Physiologically informed dynamic causal modeling of fMRI data

AbstractThe 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

Neural Basis of Functional Connectivity MRI

The brain is hierarchically organized across a range of scales. While studies based on electrophysiology and anatomy have been fruitful on the micron to millimeter scale, findings based on functional connectivity MRI (fcMRI) suggest that a higher level of brain organization has been largely overlooked. These findings show that the brain is organized into networks, and each network extends across multiple brain areas. This large-scale, across-area brain organization is functionally relevant and stable across subjects, primate species, and levels of consciousness. This dissertation addresses the neural origin of MRI functional connectivity. fcMRI relies on temporal correlation in at-rest blood oxygen level dependent (BOLD) fluctuations. Thus, understanding the neural origin of at-rest BOLD correlation is of critical significance. By shedding light on the origin of the large-scale brain organization captured by fcMRI, it will guide the design and interpretation of fcMRI studies. Prior investigations of the neural basis of BOLD have not addressed the at-rest BOLD correlation, and they have been focusing on task-related BOLD. At-rest BOLD correlation captured by fcMRI likely reflects a distinct physiological process that is different from that of task-related BOLD, since these two kinds of BOLD dynamics are different in their temporal scale, spatial spread, energy consumption, and their dependence on consciousness. To address this issue, we develop a system to simultaneously record oxygen and electrophysiology in at-rest, awake monkeys. We demonstrate that our oxygen measurement, oxygen polarography, captures the same physiological phenomenon as BOLD by showing that task-related polarographic oxygen responses and at-rest polarographic oxygen correlation are similar to those of BOLD. These results validate the use of oxygen polarography as a surrogate for BOLD to address the neural origin of MRI functional connectivity. Next, we show that at-rest oxygen correlation reflects at-rest correlation in electrophysiological signals, especially spiking activity of neurons. Using causality analysis, we show that oxygen is driven by slow changes in raw local field potential levels (slow LFP), and slow LFP itself is driven by spiking activity. These results provide critical support to the idea that oxygen correlation reflects neural activity, and pose significant challenges to the traditional view of neurohemodynamic coupling. In addition, we find that at-rest correlation does not originate from criticality, which has been the dominant hypothesis in the field. Instead, we show that at-rest correlation likely reflects a specific and potentially localized oscillatory process. We suggest that this oscillatory process could be a result of the delayed negative feedback loop between slow LFP and spiking activity. Thus, we conclude that at-rest BOLD correlation captured by fcMRI is driven by at-rest slow LFP correlation, which is itself driven by spiking activity correlation. The at-rest spiking activity correlation, itself, is likely driven by an oscillatory process. Future studies combining recording with interventional approaches, like pharmacological manipulation and microstimulation, will help to elucidate the circuitry underlying the oscillatory process and its potential functional role

Studying functional magnetic resonance imaging with artificial imaging objects

Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) is an indirect method for measuring information processing in the brain. The method has enabled mapping human brain function in an unprecedented variety of tasks and conditions, and with a spatial resolution of the order of 1 mm. In this dissertation, artificial imaging objects, or phantoms, with adjustable signal intensity were used to simulate and investigate the generation of fMRI signals. The objective was to characterise, and devise means to characterise, fMRI signal components that arise from methodological reasons, impeding the correct physiological interpretation of the signals. The first study involved building an fMRI phantom, where an electric current was applied to introduce magnetic field inhomogeneity within a magnetic resonance signal source. It was shown that the changes of field homogeneity and thus fMRI signal, largely corresponded to the human BOLD changes, even though the physical mechanisms were different. The mechanical properties of phantoms and brain however differ. Thus it was important to look into the attributes of phantoms that would make the fMRI signal from the phantom similar to brain scanning data. The second study examined geometric distortions in the echo-planar imaging method—commonly employed in both fMRI and diffusion tensor imaging—using a purpose-built structural phantom. In the third study, another fMRI activation phantom was built. There the induction wires were located outside the source of the fMRI signal, and thus the partial volume effect limiting the usability of the first fMRI phantom was abated. The phantom was applied to induce artificial activations that could be utilized to deduce periods when simultaneously measured brain activations would yield deviant activation levels due to unphysiological causes. In the last study, an fMRI phantom was used to show that transient fMRI signal components, often witnessed in brain activation data, could occur in the absence of corresponding physiological signal, resulting from the sole signal change

Multiparametric measurement of cerebral physiology using calibrated fMRI

The ultimate goal of calibrated fMRI is the quantitative imaging of oxygen metabolism (CMRO2), and this has been the focus of numerous methods and approaches. However, one underappreciated aspect of this quest is that in the drive to measure CMRO2, many other physiological parameters of interest are often acquired along the way. This can significantly increase the value of the dataset, providing greater information that is clinically relevant, or detail that can disambiguate the cause of signal variations. This can also be somewhat of a double-edged sword: calibrated fMRI experiments combine multiple parameters into a physiological model that requires multiple steps, thereby providing more opportunity for error propagation and increasing the noise and error of the final derived values. As with all measurements, there is a trade-off between imaging time, spatial resolution, coverage, and accuracy. In this review, we provide a brief overview of the benefits and pitfalls of extracting multiparametric measurements of cerebral physiology through calibrated fMRI experiments

Energetics and activation of the central nervous system by in vivo nuclear magnetic resonance

Stefano Pascarella, Mario Compiani, Staderin

An Integrated Magnetoencephalographic and Functional Magnetic Resonance Imaging Study on Temporal Asymmetry Processing in the Human Auditory Cortex

Temporal asymmetry is a fundamental property of speech and music. When a "damped" exponential is used to modulate a sinusoid, it reduces the sound quality typically associated with the carrier. When the modulator is reversed in time, producing a "ramped" sound, the sound quality of the carrier is more salient. Magnetoencephalography was used in conjunction with functional magnetic resonance imaging (fMRI) to localize and characterize cortical sources evoked by temporally asymmetric sounds. Additionally, the relation between neurophysiological and psychometric data was explored. We show that the relationship between stimulus perception and neuromagnetic responses are observed in the N100m component of auditory evoked magnetic fields. In response to ramped and damped sinusoids N100m peak amplitudes increase with the stimulus half{life time (HLT). The asymmetry in terms of N100m magnitude ratio was maximal at modulation HLT of 4 ms and greatly reduced at 0.5 ms and 32 ms which parallels the psychophysical data. fMRI revealed substantial right lateralized activation of Planum temporale in a response to a difference contrast 'ramped-damped' of 4 ms HLT, where we localized N100m sources. Temporally asymmetric sounds were simulated using auditory image model. The extracted carrier salience correlated with both, the N100m magnitude and perceived carrier salience