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Pharmacological stimulation of cholinergic activity alters brain-wide spontaneous fMRI network dynamics
Daniel Gutierrez-Barragan1,2, Carola Canella2,3, Alberto Galbusera3, Stefano Panzeri1, and Alessandro Gozzi3

1Neural Computation Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @ UNITN, Rovereto, Italy, 2CIMeC, Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy, 3Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @ UNITN, Rovereto, Italy

Synopsis

We recently demonstrated that resting-state fMRI (rsfMRI) network dynamics can be mapped with voxel-resolution in the mouse, and that oscillatory state transitions govern rsfMRI network dynamics in the resting mouse brain. Here we show that pharmacological modulation of cholinergic activity strongly affects the spatio-temporal dynamics of rsfMRI state fluctuations, encompassing novel spatial topographies and altered spatio-temporal dynamics. Our results implicate ascending cholinergic activity in the generation of oscillating fMRI states in the resting brain.

Introduction

Using resting-state fMRI (rsfMRI) frame-wise clustering in the mouse, we recently demonstrated that spontaneous fMRI activity is orchestrated by recurrent oscillatory state transitions. We also showed that the observed states are weakly-coupled and exhibit preferred occurrences within global fMRI signal(GS) oscillations1. This observation putatively links infra-slow fMRI state oscillations with the activity of ascending nuclei involved in the modulation of the fMRI global-signal and arousal2. In an attempt to establish an initial link between fMRI state oscillations and ascending neuromodulatory activity, we carried out rsfMRI recordings under pharmacological stimulation of muscarinic acetylcholine receptors using the selective M1 and M4 xanomeline3,4. This drug robustly stimulates cholinergic outputs3. Consistent with the pro-arousing properties of xanomeline, we found that cholinergic stimulation produces a generalized reduction in the power of infra-slow fluctuations5, and functional connectivity. Xanomeline also altered whole-brain dynamics, reconfiguring recurrent spontaneous fMRI activity into a novel set of states characterized by a predominant involvement of sensory-motor, hypothalamic and basal-forebrain substrates. Our data implicates a key contribution of cholinergic activity in shaping fMRI network dynamics.

Methods

Resting-state fMRI. C57BI6/J-mice were scanned under shallow halothane anesthesia(0.75%) and artificial ventilation6. Mice received either a subcutaneous injection of vehicle solution (n=19, saline), or xanomeline(n=21, 30mg/Kg). Experiments were performed on a 7T-MRI scanner using single-shot EPI-sequence(TR/TE-1200/15ms, flip-angle 30°, FOV-2×2cm2, matrix-100×100, thickness-0.5mm, 1000-volumes). Data were pre-processed as previously described7. Four subjects were rejected in the xanomeline cohort due to excessive Frame-wise displacement(FD>0.1mm in over 20% of frames). Data-analysis. We first assessed the effect of xanomeline on the fractional-Amplitude-of-Low-frequency Fluctuations(fALFF)5 and functional connectivity. To capture whole-brain dynamics, we employed clustered fMRI frames into co-activation patterns(CAPs) exhibiting congruent spontaneous BOLD-activity, as previously described1. Concatenated frames were clustered according to their spatial-similarity using k-means++8, 1000-iterations and k=2:20. We incrementally assessed each solution based on its variance explained, and reproducibility of the identified clusters in the vehicle group with independent datasets(n1=40, n2=41, n3=23 mice)1. We choose k=6 as the most reproducible solution across datasets and applied this solution in the xanomeline cohort. CAP-dynamics. Each CAP-template(mean BOLD-map) was projected(spatial-correlation) to all fMRI-frames to obtain CAP time-series for each subject, and their respective power-spectrum, also for the global-signal(GS). CAP-occurrence within GS cycles was assessed by computing the GS’s instantaneous phase with the Hilbert-Transform9 and used circular-statistics to measure the probability distribution of GS-phases at each CAP’s occurrence1.

Results and discussion

We first probed effects of xanomeline on steady-state functional connectivity, and on regional amplitude of BOLD oscillations(Fig.1), highlighting a generalized reduction in rsfMRI connectivity upon xanomeline administration. The observed connectivity decrease was particularly strong within the latero-cortical, postero-lateral, default-mode, and hippocampal networks. The relative power of infra-slow(0.01-0.03Hz) fluctuations, as measured with fALFF, revealed foci of reduced fALFF in xanomeline-treated mice peaking in prefrontal cortical sites, primary-motor areas, thalamus and hippocampus. Interestingly, fALFF was robustly increased in the hypothalamus and basal-forebrain of xanomeline-treated mice(Fig.1C-D). Interestingly, we did not observe significant changes in the fALFF within most somatosensory regions. To identify the presence of reproducible macro-scale brain functional states, we identified clusters of spatially-congruent rsfMRI-frames using a k-means++ algorithm(Figs.2, Fig.3A). We identified six stable states encompassing cortical and sub-cortical districts in the vehicle group(Fig.2A, Fig.3A). Importantly, rsfMRI clustering of the xanomeline dataset revealed a new set of recurrent co-activation states, most of which demonstrate the involvement strong co-activations and co-deactivations of the basal-forebrain, hypothalamus, and postero-lateral cortical regions(Fig.3B). The presence of opposing patterns of co-activation found in xanomeline-induced states suggested conserved oscillatory dynamics, which we probed by computing the power spectrum of CAP time-courses, showing clear infra-slow, band-limited(0.01-0.03Hz) oscillations in states for both vehicle, and xanomeline groups(Fig.4A-B). Interestingly, we found that the global-signal presents significantly lower fALFF(p=0.002,Fig.4C). Moreover, contrary to controls, most states in the xanomeline group did not exhibit narrow distributions of GS-phases at CAP-occurrences within GS-cycles(Fig4.D).

Conclusions

Our work provides a first description of the effect of cholinergic modulation on large-scale network dynamics in the mouse brain. We show that pharmacological stimulation of cholinergic activity produces a significant increase in local BOLD fluctuations of the projecting sites of the basal-forebrain, resulting in desynchronization of resting-state network structure. Importantly, we also report that cholinergic stimulation engages brain-wide spontaneous network activity in a set of unique oscillatory states, involving the contrasting co-activation of postero-lateral regions and basal-forebrain areas. These results document that oscillatory brain states previously described in the mouse brain1, undergo prominent spatial reconfiguration resulting from modulatory and arousal states, providing the basis to describe spontaneous brain activity in terms of oscillatory patterns of instantaneous activity that are morphed by external and internal modulatory input.

Acknowledgements

Daniel Gutierrez-Barragan and Carola Canella contributed equally to this work and share first authorship. A.G. acknowledges funding by the Simons Foundation (SFARI 400101, A. Gozzi) and the Brain and behavior Foundation (2017 NARSAD, Independent Investigator Grant 25861). M.A.B was supported by grants from the Simons Foundation (SFARI 344763) and Medical Research Council (MR/K022377/1).

References

  1. Gutierrez-Barragan, D., Basson, M. A., Panzeri, S. & Gozzi, A. Oscillatory brain states govern spontaneous fMRI network dynamics. bioRxiv 393389 (2018). doi:10.1101/393389
  2. Liu, X. et al. Subcortical evidence for a contribution of arousal to fMRI studies of brain activity. Nat. Commun. 9, 1–10 (2018).
  3. Mirza, N. R., Peters, D. & Sparks, R. G. Xanomeline and the Antipsychotic Potential of Muscarinic Receptor Subtype Selective Agonists. CNS Drug Rev. 9, 159–186 (2006).
  4. Andersen, M. B. et al. The muscarinic M1/M4receptor agonist xanomeline exhibits antipsychotic-like activity in cebus apella monkeys. Neuropsychopharmacology 28, 1168–1175 (2003).
  5. Zou, Q. H. et al. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF. J. Neurosci. Methods 172, 137–141 (2008).
  6. Sforazzini, F., Schwarz, A. J., Galbusera, A., Bifone, A. & Gozzi, A. Distributed BOLD and CBV-weighted resting-state networks in the mouse brain. Neuroimage 87, 403–415 (2014).
  7. Liska, A. et al. Homozygous Loss of Autism-Risk Gene CNTNAP2 Results in Reduced Local and Long-Range Prefrontal Functional Connectivity. Cereb. Cortex 1–13 (2017). doi:10.1093/cercor/bhx022
  8. Arthur, D. & Vassilvitskii, S. K-Means++: the Advantages of Careful Seeding. Proc. eighteenth Annu. ACM-SIAM Symp. Discret. algorithms 1027–1025 (2007). doi:10.1145/1283383.1283494
  9. Montemurro, M. A., Rasch, M. J., Murayama, Y., Logothetis, N. K. & Panzeri, S. Phase-of-Firing Coding of Natural Visual Stimuli in Primary Visual Cortex. Curr. Biol. 18, 375–380 (2008).

Figures

Figure 1. Xanomeline induces rsfMRI desynchronization and reduces amplitude of low frequency BOLD fluctuations.(A) Anatomical location of selected seeds embedded in known functional systems in the mouse brain (blue: Latero-cortical Network-LCN; green: Postero-lateral Network-PLN; blue: Default-mode Network-DMN; yellow: Thalamus-TH; purple: Hippocampal Network-HCN; black: Striatum-STN; light blue: Hypothalamus-HT; orange: Nucleus Basalis-NB).(B) Between-seed normalized mean correlation (functional connectivity) of vehicle (left) and xanomeline (middle) treated animals (T-test, p<0.05, FDR-corrected), and mean between group differences (right)(two-sample T-test, p<0.05, FDR-corrected).(C) fALFF (0.01-0.03 Hz band) difference-map (vehicle vs. xanomeline, two-sample T-test, cluster-corrected, p<0.01, T-threshold=2.8).(D) Mean group level power-spectra for selected regions.

Figure 2. Selection of optimal number of clusters. (A) Whole-brain representations of CAPs with k=6, 7, and 8 in the vehicle dataset(n=19), and their matched CAPs found in dataset1(n1=40, 500-frames/subject), dataset2 (n2=41, 300-frames/subject) or dataset3 (n3=23, 500-frames/subject)1. Spatial correlations to the vehicle dataset are shown above each CAP. Note that CAPs 1-6 are recurrently found in all datasets with k=7 and 8 partitions, while additional CAPs are less reproducible across datasets. (B) Explained-variance by clustering the vehicle and xanomeline datasets with k=2:20. (C) Percentage gain in explained variance when advancing from k-1 to k.

Figure 3. Xanomeline alters functional state topography in the mouse brain. (A) Whole-brain CAPs in the vehicle dataset recapitulate six reproducible functional states previously described in the mouse brain. Red/yellow indicates co‐activation, while blue indicates co‐deactivation (below baseline BOLD signal) (p<0.01, Bonferroni corrected). CAPs are ordered according to consecutive states characterized by opposing BOLD co‐activation patterns (i.e. 1‐2, 3‐4 and 5‐6), denoted by the negative correlations in the lower panels. (B) CAPs in the xanomeline dataset represent a set of independent states of average BOLD activity (p<0.01, Bonferroni-corrected), most of them having non-comparable spatial features to the vehicle CAPs.

Figure 4. Functional states exhibit oscillatory dynamics. (A) Power-spectra of CAPs(mean+/‐SEM) from the vehicle(blue) and (B) xanomeline(red) groups. Dashed vertical lines delimit the 0.01‐0.03Hz frequency band. Inset numbers represent the peak frequencies. (C) Mean power-spectral density of the GS(mean+/‐SEM) from the vehicle (blue) and xanomeline(red) groups, showing significant decrease in fALFF of the GS in the xanomeline group(**p=0.002). (D) Circular distribution of GS-phases at each CAP’s occurrence within GS-cycles. Black radial lines denote resulting vectors(magnitude and phase). GS-phase distributions at the occurrence of all CAPs significantly deviated from circular uniformity, except CAP2 under xanomeline(Raleigh-test,*p<0.01, Bonferroni-corrected).

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
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