In vivo structural connectome of arousal and motor brainstem nuclei by 7 Tesla and 3 Tesla MRI

Brainstem nuclei are key participants in the generation and maintenance of arousal, which is a basic function that modulates wakefulness/sleep, autonomic responses, affect, attention, and consciousness. Their mechanism is based on diffuse pathways ascending from the brainstem to the thalamus, hypothalamus, basal forebrain and cortex. Several arousal brainstem nuclei also participate in motor functions that allow humans to respond and interact with the surrounding through a multipathway motor network. Yet, little is known about the structural connectivity of arousal and motor brainstem nuclei in living humans. This is due to the lack of appropriate tools able to accurately visualize brainstem nuclei in conventional imaging. Using a recently developed in vivo probabilistic brainstem nuclei atlas and 7 Tesla diffusion-weighted images (DWI), we built the structural connectome of 18 arousal and motor brainstem nuclei in living humans (n = 19). Furthermore, to investigate the translatability of our findings to standard clinical MRI, we acquired 3 Tesla DWI on the same subjects, and measured the association of the connectome across scanners. For both arousal and motor circuits, our results showed high connectivity within brainstem nuclei, and with expected subcortical and cortical structures based on animal studies. The association between 3 Tesla and 7 Tesla connectivity values was good, especially within the brainstem. The resulting structural connectome might be used as a baseline to better understand arousal and motor functions in health and disease in humans

Structural connectivity of autonomic, pain, limbic, and sensory brainstem nuclei in living humans based on 7 Tesla and 3 Tesla MRI

Autonomic, pain, limbic, and sensory processes are mainly governed by the central nervous system, with brainstem nuclei as relay centers for these crucial functions. Yet, the structural connectivity of brainstem nuclei in living humans remains understudied. These tiny structures are difficult to locate using conventional in vivo MRI, and ex vivo brainstem nuclei atlases lack precise and automatic transformability to in vivo images. To fill this gap, we mapped our recently developed probabilistic brainstem nuclei atlas developed in living humans to high-spatial resolution (1.7 mm isotropic) and diffusion weighted imaging (DWI) at 7 Tesla in 20 healthy participants. To demonstrate clinical translatability, we also acquired 3 Tesla DWI with conventional resolution (2.5 mm isotropic) in the same participants. Results showed the structural connectome of 15 autonomic, pain, limbic, and sensory (including vestibular) brainstem nuclei/nuclei complex (superior/inferior colliculi, ventral tegmental area-parabrachial pigmented, microcellular tegmental-parabigeminal, lateral/medial parabrachial, vestibular, superior olivary, superior/inferior medullary reticular formation, viscerosensory motor, raphe magnus/pallidus/obscurus, parvicellular reticular nucleus-alpha part), derived from probabilistic tractography computation. Through graph measure analysis, we identified network hubs and demonstrated high intercommunity communication in these nuclei. We found good (r = .5) translational capability of the 7 Tesla connectome to clinical (i.e., 3 Tesla) datasets. Furthermore, we validated the structural connectome by building diagrams of autonomic/pain/limbic connectivity, vestibular connectivity, and their interactions, and by inspecting the presence of specific links based on human and animal literature. These findings offer a baseline for studies of these brainstem nuclei and their functions in health and disease, including autonomic dysfunction, chronic pain, psychiatric, and vestibular disorders

Models of BOLD Signal Change in Breath Hold Studies: Methodological Issues and Interpretation

This work of thesis aims at studying neural correlates of respiration control with the goal of gaining knowledge towards the determination of the physiopathology of abnormal periodic breathing patterns such as Cheyne-Stokes respiration. Functional magnetic resonance imaging is used to investigate brain areas devoted to the closed-loop control system of respiration in healthy subjects, in particular those areas related to sensing of carbon dioxide levels and integration with peripheral chemoreceptors. Subjects were asked to perform about thirty-seconds-long voluntary apnoea with periods of about sixty seconds. The analysis is focused on the brainstem, thalamic nuclei and surrounding cortical areas, and originates from the work of Pattinson et al. (2009) to investigate the presence of nonlinear responses in carbon dioxide sensing networks, and their eventual role in allowing a separation between BOLD fluctuations caused by respiration-related neural activity and changes in the fMRI signal due to physiological noise from respiration or cardiac activity. A general linear model is applied to fMRI data, including static nonlinearities in the way described in Magri et al. (2011). The critical role of retrospective correction for physiological noise is deeply studied, in order to discuss on the optimal preprocessing path to be followed whenever in fMRI studies there is an interest over respiration-related brain activity. Methodological issues regarding correction for multiple tests were faced by testing both cluster-based methods and voxel-wise correcting techniques based on permutations. Finally, since the analysis produced a huge number of models, differing in terms of complexity and delays, different statistical model selection techniques are discussed, based on different comparison criteria such as adjusted R2 or BIC, testing also different approaches such as leave-one-subject-out cross validation, that may allow to reduce problems of overfitting. The study allowed to highlight activity in the thalamus, diencephalon, globus pallidum and caudate nucleus but failed to provide inter-subject consistent activations in the brainstem. The determination of some parameters defining region-specific responses, performed though model selection, may allow to determine time constants and the presence of eventual nonlinearities characterizing the mechanisms of the ventilatory drive. In this context, this work offers a framework that may inspire future works in approaching fMRI analysis with spatially-parametrized hierarchical linear models

mICA-Based fMRI Analysis of Specific CO₂-Level-Dependent BOLD Signal Changes in the Human Brainstem

Noninvasive studies of the central respiratory control are of key importance to understanding the physiopathology of central apneas and periodic breathing. The study of the brainstem and cortical-subcortical centers may be achieved by using functional magnetic resonance imaging (fMRI) during gas challenges (hypercapnia). Nonetheless, disentangling specific from non-specific effects of hypercapnia in fMRI is a major methodological challenge, as CO2 vasodilatory effects and physiological noise do strongly impact the BOLD signal. This is particularly true in deep brainstem regions where chemoreceptors and rhythm pattern generators are located. One possibility to detect the true neural-related activation is given by the presence of a supralinear relation between CO2 changes and BOLD signal related to neurovascular coupling in overactive neural areas. Here, we test this hypothesis of a supralinear relationship between CO2 and BOLD signal, as a marker of specificity. We employed a group-masked Independent Component Analysis (mICA) approach and we compared activation levels across different mixtures of inspired CO2 using polynomial regression. Our results highlight key nodes of the central breathing control network, also including dorsal pontine and medullary regions. The suggested methodology allows a voxel-wise parametrization of the response, targeting an issue that affects many fMRI studies employing hypercapnic challenges