The Brainstem Imaging Lab aims to develop in living humans a neuroimaging-based atlas and connectivity diagram of brainstem nuclei by the use of dedicated MRI pulse sequences and scanners. Our ultimate goal is to enhance our knowledge and the quality of patient care in a vast array of brainstem-related disorders, such as disorders of consciousness, sleep disorders, vestibular disorders, autonomic dysfunction, and movement disorders (such as Parkinson’s disease).
Brainstem Nuclei Atlas
We developed an in vivo 7 Tesla multi-contrast procedure able to segment on a single-subject basis 16 brainstem and 4 diencephalic nuclei crucial for arousal, autonomic and sensory-motor functions. Further, we created a probabilistic structural atlas of these nuclei in stereotaxic (MNI) space by computing the spatial overlap across subjects of the single-subject nuclei segmentations (Bianciardi et al., Brain Connect 2015; Bianciardi et al., Neuroimage 2017, Strong et al., ISMRM 2018, see Figure above). Out atlas can be used in conventional (e.g. 3 Tesla) research and clinical MRI studies to investigate brainstem mechanisms in health and brainstem-related disorders. We are currently expanding the atlas to include additional brainstem nuclei by the use of sub-millimiter spatial resolution and additional image contrasts.
You have a 3 Tesla scanner, and you wonder where tiny raphe (or other brainstem) nuclei are in your images? You could consider using our in vivo probabilistic atlas of brainstem nuclei! Upon the release of our atlas (to be announced), you will be able to locate several brainstem nuclei (see list below) in your images by precisely aligning your 3 Tesla images to stereotaxic (MNI) space (i.e. the brainstem atlas space).
List of delineated brainstem nuclei:
1) Substantia Nigra subregion 1 (compatible with pars reticulata); 2) Substantia Nigra subregion 2 (compatible with pars compacta);
3) Red Nucleus subregion 1; 4) Red Nucleus subregion 2;
5) Inferior Olivary Nucleus; 6) Raphe Magnus;
7) Dorsal Raphe; 8) Median Raphe;
9) Paramedian Raphe; 10) Caudal Linear Raphe;
11) Periaqueductal Gray; 12) Pedunculotegmental (also called pedunculopontine) nucleus;
13) Cuneiform nucleus; 14) Oral part of the Pontine Reticular Formation (also called points oralis);
15) Inferior Colliculus; 16) Superior Colliculus.
List of delineated diencephalic nuclei:
1) Subthalamic Nucleus subregion 1; 2) Subthalamic Nucleus subregion 2;
3) Lateral Geniculate nucleus; 4) Medial Geniculate nucleus.
Brainstem Nuclei Connectome
Imaging of the connectivity pathways of brainstem nuclei in living humans is an underserved area of human brain imaging research. Most of our knowledge about brainstem pathways derives from tracing and MRI studies in animals and ex vivo humans.
To fill this gap, we are evaluating the functional and structural connectivity of brainstem nuclei with the rest of the brain. To do so, we exploit the increased sensitivity, spatial resolution and contrast of 7 Tesla MRI scanners and of our 3 Tesla “Connectome” scanner to acquire cutting edge resting state functional MRI and diffusion based MRI. After mapping our brainstem nuclei atlas to single-subject MRI data (via precise image coregistration), we are able to build connectivity diagrams (connectomes) of brainstem nuclei with target cortical and subcortical regions (Bianciardi et al., MAGMA 2016, see Figure above). To aid the interpretation of these connectomes, we are also building prediction models of brainstem networks (Satpute et al., Neurosci Lett 2018), which include the nodes and pathways expected from previous animal and ex vivo human studies.
Brainstem Mechanisms in Health and Disease
Our brainstem nuclei atlas and connectome can be applied to study brainstem mechanisms in health and in a wide array of brainstem pathologies, such as disorders of consciousness, sleep disorders, Parkinson’s and motor diseases, chronic pain, altered autonomic function, and vestibular disorders.
Our team is currently working on the translational application of the brainstem nuclei atlas and connectome to improve our understanding of arousal mechanisms in traumatic coma and its prognostication (collaboration with Dr. Edlow, MGH). We are also investigating arousal and motor mechanisms in REM-sleep behavior disorder, which is a premotor model of Parkinson’s disease, with the goal of developing prodromal (early) brainstem-based imaging biomarkers of Parkinson’s disease (collaboration with Dr. Videnovic, MGH). Our most recent efforts are devoted to map autonomic-vestibular pathways and functional interactions in healthy subjects as a baseline for future studies of vestibular disorders (collaboration with Dr. Indovina, IRCCS Santa Lucia Foundation, and Dr. Staab, Mayo Clinic).
We are also interested in developing MRI methods able to: (i) correct for physiological noise in MRI/fMRI data; (ii) extract biomarkers of brain microstructure, physiology, function and metabolism, based on multiple MRI modalities and the acquisition of physiological and electrophysiological recordings. Interestingly, our search for methods to correct for artifacts and noise (aim (i)) sometimes turned out to be an opportunity to observe interesting signals or develop novel biomarkers (aim ii)!
This was the case when we were developing methods able to remove fMRI noise related to the magnetic field changes due to respiration: once we found out how to model this type of noise from the phase fMRI signals at each time point, we were astonished to observe significant task-based and resting-state functional responses from the residual (after noise removal) phase fMRI signals (beyond the commonly employed magnitude fMRI signals). Ours was the first paper (Bianciardi et al., Human Brain Mapp 2014) reporting the measurement of task-based and resting-state fMRI resonance frequency shifts and quantitative susceptibility changes, most probably originating from neuronal activity through induced blood volume and oxygenation changes in pial and intracortical veins.
Further, our expertise in modeling and correcting for fMRI signal fluctuations due to cardiac and respiratory pulsatility effects allowed us (Bianciardi et al., Phil Trans A 2016) to develop MRI methods able to visualize MRI pulse waveforms due to pulsatility effects in several brain compartments, as well as to develop novel MRI indicators of cerebrovascular compliance (the pulsatility volume index). See the following videos!
We deeply thank our funding sources for making this work possible!
2019 Mind Brain Behavior Harvard Faculty Award
2017 MGH Distinguished Scholar Claflin Award
NIH NIBIB: K01-EB019474; P41-EB015896
NIH NIDCD: R21-DC015888
NIH NCI: U01-CA193632