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

Functional connectome of brainstem nuclei involved in autonomic, limbic, pain and sensory processing in living humans from 7 Tesla resting state fMRI

Despite remarkable advances in mapping the functional connectivity of the cortex, the functional connectivity of subcortical regions is understudied in living humans. This is the case for brainstem nuclei that control vital processes, such as autonomic, limbic, nociceptive and sensory functions. This is because of the lack of precise brainstem nuclei localization, of adequate sensitivity and resolution in the deepest brain regions, as well as of optimized processing for the brainstem. To close the gap between the cortex and the brainstem, on 20 healthy subjects, we computed a correlation-based functional connectome of 15 brainstem nuclei involved in autonomic, limbic, nociceptive, and sensory function (superior and inferior colliculi, ventral tegmental area-parabrachial pigmented nucleus complex, microcellular tegmental nucleus-prabigeminal nucleus complex, lateral and medial parabrachial nuclei, vestibular and superior olivary complex, superior and inferior medullary reticular formation, viscerosensory motor nucleus, raphe magnus, pallidus, and obscurus, and parvicellular reticular nucleus – alpha part) with the rest of the brain. Specifically, we exploited 1.1mm isotropic resolution 7 Tesla resting-state fMRI, ad-hoc coregistration and physiological noise correction strategies, and a recently developed probabilistic template of brainstem nuclei. Further, we used 2.5mm isotropic resolution resting-state fMRI data acquired on a 3 Tesla scanner to assess the translatability of our results to conventional datasets. We report highly consistent correlation coefficients across subjects, confirming available literature on autonomic, limbic, nociceptive and sensory pathways, as well as high interconnectivity within the central autonomic network and the vestibular network. Interestingly, our results showed evidence of vestibulo-autonomic interactions in line with previous work. Comparison of 7 Tesla and 3 Tesla findings showed high translatability of results to conventional settings for brainstem-cortical connectivity and good yet weaker translatability for brainstem-brainstem connectivity. The brainstem functional connectome might bring new insight in the understanding of autonomic, limbic, nociceptive and sensory function in health and disease

Functional connectome of arousal and motor brainstem nuclei in living humans by 7 Tesla resting-state fMRI

Brainstem nuclei play a pivotal role in many functions, such as arousal and motor control. Nevertheless, the connectivity of arousal and motor brainstem nuclei is understudied in living humans due to the limited sensitivity and spatial resolution of conventional imaging, and to the lack of atlases of these deep tiny regions of the brain. For a holistic comprehension of sleep, arousal and associated motor processes, we investigated in 20 healthy subjects the resting-state functional connectivity of 18 arousal and motor brainstem nuclei in living humans. To do so, we used high spatial-resolution 7 Tesla resting-state fMRI, as well as a recently developed in-vivo probabilistic atlas of these nuclei in stereotactic space. Further, we verified the translatability of our brainstem connectome approach to conventional (e.g. 3 Tesla) fMRI. Arousal brainstem nuclei displayed high interconnectivity, as well as connectivity to the thalamus, hypothalamus, basal forebrain and frontal cortex, in line with animal studies and as expected for arousal regions. Motor brainstem nuclei showed expected connectivity to the cerebellum, basal ganglia and motor cortex, as well as high interconnectivity. Comparison of 3 Tesla to 7 Tesla connectivity results indicated good translatability of our brainstem connectome approach to conventional fMRI, especially for cortical and subcortical (non-brainstem) targets and to a lesser extent for brainstem targets. The functional connectome of 18 arousal and motor brainstem nuclei with the rest of the brain might provide a better understanding of arousal, sleep and accompanying motor functions in living humans in health and disease

In vivo probabilistic atlas of white matter tracts of the human subthalamic area combining track density imaging and optimized diffusion tractography

The human subthalamic area is a region of high anatomical complexity, tightly packed with tiny fiber bundles. Some of them, including the pallidothalamic, cerebello-thalamic, and mammillothalamic tracts, are relevant targets in functional neurosurgery for various brain diseases. Diffusion-weighted imaging-based tractography has been suggested as a useful tool to map white matter pathways in the human brain in vivo and non-invasively, though the reconstruction of these specific fiber bundles is challenging due to their small dimensions and complex anatomy. To the best of our knowledge, a population-based, in vivo probabilistic atlas of subthalamic white matter tracts is still missing. In the present work, we devised an optimized tractography protocol for reproducible reconstruction of the tracts of subthalamic area in a large data sample from the Human Connectome Project repository. First, we leveraged the super-resolution properties and high anatomical detail provided by short tracks track-density imaging (stTDI) to identify the white matter bundles of the subthalamic area on a group-level template. Tracts identification on the stTDI template was also aided by visualization of histological sections of human specimens. Then, we employed this anatomical information to drive tractography at the subject-level, optimizing tracking parameters to maximize between-subject and within-subject similarities as well as anatomical accuracy. Finally, we gathered subject level tracts reconstructed with optimized tractography into a large-scale, normative population atlas. We suggest that this atlas could be useful in both clinical anatomy and functional neurosurgery settings, to improve our understanding of the complex morphology of this important brain region

Network analysis of the human structural connectome including the brainstem: a new perspective on consciousness

The underlying anatomical structure is fundamental to the study of brain networks and is likely to play a key role in the generation of conscious experience. We conduct a computational and graph-theoretical study of the human structural connectome incorporating a variety of subcortical structures including the brainstem, which is typically not considered in similar studies. Our computational scheme involves the use of Python DIPY and Nibabel libraries to develop an averaged structural connectome comprised of 100 healthy adult subjects. We then compute degree, eigenvector, and betweenness centralities to identify several highly connected structures and find that the brainstem ranks highest across all examined metrics. Our results highlight the importance of including the brainstem in structural network analyses. We suggest that structural network-based methods can inform theories of consciousness, such as global workspace theory (GWT), integrated information theory (IIT), and the thalamocortical loop theory.Comment: 23 pages, 5 figure

Methods for improved mapping of brain lesion connectivity

Recent advances over the past two decades in neuroimaging methods have enabled us to map the connectivity of the brain. In parallel, pathophysiological models of brain disease have shifted from an emphasis on understanding pathology in specific brain regions to characterizing disruptions to interconnected neural networks. Nevertheless, these recent methods for mapping brain connectivity are still under development. Every step of the mapping process becomes a potential source for additional error due to noise or artifacts that could impact final analyses. Segmentation, parcellation, registration, and tractography are some of the steps where this occurs. Moreover, mapping the connectivity in a brain lesion is even more susceptible to errors in these steps. In this body of work, I describe multiple new methods for improving the accuracy of mapping lesion connectivity by reducing errors at the tractography stage which is the most error prone stage. First, we develop an approach for directly normalizing streamlines into a template space that avoids performing tractography in the normalized template space, reducing the error of connectomes constructed in the template space with respect to the ground truth native space connectome. Second, we develop a rapid approach for performing shortest path tractography and constructing shortest path probability weighted connectomes which increases the connection specificity relative to local streamline tracking approaches. We then demonstrate how our shortest path tractography approach can be used construct a disconnectome, a connectivity map of the proportion of connections lost due to intersecting a lesion. We then develop a fast, greedy graph-theoretic algorithm that extracts the maximally disconnected subgraph containing brain regions with the greatest shared loss of connectivity. Finally, we demonstrate how combining methods from diffusion based image inpainting and optimal estimation can be used to restore or inpaint corrupted fiber diffusion models in lesioned white matter tissue, enabling tractography and the study of lesion connectivity and modeling of microstructural measures in the patient’s native space

Mapping the subcortical connectome using in vivo diffusion MRI: Feasibility and reliability

Tractography combined with regions of interest (ROIs) has been used to non-invasively study the structural connectivity of the cortex as well as to assess the reliability of these connections. However, the subcortical connectome (subcortex to subcortex) has not been comprehensively examined, in part due to the difficulty of performing tractography in this complex and compact region. In this study, we performed an in vivo investigation using tractography to assess the feasibility and reliability of mapping known connections between structures of the subcortex using the test-retest dataset from the Human Connectome Project (HCP). We further validated our observations using a separate unrelated subjects dataset from the HCP. Quantitative assessment was performed by computing tract densities and spatial overlap of identified connections between subcortical ROIs. Further, known connections between structures of the basal ganglia and thalamus were identified and visually inspected, comparing tractography reconstructed trajectories with descriptions from tract-tracing studies. Our observations demonstrate both the feasibility and reliability of using a data-driven tractography-based approach to map the subcortical connectome in vivo

Defining thalamic nuclei and topographic connectivity gradients in vivo.

The thalamus consists of multiple nuclei that have been previously defined by their chemoarchitectual and cytoarchitectual properties ex vivo. These form discrete, functionally specialized, territories with topographically arranged graduated patterns of connectivity. However, previous in vivo thalamic parcellation with MRI has been hindered by substantial inter-individual variability or discrepancies between MRI derived segmentations and histological sections. Here, we use the Euclidean distance to characterize probabilistic tractography distributions derived from diffusion MRI. We generate 12 feature maps by performing voxel-wise parameterization of the distance histograms (6 feature maps) and the distribution of three-dimensional distance transition gradients generated by applying a Sobel kernel to the distance metrics. We use these 12 feature maps to delineate individual thalamic nuclei, then extract the tractography profiles for each and calculate the voxel-wise tractography gradients. Within each thalamic nucleus, the tractography gradients were topographically arranged as distinct non-overlapping cortical networks with transitory overlapping mid-zones. This work significantly advances quantitative segmentation of the thalamus in vivo using 3T MRI. At an individual subject level, the thalamic segmentations consistently achieve a close relationship with a priori histological atlas information, and resolve in vivo topographic gradients within each thalamic nucleus for the first time. Additionally, these techniques allow individual thalamic nuclei to be closely aligned across large populations and generate measures of inter-individual variability that can be used to study both basic function and pathological processes in vivo