Tradeoffs in pushing the spatial resolution of fMRI for the 7T Human Connectome Project

Vu, An T.; Jamison, Keith; Glasser, Matthew F.; Smith, Stephen M.; Coalson, Timothy S.; Moeller, Steen; Auerbach, Edward J.; Uğurbil, Kâmil; Yacoub, Essa

doi:10.1016/j.neuroimage.2016.11.049

Cited by 130 publications

(71 citation statements)

References 66 publications

(99 reference statements)

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“…Since rhesus macaques offer an extremely promising translational model (Consortium et al, 2020;Hutchison et al, 2011;Milham et al, 2018), a detailed map of the rsfMRI 'macaque connectome' is needed to build the basis for future interventional work. While human rsfMRI studies have made significant advances in the ability to acquire highly sensitive and informative images of functional connectivity Glasser et al, 2016;Harms et al, 2018), including at relatively high spatial and temporal resolutions Vu et al, 2016), similar non-invasive neuroimaging advances in the macaque have not been as widespread or productive.…”

Section: Discussionmentioning

confidence: 99%

Ultra-high field (10.5 T) resting state fMRI in the macaque

Yacoub

Grier

Auerbach

et al. 2020

Preprint

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Resting state functional connectivity refers to the temporal correlations between spontaneous hemodynamic signals obtained using functional magnetic resonance imaging. This technique has demonstrated that the structure and dynamics of identifiable networks are altered in psychiatric and neurological disease states. Thus, resting state network organizations can be used as a diagnostic, or prognostic recovery indicator. However, much about the physiological basis of this technique is unknown. Thus, providing a translational bridge to an optimal animal model, the macaque, in which invasive circuit manipulations are possible, is of utmost importance. Current approaches to resting state measurements in macaques face unique challenges associated with signal-to-noise, the need for invasive contrast agents, and within-subject designs. These limitations can, in principle, be overcome through ultra-high magnetic fields. However, ultra-high field imaging has yet to be adapted for fMRI in macaques. Here, we demonstrate that the combination of high channel count transmitter and receiver arrays, optimized pulse sequences, and careful anesthesia regimens, allows for detailed withinsubject resting state analysis at ultra-high resolutions. In this study, we uncover thirty spatially detailed resting state components that are highly robust across individual macaques and closely resemble the quality and findings of connectomes from large human datasets. This detailed map of the rsfMRI 'macaque connectome' will be the basis for future neurobiological circuit manipulation work, providing valuable biological insights into human connectomics.

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Section: Discussionmentioning

confidence: 99%

Ultra-high field (10.5 T) resting state fMRI in the macaque

Yacoub

Grier

Auerbach

et al. 2020

Preprint

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“…Ultra-high field fMRI allows high spatial resolution, which is beneficial for investigation of subcortical structures (Metzger, van der Werf, & Walter, 2013;Walter, Stadler, Tempelmann, Speck, & Northoff, 2008) and improved functional specificity compared to lower field strengths (Vu et al, 2017).…”

Section: Mri Data Acquisition Preprocessing and Functional Connectmentioning

confidence: 99%

Functional connectivity changes following interpersonal reactivity

Krause

Čolić

Borchardt

et al. 2017

Human Brain Mapping

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Attachment experiences substantially influence emotional and cognitive development. Narratives comprising attachment-dependent content were proposed to modulate activation of cognitive-emotional schemata in listeners. We studied the effects after listening to prototypical attachment narratives on wellbeing and countertransference-reactions in 149 healthy participants. Neural correlates of these cognitive-emotional schema activations were investigated in a 7 Tesla rest-task-rest fMRI-study (23 healthy males) using functional connectivity (FC) analysis of the social approach network (seed regions: left and right Caudate Nucleus, CN). Reduced FC between left CN and bilateral dorsolateral prefrontal cortex (DLPFC) represented a general effect of prior auditory stimulation. After presentation of the insecure-dismissing narrative, FC between left CN and bilateral temporo-parietal junction, and right dorsal posterior Cingulum was reduced, compared to baseline. Post-narrative FC-patterns of insecure-dismissing and insecure-preoccupied narratives differed in strength between left CN and right DLPFC. Neural correlates of the moderating effect of individual attachment anxiety were represented in a reduced CN-DLPFC FC as a function of individual neediness-levels. These findings suggest specific neural processing of prolonged mood-changes and schema activation induced by attachment-specific speech patterns. Individual desire for interpersonal proximity was predicted by attachment anxiety and furthermore modulated FC of the social approach network in those exposed to such narratives.

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“…A great deal of neuroimaging techniques could be utilized to image human brain function and structure, e.g., diffusion tensor imaging (DTI), magnetic resonance spectroscopy (MRS), electroencephalogram (EEG), functional magnetic resonance imaging (fMRI), and so on ( Busato et al, 2016 ; Thanh Vu et al, 2017 ). Due to the advantages of high temporal and spatial resolutions, fMRI especially resting-state fMRI have gained increasingly growing popularities in the investigation of the whole-brain neural connectivity recently ( Goense et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Progression Toward Alzheimer’s Disease Based on Evolutionary Weighted Random Support Vector Machine Cluster

Bi¹,

Xu²,

Luo³

et al. 2018

Front. Neurosci.

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Alzheimer’s disease (AD) could be described into following four stages: healthy control (HC), early mild cognitive impairment (EMCI), late MCI (LMCI) and AD dementia. The discriminations between different stages of AD are considerably important issues for future pre-dementia treatment. However, it is still challenging to identify LMCI from EMCI because of the subtle changes in imaging which are not noticeable. In addition, there were relatively few studies to make inferences about the brain dynamic changes in the cognitive progression from EMCI to LMCI to AD. Inspired by the above problems, we proposed an advanced approach of evolutionary weighted random support vector machine cluster (EWRSVMC). Where the predictions of numerous weighted SVM classifiers are aggregated for improving the generalization performance. We validated our method in multiple binary classifications using Alzheimer’s Disease Neuroimaging Initiative dataset. As a result, the encouraging accuracy of 90% for EMCI/LMCI and 88.89% for LMCI/AD were achieved respectively, demonstrating the excellent discriminating ability. Furthermore, disease-related brain regions underlying the AD progression could be found out on the basis of the amount of discriminative information. The findings of this study provide considerable insight into the neurophysiological mechanisms in AD development.

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Tradeoffs in pushing the spatial resolution of fMRI for the 7T Human Connectome Project

Cited by 130 publications

References 66 publications

Ultra-high field (10.5 T) resting state fMRI in the macaque

Ultra-high field (10.5 T) resting state fMRI in the macaque

Functional connectivity changes following interpersonal reactivity

Analysis of Progression Toward Alzheimer’s Disease Based on Evolutionary Weighted Random Support Vector Machine Cluster

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