Infraslow State Fluctuations Govern Spontaneous fMRI Network Dynamics

Abstract

Spontaneous brain activity as assessed with resting-state fMRI exhibits rich spatiotemporal structure. However, the principles by which brain-wide patterns of spontaneous fMRI activity reconfigure and interact with each other remain unclear. We used a framewise clustering approach to map spatiotemporal dynamics of spontaneous fMRI activity with voxel resolution in the resting mouse brain. We show that brain-wide patterns of fMRI co-activation can be reliably mapped at the group and subject level, defining a restricted set of recurring brain states characterized by rich network structure. Importantly, we document that the identified fMRI states exhibit contrasting patterns of functional activity and coupled infraslow network dynamics, with each network state occurring at specific phases of global fMRI signal fluctuations. Finally, we show that autism-associated genetic alterations entail the engagement of atypical functional states and altered infraslow network dynamics. Our results reveal a novel set of fundamental principles guiding the spatiotemporal organization of resting-state fMRI activity and its disruption in brain disorders.

Keywords: ASD; CAPs; DMN; QPP; TPN; autism; functional connectivity; mouse; quasi-periodic patterns; task positive network.

Graphical abstract

Figure 1

Identification of Recurring Brain States via Whole-Brain fMRI Frame Clustering (A) Glass brain representation of the seed-based resting-state networks described in Figure S1 (DMN, default mode network; HCN, hippocampal network; LCN, latero-cortical network; PLN, postero-lateral network). (B) Illustrative fMRI BOLD time course (SD units) in the anterior cingulate (ACg) (red), somatosensory cortex (SSs) (blue) as well as fMRI global signal (black) in a representative subject (brain slice illustrated in A). Note the presence of peaks of concordant or diverging BOLD activity in cingulate and somatosensory areas across time, suggestive of time-varying network reconfiguration; cingulate and somatosensory regions are concurrently co-activated and co-deactivated in F1 and F4, but they exhibit opposing BOLD activity in F2 and F3. (C) These dynamic transitions can be captured and classified into recurring brain states by clustering fMRI frames into spatially congruent patterns (CAPs), using the k-means algorithm.

Figure 2

Recurring Functional States of the Mouse Brain (A) Whole-brain representation of the functional brain states (CAPs) we identified at the group level. Red-yellow indicates co-activation (i.e., high fMRI BOLD signal), and blue indicates co-deactivation (i.e., low fMRI BOLD signal; p < 0.01; Bonferroni corrected). (B) CAPs have been ordered based on their spatial properties by numbering consecutively states characterized by opposing BOLD co-activation patterns (i.e., 1-2, 3-4, and 5-6), as denoted by the negative correlations. (C and D) CAP occurrence rate (C) and mean duration (D; mean ± SEM). ACg, anterior cingulate cortex; AUD, auditory cortex; dHC, dorsal hippocampus; vHC, ventral hippocampus; HT, hypothalamus; ILA, infralimbic area; LAN, lateral amygdalar nucleus; MOp, primary motor cortex; Mos, secondary motor cortex; ORB, orbitofrontal cortex; PIR, piriform area; PL, pallidum; Rs, retrosplenial cortex; SSp, primary somatosensory cortex; SSs, secondary somatosensory cortex; ST, striatum; TeA, temporal association cortex; TH, thalamus; VIS, visual cortex. See also Videos S1, S2, S3, S4, S5, and S6 and Figures S2–S5.

Figure 3

Brain States Can Be Detected at the Single-Subject Level (A) Incidence map of each CAP at the subject level (k = 6, 30-min rsfMRI acquisitions; p < 0.05; FDR corrected). Each voxel represents the proportion of subjects with significant co-activation (p < 0.05; FDR corrected) as its corresponding group-level CAP template. (B) Spatial distribution of CAPs detected in a representative subject (p < 0.05; FDR corrected). Yellow indicates regional co-activation, blue regional co-deactivation. See also Figure S4.

Figure 4

fMRI States Exhibit Infraslow Network Dynamics (A) Illustrative CAP and GS time course from a representative subject. (B) Mean power spectral density of CAPs and the fMRI GS (mean ± SEM). Blue dashed vertical lines delimit the 0.01- to 0.03-Hz frequency band employed in subsequent phase analyses; horizontal red lines show the spectrum of randomly shuffled surrogate time courses.

Figure 5

fMRI States Occur at Specific Phases of Global Signal Fluctuations (A) Instantaneous phase of the GS in a representative subject. Colored dots mark the occurrence of each CAP. (B) CAPs occur at specific phases of global signal fluctuations. Phase dispersion is plotted as circular variance around the circular mean (top horizontal bars). CAP encoding is described according to color scheme in (A). (C) Circular distribution of GS phases at each CAP’s occurrence within a GS cycle. For each distribution, the resulting vector (magnitude and phase) is shown as a black radial line. See also Figures S3 and S6.

Figure 6

Altered Brain States in a Genetic Mouse Model of Autism (A) Representative coronal and sagittal cuts showing the functional organization of CAPs 1-6 in control (Chd8^+/+, left) and mutant mice (Chd8^+/−, right; p < 0.001; familywise error [FWE] cluster corrected). (B) Anatomical location of the regions exhibiting significant between-group differences in BOLD co-activation intensity for each of the six identified CAPs (p < 0.001; FWE cluster corrected). Red or blue indicate areas exhibiting regionally increased or decreased BOLD co-activation, respectively, in mutant mice with respect to control littermates. (C) Delayed CAP occurrence within GS cycles with respect to control mice in Chd8^+/− mutants (p < 0.01; William-Watson test for circular mean homogeneity; Bonferroni corrected; all CAPs, except for CAP3). (D) Seed-based rsfMRI correlation differences in Chd8 mutants. Inter-group differences (p < 0.05; FWE cluster corrected) are depicted with respect to a seed-pair in the SS to replicate the rsfMRI findings reported in [24]. The location of hippocampal (HCP) seed used for correlational profiling is also illustrated (HCP1, 2, and 3). (E) Seed-based correlation profiling upon regression of individual CAP time courses or the GS in both groups (^∗∗∗p < 0.001; two-way repeated-measures ANOVA; mean ± SEM). Cg, cingulate cortex; dHCP, dorsal hippocampus; HCP, hippocampus; vHCP, ventral hippocampus; SS, somatosensory cortex.