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

Abstract

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.

A novel rhesus macaque model of Huntington’s disease recapitulates key neuropathological changes along with progressive motor and cognitive decline

“…After regressing for signals of CSF and white matter, motion, and outliers’ censors 84 , FSL-MELODIC software (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MELODIC) was applied to the baseline data to derive group-level connectivity networks using Independent Component Analysis (ICA) 85 . This approach identified 4 independent components (ICs) which appeared to align with previously described Resting-State Networks identified in macaques 86, 87 and humans 85, 88–90 , overlapped primarily gray matter regions, and possessed low-frequency spectral power 91 ( Figure S5) . Based on these criteria, the remaining 4 ICs were not further examined 91 (Figure S6) .…”

Muting, not fragmentation, of functional brain networks under general anesthesia

“…Consistent with these findings, we found strong clustering of the correlation matrices at the subject level (Figure 4a, left) in the uncentered data, which we were able to remove through our centring approach (Figure 4a, right). of the gradients reported by others in both non-human primates (Margulies et al 2016, Yacoub et al 2020, and in humans (Hong et al 2020). Together, these findings support the notion that a constant network structure is present across all dose levels, and that it is only the expression of this constant network structure that changes across dose.…”

The cortical connectome of primate lateral prefrontal cortex

“…Another limitation of this study is that our resolution (2 mm) was not fine enough to capture the finer scaled regularities of connectivity, including columnar (Cavada and Goldman-Rakic, 1989;Schwartz and Goldman-Rakic, 1984;Selemon and Goldman-Rakic, 1988;Seltzer et al, 1996), laminar (Barbas, 2015), and layer-specific, stripe-like (Levitt et al, 1993;Lund et al, 1993) patterns observed in tracing studies. High-resolution fMRI (Huber et al, 2017;Yacoub et al, 2020) and ultrafast whole-brain fluorescence imaging (Xu et al, 2021) in primates will help better link connectivity patterns mapped by EM-fMRI and tracing in the future.…”