Metabolic connectivity mapping reveals effective connectivity in the resting human brain

Directionality of signaling among brain regions provides essential information about human cognition and disease states. Assessing such effective connectivity (EC) across brain states using functional magnetic resonance imaging (fMRI) alone has proven difficult, however. We propose a novel measure of EC, termed metabolic connectivity mapping (MCM), that integrates undirected functional connectivity (FC) with local energy metabolism from fMRI and positron emission tomography (PET) data acquired simultaneously. This method is based on the concept that most energy required for neuronal communication is consumed postsynaptically, i.e., at the target neurons. We investigated MCM and possible changes in EC within the physiological range using “eyes open” versus “eyes closed” conditions in healthy subjects. Independent of condition, MCM reliably detected stable and bidirectional communication between early and higher visual regions. Moreover, we found stable top-down signaling from a frontoparietal network including frontal eye fields. In contrast, we found additional top-down signaling from all major clusters of the salience network to early visual cortex only in the eyes open condition. MCM revealed consistent bidirectional and unidirectional signaling across the entire cortex, along with prominent changes in network interactions across two simple brain states. We propose MCM as a novel approach for inferring EC from neuronal energy metabolism that is ideally suited to study signaling hierarchies in the brain and their defects in brain disorders.Complex cognition emerges by integrating upstream sensory information with feedback signaling from higher cortical regions (1–4). Networks related to sensory processing or cognition reliably occur in the human brain even at rest (5, 6). These networks are identified by synchronous signal fluctuations, or functional connectivity (FC), among brain regions when neuronal activity is recorded by functional magnetic resonance imaging (fMRI). In recent years, various FC patterns have emerged as reliable indicators of different brain states, because they have been found to adapt to recent behavior or cognition (7–12) and to be disrupted in patients suffering from specific psychiatric disorders (13, 14). Further knowledge about important aspects of cognition and diseases could be gained from a better distinction between feedback and feedforward communication. Our understanding of the signaling hierarchy in different brain states remains incomplete, however.Although FC captures correlations among neuronal signals, only effective connectivity (EC) describes the influence exerted by one neuronal system over another (15). Recent approaches to modeling EC during different brain states appear promising (16, 17), but face problems inherent to fMRI. First, EC is estimated directly from the time-varying fluctuations or cross-spectra of the observed fMRI signal, and thus is prone to confounds from different hemodynamic responses across groups, particularly when studying patient populations (15, 17). Second, analyses are usually restricted to a limited number of brain regions, owing to the need for complex computations. Here we propose a novel approach integrating FC with simultaneously measured energy metabolism from positron emission tomography (PET) to derive a voxel-wise, whole-brain mapping of EC in humans.Energy consumption is an essential aspect of neuronal communication. Consistently across species, the greatest amount of energy metabolism is dedicated to signaling, with the remaining part dedicated to housekeeping functions (18). Up to 75% of signaling-related energy is consumed postsynaptically, i.e., at the target neurons (19–22). Scaled to the systems level, we assume that an increase in local metabolism reflects an increase in afferent EC from source regions. We hypothesize that the spatial profile of this relationship is expressed in terms of spatial correlations between metabolic activity and long-range FC, which we term metabolic connectivity mapping (MCM). We simultaneously acquired fMRI and PET data for the glucose analog ¹⁸F-fludeoxyglucose (FDG) during two different brain states, as reported previously (10). In individual subject space, we performed spatial correlation analyses of voxel FC and FDG to test whether the metabolic profile indicates the target area of communication between functionally connected regions (Fig. 1).Fig. 1.MCM reveals EC in the human brain. (A) FC reveals undirected pathways of synchronous fMRI signal fluctuations between two regions, X and Y. For each subject, FC is calculated as the temporal correlation, r] between the cluster time series. In our example, ...Vision is the only sensation that can be interrupted volitionally in a natural way. Opening the eyes is a fundamental behavior for directing attention to the external world, i.e., changing from an interoceptive state to an exteroceptive state (3, 23, 24). Current knowledge of the signaling hierarchy in the extended visual system has emerged from animal and tracer studies. These data reveal reciprocal (bottom-up and top-down) connections along the ventral and dorsal visual stream (25), including top-down projections from frontal back to early visual cortices (3, 4, 26, 27). To test this signaling hierarchy in humans, we scanned healthy human subjects in two brain states, lying with either eyes closed or eyes open in darkness, and calculated EC using our integrated approach. Consistent with previously reported data, MCM revealed persistent and bidirectional interactions between visual stream areas during both the “eyes closed” and “eyes open” conditions, but frontal top-down modulation of early visual areas only during the eyes open condition.In the present study, we used FDG to inform undirected FC from fMRI with a directional measure of postsynaptic neuronal activity. Our results indicate that the integrated measure of MCM serves as a proxy for EC in brain states. Our approach might be particularly useful for investigating other signaling hierarchies in higher cognition or in brain disorders involving, e.g., hippocampal-cortical circuits in Alzheimer’s disease (28) or fronto-midbrain interactions in major depression (29).