Pedestrian Safety in Frontal Tram Collision, Part 1: Historical Overview and Experimental-Data-Based Biomechanical Study of Head Clashing in Frontal and Side Impacts.

This article represents the first paper in a two-part series dealing with safety during tram-pedestrian collisions. This research is dedicated to the safety of trams for pedestrians during collisions and is motivated by the increased number of lethal cases. The first part of this paper includes an overview of tram face development from the earliest designs to the current ones in use and, at the same time, provides a synopsis and explanation of the technical context, including a link to current and forthcoming legislation. The historical design development can be characterised by three steps, from an almost vertical front face, to leaned and pointed shapes, to the current inclined low-edged windshield without a protruding coupler. However, since most major manufacturers now export their products worldwide and customisation is only of a technically insignificant nature, our conclusions are generalisable (supported by the example of Berlin). The most advantageous shape of the tram's front, minimising the effects on pedestrians in all collision phases, has evolved rather spontaneously and was unprompted, and it is now being built into the European Commission regulations. The goal of the second part of this paper is to conduct a series of tram-pedestrian collisions with a focus on the frontal and side impacts using a crash test dummy (anthropomorphic test device-ATD). Four tram types approaching the collision at four different impact speeds (5 km/h, 10 km/h, 15 km/h, and 20 km/h) were used. The primary outcome variable was the resultant head acceleration. The risk and severity of possible head injuries were assessed using the head injury criterion (HIC15) and its linkage to the injury level on the Abbreviated Injury Scale (AIS). The results showed increasing head impacts with an increasing speed for all tram types and collision scenarios. Higher values of head acceleration were reached during the frontal impact (17-124 g) compared to the side one (2-84 g). The HIC15 values did not exceed the value of 300 for any experimental setting, and the probability of AIS4+ injuries did not exceed 10%. The outcomes of tram-pedestrian collisions can be influenced by the ATD's position and orientation, the impact speed and front-end design of trams, and the site of initial contact.

Pedestrian Safety in Frontal Tram Collision, Part 2: Laminated Glass as a Crucial Part of the Absorption and Deformation Zone-Its Impact Test and Analysis.

As was shown in the previous part of the study, windshields are an important part of the passive safety means of modern low-floor trams with an extraordinary effect on pedestrian safety in a pedestrian-tram collisions. Therefore, maximum attention must be paid to the definition of tram windshield characteristics. This article describes a windshield crash test, from which data are obtained to verify the feasibility of the applied computational approaches. A developed analytical model is utilised for a simple description of the energy balance during collision with an illustrative definition of the important parameters of laminated glass as well as their clear physical interpretations. The finite element analysis (FEA) performed in Ansys software using two versions of material definition, namely a simpler (*MAT_ELASTIC with nonlocal failure criterion) and a more complex (*MAT_GLASS with brittle stress-state-dependent failure) material model, which are presented as suitable for obtaining a detailed description of the shattering process of laminated glass, which can also be used effectively in windshield engineering.

Predicting neuronal response properties from hemodynamic responses in the auditory cortex.

Recent functional MRI (fMRI) studies have highlighted differences in responses to natural sounds along the rostral-caudal axis of the human superior temporal gyrus. However, due to the indirect nature of the fMRI signal, it has been challenging to relate these fMRI observations to actual neuronal response properties. To bridge this gap, we present a forward model of the fMRI responses to natural sounds combining a neuronal model of the auditory cortex with physiological modeling of the hemodynamic BOLD response. Neuronal responses are modeled with a dynamic recurrent firing rate model, reflecting the tonotopic, hierarchical processing in the auditory cortex along with the spectro-temporal tradeoff in the rostral-caudal axis of its belt areas. To link modeled neuronal response properties with human fMRI data in the auditory belt regions, we generated a space of neuronal models, which differed parametrically in spectral and temporal specificity of neuronal responses. Then, we obtained predictions of fMRI responses through a biophysical model of the hemodynamic BOLD response (P-DCM). Using Bayesian model comparison, our results showed that the hemodynamic BOLD responses of the caudal belt regions in the human auditory cortex were best explained by modeling faster temporal dynamics and broader spectral tuning of neuronal populations, while rostral belt regions were best explained through fine spectral tuning combined with slower temporal dynamics. These results support the hypotheses of complementary neural information processing along the rostral-caudal axis of the human superior temporal gyrus.

A dynamical model of the laminar BOLD response.

High-resolution functional magnetic resonance imaging (fMRI) using blood oxygenation dependent level-dependent (BOLD) signal is an increasingly popular tool to non-invasively examine neuronal processes at the mesoscopic level. However, as the BOLD signal stems from hemodynamic changes, its temporal and spatial properties do not match those of the underlying neuronal activity. In particular, the laminar BOLD response (LBR), commonly measured with gradient-echo (GE) MRI sequence, is confounded by non-local changes in deoxygenated hemoglobin and cerebral blood volume propagated within intracortical ascending veins, leading to a unidirectional blurring of the neuronal activity distribution towards the cortical surface. Here, we present a new cortical depth-dependent model of the BOLD response based on the principle of mass conservation, which takes the effect of ascending (and pial) veins on the cortical BOLD responses explicitly into account. It can be used to dynamically model cortical depth profiles of the BOLD signal as a function of various baseline- and activity-related physiological parameters for any spatiotemporal distribution of neuronal changes. We demonstrate that the commonly observed spatial increase of LBR is mainly due to baseline blood volume increase towards the surface. In contrast, an occasionally observed local maximum in the LBR (i.e. the so-called "bump") is mainly due to spatially inhomogeneous neuronal changes rather than locally higher baseline blood volume. In addition, we show that the GE-BOLD signal laminar point-spread functions, representing the signal leakage towards the surface, depend on several physiological parameters and on the level of neuronal activity. Furthermore, even in the case of simultaneous neuronal changes at each depth, inter-laminar delays of LBR transients are present due to the ascending vein. In summary, the model provides a conceptual framework for the biophysical interpretation of common experimental observations in high-resolution fMRI data. In the future, the model will allow for deconvolution of the spatiotemporal hemodynamic bias of the LBR and provide an estimate of the underlying laminar excitatory and inhibitory neuronal activity.

Impact of acquisition and analysis strategies on cortical depth-dependent fMRI.

Functional MRI at ultra-high magnetic fields (≥ 7T) provides the opportunity to probe columnar and laminar processing in the human brain in vivo at sub-millimeter spatial scales. However, fMRI data only indirectly reflects the neuronal laminar profile due to a bias to ascending and pial veins inherent in gradient- and spin-echo BOLD fMRI. In addition, accurate delineation of the cortical depths is difficult, due to the relatively large voxel sizes and lack of sufficient tissue contrast in the functional images. In conventional depth-dependent fMRI studies, anatomical and functional data are acquired with different image read-out modules, the fMRI data are distortion-corrected and vascular biases are accounted for by subtracting the depth-dependent activation profiles of different stimulus conditions. In this study, using high-resolution gradient-echo fMRI data (0.7 mm isotropic) of the human visual cortex, we propose instead, that depth-dependent functional information is best preserved if data analysis is performed in the original functional data space. To achieve this, we acquired anatomical images with high tissue contrast and similar distortion to the functional images using multiple inversion-recovery time EPI, thereby eliminating the need to un-distort the fMRI data. We demonstrate higher spatial accuracy for the cortical layer definitions of this approach as compared to the more conventional approach using MP2RAGE anatomy. In addition, we provide theoretical arguments and empirical evidence that vascular biases can be better accounted for using division instead of subtraction of the depth-dependent profiles. Finally, we show that the hemodynamic response of grey matter has relatively stronger post-stimulus undershoot than the pial vein voxels. In summary, we show that the choice of fMRI data acquisition and processing can impact observable differences in the cortical depth profiles and present evidence that cortical depth-dependent modulation of the BOLD signal can be resolved using gradient-echo imaging.

Echo-time dependence of the BOLD response transients - A window into brain functional physiology.

The blood oxygenation level-dependent (BOLD) fMRI response to neuronal activation results from a complex interplay of induced metabolic and vascular changes. Thus, its transients, such as initial overshoot and post-stimulus undershoot, provide a window into the dynamic relationships of the underlying physiological variables. In this study, we propose multi-echo fMRI as a tool to investigate the physiological underpinnings of the BOLD signal, in particular, and brain functional physiology, in general. In the human visual cortex at 3 T, we observed that the BOLD response is nonlinearly dependent on echo-time (TE) and the amount of nonlinearity varies during the entire time-course. Fitting a linear model to this nonlinear relationship resulted in a positive intercept at TE = 0 ms. The time-course of the intercept exhibited fast and slow modulations, distinctly different both from the BOLD response and cerebral blood flow (CBF). In order to shed light on the TE-dependence of the BOLD signal and the intercept time-course, we performed simulations based on a nonlinear two-compartmental BOLD signal model combined with the dynamic balloon model. The modeling suggests that the intercept time-course reflects a weighted sum of deoxyhemoglobin concentration and venous CBV signal changes. We demonstrate that only CBF-venous blood volume (CBV) uncoupling but not CBF-oxygen metabolism (CMRO2) uncoupling can fully account for our experimental observations. In particular, these results strongly argue for a slow evolution of the venous CBV together with stimulus-type-dependent CBF transients (the latter being tightly coupled with CMRO2) to be responsible for the BOLD signal adaptation during stimulation and for the post-stimulus undershoot. Thus, BOLD signal transients are composed of smoothed version of neuronal time-course as reflected in CBF and CMRO2 and secondary vascular processes due to biomechanics of venous blood vessels, and multi-echo fMRI in combination with modeling provides invaluable insights into these physiological processes.

On the importance of modeling fMRI transients when estimating effective connectivity: A dynamic causal modeling study using ASL data.

Effective connectivity is commonly assessed using blood oxygenation level-dependent (BOLD) signals. In (Havlicek et al., 2015), we presented a novel, physiologically informed dynamic causal model (P-DCM) that extends current generative models. We demonstrated the improvements afforded by P-DCM in terms of the ability to model commonly observed neuronal and vascular transients in single regions. Here, we assess the ability of the novel and previous DCM variants to estimate effective connectivity among a network of five ROIs driven by a visuo-motor task. We demonstrate that connectivity estimates depend sensitively on the DCM used, due to differences in the modeling of hemodynamic response transients; such as the post-stimulus undershoot or adaptation during stimulation. In addition, using a novel DCM for arterial spin labeling (ASL) fMRI that measures BOLD and CBF signals simultaneously, we confirmed our findings (by using the BOLD data alone and in conjunction with CBF). We show that P-DCM provides better estimates of effective connectivity, regardless of whether it is applied to BOLD data alone or to ASL time-series, and that all new aspects of P-DCM (i.e. neuronal, neurovascular, hemodynamic components) constitute an improvement compared to those in the previous DCM variants. In summary, (i) accurate modeling of fMRI response transients is crucial to obtain valid effective connectivity estimates and (ii) any additional hemodynamic data, such as provided by ASL, increases the ability to disambiguate neuronal and vascular effects present in the BOLD signal.

Tonotopic maps in human auditory cortex using arterial spin labeling.

A tonotopic organization of the human auditory cortex (AC) has been reliably found by neuroimaging studies. However, a full characterization and parcellation of the AC is still lacking. In this study, we employed pseudo-continuous arterial spin labeling (pCASL) to map tonotopy and voice selective regions using, for the first time, cerebral blood flow (CBF). We demonstrated the feasibility of CBF-based tonotopy and found a good agreement with BOLD signal-based tonotopy, despite the lower contrast-to-noise ratio of CBF. Quantitative perfusion mapping of baseline CBF showed a region of high perfusion centered on Heschl's gyrus and corresponding to the main high-low-high frequency gradients, co-located to the presumed primary auditory core and suggesting baseline CBF as a novel marker for AC parcellation. Furthermore, susceptibility weighted imaging was employed to investigate the tissue specificity of CBF and BOLD signal and the possible venous bias of BOLD-based tonotopy. For BOLD only active voxels, we found a higher percentage of vein contamination than for CBF only active voxels. Taken together, we demonstrated that both baseline and stimulus-induced CBF is an alternative fMRI approach to the standard BOLD signal to study auditory processing and delineate the functional organization of the auditory cortex. Hum Brain Mapp 38:1140-1154, 2017. © 2016 Wiley Periodicals, Inc.

High prevalence of Salmonella and IMP-4-producing Enterobacteriaceae in the silver gull on Five Islands, Australia.

OBJECTIVES: The objective of this study was to investigate the silver gull as an indicator of environmental contamination by salmonellae and carbapenemase-producing Enterobacteriaceae (CPE) in south-east Australia. METHODS: A total of 504 cloacal samples were collected from gull chicks at three nesting colonies in New South Wales, Australia [White Bay (n = 144), Five Islands (n = 200) and Montague Island (n = 160)] and were examined for salmonellae and CPE. Isolates were tested for carbapenemase genes and susceptibility to 14 antibiotics. Clonality was determined by PFGE and MLST. Genetic context and conjugative transfer of the carbapenemase gene were determined. RESULTS: A total of 120 CPE of 10 species, mainly Escherichia coli (n = 85), carrying the gene blaIMP-4, blaIMP-38 or blaIMP-26 were obtained from 80 (40%) gulls from Five Islands. Thirty percent of birds from this colony were colonized by salmonellae. Most isolates contained the gene within a class 1 integron showing a blaIMP-4-qacG-aacA4-catB3 array. The blaIMP gene was carried by conjugative plasmids of variable sizes (80-400 kb) and diverse replicons, including HI2-N (n = 30), HI2 (11), A/C (17), A/C-Y (2), L/M (5), I1 (1) and non-typeable (6). Despite the overall high genetic variability, common clones and plasmid types were shared by different birds and bacterial isolates, respectively. CONCLUSIONS: Our data demonstrate a large-scale transmission of carbapenemase-producing bacteria into wildlife, likely as a result of the feeding habits of the birds at a local waste depot. The isolates from gulls showed significant similarities with clinical isolates from Australia, suggesting the human origin of the isolates. The sources of CPE for gulls on Five Islands should be explored and proper measures applied to stop the transmission into the environment.

Primary isolated osteoma cutis causing eyelid deformation and strabismus in a dog.

Osteoma cutis describes bone formation in skin and is well documented in the medical literature, but veterinary reports are few. We report a single case of a juvenile samoyed that was referred for assessment of a superior eyelid anomaly. Exploratory surgery and histopathology revealed the presence of mature, lamellar bone within the superior eyelid. The histologic appearance was consistent with primary osteoma cutis. The presence of the ossification within the deep dermis of the eyelid was associated with an abnormal conformation causing trichiasis, keratitis and dorsal strabismus. Identification of the osseous lesion during surgery and its removal was curative with no recurrence of disease during the 32 month follow-up period.

Physiologically informed dynamic causal modeling of fMRI data.

The functional MRI (fMRI) signal is an indirect measure of neuronal activity. In order to deconvolve the neuronal activity from the experimental fMRI data, biophysical generative models have been proposed describing the link between neuronal activity and the cerebral blood flow (the neurovascular coupling), and further the hemodynamic response and the BOLD signal equation. These generative models have been employed both for single brain area deconvolution and to infer effective connectivity in networks of multiple brain areas. In the current paper, we introduce a new fMRI model inspired by experimental observations about the physiological underpinnings of the BOLD signal and compare it with the generative models currently used in dynamic causal modeling (DCM), a widely used framework to study effective connectivity in the brain. We consider three fundamental aspects of such generative models for fMRI: (i) an adaptive two-state neuronal model that accounts for a wide repertoire of neuronal responses during and after stimulation; (ii) feedforward neurovascular coupling that links neuronal activity to blood flow; and (iii) a balloon model that can account for vascular uncoupling between the blood flow and the blood volume. Finally, we adjust the parameterization of the BOLD signal equation for different magnetic field strengths. This paper focuses on the form, motivation and phenomenology of DCMs for fMRI and the characteristics of the various models are demonstrated using simulations. These simulations emphasize a more accurate modeling of the transient BOLD responses - such as adaptive decreases to sustained inputs during stimulation and the post-stimulus undershoot. In addition, we demonstrate using experimental data that it is necessary to take into account both neuronal and vascular transients to accurately model the signal dynamics of fMRI data. By refining the models of the transient responses, we provide a more informed perspective on the underlying neuronal process and offer new ways of inferring changes in local neuronal activity and effective connectivity from fMRI.

Functional network connectivity during rest and task conditions: a comparative study.

Functional connectivity (FC) examines temporal statistical dependencies among distant brain regions by means of seed-based analysis or independent component analysis (ICA). Spatial ICA also makes it possible to investigate FC at the network level, termed functional network connectivity (FNC). The dynamics of each network (ICA component), which may consist of several remote regions is described by the ICA time-course of that network; hence, FNC studies statistical dependencies among ICA time-courses. In this article, we compare comprehensively FNC in the resting state and during performance of an auditory oddball (AOD) task in 28 healthy subjects on relevant (nonartifactual) brain networks. The results show global FNC decrease during the performance of the task. In addition, we show that specific networks enlarge and/or demonstrate higher activity during the performance of the task. The results suggest that performing an active task like AOD may be facilitated by recruiting more neurons and higher activation of related networks rather than collaboration among different brain networks. We also evaluated the impact of temporal filtering on FNC analyses. Results showed that the results are not significantly affected by filtering.

Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering.

This paper presents a new approach to inverting (fitting) models of coupled dynamical systems based on state-of-the-art (cubature) Kalman filtering. Crucially, this inversion furnishes posterior estimates of both the hidden states and parameters of a system, including any unknown exogenous input. Because the underlying generative model is formulated in continuous time (with a discrete observation process) it can be applied to a wide variety of models specified with either ordinary or stochastic differential equations. These are an important class of models that are particularly appropriate for biological time-series, where the underlying system is specified in terms of kinetics or dynamics (i.e., dynamic causal models). We provide comparative evaluations with generalized Bayesian filtering (dynamic expectation maximization) and demonstrate marked improvements in accuracy and computational efficiency. We compare the schemes using a series of difficult (nonlinear) toy examples and conclude with a special focus on hemodynamic models of evoked brain responses in fMRI. Our scheme promises to provide a significant advance in characterizing the functional architectures of distributed neuronal systems, even in the absence of known exogenous (experimental) input; e.g., resting state fMRI studies and spontaneous fluctuations in electrophysiological studies. Importantly, unlike current Bayesian filters (e.g. DEM), our scheme provides estimates of time-varying parameters, which we will exploit in future work on the adaptation and enabling of connections in the brain.

Determining laminar neuronal activity from BOLD fMRI using a generative model.

Laminar fMRI using the BOLD contrast enables the non-invasive investigation of mesoscopic functional circuits in the human brain. However, the laminar neuronal activity is spatiotemporally biased in the observed cortical depth profiles of the BOLD signal. In this study, we propose a generative fMRI signal model, comprehensively covering the relationship between cortical depth-dependent changes in excitatory and inhibitory neuronal activity with the sampling of the BOLD signal with finite voxels. The generative model allowed us to investigate pertinent questions regarding the accuracy of the laminar BOLD signal relative to the neuronal activity, and we found that: a) condition differences in laminar BOLD signals may be more reflective of neuronal activity than single condition BOLD signal depth profiles; b) angular dependence of the BOLD signal induces significant signal variability, which can mask underlying activity profiles; c) even if only three neuronal depths are of interest, more BOLD signal depths should be considered in the analysis. In addition, we recommend that the laminar BOLD data should be displayed using the centroid method to appreciate its spatial distribution in the original resolution. Finally, we showed that Bayesian model inversion of the generative model can improve sensitivity and specificity of assessing depth-dependent neuronal changes both for steady-state and dynamically.

Sub-millimetre resolution laminar fMRI using Arterial Spin Labelling in humans at 7 T.

Laminar fMRI at ultra-high magnetic field strength is typically carried out using the Blood Oxygenation Level-Dependent (BOLD) contrast. Despite its unrivalled sensitivity to detecting activation, the BOLD contrast is limited in its spatial specificity due to signals stemming from intra-cortical ascending and pial veins. Alternatively, regional changes in perfusion (i.e., cerebral blood flow through tissue) are colocalised to neuronal activation, which can be non-invasively measured using Arterial Spin Labelling (ASL) MRI. In addition, ASL provides a quantitative marker of neuronal activation in terms of perfusion signal, which is simultaneously acquired along with the BOLD signal. However, ASL for laminar imaging is challenging due to the lower SNR of the perfusion signal and higher RF power deposition i.e., specific absorption rate (SAR) of ASL sequences. In the present study, we present for the first time in humans, isotropic sub-millimetre spatial resolution functional perfusion images using Flow-sensitive Alternating Inversion Recovery (FAIR) ASL with a 3D-EPI readout at 7 T. We show that robust statistical activation maps can be obtained with perfusion-weighting in a single session. We observed the characteristic BOLD amplitude increase towards the superficial laminae, and, in apparent discrepancy, the relative perfusion profile shows a decrease of the amplitude and the absolute perfusion profile a much smaller increase towards the cortical surface. Considering the draining vein effect on the BOLD signal using model-based spatial "convolution", we show that the empirically measured perfusion and BOLD profiles are, in fact, consistent with each other. This study demonstrates that laminar perfusion fMRI in humans is feasible at 7 T and that caution must be exercised when interpreting BOLD signal laminar profiles as direct representation of the cortical distribution of neuronal activity.

Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data.

Increasing interest in understanding dynamic interactions of brain neural networks leads to formulation of sophisticated connectivity analysis methods. Recent studies have applied Granger causality based on standard multivariate autoregressive (MAR) modeling to assess the brain connectivity. Nevertheless, one important flaw of this commonly proposed method is that it requires the analyzed time series to be stationary, whereas such assumption is mostly violated due to the weakly nonstationary nature of functional magnetic resonance imaging (fMRI) time series. Therefore, we propose an approach to dynamic Granger causality in the frequency domain for evaluating functional network connectivity in fMRI data. The effectiveness and robustness of the dynamic approach was significantly improved by combining a forward and backward Kalman filter that improved estimates compared to the standard time-invariant MAR modeling. In our method, the functional networks were first detected by independent component analysis (ICA), a computational method for separating a multivariate signal into maximally independent components. Then the measure of Granger causality was evaluated using generalized partial directed coherence that is suitable for bivariate as well as multivariate data. Moreover, this metric provides identification of causal relation in frequency domain, which allows one to distinguish the frequency components related to the experimental paradigm. The procedure of evaluating Granger causality via dynamic MAR was demonstrated on simulated time series as well as on two sets of group fMRI data collected during an auditory sensorimotor (SM) or auditory oddball discrimination (AOD) tasks. Finally, a comparison with the results obtained from a standard time-invariant MAR model was provided.

Surgical management of vertebral malformation in a Manx cat.

A 15-week-old Manx kitten was presented for evaluation of progressive hind limb paresis and ataxia. Spinal radiographs and computed tomography revealed a malformation of T3 causing thoracic spinal cord compression. Decompressive surgery consisting of dorsal laminectomy, without vertebral stabilisation, resulted in improvement of clinical signs.

Resolving laminar activation in human V1 using ultra-high spatial resolution fMRI at 7T.

The mesoscopic organization of the human neocortex is of great interest for cognitive neuroscience. However, fMRI in humans typically maps the functional units of cognitive processing on a macroscopic level. With the advent of ultra-high field MRI (≥7T), it has become possible to acquire fMRI data with sub-millimetre resolution, enabling probing the laminar and columnar circuitry in humans. Currently, laminar BOLD responses are not directly observed but inferred via data analysis, due to coarse spatial resolution of fMRI (e.g. 0.7-0.8 mm isotropic) relative to the extent of histological laminae. In this study, we introduce a novel approach for mapping the cortical BOLD response at the spatial scale of cortical layers and columns at 7T (an unprecedented 0.1 mm, either in the laminar or columnar direction). We demonstrate experimentally and using simulations, the superiority of the novel approach compared to standard approaches for human laminar fMRI in terms of effective spatial resolution in either laminar or columnar direction. In addition, we provide evidence that the laminar BOLD signal profile is not homogeneous even over short patches of cortex. In summary, the proposed novel approach affords the ability to directly study the mesoscopic organization of the human cortex, thus, bridging the gap between human cognitive neuroscience and invasive animal studies.

Determining Excitatory and Inhibitory Neuronal Activity from Multimodal fMRI Data Using a Generative Hemodynamic Model.

Hemodynamic responses, in general, and the blood oxygenation level-dependent (BOLD) fMRI signal, in particular, provide an indirect measure of neuronal activity. There is strong evidence that the BOLD response correlates well with post-synaptic changes, induced by changes in the excitatory and inhibitory (E-I) balance between active neuronal populations. Typical BOLD responses exhibit transients, such as the early-overshoot and post-stimulus undershoot, that can be linked to transients in neuronal activity, but they can also result from vascular uncoupling between cerebral blood flow (CBF) and venous cerebral blood volume (venous CBV). Recently, we have proposed a novel generative hemodynamic model of the BOLD signal within the dynamic causal modeling framework, inspired by physiological observations, called P-DCM (Havlicek et al., 2015). We demonstrated the generative model's ability to more accurately model commonly observed neuronal and vascular transients in single regions but also effective connectivity between multiple brain areas (Havlicek et al., 2017b). In this paper, we additionally demonstrate the versatility of the generative model to jointly explain dynamic relationships between neuronal and hemodynamic physiological variables underlying the BOLD signal using multi-modal data. For this purpose, we utilized three distinct data-sets of experimentally induced responses in the primary visual areas measured in human, cat, and monkey brain, respectively: (1) CBF and BOLD responses; (2) CBF, total CBV, and BOLD responses (Jin and Kim, 2008); and (3) positive and negative neuronal and BOLD responses (Shmuel et al., 2006). By fitting the generative model to the three multi-modal experimental data-sets, we showed that the presence or absence of dynamic features in the BOLD signal is not an unambiguous indication of presence or absence of those features on the neuronal level. Nevertheless, the generative model that takes into account the dynamics of the physiological mechanisms underlying the BOLD response allowed dissociating neuronal from vascular transients and deducing excitatory and inhibitory neuronal activity time-courses from BOLD data alone and from multi-modal data.

Nonparametric hemodynamic deconvolution of FMRI using homomorphic filtering.

Functional magnetic resonance imaging (fMRI) is an indirect measure of neural activity which is modeled as a convolution of the latent neuronal response and the hemodynamic response function (HRF). Since the sources of HRF variability can be nonneural in nature, the measured fMRI signal does not faithfully represent underlying neural activity. Therefore, it is advantageous to deconvolve the HRF from the fMRI signal. However, since both latent neural activity and the voxel-specific HRF is unknown, the deconvolution must be blind. Existing blind deconvolution approaches employ highly parameterized models, and it is unclear whether these models have an over fitting problem. In order to address these issues, we 1) present a nonparametric deconvolution method based on homomorphic filtering to obtain the latent neuronal response from the fMRI signal and, 2) compare our approach to the best performing existing parametric model based on the estimation of the biophysical hemodynamic model using the Cubature Kalman Filter/Smoother. We hypothesized that if the results from nonparametric deconvolution closely resembled that obtained from parametric deconvolution, then the problem of over fitting during estimation in highly parameterized deconvolution models of fMRI could possibly be over stated. Both simulations and experimental results demonstrate support for our hypothesis since the estimated latent neural response from both parametric and nonparametric methods were highly correlated in the visual cortex. Further, simulations showed that both methods were effective in recovering the simulated ground truth of the latent neural response.