elucidating neurovascular coupling mechanisms and improving the accuracy of fMRI-based functional connectivity analyses. In particular, HRF estimation using BOLD-fMRI is challenging particularly in the case of resting-state data, due to the absence of information about the underlying neuronal dynamics. To this end, using simultaneously recorded electroencephalography (EEG) and fMRI data is a promising approach, as EEG provides a more direct measure of neural activations. In the present work, we employ simultaneous EEG-fMRI to investigate the regional characteristics of the HRF using measurements acquired during resting conditions. We propose a novel methodological approach based on combining distributed EEG source space reconstruction, which improves the spatial resolution of HRF estimation and using block-structured linear and nonlinear models, which enables us to simultaneously obtain HRF estimates and the contribution of different EEG frequency bands. Our results suggest that the dynamics of the resting-state BOLD signal can be sufficiently described using linear models and that the contribution of each band is region specific. Specifically, it was found that sensory-motor cortices exhibit positive HRF shapes, whereas the lateral occipital cortex and areas in the parietal cortex, such as the inferior and superior parietal lobule exhibit negative HRF shapes. To validate the proposed method, we repeated the analysis using simultaneous EEG-fMRI measurements acquired during execution of a unimanual hand-grip task. Our results reveal significant associations between BOLD signal variations and electrophysiological power fluctuations in the ipsilateral primary motor cortex, particularly for the EEG beta band, in agreement with previous studies in the literature.

Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph.

The blood oxygenation level-dependent (BOLD) contrast mechanism allows the noninvasive monitoring of changes in deoxyhemoglobin content. As such, it is commonly used in functional magnetic resonance imaging (fMRI) to study brain activity since levels of deoxyhemoglobin are indirectly related to local neuronal activity through neurovascular coupling mechanisms. However, the BOLD signal is severely affected by physiological processes as well as motion. Due to this, several noise correction techniques have been developed to correct for the associated confounds. The present study focuses on cardiac pulsatility fMRI confounds, aiming to refine model-based techniques that utilize the photoplethysmograph (PPG) signal. Specifically, we propose a new technique based on convolution filtering, termed cardiac pulsatility model (CPM) and compare its performance with the cardiac-related RETROICOR (Card-RETROICOR), which is a technique commonly used to model fMRI fluctuations due to cardiac pulsatility. Further, we investigate whether variations in the amplitude of the PPG pulses (PPG-Amp) covary with variations in amplitude of pulse-related fMRI fluctuations, as well as with the systemic low frequency oscillations (SLFOs) component of the fMRI global signal (GS - defined as the mean signal across all gray matter voxels). Capitalizing on 3T fMRI data from the Human Connectome Project, CPM was found to explain a significantly larger fraction of the fMRI signal variance compared to Card-RETROICOR, particularly for subjects with larger heart rate variability during the scan. The amplitude of the fMRI pulse-related fluctuations did not covary with PPG-Amp; however, PPG-Amp explained significant variance in the GS that was not attributed to variations in heart rate or breathing patterns. Our results suggest that the proposed approach can model high-frequency fluctuations due to pulsation as well as low-frequency physiological fluctuations more accurately compared to model-based techniques commonly employed in fMRI studies.

Estimating brain age from structural MRI and MEG data: Insights from dimensionality reduction techniques.

Brain age prediction studies aim at reliably estimating the difference between the chronological age of an individual and their predicted age based on neuroimaging data, which has been proposed as an informative measure of disease and cognitive decline. As most previous studies relied exclusively on magnetic resonance imaging (MRI) data, we hereby investigate whether combining structural MRI with functional magnetoencephalography (MEG) information improves age prediction using a large cohort of healthy subjects (N = 613, age 18-88 years) from the Cam-CAN repository. To this end, we examined the performance of dimensionality reduction and multivariate associative techniques, namely Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA), to tackle the high dimensionality of neuroimaging data. Using MEG features (mean absolute error (MAE) of 9.60 years) yielded worse performance when compared to using MRI features (MAE of 5.33 years), but a stacking model combining both feature sets improved age prediction performance (MAE of 4.88 years). Furthermore, we found that PCA resulted in inferior performance, whereas CCA in conjunction with Gaussian process regression models yielded the best prediction performance. Notably, CCA allowed us to visualize the features that significantly contributed to brain age prediction. We found that MRI features from subcortical structures were more reliable age predictors than cortical features, and that spectral MEG measures were more reliable than connectivity metrics. Our results provide an insight into the underlying processes that are reflective of brain aging, yielding promise for the identification of reliable biomarkers of neurodegenerative diseases that emerge later during the lifespan.

Phase dynamics of cerebral blood flow in subarachnoid haemorrhage in response to sodium nitrite infusion.

Aneurysmal subarachnoid haemorrhage (SAH) is a devastating subset of stroke. One of the major determinates of morbidity is the development of delayed cerebral ischemia (DCI). Disruption of the nitric oxide (NO) pathway and consequently the control of cerebral blood flow (CBF), known as cerebral autoregulation, is believed to play a role in its pathophysiology. Through the pharmacological manipulation of in vivo NO levels using an exogenous NO donor we sought to explore this relationship. Phase synchronisation index (PSI), an expression of the interdependence between CBF and arterial blood pressure (ABP) and thus cerebral autoregulation, was calculated before and during sodium nitrite administration in 10 high-grade SAH patients acutely post-rupture. In patients that did not develop DCI, there was a significant increase in PSI around 0.1 Hz during the administration of sodium nitrite (33%; p-value 0.006). In patients that developed DCI, PSI did not change significantly. Synchronisation between ABP and CBF at 0.1 Hz has been proposed as a mechanism by which organ perfusion is maintained, during periods of physiological stress. These findings suggest that functional NO depletion plays a role in impaired cerebral autoregulation following SAH, but the development of DCI may have a distinct pathophysiological aetiology.

Functional brain networks of patients with epilepsy exhibit pronounced multiscale periodicities, which correlate with seizure onset.

Epileptic seizure detection and prediction by using noninvasive measurements such as scalp EEG signals or invasive, intracranial recordings, has been at the heart of epilepsy studies for at least three decades. To this end, the most common approach has been to consider short-length recordings (several seconds to a few minutes) around a seizure, aiming to identify significant changes that occur before or during seizures. An inherent assumption in this approach is the presence of a relatively constant EEG activity in the interictal period, which is interrupted by seizure occurrence. Here, we examine this assumption by using long-duration scalp EEG data (21-94 hr) in nine patients with epilepsy, based on which we construct functional brain networks. Our results reveal that these networks vary over time in a periodic fashion, exhibiting multiple peaks at periods ranging between 1 and 24 hr. The effects of seizure onset on the functional brain network properties were found to be considerably smaller in magnitude compared to the changes due to these inherent periodic cycles. Importantly, the properties of the identified network periodic components (instantaneous phase) were found to be strongly correlated to seizure onset, especially for the periodicities around 3 and 5 hr. These correlations were found to be largely absent between EEG signal periodicities and seizure onset, suggesting that higher specificity may be achieved by using network-based metrics. In turn, this implies that more robust seizure detection and prediction can be achieved if longer term underlying functional brain network periodic variations are taken into account.

Identification of physiological response functions to correct for fluctuations in resting-state fMRI related to heart rate and respiration.

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 min, 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.

Modeling of dynamic cerebrovascular reactivity to spontaneous and externally induced CO2 fluctuations in the human brain using BOLD-fMRI.

In this work, we investigate the regional characteristics of the dynamic interactions between arterial CO2 and BOLD (dynamic cerebrovascular reactivity - dCVR) during normal breathing and hypercapnic, externally induced step CO2 challenges. To obtain dCVR curves at each voxel, we use a custom set of basis functions based on the Laguerre and gamma basis sets. This allows us to obtain robust dCVR estimates both in larger regions of interest (ROIs), as well as in individual voxels. We also implement classification schemes to identify brain regions with similar dCVR characteristics. Our results reveal considerable variability of dCVR across different brain regions, as well as during different experimental conditions (normal breathing and hypercapnic challenges), suggesting a differential response of cerebral vasculature to spontaneous CO2 fluctuations and larger, externally induced CO2 changes that are possibly associated with the underlying differences in mean arterial CO2 levels. The clustering results suggest that anatomically distinct brain regions are characterized by different dCVR curves that in some cases do not exhibit the standard, positive valued curves that have been previously reported. They also reveal a consistent set of dCVR cluster shapes for resting and forcing conditions, which exhibit different distribution patterns across brain voxels.

Older adults exhibit a more pronounced modulation of beta oscillations when performing sustained and dynamic handgrips.

Muscle contractions are associated with a decrease in beta oscillatory activity, known as movement-related beta desynchronization (MRBD). Older adults exhibit a MRBD of greater amplitude compared to their younger counterparts, even though their beta power remains higher both at rest and during muscle contractions. Further, a modulation in MRBD has been observed during sustained and dynamic pinch contractions, whereby beta activity during periods of steady contraction following a dynamic contraction is elevated. However, how the modulation of MRBD is affected by aging has remained an open question. In the present work, we investigated the effect of aging on the modulation of beta oscillations and their putative link with motor performance. We collected magnetoencephalography (MEG) data from younger and older adults during a resting-state period and motor handgrip paradigms, which included sustained and dynamic contractions, to quantify spontaneous and motor-related beta oscillatory activity. Beta power at rest was found to be significantly increased in the motor cortex of older adults. During dynamic hand contractions, MRBD was more pronounced in older participants in frontal, premotor and motor brain regions. These brain areas also exhibited age-related decreases in cortical thickness; however, the magnitude of MRBD and cortical thickness were not found to be associated after controlling for age. During sustained hand contractions, MRBD exhibited a decrease in magnitude compared to dynamic contraction periods in both groups and did not show age-related differences. This suggests that the amplitude change in MRBD between dynamic and sustained contractions is larger in older compared to younger adults. We further probed for a relationship between beta oscillations and motor behaviour and found that greater MRBD in primary motor cortices was related to degraded motor performance beyond age, but our results suggested that age-related differences in beta oscillations were not predictive of motor performance.

Functional and effective reorganization of the aging brain during unimanual and bimanual hand movements.

Motor performance decline observed during aging is linked to changes in brain structure and function, however, the precise neural reorganization associated with these changes remains largely unknown. We investigated the neurophysiological correlates of this reorganization by quantifying functional and effective brain network connectivity in elderly individuals (n = 11; mean age = 67.5 years), compared to young adults (n = 12; mean age = 23.7 years), while they performed visually-guided unimanual and bimanual handgrips inside the magnetoencephalography (MEG) scanner. Through a combination of principal component analysis and Granger causality, we observed age-related increases in functional and effective connectivity in whole-brain, task-related motor networks. Specifically, elderly individuals demonstrated (i) greater information flow from contralateral parietal and ipsilateral secondary motor regions to the left primary motor cortex during the unimanual task and (ii) decreased interhemispheric temporo-frontal communication during the bimanual task. Maintenance of motor performance and task accuracy in elderly was achieved by hyperactivation of the task-specific motor networks, reflecting a possible mechanism by which the aging brain recruits additional resources to counteract known myelo- and cytoarchitectural changes. Furthermore, resting-state sessions acquired before and after each motor task revealed that both older and younger adults maintain the capacity to adapt to task demands via network-wide increases in functional connectivity. Collectively, our study consolidates functional connectivity and directionality of information flow in systems-level cortical networks during aging and furthers our understanding of neuronal flexibility in motor processes.

Prediction of the Time to Syncope Occurrence in Patients Diagnosed with Vasovagal Syncope.

OBJECTIVE: In this study we aimed to predict the time to syncope occurrence (TSO) in patients with vasovagal syncope (VVS), solely based on measurements recorded during the supine position of the head-up tilt (HUT) testing protocol. METHODS: We extracted various time and frequency domain features related to morphological aspects of arterial blood pressure (ABP) and the electrocardiogram (ECG) raw signals as well as to dynamic interactions between beat-to-beat ABP, heart rate, and cerebral blood flow velocity. From these we identified the most predictive features related to TSO. RESULTS: Specifically, when no orthostatic stress is involved, TSO in VVS patients can be predicted with high accuracy from a set of only five ECG features.

Effects of continuous positive airway pressure and isocapnic-hypoxia on cerebral autoregulation in patients with obstructive sleep apnoea.

KEY POINTS: Altered cerebral autoregulation (CA) in obstructive sleep apnoea (OSA) patients may contribute to increased stroke risk in this population; the gold standard treatment for OSA is continuous positive airway pressure, which improves cerebrovascular regulation and may decrease the risk of stroke. Isocapnic-hypoxia impairs CA in healthy subjects, but it remains unknown in OSA whether impaired CA is further exacerbated by isocapnic-hypoxia and whether it is improved by treatment with continuous positive airway pressure. During normoxia, CA was altered in the more severe but not in the less severe OSA patients, while, in contrast, during isocapnic-hypoxia, CA was similar between groups and tended to improve in patients with more severe OSA compared to normoxia. From a clinical perspective, one month of continuous positive airway pressure treatment does not improve CA. From a physiological perspective, this study suggests that sympathetic overactivity may be responsible for altered CA in the more severe OSA patients. ABSTRACT: Cerebral autoregulation (CA) impairment may contribute to the increased risk of stroke associated with obstructive sleep apnoea (OSA). It is unknown if impaired CA is further exacerbated by isocapnic-hypoxia and whether it is improved by treatment of OSA with continuous positive airway pressure (CPAP). CA was assessed during wakefulness in 53 OSA patients (50.3 ± 9.3 years) and 21 controls (49.8 ± 8.6 years) at baseline and following a minimum of 1 month of effective CPAP therapy (OSA patients, n = 40). Control participants (n = 21) performed a follow-up visit to control for time effects within OSA patients between baseline and the post-CPAP visit. Beat-by-beat middle cerebral artery blood flow velocity and mean arterial blood pressure (MBP), and breath-by-breath end-tidal partial pressure of CO2 (P ET ,CO2) were monitored. CA was determined during normoxia and isocapnic-hypoxia using transfer function (phase and gain) and coherence analysis (including multiple and partial coherence (using MBP and P ET ,CO2 as inputs)) in the very low frequency range (0.03-0.07 Hz). OSA patients were divided into two subgroups (less severe and more severe) based upon the median respiratory disturbance index (RDI). During normoxia, the more severe OSA patients (RDI 45.9 ± 10.3) exhibited altered CA compared to controls and the less severe OSA patients (RDI 24.5 ± 5.9). In contrast, during isocapnic-hypoxia, CA was similar between groups. CPAP had no effect on CA. In conclusion, CA is altered in the more severe OSA patients during normoxia but not during isocapnic-hypoxia and CPAP treatment does not impact CA.

Assessment of nonlinear interactions in event-related potentials elicited by stimuli presented at short interstimulus intervals using single-trial data.

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.

On the variability of dynamic functional connectivity assessment methods.

BACKGROUND: Dynamic functional connectivity (dFC) has become an important measure for understanding brain function and as a potential biomarker. However, various methodologies have been developed for assessing dFC, and it is unclear how the choice of method affects the results. In this work, we aimed to study the results variability of commonly used dFC methods. METHODS: We implemented 7 dFC assessment methods in Python and used them to analyze the functional magnetic resonance imaging data of 395 subjects from the Human Connectome Project. We measured the similarity of dFC results yielded by different methods using several metrics to quantify overall, temporal, spatial, and intersubject similarity. RESULTS: Our results showed a range of weak to strong similarity between the results of different methods, indicating considerable overall variability. Somewhat surprisingly, the observed variability in dFC estimates was found to be comparable to the expected functional connectivity variation over time, emphasizing the impact of methodological choices on the final results. Our findings revealed 3 distinct groups of methods with significant intergroup variability, each exhibiting distinct assumptions and advantages. CONCLUSIONS: Overall, our findings shed light on the impact of dFC assessment analytical flexibility and highlight the need for multianalysis approaches and careful method selection to capture the full range of dFC variation. They also emphasize the importance of distinguishing neural-driven dFC variations from physiological confounds and developing validation frameworks under a known ground truth. To facilitate such investigations, we provide an open-source Python toolbox, PydFC, which facilitates multianalysis dFC assessment, with the goal of enhancing the reliability and interpretability of dFC studies.

Spiking Laguerre Volterra networks-predicting neuronal activity from local field potentials.

Objective.Understanding the generative mechanism between local field potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations.Approach.Here, we fill this gap by proposing novel spiking Laguerre-Volterra network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics.Main results.The proposed networks were applied on extracellular microelectrode recordings of Parkinson's Disease patients during deep brain stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features.Significance.These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.

Time-domain methods for quantifying dynamic cerebral blood flow autoregulation: Review and recommendations. A white paper from the Cerebrovascular Research Network (CARNet).

Cerebral Autoregulation (CA) is an important physiological mechanism stabilizing cerebral blood flow (CBF) in response to changes in cerebral perfusion pressure (CPP). By maintaining an adequate, relatively constant supply of blood flow, CA plays a critical role in brain function. Quantifying CA under different physiological and pathological states is crucial for understanding its implications. This knowledge may serve as a foundation for informed clinical decision-making, particularly in cases where CA may become impaired. The quantification of CA functionality typically involves constructing models that capture the relationship between CPP (or arterial blood pressure) and experimental measures of CBF. Besides describing normal CA function, these models provide a means to detect possible deviations from the latter. In this context, a recent white paper from the Cerebrovascular Research Network focused on Transfer Function Analysis (TFA), which obtains frequency domain estimates of dynamic CA. In the present paper, we consider the use of time-domain techniques as an alternative approach. Due to their increased flexibility, time-domain methods enable the mitigation of measurement/physiological noise and the incorporation of nonlinearities and time variations in CA dynamics. Here, we provide practical recommendations and guidelines to support researchers and clinicians in effectively utilizing these techniques to study CA.

Non-invasive estimation of arterial blood pressure fluctuations using a peripheral photoplethysmograph inside the MRI scanner.

The blood-oxygen-level-dependent (BOLD) signal measured by functional magnetic resonance imaging (fMRI) is modulated by neural activity through the neurovascular coupling effect, as well as non-neural factors of physiological origin such as heart rate, respiration, and arterial blood pressure (ABP). While the former two effects have been previously characterized, the modulation of the BOLD signal by ABP fluctuations is still poorly understood. This is largely due to the difficulty of obtaining reliable ABP measurements in the MRI environment. Here, we propose a combined experimental and mathematical modeling framework to estimate ABP fluctuations inside the MRI scanner using photoplethysmography (PPG). Specifically, we used concurrent PPG and ABP measurements obtained outside the scanner to train the mathematical model and applied it to PPG measurements obtained inside the MRI scanner. Our results suggest good agreement between the model-predicted and experimentally measured ABP fluctuations and region specific correlations with the BOLD fluctuations.