Time-resolved resting-state brain networks

Neuronal dynamics display a complex spatiotemporal structure involving the precise, context-dependent coordination of activation patterns across a large number of spatially distributed regions. Functional magnetic resonance imaging (fMRI) has played a central role in demonstrating the nontrivial spatial and topological structure of these interactions, but thus far has been limited in its capacity to study their temporal evolution. Here, using high-resolution resting-state fMRI data obtained from the Human Connectome Project, we mapped time-resolved functional connectivity across the entire brain at a subsecond resolution with the aim of understanding how nonstationary fluctuations in pairwise interactions between regions relate to large-scale topological properties of the human brain. We report evidence for a consistent set of functional connections that show pronounced fluctuations in their strength over time. The most dynamic connections are intermodular, linking elements from topologically separable subsystems, and localize to known hubs of default mode and fronto-parietal systems. We found that spatially distributed regions spontaneously increased, for brief intervals, the efficiency with which they can transfer information, producing temporary, globally efficient network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time, possibly achieving a balance between efficient information-processing and metabolic expenditure.The coordination of brain activity between disparate neural populations is a dynamic and context-dependent process (1–3). Although dynamic patterns of neural synchronization may be evident in time-dependent measures of functional connectivity (4, 5), the temporal stability of high-level topological properties is unknown. The topology of large-scale cortical activity—such as its efficient network layout (6), community structure (7), network hubs (8), rich-club organization (9, 10), and small worldness (11, 12)—may reflect fundamental aspects of cortical computation. Temporal fluctuations in these graph-theoretic measures may hence speak to adaptive properties of neuronal information processing.With international connectome mapping consortia such as the Human Connectome Project (HCP) (13) and the developing Human Connectome Project in full swing, resting-state functional magnetic resonance imaging (rsfMRI) data of unprecedented temporal resolution are now available to map the time-resolved properties of functional brain networks. Imaging the brain at rest reveals spontaneous low-frequency fluctuations in brain activity that are temporally correlated between functionally related regions (14–17). Interregional correlations are referred to as functional connections, and they collectively form a complex network (18).Functional brain networks are typically mapped in a time-averaged sense, based on the assumption that functional connections remain relatively static (stationary) in the resting brain. However, recent investigations have furnished compelling evidence challenging the “static” conceptualization of resting-state functional connectivity (5). In particular, the application of time-resolved methodologies for analyzing time series data has consistently revealed fluctuations in resting-state functional connectivity at timescales ranging from tens of seconds to a few minutes (19–24). Furthermore, the modular organization of functional brain networks appears to be time-dependent in the resting state (25, 26) and modulated by learning (27) and cognitive effort (28, 29). It is therefore apparent that reducing fluctuations in functional connectivity to time averages has led to a very useful but static and possibly oversimplified characterization of the brain’s functional networks. For example, connections that toggle between correlated and anticorrelated states are reduced to zero in a time-averaged sense, assuming equal dwell times in each state.Conventionally, rsfMRI data are sampled at a resolution of 2 s or slower. Using multiband accelerated echo planar imaging, the HCP has acquired high-quality rsfMRI data at a subsecond resolution (30). This order of magnitude improvement in temporal resolution is highly advantageous to the feasibility of time-resolved functional connectomics. Faster sampling rates enable a richer temporal characterization of resting-state fluctuations, denser sampling of physiological confounds, and greater degrees of freedom (30).Using a sliding-window approach applied to HCP rsfMRI data, we mapped the evolution of functional brain networks over a continuous 15-min interval at a subsecond resolution. For each of 10 individuals, this yielded a time series of correlation matrices (regions × regions × time), where matrix elements quantified the functional connectivity at a given time instant between cortical and subcortical regions comprising established brain parcellation atlases. We developed a statistic to test the time-resolved connectome for evidence of nonstationary temporal dynamics and applied it to the 10 individuals as well as a replication data set and simulated rsfMRI data.Our main aim was to investigate the consequences of nonstationary fluctuations on the topological organization of functional brain networks. We hypothesized that dynamic behavior is coordinated across the brain so that transitions between distinct states are marked by reorganization of the brain’s functional topology. Evidence for this hypothesis is provided by the coordinated fluctuations in network measures, such as hub centrality (31), that have been observed in simulated rsfMRI data (32, 33).     

Functional hierarchy underlies preferential connectivity disturbances in schizophrenia

Schizophrenia may involve an elevated excitation/inhibition (E/I) ratio in cortical microcircuits. It remains unknown how this regulatory disturbance maps onto neuroimaging findings. To address this issue, we implemented E/I perturbations within a neural model of large-scale functional connectivity, which predicted hyperconnectivity following E/I elevation. To test predictions, we examined resting-state functional MRI in 161 schizophrenia patients and 164 healthy subjects. As predicted, patients exhibited elevated functional connectivity that correlated with symptom levels, and was most prominent in association cortices, such as the fronto-parietal control network. This pattern was absent in patients with bipolar disorder (n = 73). To account for the pattern observed in schizophrenia, we integrated neurobiologically plausible, hierarchical differences in association vs. sensory recurrent neuronal dynamics into our model. This in silico architecture revealed preferential vulnerability of association networks to E/I imbalance, which we verified empirically. Reported effects implicate widespread microcircuit E/I imbalance as a parsimonious mechanism for emergent inhomogeneous dysconnectivity in schizophrenia.Schizophrenia (SCZ) is a disabling psychiatric disease associated with widespread neural disturbances. These involve abnormal neurodevelopment (1–3), neurochemistry (4–7), neuronal gene expression (8–11), and altered microscale neural architecture (2). Such deficits are hypothesized to impact excitation-inhibition (E/I) balance in cortical microcircuits (12). Clinically, SCZ patients display a wide range of symptoms, including delusions, hallucinations (13, 14), higher-level cognitive deficits (15, 16), and lower-level sensory alterations (17). This display is consistent with a widespread neuropathology (18), such as the E/I imbalance suggested by the NMDA receptor (NMDAR) hypofunction model (19–21). However, emerging resting-state functional magnetic resonance imaging (rs-fMRI) studies implicate more network-specific abnormalities in SCZ. Typically, these alterations are localized to higher-order association regions, such as the fronto-parietal control network (FPCN) (18, 22) and the default mode network (DMN) (23, 24), with corresponding disturbances in thalamo-cortical circuits connecting to association regions (25, 26). It remains unknown how to reconcile widespread cellular-level neuropathology in SCZ (20, 21, 27, 28) with preferential association network disruptions (29, 30).Currently a tension exists between two competing frameworks: global versus localized neural dysfunction in SCZ. Association network alterations in SCZ, identified via neuroimaging, may arise from a localized dysfunction (3, 9, 31, 32). Alternatively, they may represent preferential abnormalities arising emergently from a nonspecific global microcircuit disruption (20, 33). Mechanistically, an emergent preferential effect could occur because of intrinsic differences between cortical areas in the healthy brain, leading to differential vulnerability toward a widespread homogenous neuropathology. For example, histological studies of healthy primate brains show interregional variation in cortical cytoarchitectonics (34–38). Additional studies reveal differences in microscale organization and activity timescales for neuronal populations in higher-order association cortex compared with lower-order sensory regions (38–40). However, these well-established neuroanatomical and neurophysiological hierarchies have yet to be systematically applied to inform network-level neuroimaging disturbances in SCZ. In this study, we examined the neuroimaging consequences of cortical hierarchy as defined by neurophysiological criteria (i.e., functional) rather than anatomical or structural criteria.One way to link cellular-level neuropathology hypotheses with neuroimaging is via biophysically based computational models (18, 41). Although these models have been applied to SCZ, none have integrated cortical hierarchy into their architecture. Here we initially implemented elevated E/I ratio within our well-validated computational model of resting-state neural activity (18, 42, 43) without assuming physiological differences between brain regions, but maintaining anatomical differences. The model predicted widespread elevated functional connectivity as a consequence of elevated E/I ratio. In turn, we tested this connectivity prediction across 161 SCZ patients and 164 matched healthy comparison subjects (HCS). However, we discovered an inhomogeneous spatial pattern of elevated connectivity in SCZ generally centered on association cortices.To capture the observed inhomogeneity, we hypothesized that pre-existing intrinsic regional differences between association and lower-order cortical regions may give rise to preferential network-level vulnerability to elevated E/I. Guided by primate studies examining activity timescale differences across the cortical hierarchy (39, 44), we incorporated physiological differentiation across cortical regions in the model. Specifically, we tested whether pre-existing stronger recurrent excitation in “association” networks (39, 40) would preferentially increase their functional connectivity in response to globally elevated E/I. Indeed, modeling simulations predicted preferential effects of E/I elevation in association networks, which could not be explained by structural connectivity differences alone.Finally, we empirically tested all model-derived predictions by examining network-specific disruptions in SCZ. To investigate diagnostic specificity of SCZ effects, we examined an independent sample of bipolar disorder (BD) patients (n = 73) that did not follow model-derived predictions. These results collectively support a parsimonious theoretical framework whereby emergent preferential association network disruptions in SCZ can arise from widespread and nonspecific E/I elevations at the microcircuit level. This computational psychiatry study (45) illustrates the productive interplay between biologically grounded modeling and clinical effects, which may inform refinement of neuroimaging markers and ultimately rational development of treatments for SCZ.     

Intrinsically organized resting state networks in the human spinal cord

Spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals of the brain have repeatedly been observed when no task or external stimulation is present. These fluctuations likely reflect baseline neuronal activity of the brain and correspond to functionally relevant resting-state networks (RSN). It is not known however, whether intrinsically organized and spatially circumscribed RSNs also exist in the spinal cord, the brain’s principal sensorimotor interface with the body. Here, we use recent advances in spinal fMRI methodology and independent component analysis to answer this question in healthy human volunteers. We identified spatially distinct RSNs in the human spinal cord that were clearly separated into dorsal and ventral components, mirroring the functional neuroanatomy of the spinal cord and likely reflecting sensory and motor processing. Interestingly, dorsal (sensory) RSNs were separated into right and left components, presumably related to ongoing hemibody processing of somatosensory information, whereas ventral (motor) RSNs were bilateral, possibly related to commissural interneuronal networks involved in central pattern generation. Importantly, all of these RSNs showed a restricted spatial extent along the spinal cord and likely conform to the spinal cord’s functionally relevant segmental organization. Although the spatial and temporal properties of the dorsal and ventral RSNs were found to be significantly different, these networks showed significant interactions with each other at the segmental level. Together, our data demonstrate that intrinsically highly organized resting-state fluctuations exist in the human spinal cord and are thus a hallmark of the entire central nervous system.Functional magnetic resonance imaging (fMRI) has been used to study the functional connectivity of the human brain, with spontaneous fluctuations in the resting-state fMRI signal (1–3) attracting much attention in the past few years (for review, see refs. 4–6). Brain regions showing temporally coherent spontaneous fluctuations constitute several anatomically consistent “resting state networks” (RSNs), such as visual, auditory, sensory-motor, executive control, and default mode networks (7–11). Consequently, analyses of RSNs are rapidly emerging as a powerful tool for in vivo mapping of neural circuitry in the human brain and one such approach for exploring RSNs is independent component analysis (ICA) (12–14). ICA decomposes the data into spatially independent and temporally coherent source signals/components. The advantage of ICA over more traditional seed-based approaches (15) is that it is a model-free, data-driven multiple-regression approach, i.e., within the ICA framework we can account for multiple underlying signal contributions (artifactual or neuronal in origin) simultaneously and thereby disentangle these different contributions to the measured observations (16). To date, ICA has been used not only to characterize brain connectivity in healthy adults (7, 10, 17), but also to assess the development of brain connectivity at various stages of (18, 19) as well as across the lifespan (20) and to investigate connectivity alterations in clinical populations (21–24).Here, we use this approach to investigate the intrinsic organization of RSNs in the human spinal cord. The spinal cord is the first part of the central nervous system (CNS) involved in the transmission of somatosensory information from the body periphery to the brain, as well as the last part of the CNS involved in relaying motor signals to the body periphery. This functional separation is also evident in the anatomical organization of the spinal cord, with the ventral part of gray matter involved in motor function and the dorsal part involved in somatosensory processing. The corresponding pairs of ventral and dorsal nerve roots convey information to and from the body periphery with a rostro-caudal topographical arrangement for both sensory (dermatomes) and motor innervation (myotomes).Although such a precise anatomical layout would suggest clear organizational principles for intrinsic spinal cord networks (similar to e.g., the visual and auditory RSNs in the brain), it is not known whether spatially consistent RSNs exist in the spinal cord. Distinct spatial maps due to cardiac and respiratory noise sources have been revealed by single subject ICA (25–27), and a seed-based approach demonstrated correlations between ventral horns and between dorsal horns (28), but no group patterns of circumscribed motor or sensory networks have yet been found; also only a few investigations of task-based functional connectivity have been performed (29–31). One reason for the apparent lack of relevant data is that fMRI is more challenging to perform in the spinal cord than in the brain (32, 33). The difficulties faced are mostly due to its small cross-sectional area (∼1 cm², necessitating the use of small voxel sizes, which leads to a low signal-to-noise ratio), magnetic susceptibility differences in tissues adjacent to the cord, e.g., vertebral bodies and spinous processes (causing signal loss and image distortion), as well as the influence of physiological noise (obscuring neuronally induced signal changes).Here, we used recent improvements in spinal fMRI [i.e., acquisition techniques that mitigate magnetic susceptibility differences (34), validated procedures for physiological noise reduction (35, 36) and techniques that allow voxel-wise group analyses (37, 38)] to overcome these difficulties and investigate the organizational principles of RSNs in the human spinal cord. We hypothesized that dorsal and ventral regions of the spinal cord would show different patterns of resting activity and furthermore investigated whether the segmental organization of the spinal cord would be evident in the rostro-caudal spatial layout of spinal RSNs.     

Large-scale topology and the default mode network in the mouse connectome

Noninvasive functional imaging holds great promise for serving as a translational bridge between human and animal models of various neurological and psychiatric disorders. However, despite a depth of knowledge of the cellular and molecular underpinnings of atypical processes in mouse models, little is known about the large-scale functional architecture measured by functional brain imaging, limiting translation to human conditions. Here, we provide a robust processing pipeline to generate high-resolution, whole-brain resting-state functional connectivity MRI (rs-fcMRI) images in the mouse. Using a mesoscale structural connectome (i.e., an anterograde tracer mapping of axonal projections across the mouse CNS), we show that rs-fcMRI in the mouse has strong structural underpinnings, validating our procedures. We next directly show that large-scale network properties previously identified in primates are present in rodents, although they differ in several ways. Last, we examine the existence of the so-called default mode network (DMN)—a distributed functional brain system identified in primates as being highly important for social cognition and overall brain function and atypically functionally connected across a multitude of disorders. We show the presence of a potential DMN in the mouse brain both structurally and functionally. Together, these studies confirm the presence of basic network properties and functional networks of high translational importance in structural and functional systems in the mouse brain. This work clears the way for an important bridge measurement between human and rodent models, enabling us to make stronger conclusions about how regionally specific cellular and molecular manipulations in mice relate back to humans.Understanding the functional architecture of brain systems in both typical and atypical populations has the potential to improve diagnosis, prevention, and treatment of various neurologic and mental illnesses. Human functional neuroimaging, because of its ease of use, noninvasive nature, and wide availability, has significantly advanced this goal. However, because functional brain imaging is an indirect measure of the underlying neuronal dynamics (1), a number of basic questions about the molecular and structural underpinnings of these functional signals needs to be answered before the full clinical promise of the technique can be realized. Insight into these underpinnings would be vastly enhanced by translation to rodent models, where rich methodology for studying high-throughput genetic, histological, and therapeutic conditions in a tightly controlled environment exists. Mouse models, in particular, are likely to contribute significantly to this end.Efforts aimed at using mouse models to enrich findings obtained in humans with noninvasive imaging would benefit greatly from bridge measurements—measurements that can be obtained and compared directly between species, such as resting-state functional connectivity MRI (rs-fcMRI). Importantly, rs-fcMRI has provided invaluable insight into the large-scale topological organization of the human brain (2–4), how it relates to complex behaviors, and how it can be disrupted in disordered populations (5–8). In addition, rs-fcMRI is comparable across species, persists under light anesthesia, and allows for a broad view of intricate regional functional interactions without task inputs (9, 10). The capacity to image the murine brain with rs-fcMRI would effectively bridge clinical studies of human subjects with a vast array of techniques used to understand brain function with mouse models.Although functional brain networks have been well-characterized in humans and to an increasing extent, macaques, a remaining question is whether there is conservation between species in large-scale topological features, such as the “Rich Club”—a system where highly connected brain regions (or hubs) also connect strongly with each other (11–13). Advances in rs-fcMRI and its computational evaluation have begun to shed some light on homology between brain networks in primates (14); however, there is a paucity of studies comparing primates with rodents. Despite evidence for intrinsic functional connectivity in rats (15–19) and to a lesser extent, mice (20–24), comparing large-scale network organization between mice and primates has proven difficult.Of particular interest are prototypical functional networks, such as the default mode network (DMN). The DMN is a set of interconnected brain regions that were originally shown to decrease their level of activity in humans during goal-directed tasks (25, 26). These regions have subsequently been shown to be highly functionally connected in the human (27) and the macaque (9, 28). In addition, strength of functional connectivity in this system has been tied to several neurologic and psychiatric conditions, including Alzheimer’s disease, Autism Spectrum Disorders, and Attention Deficit Hyperactivity Disorder (ADHD) among others (29). In rats, functional connectivity work has now revealed a potential default system surrogate (16); however, this network has yet to be revealed in the mouse, where rich genetic models, behavioral methodology, and the complete structural connectome exist. In addition, it is unclear whether this surrogate default system corresponds to direct connections of the underlying structural connectome.This report fills this void by developing a high-resolution rs-fcMRI approach in mice, which combines with a brain-wide axonal projection mapping matrix [Allen Institute for Brain Science; connectivity.brain-map.org (30)] to (i) examine the structure–function relationships of rs-fcMRI in the mouse, (ii) directly test how well basic functional connectional topology is conserved between primates and the mouse, and (iii) considering this topology, identify whether a default mode-like network in mice exists.     

Neurobiological basis of head motion in brain imaging

Individual differences in brain metrics, especially connectivity measured with functional MRI, can correlate with differences in motion during data collection. The assumption has been that motion causes artifactual differences in brain connectivity that must and can be corrected. Here we propose that differences in brain connectivity can also represent a neurobiological trait that predisposes to differences in motion. We support this possibility with an analysis of intra- versus intersubject differences in connectivity comparing high- to low-motion subgroups. Intersubject analysis identified a correlate of head motion consisting of reduced distant functional connectivity primarily in the default network in individuals with high head motion. Similar connectivity differences were not found in analysis of intrasubject data. Instead, this correlate of head motion was a stable property in individuals across time. These findings suggest that motion-associated differences in brain connectivity cannot fully be attributed to motion artifacts but rather also reflect individual variability in functional organization.Head motion has long been known as a confounding factor in brain imaging including MRI (1, 2), PET (3, 4), single-photon emission computerized tomography (5, 6), and near infrared spectroscopy (7), but has raised particular concerns recently following the growing prominence of resting-state functional connectivity MRI. Studies found that head motion can vary considerably across individuals and often demonstrates systematic group effects when contrasting different populations, especially in neurodevelopmental (8–10), aging (11, 12), and neuropsychiatric studies (13). Some recent work reported that head motion augmented local coupling of the blood oxygenation level-dependent (BOLD) signal but reduced distant coupling (14–16). These correlations between connectivity measures and head motion have raised appropriate concern that previously observed differences in connectivity are due to artifact induced by differences in head motion. For example, developmental changes in functional connectivity might also be predicted by head motion (15). The assumption has been that head motion causes distorted connectivity measurements that must be addressed through improved motion-correction techniques (15). However, this correlation could be driven by causal factors in the other direction. Specifically, individual differences in brain connectivity could determine how well a subject can lie still in the scanner. This is not unreasonable as individual differences in structural connectivity can predict trait anxiety and can be related to attention deficits (17, 18) and individual differences in resting-state functional MRI (fMRI) measures may relate to various behavioral differences, including impulsivity (19–22). In such a scenario, certain intersubject differences in connectivity measures could persist even after the most rigorous motion correction, as has been suggested in several earlier studies (23, 24).To explore the relation between head motion and brain connectivity, we examined functional connectivity in different subject groups selected on the basis of head motion parameters from a large database of 3,000+ participants, many of whom were scanned multiple times. These cohorts allowed us to compare intersubject and intrasubject differences in connectivity in high- versus low-motion scans. If motion causes connectivity differences, these should be similar both inter- and intrasubject. However, if connectivity differences include a stable trait that predisposes to head motion, then these differences should be present between subjects but not within subjects.     

Long-term effects of marijuana use on the brain

Questions surrounding the effects of chronic marijuana use on brain structure continue to increase. To date, however, findings remain inconclusive. In this comprehensive study that aimed to characterize brain alterations associated with chronic marijuana use, we measured gray matter (GM) volume via structural MRI across the whole brain by using voxel-based morphology, synchrony among abnormal GM regions during resting state via functional connectivity MRI, and white matter integrity (i.e., structural connectivity) between the abnormal GM regions via diffusion tensor imaging in 48 marijuana users and 62 age- and sex-matched nonusing controls. The results showed that compared with controls, marijuana users had significantly less bilateral orbitofrontal gyri volume, higher functional connectivity in the orbitofrontal cortex (OFC) network, and higher structural connectivity in tracts that innervate the OFC (forceps minor) as measured by fractional anisotropy (FA). Increased OFC functional connectivity in marijuana users was associated with earlier age of onset. Lastly, a quadratic trend was observed suggesting that the FA of the forceps minor tract initially increased following regular marijuana use but decreased with protracted regular use. This pattern may indicate differential effects of initial and chronic marijuana use that may reflect complex neuroadaptive processes in response to marijuana use. Despite the observed age of onset effects, longitudinal studies are needed to determine causality of these effects.The rate of marijuana use has had a steady increase since 2007 (1). Among >400 chemical compounds, marijuana’s effects are primarily attributed to δ-9-tetrahydrocannabinol (THC), which is the main psychoactive ingredient in the cannabis plant. THC binds to cannabinoid receptors, which are ubiquitous in the brain. Consequently, exposure to THC leads to neural changes affecting diverse cognitive processes. These changes have been observed to be long-lasting, suggesting that neural changes due to marijuana use may affect neural architecture (2). However, to date, these brain changes as a result of marijuana use remains equivocal. Specifically, although functional changes have been widely reported across cognitive domains in both adult and adolescent cannabis users (3–6), structural changes associated with marijuana use have not been consistent. Although some have reported decreases in regional brain volume such as in the hippocampus, orbitofrontal cortex, amygdala, and striatum (7–12), others have reported increases in amygdala, nucleus accumbens, and cerebellar volumes in chronic marijuana users (13–15). However, others have reported no observable difference in global or regional gray or white matter volumes in chronic marijuana users (16, 17). These inconsistencies could be attributed to methodological differences across studies pertaining to study samples (e.g., severity of marijuana use, age, sex, comorbidity with other substance use or psychiatric disorders) and/or study design (e.g., study modality, regions of interest).Because THC binds to cannabinoid 1 (CB1) receptors in the brain, when differences are observed, these morphological changes associated with marijuana use have been reported in CB1 receptor-enriched areas such as the orbitofrontal cortex, anterior cingulate, striatum, amygdala, insula, hippocampus, and cerebellum (2, 11, 13, 18). CB1 receptors are widely distributed in the neocortex, but more restricted in the hindbrain and the spinal cord (19). For example, in a recent study by Battistella et al. (18), they found significant brain volume reductions in the medial temporal cortex, temporal pole, parahippocampal gyrus, insula, and orbitofrontal cortex (OFC) in regular marijuana users compared with occasional users. Whether these reductions in brain volume lead to downstream changes in brain organization and function, however, is still unknown.Nevertheless, emergent studies have demonstrated a link between brain structure and connectivity. For example, Van den Heuvel et al. and Greicius et al. demonstrated robust structural connections between white matter indexes and functional connectivity strength within the default mode network (20, 21). Similarly, others have reported correlated patterns of gray matter structure and connectivity that are in many ways reflective of the underlying intrinsic networks (22). Thus, given the literature suggesting a direct relationship between structural and functional connectivity, it is likely that connectivity changes would also be present where alterations in brain volume are observed as a result of marijuana use.The goal of this study was to characterize alterations in brain morphometry and determine potential downstream effects in connectivity as a result of chronic marijuana use. To address the existing inconsistencies in the literature that may be in part due to methodological issues, we (i) used three different MRI techniques to investigate a large cohort of well-characterized chronic cannabis users with a wide age range (allowing for characterization without developmental or maturational biases) and compared them to age- and sex-matched nonusing controls; (ii) examined observable global (rather than select) gray matter differences between marijuana users and nonusing controls; and (iii) performed subsequent analyses to determine how these changes relate to functional and structural connectivity, as well as behavior. Given the existing literature on morphometric reductions associated with long-term marijuana use, we expected gray matter reductions in THC-enriched areas in chronic marijuana users that will be associated with changes in brain connectivity and marijuana-related behavior.     

From the Cover: Bioengineered functional brain-like cortical tissue

The brain remains one of the most important but least understood tissues in our body, in part because of its complexity as well as the limitations associated with in vivo studies. Although simpler tissues have yielded to the emerging tools for in vitro 3D tissue cultures, functional brain-like tissues have not. We report the construction of complex functional 3D brain-like cortical tissue, maintained for months in vitro, formed from primary cortical neurons in modular 3D compartmentalized architectures with electrophysiological function. We show that, on injury, this brain-like tissue responds in vitro with biochemical and electrophysiological outcomes that mimic observations in vivo. This modular 3D brain-like tissue is capable of real-time nondestructive assessments, offering previously unidentified directions for studies of brain homeostasis and injury.The brain possesses extraordinary connectivity of neural networks. This complexity is evident at multiple levels of structural and functional hierarchy, including microcircuits dominated by neuronal clusters and larger distinctive regions of grey matter interconnected by white matter axon tracts. These features are highlighted in the Blue Brain project (1) and the Human Connectom Project (2) that aim to compile detailed information about connectivity at various levels and ultimately, reconstruct the human brain as a large-scale network. However, at the tissue level, the complex interconnectivity is masked by the distribution of neurons, such as in the stratified laminar layers of the neocortex. Although functionally related neurons generally group together (3, 4), boundaries of functional units cannot be readily revealed with phenotype markers, necessitating electrophysiological studies and correlative functional outcomes. It is, therefore, necessary to differentiate physical and functional associations of neuronal populations to unravel complex networks.Three-dimensional tissue engineering could provide compartmentalized cultures of discrete and identifiable structures to emulate native tissues and thereby, provide insight into the complexities. By recreating cell–cell and cell–ECM interactions, 3D structures enable the formation of tissue-mimetic architectures and promote more realistic physiological responses than conventional 2D cultures (5). Toward this goal, multilayer lithography (6), 3D patterning of bulk structures (7), and 3D tissue printing (8) are used. These rationally designed structures have been generated for tissue engineering of the lung, liver, and kidney, for which the structure–function relationships are modular-based and well-defined. Recent advances in stem and progenitor cell technology have induced cells to differentiate into and produce tissue-appropriate cell compositions and ECM components and form biomimetic tissues with nascent functions, including the cerebral organoids (9). These technologies show self-organization capability of cells in tissue-mimetic environments, such as native tissue-derived decellularized scaffolds (10–13). However, densely packed brain tissue with an architecture defined by neuronal connectivity (14, 15) presents a unique challenge to define modular structures with specific functions. Rather than reconstructing a whole-brain network, we aimed at reducing the structural complexity to fundamental features that are relevant to tissue-level physiological functions.Neural connectivity at the basic level, which includes segregated neuronal and axonal compartments, is particularly relevant for brain disorders, such as diffuse axonal injury in brain trauma (16, 17). However, ECM gel-based in vitro 3D systems have not yielded tissue-level functional assessments, possibly because of their inadequate mechanical properties and fast degradation compared with brain tissue. Here, we developed 3D compartmentalized neuronal cultures with silk fibroin-based biomaterials offering tunable mechanical properties, versatile structural forms, and brain and neural culture compatibility (18–21). This brain-like tissue provides rudimentary but relevant features of brain neural networks. The physiologically relevant and responsive 3D brain-like tissue also shows capability for the assessment of brain disorders, such as traumatic brain injury (TBI).     

Stable long-range interhemispheric coordination is supported by direct anatomical projections

The functional interaction between the brain’s two hemispheres includes a unique set of connections between corresponding regions in opposite hemispheres (i.e., homotopic regions) that are consistently reported to be exceptionally strong compared with other interhemispheric (i.e., heterotopic) connections. The strength of homotopic functional connectivity (FC) is thought to be mediated by the regions’ shared functional roles and their structural connectivity. Recently, homotopic FC was reported to be stable over time despite the presence of dynamic FC across both intrahemispheric and heterotopic connections. Here we build on this work by considering whether homotopic FC is also stable across conditions. We additionally test the hypothesis that strong and stable homotopic FC is supported by the underlying structural connectivity. Consistent with previous findings, interhemispheric FC between homotopic regions were significantly stronger in both humans and macaques. Across conditions, homotopic FC was most resistant to change and therefore was more stable than heterotopic or intrahemispheric connections. Across time, homotopic FC had significantly greater temporal stability than other types of connections. Temporal stability of homotopic FC was facilitated by direct anatomical projections. Importantly, temporal stability varied with the change in conductive properties of callosal axons along the anterior–posterior axis. Taken together, these findings suggest a notable role for the corpus callosum in maintaining stable functional communication between hemispheres.The brain’s capacity for processing information relies on a modular and hierarchical functional architecture that allows both functional segregation and integration (1, 2). Distributed processing occurs within segregated communities responsible for highly specialized functions, whereas more comprehensive functions require long-range integration across communities. This long-range integration is especially important for coordinating functions between the two hemispheres. Functional connectivity (FC), computed as the temporal correlation or covariance between regionwise signals, varies across different interhemispheric regions. FC is significantly stronger between homotopic regions than between heterotopic regions (3) and is greater than would be expected from the anatomical distance between the homotopic regions (4). This strong homotopic FC is thought to be mediated by the strong underlying structural connectivity of the corpus callosum (CC). Indeed, the majority of callosal fibers are between homotopic brain regions (5–7), and the loss of callosal integrity leads to a loss in homotopic FC (8, 9).Recently, a growing number of resting-state functional MRI (fMRI) studies have reported how FC varies over time (10). Flexibility in cognitive processing is thought to arise from the ability of certain regions to participate dynamically in different network configurations (11). For instance, interhemispheric functional interactions between different communities are highly variable over the course of a resting-state scan. Meanwhile, interhemispheric connections within the same community, especially homotopic connections, are temporally stable (12–14). Together, these findings suggest that interhemispheric coordination may occur predominantly via homotopic functional connections.FC also is known to vary between task and resting-state conditions (15). The extent to which interhemispheric coordination relies on homotopic functional connections across conditions remains to be determined. Moreover, whether interhemispheric coordination is mediated by the underlying structural connectivity has yet to be demonstrated empirically. In this study, we tested the hypothesis that FC between homotopic regions is stable across both time and conditions using blood oxygen level-dependent (BOLD) fMRI data collected from humans and macaques. We additionally used two approaches to examine the extent to which the stability of homotopic functional connections is mediated by the underlying anatomical projections. First, we directly compared macaque BOLD-fMRI data with structural connectivity data derived from axonal tract-tracing studies in macaque monkeys. Second, we compared the patterns of observed functional stability with known patterns of callosal fiber conductive properties in both humans and macaques.     

Specialization and integration of functional thalamocortical connectivity in the human infant

Connections between the thalamus and cortex develop rapidly before birth, and aberrant cerebral maturation during this period may underlie a number of neurodevelopmental disorders. To define functional thalamocortical connectivity at the normal time of birth, we used functional MRI (fMRI) to measure blood oxygen level-dependent (BOLD) signals in 66 infants, 47 of whom were at high risk of neurocognitive impairment because of birth before 33 wk of gestation and 19 of whom were term infants. We segmented the thalamus based on correlation with functionally defined cortical components using independent component analysis (ICA) and seed-based correlations. After parcellating the cortex using ICA and segmenting the thalamus based on dominant connections with cortical parcellations, we observed a near-facsimile of the adult functional parcellation. Additional analysis revealed that BOLD signal in heteromodal association cortex typically had more widespread and overlapping thalamic representations than primary sensory cortex. Notably, more extreme prematurity was associated with increased functional connectivity between thalamus and lateral primary sensory cortex but reduced connectivity between thalamus and cortex in the prefrontal, insular and anterior cingulate regions. This work suggests that, in early infancy, functional integration through thalamocortical connections depends on significant functional overlap in the topographic organization of the thalamus and that the experience of premature extrauterine life modulates network development, altering the maturation of networks thought to support salience, executive, integrative, and cognitive functions.The formation of topographically organized neural connections between cerebral cortex and thalamus is necessary for normal cortical morphogenesis (1), and development of these connections requires thalamocortical projections to synapse transiently in the temporary cortical subplate before penetrating the cortical plate (2–4). In humans, the subplate is at maximal extent in the last trimester of gestation (5), a time of rapid growth for thalamocortical fibers and the cortical dendritic tree, particularly in heteromodal cortex (6, 7). This process has been shown to be disrupted by preterm birth (8). Premature delivery is associated with increased risk of neurocognitive impairment, and it is widely hypothesized that abnormal development of brain structure during this period is the cause of these problems and may also underlie the development of autistic spectrum disorders and attention deficit disorders in genetically predisposed individuals.During the last trimester of pregnancy, functional MRI (fMRI) detects the emergence of coordinated, spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signals, which are closely linked with the development of electroencephalographic activity (9–11) and develop into a near-facsimile of the mature adult resting-state network architecture by the normal age of birth at 38–42 wk gestational age (12). However, little is known about the growth of functional connectivity between the thalamus and cortex during this period.Anatomical studies in animals and postmortem adult human subjects have defined the thalamic microstructure and described a corticotopic parcellation of the thalamus with precise connectivity to specific cortical regions (13, 14). Diffusion tensor imaging studies have described a similar pattern of structural thalamocortical connectivity (15, 16), with evidence in adults that some thalamocortical circuits share common thalamic territory, giving the potential for integrative functions (17). Functional connectivity MRI analysis between the thalamus and the cortex has also shown corticotopic organization in the thalamus (18, 19).It is not known, however, when this thalamocortical mapping develops or how it might be disrupted during development. We, therefore, used connectivity fMRI to address a series of questions. First, is the pattern of dominant thalamocortical connectivity at the time of normal birth already similar to the mature adult pattern? Second, in addition to the dominant thalamocortical correlations, is there a pattern of overlapping cortical representations in the neonatal thalamus that might reflect developing integration of functional cortical regions? Third, does the experience of preterm delivery and premature extrauterine life affect the development of thalamocortical connectivity, and is the effect more marked in rapidly developing heteromodal cortex than in more mature primary cortex?     

Breakdown of the brain’s functional network modularity with awareness

Neurobiological theories of awareness propose divergent accounts of the spatial extent of brain changes that support conscious perception. Whereas focal theories posit mostly local regional changes, global theories propose that awareness emerges from the propagation of neural signals across a broad extent of sensory and association cortex. Here we tested the scalar extent of brain changes associated with awareness using graph theoretical analysis applied to functional connectivity data acquired at ultra-high field while subjects performed a simple masked target detection task. We found that awareness of a visual target is associated with a degradation of the modularity of the brain’s functional networks brought about by an increase in intermodular functional connectivity. These results provide compelling evidence that awareness is associated with truly global changes in the brain’s functional connectivity.Three broad classes of models have been proposed to explain the neural basis of awareness, with these classes primarily differing on the predicted extent of neural information changes associated with conscious perception. According to focal theories, awareness results from local changes in neural activity in either the perceptual substrates (1–3) or in higher-level nodes of information processing pathways (4). By contrast, network-level theories posit that awareness is tightly associated with activation of parietofrontal attention networks of the brain (5–11). Finally, global models propose that awareness results from widespread changes in the activation state (12–15) and functional connectivity (16–19) of the brain. Though there is strong experimental support for network-level theories, there is scant experimental evidence in favor of truly sweeping, widespread changes in brain activity with conscious perception despite the fact that global scale models have recently come to prominence in the theoretical landscape of this field.Using a graph theoretical approach applied to ultra-high-field fMRI data, here we experimentally tested a key tenet of global theories: the widespread emergence of large-scale functional connectivity with awareness. Graph theory analyses are ideal tools to test global models of awareness because they can provide concise measures of the integration and segregation of interconnected nodes of a system (20). Applied to functional imaging data, we treat individual brain regions of interest (ROIs) as nodes, functional connectivity between ROIs as edges, and functional brain networks as interconnected modules of nodes. When examining a large set of ROIs that encompass the different networks of the human cerebral cortex (21, 22), we can apply graph theory analyses to estimate the extent to which key measures of global information processing are altered by the state of awareness. This approach has been previously applied to study differences in cognitive states (23–31). Although recent studies have taken advantage of graph theory analysis to examine the connectivity patterns that precede a conscious event (32) or following pharmacologically induced loss of consciousness (33), this approach had yet to be used for characterizing the topology associated with conscious target perception per se, a necessary test for global theories of awareness.If the changes with awareness are truly global, one should see such changes even if the task does not require complex discrimination, identification, and semantic processes that may recruit vast extents of cortical tissue that are not necessarily associated with conscious perception; in other words, these global changes should appear even for the simple conscious detection of a flashed disk. For this reason, we had participants perform an elementary masked target detection task (Fig. 1) while being scanned at ultra-high field (7 T). The task included three trial types: forward-masked, backward-masked, and no-target conditions. In the forward-masked (paracontrast) condition, a 133-ms-duration annular mask offset 33 ms before the target (a disk whose exterior border coincided with the interior border of the annulus) presented for 33 ms. In the backward-masked (metacontrast) condition, the order of mask/target presentation was reversed while keeping all timing parameters the same. Under such conditions, forward masking of targets has been shown to impair target detection more than backward masking (34, 35). Consequently, the mask/target orderings provided a manipulation of target awareness while maintaining the same mask and target presentation times across both forward- and backward-masked conditions. Because on each trial, participants made a detection response about the presence or absence of the target followed by a confidence rating on their response, subjects’ performance could be assessed on both an objective (discriminability index d′) and subjective (confidence rating) measure of awareness (36). In turn, only trials in which the target was either seen (aware) or unseen (unaware) at high confidence levels were used for analysis of brain imaging data. Finally, because the report of the percept was 12 s removed from the stimulus presentations (Fig. 1), the task design precluded initiation of the motor response itself from influencing estimates of awareness. Although response selection and motor preparation processes likely occur during this period, similar preparation would occur across all conditions.Open in a separate windowFig. 1.Schematic of behavioral paradigm with forward-masked and backward-masked trial types (no-target trials not shown). On each trial, participants responded whether they detected the target stimulus and indicated a confidence rating for their answer (Methods).     

High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain

The increasing use of mouse models for human brain disease studies presents an emerging need for a new functional imaging modality. Using optical excitation and acoustic detection, we developed a functional connectivity photoacoustic tomography system, which allows noninvasive imaging of resting-state functional connectivity in the mouse brain, with a large field of view and a high spatial resolution. Bilateral correlations were observed in eight functional regions, including the olfactory bulb, limbic, parietal, somatosensory, retrosplenial, visual, motor, and temporal regions, as well as in several subregions. The borders and locations of these regions agreed well with the Paxinos mouse brain atlas. By subjecting the mouse to alternating hyperoxic and hypoxic conditions, strong and weak functional connectivities were observed, respectively. In addition to connectivity images, vascular images were simultaneously acquired. These studies show that functional connectivity photoacoustic tomography is a promising, noninvasive technique for functional imaging of the mouse brain.Resting-state functional connectivity (RSFC) is an emerging neuroimaging approach that aims to identify low-frequency, spontaneous cerebral hemodynamic fluctuations and their associated functional connections (1, 2). Recent research suggests that these fluctuations are highly correlated with local neuronal activity (3, 4). The spontaneous fluctuations relate to activity that is intrinsically generated by the brain, instead of activity attributable to specific tasks or stimuli (2). A hallmark of functional organization in the cortex is the striking bilateral symmetry of corresponding functional regions in the left and right hemispheres (5). This symmetry also exists in spontaneous resting-state hemodynamics, where strong correlations are found interhemispherically between bilaterally homologous regions as well as intrahemispherically within the same functional regions (3). Clinical studies have demonstrated that RSFC is altered in brain disorders such as stroke, Alzheimer’s disease, schizophrenia, multiple sclerosis, autism, and epilepsy (6–12). These diseases disrupt the healthy functional network patterns, most often reducing correlations between functional regions. Due to its task-free nature, RSFC imaging requires neither stimulation of the subject nor performance of a task during imaging (13). Thus, it can be performed on patients under anesthesia (14), on patients unable to perform cognitive tasks (15, 16), and even on patients with brain injury (17, 18).RSFC imaging is also an appealing technique for studying brain diseases in animal models, in particular the mouse, a species that holds the largest variety of neurological disease models (3, 13, 19, 20). Compared with clinical studies, imaging genetically modified mice allows exploration of molecular pathways underlying the pathogenesis of neurological disorders (21). The connection between RSFC maps and neurological disorders permits testing and validation of new therapeutic approaches. However, conventional neuroimaging modalities cannot easily be applied to mice. For instance, in functional connectivity magnetic resonance imaging (fcMRI) (22), the resting-state brain activity is determined via the blood-oxygen-level–dependent (BOLD) signal contrast, which originates mainly from deoxy-hemoglobin (23). The correlation analysis central to functional connectivity requires a high signal-to-noise ratio (SNR). However, achieving a sufficient SNR is made challenging by the high magnetic fields and small voxel size needed for imaging the mouse brain, as well as the complexity of compensating for field inhomogeneities caused by tissue–bone or tissue–air boundaries (24). Functional connectivity mapping with optical intrinsic signal imaging (fcOIS) was recently introduced as an alternative method to image functional connectivity in mice (3, 20). In fcOIS, changes in hemoglobin concentrations are determined based on changes in the reflected light intensity from the surface of the brain (3, 25). Therefore, neuronal activity can be measured through the neurovascular response, similar to the method used in fcMRI. However, due to the diffusion of light in tissue, the spatial resolution of fcOIS is limited, and experiments have thus far been performed using an exposed skull preparation, which increases the complexity for longitudinal imaging.Photoacoustic imaging of the brain is based on the acoustic detection of optical absorption from tissue chromophores, such as oxy-hemoglobin (HbO₂) and deoxy-hemoglobin (Hb) (26, 27). This imaging modality can simultaneously provide high-resolution images of the brain vasculature and hemodynamics with intact scalp (28, 29). In this article, we perform functional connectivity photoacoustic tomography (fcPAT) to study RSFC in live mice under either hyperoxic or hypoxic conditions, as well as in dead mice. Our experiments show that fcPAT is able to detect connectivities between different functional regions and even between subregions, promising a powerful functional imaging modality for future brain research.     

Dynamic reorganization of human resting-state networks during visuospatial attention

Fundamental problems in neuroscience today are understanding how patterns of ongoing spontaneous activity are modified by task performance and whether/how these intrinsic patterns influence task-evoked activation and behavior. We examined these questions by comparing instantaneous functional connectivity (IFC) and directed functional connectivity (DFC) changes in two networks that are strongly correlated and segregated at rest: the visual (VIS) network and the dorsal attention network (DAN). We measured how IFC and DFC during a visuospatial attention task, which requires dynamic selective rerouting of visual information across hemispheres, changed with respect to rest. During the attention task, the two networks remained relatively segregated, and their general pattern of within-network correlation was maintained. However, attention induced a decrease of correlation in the VIS network and an increase of the DAN→VIS IFC and DFC, especially in a top-down direction. In contrast, within the DAN, IFC was not modified by attention, whereas DFC was enhanced. Importantly, IFC modulations were behaviorally relevant. We conclude that a stable backbone of within-network functional connectivity topography remains in place when transitioning between resting wakefulness and attention selection. However, relative decrease of correlation of ongoing “idling” activity in visual cortex and synchronization between frontoparietal and visual cortex were behaviorally relevant, indicating that modulations of resting activity patterns are important for task performance. Higher order resting connectivity in the DAN was relatively unaffected during attention, potentially indicating a role for simultaneous ongoing activity as a “prior” for attention selection.The function of the brain has been traditionally studied in response to controlled stimuli at the level of single neurons, cortical circuits, or systems, and spontaneous activity has been modeled as stochastic noise, with its variability randomly affecting the threshold of postsynaptic firing (hence, the forward transmission of information through cortical circuits) (1). However, in the last two decades, it has become apparent that spontaneous activity is far from random but organized in space and time at the level of micro- and macrocircuitries (2) as well as at the level of large-scale distributed neuroanatomical systems (3). The large-scale organization of spontaneous activity has been most effectively studied by computing the temporal correlation of the blood oxygenation level-dependent (BOLD) signal (or functional connectivity) measured at rest with functional magnetic resonance imaging (fMRI) in the absence of any task or stimulus. The whole cerebral cortex has been subdivided in a relatively small number of networks formed by regions that show correlated activity over long periods of time [resting-state networks (RSNs)] (4, 5). The relatively small number of RSNs raises the question of how these networks can support the presumably very large number of sensory–motor–cognitive states that form our behavior, which undoubtedly must require the dynamic and flexible coordination of brain regions.One leading idea is that RSNs represent spatiotemporal “priors” for task networks and that their modulation contributes to task-evoked responses (6, 7). According to this view, the connectivity at rest reflects experience-dependent plasticity that constrains subsequent activity during stimulus processing and maintains predictions about forthcoming stimuli. Another hypothesis considers RSNs as reflecting a state of “idling” (or inactivity) of the brain that must be reorganized for task-dependent interactions to emerge (8). The former view is supported by the stability of RSNs topography across behavioral states (9, 10) and the similarity of RSNs to task networks recruited by common cognitive tasks (7, 11). The latter view is, instead, supported by studies showing that task execution reconfigures resting connectivity to allow task-dependent interactions (12, 13).To address this fundamental question, we examined how resting functional connectivity is modulated during the execution of a spatial attention task with underlying circuitry that is well-understood (14–19). The task involves either maintaining attention to a stream of sensory stimuli or shifting attention to a different stream simultaneously presented in the opposite visual field. After each attention shift, visual information must be then dynamically rerouted from one visual field/hemisphere to the other. Task activation studies have shown that this task recruits both frontal and parietal control regions of the dorsal attention network (DAN) and occipital visual (VIS) network regions involved in sensory processing, but their dynamic interaction has never been studied. Critically, in a state of idle wakefulness (visual fixation), regions in the DAN and VIS are largely segregated (i.e., their within-network temporal correlation is stronger than their between-network correlation) (4, 5). Therefore, these networks represent an ideal system for examining the questions of how RSNs are modified by task performance and specifically, how functional connections are dynamically modulated when transitioning from a resting to an attentive state. If RSNs represent priors of task networks, then performing the attention task should maintain and even strengthen RSNs interaction. If, however, RSNs represent idling cortical rhythms, then task performance should induce a reorganization of functional connectivity patterns in a task- and behavior-dependent manner.     

Identification of gene ontologies linked to prefrontal–hippocampal functional coupling in the human brain

Functional interactions between the dorsolateral prefrontal cortex and hippocampus during working memory have been studied extensively as an intermediate phenotype for schizophrenia. Coupling abnormalities have been found in patients, their unaffected siblings, and carriers of common genetic variants associated with schizophrenia, but the global genetic architecture of this imaging phenotype is unclear. To achieve genome-wide hypothesis-free identification of genes and pathways associated with prefrontal–hippocampal interactions, we combined gene set enrichment analysis with whole-genome genotyping and functional magnetic resonance imaging data from 269 healthy German volunteers. We found significant enrichment of the synapse organization and biogenesis gene set. This gene set included known schizophrenia risk genes, such as neural cell adhesion molecule (NRCAM) and calcium channel, voltage-dependent, beta 2 subunit (CACNB2), as well as genes with well-defined roles in neurodevelopmental and plasticity processes that are dysfunctional in schizophrenia and have mechanistic links to prefrontal–hippocampal functional interactions. Our results demonstrate a readily generalizable approach that can be used to identify the neurogenetic basis of systems-level phenotypes. Moreover, our findings identify gene sets in which genetic variation may contribute to disease risk through altered prefrontal–hippocampal functional interactions and suggest a link to both ongoing and developmental synaptic plasticity.Imaging genetics is widely used to identify neural circuits linked to genetic risk for heritable neuropsychiatric disorders, such as schizophrenia, autism, or bipolar disorder (1). A well-established imaging genetics phenotype is functional connectivity between the right dorsolateral prefrontal cortex (DLPFC) and the left hippocampus (HC) during working memory (WM) performance (2–4). Specifically, impaired interaction of the HC and prefrontal cortex (PFC) has been proposed as a core abnormality during neurodevelopment in schizophrenia. The hippocampus provides input to the DLPFC through long-range glutamatergic connections, which have been linked to the glutamate hypothesis of the illness. Moreover, selective lesions of the hippocampus in primates and rodents have been shown to result in postpubescent changes in prefrontal regions that are consistent with neuropathological findings in schizophrenic patients (5, 6). Brain physiology during WM performance is highly heritable (7), and anomalies of prefrontal–hippocampal functional coupling during WM have been identified in schizophrenia patients (1, 2, 4, 8), their unaffected first-grade relatives (4), healthy carriers of genome-wide supported schizophrenia risk variants and subjects at risk (4, 9–12), and in genetic animal models of the disorder (13). These studies provide strong support for a role of this neural systems-level phenotype in schizophrenia pathophysiology and correspond well to current theories that conceptualize the illness as a “brain disconnection syndrome” rooted in disturbed synaptic plasticity processes (14, 15).Previous studies have characterized abnormal prefrontal–hippocampal interactions in subjects with genetic risk factors for schizophrenia (4, 9, 10, 16). In particular, genome-wide association studies (GWAS) have become a standard approach for identifying common variants that may contribute to risk phenotypes in structural and functional neuroimaging data (10, 16, 17). However, although this approach has been effective in identifying genetic risk variants for imaging phenotypes, post hoc interpretation of results is challenging. Detected risk variants often fall within intronic sequences, where a lack of prior knowledge on functionality hinders a mechanistic explanation of how they impact brain function (18).Increasing evidence suggests that common genetic risk variants for psychiatric disorders are not distributed randomly but rather lie among sets of genes with overlapping functions (19–22). Gene set enrichment analysis (GSEA) is a data analytical approach that leverages a priori knowledge to gain insight into the biological functions of genes and pathways in the analysis of genetic data (23, 24). This approach relies on analysis of sets of genes grouped by common biological characteristics, such as a shared role in particular molecular functions or metabolic pathways. GSEA can then be used to test whether genes that are more strongly associated with a phenotype of interest tend to significantly aggregate within specific biologically based “gene sets.” As an adjunct to established GWA studies and candidate gene approaches, GSEA has successfully identified genes sets with established risk genes for complex diseases such as lung cancer, Parkinson’s disease, and psychiatric disorders, yielding insight into plausible biological processes and molecular mechanisms warranting further investigation (24–26).Although in principle the same strategy can be applied to other quantitative risk-associated phenotypes (27), no prior study has attempted to identify shared biological pathways linked to individual variation in DLPFC–HC functional coupling through a combination of GSEA, whole-genome genotype data, and neuroimaging. Here we used GSEA to test the association of ontology-based gene sets derived from common genetic variants with prefrontal–hippocampal interactions in 269 healthy volunteers who performed the n-back WM task during functional magnetic resonance imaging (fMRI), a well-established paradigm to challenge DLPFC–HC interactions. Given the reviewed evidence (14, 15), we hypothesized that we would identify gene sets linked to developmental plasticity and synaptic neurotransmission, including previously identified risk genes for schizophrenia.     

Resting-brain functional connectivity predicted by analytic measures of network communication

The complex relationship between structural and functional connectivity, as measured by noninvasive imaging of the human brain, poses many unresolved challenges and open questions. Here, we apply analytic measures of network communication to the structural connectivity of the human brain and explore the capacity of these measures to predict resting-state functional connectivity across three independently acquired datasets. We focus on the layout of shortest paths across the network and on two communication measures—search information and path transitivity—which account for how these paths are embedded in the rest of the network. Search information is an existing measure of information needed to access or trace shortest paths; we introduce path transitivity to measure the density of local detours along the shortest path. We find that both search information and path transitivity predict the strength of functional connectivity among both connected and unconnected node pairs. They do so at levels that match or significantly exceed path length measures, Euclidean distance, as well as computational models of neural dynamics. This capacity suggests that dynamic couplings due to interactions among neural elements in brain networks are substantially influenced by the broader network context adjacent to the shortest communication pathways.The topology and dynamics of brain networks are a central focus of the emerging field of connectomics (1). A growing number of studies of human brain networks carried out with modern noninvasive neuroimaging methods have begun to characterize the architecture of structural networks (2–4), as well as spatially distributed components (5–7) and time-varying dynamics (8) of functional networks. Although structural connectivity (SC) is inferred from diffusion imaging and tractography, functional connectivity (FC) is generally derived from pairwise correlations of time series recorded during “resting” brain activity, measured with functional magnetic resonance imaging (fMRI). Both networks define a multiplex system (9) in which the SC level shapes or imposes constraints on the FC level. Indeed, mounting evidence indicates that SC and FC are robustly related. Numerous studies have documented strong and significant correlations between the strengths of structural and functional connections at whole-brain (2, 10–13) and mesoscopic scales (14), as well as acute changes in FC after perturbation of SC (15).Although there is ample evidence documenting statistical relationships between SC and FC, the causal role of SC in shaping whole-brain patterns of FC is still only incompletely understood. There are numerous reports of strong FC among brain regions that are not directly structurally connected, an effect that has been ascribed to signal propagation along one or more indirect structural paths (11), or to network-wide contextual influence (16). The present paper builds on two interrelated premises. First, if SC plays a major causal role in shaping resting-state FC, then appropriately configured generative models that incorporate SC topology should be able to predict, at least to some extent, FC patterns. To this end, a number of models have been proposed, including large-scale neural mass models generating synthetic fMRI time series (11, 17, 18) as well as analytic models based on distance and topological measures (19) or attractor dynamics (20, 21). Second, the extent to which the resting-state time courses of two brain regions become temporally aligned (i.e., highly functionally correlated) should be at least partially related to the ease with which mutual dynamic influences or perturbations can spread within the underlying structural brain network.Both premises imply that the strength of FC is related to measures of network communication. The principal communication measure applied previously in studies of brain networks is the efficiency (22), computed as the averaged inverse of the lengths of the shortest paths between node pairs. The use of this measure is based on the assumption that short paths are dynamically favored, as they allow more direct (faster, less noisy) transmission of neural signals. However, relying on path length as the sole measure of communication does not take into account how these paths are embedded in the rest of the network, which may further modulate the dynamic interactions of neuronal populations. For example, along a given path, branch points may lead to signal dispersion and hence attenuate FC, whereas local detours may offer alternative routes that amplify FC.Here, we present an approach toward predicting FC from SC based on several analytic measures of network communication. We used sets of high-resolution SC and FC maps of the cerebral cortex, obtained from three separate cohorts of participants and acquired using different scanners and imaging protocols. First, the relationship of FC to spatial embedding and path length was explored. Next, we attempted to predict FC from SC by implementing both linear and nonlinear computational models. We then examined the capacity of several analytic measures of network communication along shortest paths to predict FC from SC, singly and in the simple form of a joint multilinear model. Our results demonstrate that analytic measures that take into account the structural embedding of short paths are indeed capable of predicting a large portion of the variance observed in long-time averages of resting-brain FC.     

Elevated hippocampal resting-state connectivity underlies deficient neurocognitive function in aging

The brain is not idle during rest. Functional MRI (fMRI) studies have identified several resting-state networks, including the default mode network (DMN), which contains a set of cortical regions that interact with a hippocampus (HC) subsystem. Age-related alterations in the functional architecture of the DMN and HC may influence memory functions and possibly constitute a sensitive biomarker of forthcoming memory deficits. However, the exact form of DMN–HC alterations in aging and concomitant memory deficits is largely unknown. Here, using both task and resting data from 339 participants (25–80 y old), we have demonstrated age-related decrements in resting-state functional connectivity across most parts of the DMN, except for the HC network for which age-related elevation of connectivity between left and right HC was found along with attenuated HC–cortical connectivity. Elevated HC connectivity at rest, which was partly accounted for by age-related decline in white matter integrity of the fornix, was associated with lower cross-sectional episodic memory performance and declining longitudinal memory performance over 20 y. Additionally, elevated HC connectivity at rest was associated with reduced HC neural recruitment and HC–cortical connectivity during active memory encoding, which suggests that strong HC connectivity restricts the degree to which the HC interacts with other brain regions during active memory processing revealed by task fMRI. Collectively, our findings suggest a model in which age-related disruption in cortico–hippocampal functional connectivity leads to a more functionally isolated HC at rest, which translates into aberrant hippocampal decoupling and deficits during mnemonic processing.The brain is not idle at rest (1). Rather, intrinsic neuronal signaling, which manifests as spontaneous fluctuations in the blood oxygen level-dependent (BOLD) functional MRI (fMRI) signal, is ubiquitous in the human brain and consumes a substantial portion of the brain’s energy (2). Coherent spontaneous activity has been revealed in a hierarchy of networks that span large-scale functional circuits in the brain (3–6). These resting-state networks (RSNs) show moderate-to-high test–retest reliability (7) and replicability (8), and some have been found in the monkey (9) and infant (10) brain. In the adult human brain, RSNs include sensory motor, visual, attention, and mnemonic networks, as well as the default mode network (DMN). There is evidence that the DMN entails interacting subsystems and hubs that are implicated in episodic memory (11–13). One major hub encompasses the posterior cingulate cortex and the retrosplenial cortex. Other hubs include the lateral parietal cortex and the medial prefrontal cortex. In addition, a hippocampus (HC) subsystem is distinct from, yet interrelated with, the major cortical DMN hubs (12, 14).The functional architecture of the DMN and other RSNs is affected by different conditions, such as Alzheimer’s disease (AD), Parkinson’s disease, and head injury, suggesting that measurements of the brain’s intrinsic activity may be a sensitive biomarker and a putative diagnostic tool (for a review, see ref. 15). Alterations of the DMN have also been shown in age-comparative studies (16, 17), but the patterns of alterations are not homogeneous across different DMN components (18). Reduced functional connectivity among major cortical DMN nodes has been reported in aging (16, 17) and also in AD (19) and for asymptomatic APOE e4 carriers at increased risk of developing AD (20). Reduced cortical DMN connectivity has been linked to age-impaired performance on episodic memory (EM) tasks (21, 22). For instance, Wang and colleagues (21) showed that functional connectivity between cortical and HC hubs promoted performance on an EM task and was substantially weaker among low-performing elderly. This and other findings suggest that reductions in the DMN may be a basis for age-related EM impairment. However, elevated connectivity has been observed for the HC in individuals at genetic risk for AD (23, 24) and for elderly with memory complaints (25). Furthermore, a trend toward elevated functional connectivity for the medial temporal lobe (MTL) subsystem was observed in healthy older adults (26). Critically, higher subcortical RSN connectivity was found to correlate negatively with EM performance in an aging sample (27). Moreover, a recent combined fMRI/EEG study observed age increases in HC EEG beta power during rest (28).Thus, the association of aging with components of the DMN is complex, and it has been argued that age-related increases in functional connectivity need further examination (18). Such increases could reflect a multitude of processes, including age-related degenerative effects on the brain’s gray and white matter (18). Additionally, increases in HC functional connectivity may reflect alterations in proteolytic processes, such as amyloid deposition (29). Amyloid deposition is most prominent in posterior cortical regions of the DMN (29). It has been argued that there is a topological relationship between high neural activity over a lifetime within the DMN and amyloid deposition (30). Increased amyloid β protein burden within the posterior cortical DMN may cause cortico–hippocampal functional connectivity disruption (31), leading to a more functionally isolated HC network, which translates into aberrant hippocampal decoupling (30, 32, 33). Correspondingly, a recent model hypothesized that progressively less inhibitory cortical input would cause HC hyperactivity in aging (34).Elevated HC resting-state connectivity might thus be a sign of brain dysfunction, but the evidence remains inconclusive. Here, using data from a population-based sample covering the adult age span (n = 339, 25–80 y old), we tested the hypothesis that aging differentially affects distinct DMN components. A data-driven approach, independent component analysis (ICA), was used to identify DMN subsystems (4). We expected to observe age-related decreases in the connectivity of the cortical DMN. We also examined age-related alterations of HC RSN connectivity, and tested whether such alterations were related to HC volume and white matter integrity. We predicted that if increased HC connectivity was found, it would be accompanied by age-related decreases in internetwork connectivity of the HC RSN with cortical DMN regions. To constrain interpretations of age-related alterations, the DMN components were related to cognitive performance. Elevated HC RSN should negatively correlate with level and longitudinal change in EM performance. Such negative correlations could reflect an inability to flexibly recruit the HC and functionally associated areas during EM task performance due to aberrant hippocampal decoupling (23, 24). We tested this prediction by relating the HC RSN, within-person, to HC recruitment during an EM fMRI task (35, 36).     

Sleep deprivation increases dorsal nexus connectivity to the dorsolateral prefrontal cortex in humans

In many patients with major depressive disorder, sleep deprivation, or wake therapy, induces an immediate but often transient antidepressant response. It is known from brain imaging studies that changes in anterior cingulate and dorsolateral prefrontal cortex activity correlate with a relief of depression symptoms. Recently, resting-state functional magnetic resonance imaging revealed that brain network connectivity via the dorsal nexus (DN), a cortical area in the dorsomedial prefrontal cortex, is dramatically increased in depressed patients. To investigate whether an alteration in DN connectivity could provide a biomarker of therapy response and to determine brain mechanisms of action underlying sleep deprivations antidepressant effects, we examined its influence on resting state default mode network and DN connectivity in healthy humans. Our findings show that sleep deprivation reduced functional connectivity between posterior cingulate cortex and bilateral anterior cingulate cortex (Brodmann area 32), and enhanced connectivity between DN and distinct areas in right dorsolateral prefrontal cortex (Brodmann area 10). These findings are consistent with resolution of dysfunctional brain network connectivity changes observed in depression and suggest changes in prefrontal connectivity with the DN as a brain mechanism of antidepressant therapy action.Sleep deprivation has been used for decades as a rapid-acting and effective treatment in patients with major depressive disorder (MDD) (1, 2). Although clinically well established, the mechanisms of action are largely unknown.Brain imaging studies have shown that sleep deprivation in depressed patients is associated with renormalized metabolic activity, mainly in limbic structures including anterior cingulate (ACC) as well as dorsolateral prefrontal cortex (DLPFC) (3–6), and that changes in limbic and DLPFC activity correlated with a relief of depression symptoms (7–9). Recent studies in patients with depression point to a critical importance of altered large-scale brain network connectivity during the resting state (10, 11). Among these networks, the default mode network (DMN), which mainly comprises cortical midline structures including precuneus and medial frontal cortex as well as the inferior parietal lobule (12–15), is most consistently characterized. In functional magnetic resonance imaging (fMRI) studies, the DMN shows the strongest blood oxygenation level–dependent (BOLD) activity during rest and decreased BOLD reactivity during goal-directed task performance. The DMN is anticorrelated with the cognitive control network (CCN), a corresponding task-positive network, which encompasses bilateral fronto-cingulo-parietal structures including lateral prefrontal and superior parietal areas (16). A third system with high relevance for depression—the affective network (AN)—is based in the subgenual and pregenual parts of the ACC [Brodman area (BA) 32] (17). The AN is active during both resting and task-related emotional processing, and forms strong functional and structural connections to other limbic areas such as hypothalamus, amygdala, entorhinal cortex, and nucleus accumbens (18, 19).Increased connectivity of DMN, CCN, and AN with a distinct area in the bilateral dorsomedial prefrontal cortex (DMPFC) was recently found in patients with depression compared with healthy controls (20). This area within the DMPFC was termed dorsal nexus (DN) and was postulated to constitute a converging node of depressive “hot wiring,” which manifests itself in symptoms of emotional, cognitive, and vegetative dysregulation. This led to the hypothesis that a modification in connectivity via the DN would be a potential target for antidepressant treatments (20).Recent studies in healthy subjects reported reduced functional connectivity within DMN and between DMN and CCN in the morning after total (21) and in the evening after partial sleep deprivation (22). However, brain network connectivity via the DN was not examined in these studies. Given the recently proposed role of the DN in mood regulation, here we specifically tested whether sleep deprivation as a well-known antidepressant treatment modality affects connectivity via the DN. Based on our previous findings on network changes by ketamine (23), we hypothesized that sleep deprivation leads to a reduction in connectivity via the DN.     

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).     

Functional connectivity arises from a slow rhythmic mechanism

The mechanism underlying temporal correlations among blood oxygen level-dependent signals is unclear. We used oxygen polarography to better characterize oxygen fluctuations and their correlation and to gain insight into the driving mechanism. The power spectrum of local oxygen fluctuations is inversely proportional to frequency raised to a power (1/f) raised to the beta, with an additional positive band-limited component centered at 0.06 Hz. In contrast, the power of the correlated oxygen signal is band limited from ∼0.01 Hz to 0.4 Hz with a peak at 0.06 Hz. These results suggest that there is a band-limited mechanism (or mechanisms) driving interregional oxygen correlation that is distinct from the mechanism(s) driving local (1/f) oxygen fluctuations. Candidates for driving interregional oxygen correlation include rhythmic or pseudo-oscillatory mechanisms.Resting-state functional connectivity MRI (rs-fcMRI) analyses provide insight into the functional architecture of the brain. The method is based on slow correlations (e.g., 0.01–0.1 Hz) in blood oxygen level-dependent (BOLD) signal across the brain. The pattern of these slow correlations has been used to trace out functional networks and to describe how these networks develop, change with experience, vary across individuals, and are disturbed in disease (1–8). Slow BOLD fluctuations and their correlations are thought to reflect neuronal processes, yet the underlying mechanisms remain unknown (9, 10). We used a high temporal resolution method, oxygen polarography, to characterize the dynamics of oxygen fluctuations and thereby gain insight into the underlying neuronal mechanisms.Two types of dynamics commonly observed in the brain may be associated with two distinct types of underlying mechanisms or processes. Dynamics with narrow band-limited power may reflect the influence of specific pacemaker units. For instance, the occipital alpha rhythm, which dominates the EEG during relaxed wakefulness, may originate from an alpha pacemaker unit, which consists of a specialized subset of gap-junction–coupled thalamocortical neurons that exhibit intrinsic rhythmic bursting at alpha frequencies (11–13). Although much evidence supports this oscillatory model of resting-state activity (e.g., refs. 14 and 15), the dominant hypothesis in the field is that correlations arise from neural activity propagating within an anatomically constrained small world network (e.g., refs. 16 and 17). This model predicts scale-free dynamics, also known as 1/f dynamics (17, 18). With 1/f dynamics, event amplitude varies inversely with frequency, so that large events are rare whereas small events are common. More precisely, power may vary inversely with frequency raised by a (small) exponent: P ∝ 1/f^β, with typical exponents from 0 to 3 (19). The 1/f dynamics are a hallmark of a complex dynamic system operating at a critical point, at which the system is balanced between ordered and disordered phases (20–22), although 1/f dynamics may also arise in other noncritical systems (23). The fact that various neural signals, such as local field potentials, show 1/f characteristics has inspired models of the brain as operating at a critical point through a process of self-organization (17, 24–28).Local BOLD fluctuations have a 1/f power spectrum (29–31). This has led to the suggestion that the slow correlations of resting-state connectivity may reflect a critical process (18, 19). This assumes that the dynamics of interregional oxygen correlation match the dynamics of local fluctuations. Indeed, three studies report that BOLD correlations vary inversely with frequency (1/f), much like local oxygen (32–34). However, Sasai et al. (35), Achard et al. (36), and Cordes et al. (37) report instead that oxygen correlation peaks around 0.04–0.06 Hz, with less correlation at lower frequencies—a band-limited pattern that is distinctly different from 1/f (38–40). Finally, other studies report that BOLD correlations reach a plateau at low frequencies, a result that is intermediate between 1/f and band-limited dynamics (41, 42).We used oxygen polarography to directly measure the spectrum of interregional oxygen correlation. Polarography is an invasive alternative to BOLD fMRI that allows robust recording of local oxygen fluctuations with higher temporal resolution, higher frequency specificity, and broader frequency range than can be achieved with standard fMRI techniques. We measured oxygen fluctuations in the default network [bilateral posterior cingulate cortex (PCC) area 23] and the visual/attention network (bilateral V3) in the awake, resting macaque. Here, we report that correlations between homotopic regions are band limited rather than 1/f. Further, we show that the variance of local oxygen fluctuations can be separated into a 1/f component and a band-limited component. Only the band-limited component relates to long-range correlation. This suggests that there is a band-limited mechanism (or mechanisms) driving interregional oxygen correlation that is distinct from the mechanism(s) driving local (1/f) oxygen fluctuations. The fact that correlation is band limited is suggestive of a rhythmic or pseudo-oscillatory mechanism.