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.

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Arterial spin labeling (ASL) is the primary non-invasive MRI approach to measure baseline cerebral blood flow (CBF) in healthy subjects and patients. ASL also allows concurrent functional BOLD signal and CBF measurements, but the latter typically suffer from low contrast-to-noise (CNR) ratio. Ultra-high-field imaging significantly boosts BOLD signal CNR. However, it is contested whether also CBF CNR benefits from increasing magnetic field strength, especially given that technical challenges related to field inhomogeneities and power deposition constraints exist. Recently, we presented an optimized PASL technique that utilizes tr-FOCI inversion pulses and dielectric pads to overcome the temporal resolution limitations of previous 7T ASL implementations (Ivanov et al., in press; 2017). The primary goal of this study was to compare its performance to that of 3T ASL approaches - both pulsed ASL (PASL) and pseudo-continuous (pCASL) - concerning functional studies using simultaneous CBF and BOLD signal acquisition. To this aim, we investigated a wide range of parameters that can influence CBF and BOLD signal sensitivities: spatial resolution, labeling scheme, parallel imaging and echo time. We found that 7T ASL is superior in terms of CBF and BOLD temporal signal-to-noise ratio (SNR) and activation volume compared to all 3T ASL variants, in particular at high spatial resolution. Our results show that the advantages of 7T for ASL stem from increased image SNR, especially when parallel imaging is used. The gray matter baseline CBF was in good agreement for all 3T ASL variants, but a significantly lower value was obtained at 7T. The labeling scheme utilized was also found to significantly influence the measured perfusion territories CBF. In conclusion, a single-echo accelerated 7T PASL is recommended for high spatial and temporal resolution CBF and BOLD imaging, while a 3T dual-echo pCASL approach without parallel imaging may be preferred for low (i.e., 3mm isotropic and lower) resolution functional perfusion and BOLD applications.

In diagnostic nuclear medicine, mean absorbed doses to patients' organs and effective doses are published for standard stylised anatomic models. To provide more realistic and detailed geometries of the human morphology, the International Commission on Radiological Protection (ICRP) has recently adopted male and female voxel phantoms to represent the reference adult. This work investigates the impact of the use of these new computational phantoms. The absorbed doses were calculated for 11 different radiopharmaceuticals currently used in diagnostic nuclear medicine. They were calculated for the ICRP 110 reference computational phantoms using the OEDIPE software and the MCNP extended Monte Carlo code. The biokinetic models were issued from ICRP Publications 53, 80 and 106. The results were then compared with published values given in these ICRP Publications. To discriminate the effect of anatomical differences on organ doses from the effect of the calculation method, the Monte Carlo calculations were repeated for the reference adult stylised phantom. The voxel effect, the influence of the use of different densities and nuclear decay data were also investigated. Effective doses were determined for the ICRP 110 adult reference computational phantom with the tissue weighting factor of ICRP Publication 60 and the tissue weighting factors of ICRP Publication 103. The calculation method and, in particular, the simulation of the electron transport have a significant influence on the calculated doses, especially, for small and walled organs. Overestimates of >200 % were observed for the urinary bladder wall of the stylised phantom compared with the computational phantoms. The unrealistic organ topology of the stylised phantom leads to important dose differences, sometimes by an order of magnitude. The effective doses calculated using the new computational phantoms and the new tissue weighting factors are globally lower than the published ones, except for some radiopharmaceuticals, where the differences can reach 60 % higher than the published values. This study analyses the first set of absorbed and effective doses with the new ICRP male and female reference computational phantoms for different radiopharmaceuticals. It highlights the importance of taking into account the electron transport and the realism of the shape and inter-organ distances of the anthropomorphic model used.

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.