Functional connectome of arousal and motor brainstem nuclei in living humans by 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.

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

10.1101/2021.10.18.464881 ◽

2021 ◽

Author(s):

kavita singh ◽

Simone Cauzzo ◽

Maria Guadalupe Garcia-Gomar ◽

Matthew Stauder ◽

Nicola Vanello ◽

...

Keyword(s):

Spatial Resolution ◽

Resting State ◽

Resting State Fmri ◽

3 Tesla ◽

7 Tesla ◽

Resting State Functional Connectivity ◽

Functional Connectome ◽

Brainstem Nuclei ◽

The Brain

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 function in living humans in health and disease.

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A high resolution 7-Tesla resting-state fMRI test-retest dataset with cognitive and physiological measures

Scientific Data ◽

10.1038/sdata.2014.54 ◽

2015 ◽

Vol 2 (1) ◽

Cited By ~ 24

Author(s):

Krzysztof J Gorgolewski ◽

Natacha Mendes ◽

Domenica Wilfling ◽

Elisabeth Wladimirow ◽

Claudine J Gauthier ◽

...

Keyword(s):

High Resolution ◽

Resting State ◽

Resting State Fmri ◽

7 Tesla ◽

Physiological Measures

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Working memory capacity and the functional connectome - insights from resting-state fMRI and voxelwise centrality mapping

Brain Imaging and Behavior ◽

10.1007/s11682-017-9688-9 ◽

2017 ◽

Vol 12 (1) ◽

pp. 238-246 ◽

Cited By ~ 2

Author(s):

Sebastian Markett ◽

Martin Reuter ◽

Behrend Heeren ◽

Bernd Lachmann ◽

Bernd Weber ◽

...

Keyword(s):

Working Memory ◽

Resting State ◽

Working Memory Capacity ◽

Resting State Fmri ◽

Memory Capacity ◽

Functional Connectome

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Extraction and Analysis of Dynamic Functional Connectome Patterns in Migraine Sufferers: A Resting-State fMRI Study

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/6614520 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Weifang Nie ◽

Weiming Zeng ◽

Jiajun Yang ◽

Yuhu Shi ◽

Le Zhao ◽

...

Keyword(s):

Resting State ◽

Occipital Lobe ◽

Resting State Fmri ◽

Brain Regions ◽

Neural Mechanisms ◽

Imaging Data ◽

Physical And Mental Health ◽

Brain Areas ◽

Functional Connectome ◽

Network Metrics

Migraine seriously affects the physical and mental health of patients because of its recurrence and the hypersensitivity to the environment that it causes. However, the pathogenesis and pathophysiology of migraine are not fully understood. We addressed this issue in the present study using an autodynamic functional connectome model (A-DFCM) with twice-clustering to compare dynamic functional connectome patterns (DFCPs) from resting-state functional magnetic resonance imaging data from migraine patients and normal control subjects. We used automatic localization of segment points to improve the efficiency of the model, and intergroup differences and network metrics were analyzed to identify the neural mechanisms of migraine. Using the A-DFCM model, we identified 17 DFCPs—including 1 that was specific and 16 that were general—based on intergroup differences. The specific DFCP was closely associated with neuronal dysfunction in migraine, whereas the general DFCPs showed that the 2 groups had similar functional topology as well as differences in the brain resting state. An analysis of network metrics revealed the critical brain regions in the specific DFCP; these were not only distributed in brain areas related to pain such as Brodmann area 1/2/3, basal ganglia, and thalamus but also located in regions that have been implicated in migraine symptoms such as the occipital lobe. An analysis of the dissimilarities in general DFCPs between the 2 groups identified 6 brain areas belonging to the so-called pain matrix. Our findings provide insight into the neural mechanisms of migraine while also identifying neuroimaging biomarkers that can aid in the diagnosis or monitoring of migraine patients.

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Benchmarking functional connectome-based predictive models for resting-state fMRI

NeuroImage ◽

10.1016/j.neuroimage.2019.02.062 ◽

2019 ◽

Vol 192 ◽

pp. 115-134 ◽

Cited By ~ 54

Author(s):

Kamalaker Dadi ◽

Mehdi Rahim ◽

Alexandre Abraham ◽

Darya Chyzhyk ◽

Michael Milham ◽

...

Keyword(s):

Predictive Models ◽

Resting State ◽

Resting State Fmri ◽

Functional Connectome

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Improved 7 Tesla resting-state fMRI connectivity measurements by cluster-based modeling of respiratory volume and heart rate effects

NeuroImage ◽

10.1016/j.neuroimage.2017.04.009 ◽

2017 ◽

Vol 153 ◽

pp. 262-272 ◽

Cited By ~ 7

Author(s):

Joana Pinto ◽

Sandro Nunes ◽

Marta Bianciardi ◽

Afonso Dias ◽

L. Miguel Silveira ◽

...

Keyword(s):

Heart Rate ◽

Resting State ◽

Resting State Fmri ◽

7 Tesla ◽

Respiratory Volume ◽

Rate Effects

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Understanding the Effect of Left Prefrontal Stimulation on Positive Symptoms of Schizophrenia: A Dynamic Causal Modeling Study of Ultra-high field (7-Tesla) Resting-state fMRI

10.1101/2020.02.07.939470 ◽

2020 ◽

Author(s):

Roberto Limongi ◽

Michael Mackinley ◽

Kara Dempster ◽

Ali R. Khan ◽

Joseph S. Gati ◽

...

Keyword(s):

Resting State ◽

High Field ◽

Negative Symptoms ◽

Resting State Fmri ◽

Causal Modeling ◽

7 Tesla ◽

Positive Symptoms ◽

Dynamic Causal Modeling ◽

Ultra High Field ◽

Inhibitory Tone

AbstractRepetitive transcranial magnetic stimulation (rTMS), when applied to left dorsolateral prefrontal cortex (LDLPFC), reduces negative symptoms of schizophrenia, but has no effect on positive symptoms. In a small number of cases, it appears to worsen the severity of positive symptoms. It has been hypothesized that high frequency rTMS of the LDLPFC might increase the dopaminergic neurotransmission by driving the activity of the left striatum in the basal ganglia (LSTR)—increasing striatal dopaminergic activity. This hypothesis relies on the assumption that either the frontal-striatal connection or the intrinsic frontal and/or striatal connections covary with the severity of positive symptoms. The current work aimed to evaluate this assumption by studying the association between positive and negative symptoms severity and the effective connectivity within the frontal and striatal network using dynamic causal modeling (DCM) of ultra-high field (7 Tesla) resting state fMRI in a sample of 19 first episode psychosis (FEP) subjects. We found that of all core symptoms of schizophrenia, only delusions are strongly associated with the fronto striatal circuitry. Stronger intrinsic inhibitory tone of LDLPFC and LSTR, as well as a pronounced backward inhibition of the LDLPFC on the LSTR related to the severity of delusions. We interpret that an increase in striatal dopaminergic tone that underlies delusional symptoms, is likely associated with increased prefrontal inhibitory tone, strengthening the frontostriatal ‘brake’. Furthermore, based on our model, we propose that lessening of positive symptoms could be achieved by means of continuous theta-burst or low frequency (1Hz) rTMS of the prefrontal area.

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