The functional connectome across spatiotemporal scales: How integrating fMRI and (i)EEG changes our understanding of the human brain

Abstract

The view of human brain function has drastically shifted over the last decade, owing to the observation that the majority of brain activity is intrinsic rather than driven by external stimuli or cognitive demands. Specifically, all brain regions continuously communicate in spatiotemporally organized patterns that constitute the functional connectome, with consequences for cognition and behavior. In this talk, I will argue that another shift is underway, driven by new insights from synergistic interrogation of the functional connectome using different acquisition methods. The human functional connectome is typically investigated with functional magnetic resonance imaging (fMRI) that relies on the indirect hemodynamic signal, thereby emphasizing very slow connectivity across brain regions. Conversely, more recent methodological advances demonstrate that fast connectivity within the whole-brain connectome can be studied with real-time methods such as electroencephalography (EEG). Our findings show that combining fMRI with scalp or intracranial EEG, especially when recorded concurrently, paints a rich picture of neural communication across the connectome. Specifically, the large-scale connectome comprises both fast, oscillation-based connectivity observable with EEG, as well as extremely slow processes captured by fMRI. While the fast and slow processes share an important degree of spatio-temporal organization, a considerable proportion of these processes is independent. This observation motivates revision of the viewpoint that fMRI and EEG provide different windows onto the same neural processes. Rather, the exciting view arises that the functional connectome comprises distinct processes unfolding in partially non-overlapping spatial and temporal patterns. Depending on their timescale, these patterns dominate the signals in hemodynamic and real-time acquisition methods, respectively. The presented findings highlight the importance of multi-modal approaches for understanding brain function.