A consensus protocol for functional connectivity analysis in the rat brain

Abstract

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

Extended Data Fig. 1 |. Age and weight distributions.

Age (a) and weight (b) distribution for the rats in the MultiRat_rest collection.

Extended Data Fig. 2 |. Quality control examples.

Failed quality controls for anatomical to template registrations (a) and functional to anatomical registrations (b). The top rows are the moving objects, bottom rows are the reference objects. The red lines indicate the outlines of the other object. Four slices along the sagittal, axial, and coronal axis are shown for each case.

Extended Data Fig. 3 |. Temporal signal-to-noise ratio.

Temporal signal-to-noise ratio in the sensory cortex (tSNR S1) in the MultiRat_rest dataset collection as a function of (a) magnetic field strength, (b) repetition time, (c) echo time, (d) temporal signal-to-noise ratio in the striatum.

Extended Data Fig. 4 |. Framewise displacement.

MFW in the MultiRat_rest dataset collection as a function of (a) strain, (b) anesthesia, (c) breathing rate, (d) maximal framewise displacement.

Extended Data Fig. 5 |. FC in the default-mode network.

The reference seed is positioned in the anterior cingulate cortex (Fig. 2a), the specific region-of-interest is positioned 3.3 mm posterior in the cingulate cortex and the nonspecific region-of-interest is positioned in the S1bf.

Extended Data Fig. 6 |. StandardRat dataset description.

a. Strain. b. Sex. c. Field strength. d. Weight. e. Breathing rate as a function of MFW. f. FC specificity as a function of confound correction models.

Extended Data Fig. 7 |. FC incidence.

Incidence of FC at the group level in the StandardRat collection for four selected seeds (n = 21 datasets, n ~ 10 subjects per dataset). Connectivity incidence is improved in the StandardRat collection relative to MultiRat_rest (Fig. 3).

Extended Data Fig. 8 |. Between-datasets connectivity comparisons.

FC category comparison between MultiRat_rest and StandardRat (a) and between the awake datasets of MultiRat_rest and the awake dataset from Lui et al. 2020 (b).

Extended Data Fig. 9 |. Connectivity specificity as a function of breathing rate and signal-to-noise ratio.

FC specificity as a function of binned breathing rate (a) AND temporal signal-to-noise ratio (b) in the StandardRat collection. The percentage of each condition is size and color-coded. High levels of connectivity specificity were achieved in scans where the breathing rates were in the 84 to 114 bpm range. Similarly, higher connectivity specificity incidences were found when the cortical temporal signal-to-noise ratio was > 53. These observations support the notion of an optimal breathing rate when applying the StandardRat protocol, along with temporal signal-to-noise ratio and movement targets.

Extended Data Fig. 10 |. Group independent components analysis.

Plausible independent components overlapping with known rodent networks, obtained after group-level decomposition with n = 20 components. Labels are based on the SIGMA anatomical atlas.

Fig. 1 |. MultiRat_rest dataset description.

a, Sex. b, Strain. c, Anesthesia. d, Magnetic field strength. e, Breathing rate as a function of anesthesia. f, Repetition time. g, Echo time as a function of magnetic field strength. h, Slice position for the examples. i, Example of representative raw functional images. Arrows indicate different susceptibility artifact-related geometric distortions in the amygdala. j, Successful anatomical (top) to standard (bottom) space registration. Red lines indicate the outlines of the standard image (top) and the anatomical (bottom). k, Successful functional (top) to anatomical (bottom) registration. Red lines indicate the outlines of the anatomical image (top) and the functional (bottom).

Fig. 2 |. FC specificity.

a, Diagram illustrating the logic behind FC specificity. The sensory (barrel field, S1bf) area (blue) chiefly projects to the contralateral homotopic area (light blue) but not to the ACA area (purple). b, Example of temporal dynamics in the resting-state signal. Correlated signal between the ipsilateral and contralateral S1bf and anti-correlated signal from the ACA. c, Distribution of FC categories as a function of confound correction models. d, FC in left S1bf relative to specific (right S1bf) and non-specific (ACA) regions of interest using the global regression correction model. Dots represent scans (n = 638 rats); dotted lines indicate the thresholds used to delineate the categories. e, Distribution of connectivity categories as a function of anesthesia. Example of individual seed-based analysis maps for each connectivity category. f, Distribution of connectivity categories as a function of imaging sequence (EPI, echo-planar imaging; GE, gradient echo; SE, spin echo). Group-level FC incidence map (n = 65 datasets). g, Example of individual seed-based analysis maps for each connectivity category. a.u., arbitrary units; ROI, region of interest, WMCSFs, white-matter + cerebrospinal fluid.

Fig. 3 |. Incidence of FC at the group level (n = 65 datasets of n ~ 10 subjects per dataset) for four seeds.

Cpu, caudate-putamen; MOp, primary motor area. MOp and CPu seeds were generally observed in 50–75% of the datasets.

Fig. 4 |. StandardRat dataset description.

a, Breathing rate (bpm) as a function of strain. b, MFW as a function of strain. c, Temporal signal-to-noise ratio in the sensory cortex as a function of field strength. d, FC in left S1bf relative to specific (right S1bf) and non-specific (ACA) regions of interest using the global regression correction model. Dots represent scans (n = 207 rats); dotted lines indicate the thresholds used to delineate the categories. e, Representative independent components. a.u., arbitrary units.