The brain exhibits highly organized patterns of spontaneous activity as measured by resting-state functional magnetic resonance imaging (fMRI) fluctuations that are being widely used to assess the brain’s functional connectivity. Some evidence suggests that spatiotemporally coherent waves are a core feature of spontaneous activity that shapes functional connectivity, although this has been difficult to establish using fMRI given the temporal constraints of the hemodynamic signal. Here, we investigated the structure of spontaneous waves in human fMRI and monkey electrocorticography. In both species, we found clear, repeatable, and directionally constrained activity waves coursed along a spatial axis approximately representing cortical hierarchical organization. These cortical propagations were closely associated with activity changes in distinct subcortical structures, particularly those related to arousal regulation, and modulated across different states of vigilance. The findings demonstrate a neural origin of spatiotemporal fMRI wave propagation at rest and link it to the principal gradient of resting-state fMRI connectivity.

Correlations of resting-state functional magnetic resonance imaging (rsfMRI) signals are being widely used for assessing the functional brain connectivity in health and disease. However, an association was recently observed between rsfMRI connectivity modulations and the head motion parameters and regarded as a causal relationship, which has raised serious concerns about the validity of many rsfMRI findings. Here, we studied the origin of this rsfMRI-motion association and its relationship to arousal modulations. By using a template-matching method to locate arousal-related fMRI changes, we showed that the effects of high motion time points on rsfMRI connectivity are largely due to their significant overlap with arousal-affected time points. The finding suggests that the association between rsfMRI connectivity and the head motion parameters arises from their comodulations at transient arousal modulations, and this information is critical not only for proper interpretation of motion-associated rsfMRI connectivity changes, but also for controlling the potential confounding effects of arousal modulation on rsfMRI metrics.

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Temporal lobe epilepsy presents a unique situation where confident clinical localization of the seizure focus does not always result in a seizure free or favorable outcome after mesial temporal surgery. In this work magnetic resonance imaging derived functional and structural whole brain connectivity was used to compute a network fingerprint that captures the connectivity profile characteristics that are common across a group of nine of these patients with seizure free outcome. The connectivity profile was then computed for 38 left-out patients with the hypothesis that similarity to the fingerprint indicates seizure free surgical outcome. Patient profile distance to the fingerprint was compared to one-year seizure outcome and standard clinical parameters. Distance to the fingerprint was higher for patients with Engel III-IV one-year outcome compared to those with Engel Ia, Ib-d, and II outcome (Kruskal Wallis, p < 0.01; Wilcoxon rank-sum pcorr < 0.05 Bonferroni corrected). Receiver operator characteristic analysis revealed 100% sensitivity and 90% specificity in identifying patients with Engel III-IV outcome based on distance to the fingerprint in the left-out patients. Furthermore, distance to the fingerprint was not related to any individual clinical parameter including age at scan, duration of disease, total seizure frequency, presence of mesial temporal sclerosis, lateralizing ictal, interictal scalp electroencephalography, invasive stereo-encephalography or positron emission tomography. And two published algorithms utilizing multiple clinical measures for predicting seizure outcome were not related to distance to the fingerprint, nor predictive of seizure outcome in this cohort. The functional and structural connectome fingerprint provides quantitative, clinically interpretable and significant information not captured by standard clinical assessments alone or in combinations. This automated and simple method may improve patient specific prediction of seizure outcome in patients with a clinically identified focus in the mesial temporal lobe.