Regional specialization within the human striatum for diverse psychological functions

Decades of animal and human neuroimaging research have identified distinct, but overlapping, striatal zones, which are interconnected with separable corticostriatal circuits, and are crucial for the organization of functional systems. Despite continuous efforts to subdivide the human striatum based on anatomical and resting-state functional connectivity, characterizing the different psychological processes related to each zone remains a work in progress. Using an unbiased, data-driven approach, we analyzed large-scale coactivation data from 5,809 human imaging studies. We (i) identified five distinct striatal zones that exhibited discrete patterns of coactivation with cortical brain regions across distinct psychological processes and (ii) identified the different psychological processes associated with each zone. We found that the reported pattern of cortical activation reliably predicted which striatal zone was most strongly activated. Critically, activation in each functional zone could be associated with distinct psychological processes directly, rather than inferred indirectly from psychological functions attributed to associated cortices. Consistent with well-established findings, we found an association of the ventral striatum (VS) with reward processing. Confirming less well-established findings, the VS and adjacent anterior caudate were associated with evaluating the value of rewards and actions, respectively. Furthermore, our results confirmed a sometimes overlooked specialization of the posterior caudate nucleus for executive functions, often considered the exclusive domain of frontoparietal cortical circuits. Our findings provide a precise functional map of regional specialization within the human striatum, both in terms of the differential cortical regions and psychological functions associated with each striatal zone.In addition to its central role in selecting, planning, and executing motor behavior (1, 2), the human striatum has been reported to be involved in diverse psychological functions, including emotion generation and regulation (3, 4), reward-related processes and decision making (5, 6), and executive functions (7, 8). These discrete functions are thought to map onto distinct functional striatal zones, which participate in separable basal ganglia-thalamocortical circuits (9–12) and are critical for the organization of behavior.Following this logic, recent attempts to parcellate the human striatum using diffusion tensor imaging (DTI) (13, 14) and resting-state functional connectivity (RSFC) (15–17) have relied on patterns of corticostriatal connectivity to identify striatal zones. Although very useful, these studies have been limited to inferring striatal function indirectly via psychological functions of connected cortical regions. In addition, it remains unclear how anatomical connections and RSFC map onto different psychological processes (18). Finally, RSFC is sometimes epiphenomenal (19), and fiber tract reconstruction with DTI is inaccurate for complex axonal projections underlying frontal corticostriatal connectivity (17).A separate body of work has attempted to differentiate striatal contributions directly to behavior empirically. However, these empirical investigations have focused on a restricted set of paradigms that may fail to capture the full range of striatal function, especially in humans. For example, converging evidence suggests a division of labor between the ventral striatum (VS) and dorsal striatum for Pavlovian and instrumental conditioning, respectively (20, 21). Within the dorsal striatum, medial regions support behavioral flexibility and lateral regions support well-learned behavior (22, 23). This work has greatly advanced our understanding of the similarities of striatal function in human and nonhuman animals. However, because of this strong reliance on classic learning paradigms, the integration of ideas about how the striatum is involved in uniquely human psychological functions, such as working memory, planning, and language, remains a work in progress.To generate a comprehensive and precise functional map of the human striatum, in terms of associations with both cortical brain regions and psychological tasks, we simultaneously analyzed corticostriatal coactivation patterns and the frequency of psychological terms in the full text of 5,809 neuroimaging studies (24). In contrast to studies of RSFC, we defined corticostriatal associations based on coactivation in task-related responses across studies. A cortical voxel and a striatal voxel were coactivated if a study reported activation in both voxels. This metric groups voxels that are associated with similar psychological processes. Similar to RSFC, this metric does not imply direct functional coupling of coactivated voxels. In contrast to existing work, we attempted to associate psychological functions with striatal areas directly, rather than inferring them indirectly based on the psychological functions of connected cortical areas. Sampling across a broad spectrum of neuroimaging studies, without regard for the psychological process under investigation, allowed a large-scale comparison of associations of striatal subregions with diverse psychological processes. This data-driven approach allowed us to (i) localize five striatal zones based on their coactivation with cortical brain areas and (ii) simultaneously characterize the association of each zone with psychological states in a relatively unbiased manner, including potential associations overlooked in both individual hypothesis-driven studies and studies of RSFC.     

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.     

Signature of consciousness in the dynamics of resting-state brain activity

At rest, the brain is traversed by spontaneous functional connectivity patterns. Two hypotheses have been proposed for their origins: they may reflect a continuous stream of ongoing cognitive processes as well as random fluctuations shaped by a fixed anatomical connectivity matrix. Here we show that both sources contribute to the shaping of resting-state networks, yet with distinct contributions during consciousness and anesthesia. We measured dynamical functional connectivity with functional MRI during the resting state in awake and anesthetized monkeys. Under anesthesia, the more frequent functional connectivity patterns inherit the structure of anatomical connectivity, exhibit fewer small-world properties, and lack negative correlations. Conversely, wakefulness is characterized by the sequential exploration of a richer repertoire of functional configurations, often dissimilar to anatomical structure, and comprising positive and negative correlations among brain regions. These results reconcile theories of consciousness with observations of long-range correlation in the anesthetized brain and show that a rich functional dynamics might constitute a signature of consciousness, with potential clinical implications for the detection of awareness in anesthesia and brain-lesioned patients.During the awake resting state, spontaneous brain activity is highly structured. Functional MRI (fMRI) recordings indicate that brain activity constantly waxes and wanes in a tightly correlated manner across distant brain regions, forming reproducible patterns of functional connectivity that exhibit both a rich temporal dynamics (1, 2) and spatial organization into functional networks (3, 4). Ever since the discovery of these resting state patterns, their interpretation has been debated. Many of these patterns match those observed during active cognitive tasks, suggesting that they might arise from a spontaneous, endogenous activation of cognitive processes (5, 6). Indeed, the default mode network (DMN), the most prominent functional network at rest, is most active when subjects direct attention to inward processes, such as daydreaming or imagining (6). Furthermore, when a subject is interrupted during the resting state, functional connectivity patterns at the time of interruption can partially predict whether a subject was imagining or mind wandering (7, 8), and what was the focus of attention (9).Although these findings show that part of the resting state brain activity, at least, indexes ongoing mental content, this conclusion appears to be in conflict with other studies showing that long-range resting-state functional connectivity persists even after loss of consciousness (LOC) due to general anesthesia (10, 11) or in vegetative state (VS) patients (12, 13). Although a small proportion of VS patients show a high degree of residual cognitive activity (14, 15), this is unlikely to be the case during general anesthesia, suggesting that complex functional connectivity patterns can also arise purely as the result of a semirandom circulation of spontaneous neural activity along fixed anatomical routes. Indeed, mean-field simulations models of resting state brain activity provide a relatively good match to the observed static functional connectivity patterns by simply implementing a noisy reverberation within the known whole-brain connectivity matrix, without making any specific assumption about ongoing cognitive processes (16, 17).These studies raise the following questions. Is resting-state activity a mere manifestation of the organized structural connectivity matrix, which is hence preserved even in absence of consciousness, for instance, during general anesthesia? Or alternatively, do some aspects of resting state brain activity specifically reflect the flow of cognitive processes that characterizes the conscious state? If so, how should functional connectivity data be processed to extract signatures of the conscious state, i.e., features of resting state activity that are only present in the awake state and disappear with the loss of consciousness? Identifying such signatures may have important consequences for clinical practice, as it would add to the small number of brain-imaging paradigms that are currently available to diagnose residual consciousness in VS patients (15, 18–20).Here, we set out to address these questions by comparing fMRI images of spontaneous fluctuations in monkey brain activity either in the awake state or while undergoing general anesthesia. Our working hypothesis, based on dynamical systems simulations of resting state brain activity (16, 21–24), is that signatures of the conscious state lie in the dynamics of spontaneous brain activity. When averaged across a long time period, functional brain activity may appear similar in wakefulness and anesthesia (10, 11, 25), due to the existence of a backbone anatomical connectivity. However, the implicit assumption of temporal stationarity underlying typical resting state analyses, while useful, might provide a wrong image of the underlying functional configurations (just like the averaged outcome of tossing a fair coin, i.e., 50% heads, may not even be a possible state of the world). Indeed, several techniques have recently emerged to avoid the temporal averaging step and replace it with a direct visualization of the temporal dynamics of spontaneous brain activity patterns (26–28). These techniques have revealed a far richer picture of the nature, duration, and transition probabilities of human resting state activity in the awake condition (26–28), but they have not yet been applied to the identification of how these characteristics change during the loss of consciousness.Theories and simulations of brain operations suggest that the temporal dynamics of brain networks should be very different during wakefulness and after loss of consciousness due to anesthesia, coma, or sleep. The awake condition should be characterized by an active exploration of a high diversity of network states (22–24), forming the ceaselessly fluctuating “stream of consciousness” described by William James. During the nonconscious condition, however, spontaneous activity should reduce to the circulation of a more random pattern of neural activity shaped and constrained by the anatomical connectivity (17, 21). According to this view, the role of structural connectivity in sculpting functional connectivity maps should vary during wakefulness and anesthesia. Although wakefulness should be characterized by a rich repertoire of connectivity patterns (23), the functional connectivity patterns of the sedated brain should highly resemble the underlying structural map (16).     

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.     

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).     

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).     

Decreased segregation of brain systems across the healthy adult lifespan

Healthy aging has been associated with decreased specialization in brain function. This characterization has focused largely on describing age-accompanied differences in specialization at the level of neurons and brain areas. We expand this work to describe systems-level differences in specialization in a healthy adult lifespan sample (n = 210; 20–89 y). A graph-theoretic framework is used to guide analysis of functional MRI resting-state data and describe systems-level differences in connectivity of individual brain networks. Young adults’ brain systems exhibit a balance of within- and between-system correlations that is characteristic of segregated and specialized organization. Increasing age is accompanied by decreasing segregation of brain systems. Compared with systems involved in the processing of sensory input and motor output, systems mediating “associative” operations exhibit a distinct pattern of reductions in segregation across the adult lifespan. Of particular importance, the magnitude of association system segregation is predictive of long-term memory function, independent of an individual’s age.Healthy adult aging is characterized by a progressive degradation of brain structure and function associated with gradual changes in cognition (see reviews in refs. 1, 2). Among the age-accompanied functional changes, one prominent observation is a reduction in the specificity with which distinct neural structures mediate particular processing roles [i.e., a reduction in functional specialization, or “dedifferentiation” (3)]. A reduction in functional specificity has been observed across multiple spatial scales of brain organization, ranging from the firing patterns of single neurons (e.g., refs. 4, 5) to the evoked activity of individual brain areas (6–10). (For additional discussion see ref. 11.)Despite the compelling evidence for age-accompanied reductions in functional specialization across numerous brain areas, the relationship between neural specialization and cognition generally is weak. This likely is related to the fact that broad cognitive domains such as “long-term memory” and “executive control” are mediated by distributed and interacting brain systems, each consisting of multiple interacting brain areas. Thus, relating functional specialization in a single brain area to general measures of cognition likely will be unsuccessful. Such an argument is consistent with the view that severe impairment in cognitive function due to injury or insult typically is a consequence of damage to multiple brain locations (e.g., refs. 12, 13). Based on these considerations, it seems plausible that the cognitive decline evident even in healthy older adults may be related to decreased functional integrity at a systems level of organization. The present report approaches healthy aging from this systems-level perspective in an effort to relate systems-related functional specialization to age-accompanied differences in cognition.Before proceeding, it is important to clarify the meaning of system. The term “system” often is used in relation to brain organization when referring to any group of areas that subserve common processing roles. For example, the visual system comprises brain areas primarily defined by their role in processing visual stimuli (e.g., ref. 14), and the frontal–parietal task control system consists of brain areas involved mainly in adaptive task control (15). Identifying distinct brain systems and defining their functional roles by examining how their constituent areas are modulated by experimental testing or are impaired by brain damage is not an easy endeavor; systems of brain areas typically mediate processing roles that span multiple stimulus and task demands. This reality makes assessing changes in the functional specialization of systems across cohorts of individuals extremely challenging.An alternative formal and complementary approach to defining a brain system involves understanding how brain areas relate to one another via their patterns of shared functional or anatomical relationships in the context of a large-scale network (16, 17). Like many other complex networks, brain networks may be analyzed as a graph of connected or interacting elements. When a brain network graph represents the interaction of areas, one prominent feature is the presence of subsets of areas that are highly interactive with one another and less interactive with other subsets of areas. This pattern of organization reflects the presence of distinct “modules” or “communities” (e.g., ref. 18). Importantly, numerous connectivity-defined human brain modules have been shown to overlap closely with functional systems as defined by other methods of assessing information processing [e.g., task-evoked activity, lesion-mapping (19, 20)]. The close correspondence between differing methods of system identification provides a basis for using connectivity to understand the organization of brain systems and how they may differ with age.Modular brain networks are characterized by a fine balance of dense within-system relationships among brain areas (nodes) that have highly related processing roles, as well as sparser (but not necessarily absent) relationships between areas in systems with divergent processing roles. This pattern of system segregation facilitates communication among brain areas that may be distributed anatomically but nevertheless are in the service of related sets of processing operations, and simultaneously reinforces the functional specialization of systems that perform different sets of processing operations (21). Importantly, segregated systems can communicate with one another via the presence of the sparser connections between them. As such, any deviation in the patterns of within- and between-system connectivity may be considered evidence for a change in the system’s specialization. Furthermore, if aging is associated with changes in functional specialization at the level of brain systems, this may be revealed by examining the differences in patterns of within- and between-system areal connectivity across age.We use functional connectivity, as measured by blood oxygen-level–dependent (BOLD) functional MRI (fMRI) during rest [resting-state functional correlations (RSFCs), see ref. 22], to assess age-related differences in the organization of brain systems. Changes in RSFC patterns between sets of areas have been observed following extensive directed training (23–25), and differences in RSFC patterns also have been reported in cross-sectional comparisons spanning from childhood to older age (e.g., refs. 26–29). The extant data suggest that RSFCs are malleable and reflect sensitivity to a history of coactivation: changes in the processing roles of areas may be characterized by changes in their RSFCs with other areas (for discussion, see ref. 17). This feature makes RSFCs particularly useful in assessing differences in the organization and specialization of brain systems.In the present study, the age-accompanied differences in the functional specialization of brain systems are revealed by examining patterns of within- and between-system areal RSFCs in a large healthy adult lifespan sample (n = 210; age range, 20–89 y). The inclusion of subjects distributed across each decade of adulthood not only allows us to assess how older and younger adults differ in their organization of brain systems, but also provides insight as to whether there is a critical stage of the adult lifespan when differences in system organization may appear. Previous reports attempted to address related questions by examining end points of the adult aging spectrum, focusing on the organization within specific systems (e.g., refs. 26, 28, 30), or using area nodes that are not representative of functional areas [e.g., structural parcels (31–34)]. The latter feature likely contributes to the inconsistent findings observed in the relationship between summary network measures and age groups (e.g., refs. 31, 35 vs. refs. 30, 36). In addition to examining age-related differences in system organization developed from a biologically plausible cortical parcellation of the human brain network, we also relate systems-level differences in organization to differences in general measures of cognitive ability. To foreshadow the results that follow, we report that aging is associated with differences in patterns of connectivity within and between brain systems, that these differences are not uniform across all systems, and that the segregation of brain systems has a direct relationship to measures of cognitive ability independent of age.     

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.     

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.     

Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in autism

Although typically identified in early childhood, the social communication symptoms and adaptive behavior deficits that are characteristic of autism spectrum disorder (ASD) persist throughout the lifespan. Despite this persistence, even individuals without cooccurring intellectual disability show substantial heterogeneity in outcomes. Previous studies have found various behavioral assessments [such as intelligence quotient (IQ), early language ability, and baseline autistic traits and adaptive behavior scores] to be predictive of outcome, but most of the variance in functioning remains unexplained by such factors. In this study, we investigated to what extent functional brain connectivity measures obtained from resting-state functional connectivity MRI (rs-fcMRI) could predict the variance left unexplained by age and behavior (follow-up latency and baseline autistic traits and adaptive behavior scores) in two measures of outcome—adaptive behaviors and autistic traits at least 1 y postscan (mean follow-up latency = 2 y, 10 mo). We found that connectivity involving the so-called salience network (SN), default-mode network (DMN), and frontoparietal task control network (FPTCN) was highly predictive of future autistic traits and the change in autistic traits and adaptive behavior over the same time period. Furthermore, functional connectivity involving the SN, which is predominantly composed of the anterior insula and the dorsal anterior cingulate, predicted reliable improvement in adaptive behaviors with 100% sensitivity and 70.59% precision. From rs-fcMRI data, our study successfully predicted heterogeneity in outcomes for individuals with ASD that was unaccounted for by simple behavioral metrics and provides unique evidence for networks underlying long-term symptom abatement.Although typically identified in childhood, the social communication symptoms that are characteristic of autism spectrum disorder (ASD) persist throughout the lifespan (1, 2). On average, individuals with ASD show smaller age-related improvements in adaptive behaviors, including daily living skills critical for independent living, than do typically developing (TD) peers (2–4). The burden of prolonged clinical symptom expression, coupled with limited adaptive behaviors, leads to a relatively poor prognosis for a majority of adults with ASD. For example, only 12% of adults with ASD achieve “very good” outcomes, defined by a high level of independence (5). Adolescence and young adulthood are poorly understood in ASD. Although there seems to be a great deal of change during this time, the nature of this change varies across studies, with a handful of studies reporting a decline in functioning (2, 3, 6), others reporting general improvement (7, 8), and still others reporting a quadratic course of autistic symptoms and adaptive functioning where the trajectory peaks in late adolescence (9) or the late 20s (10) and begins to fall subsequently.Predictors of positive outcomes in ASD include higher intelligence quotient (IQ) (11–13), language ability (9, 14), less severe ASD symptoms (15), and stronger adaptive behaviors (16, 17). However, there is substantial variability in outcome even among individuals with ASD without cooccurring intellectual disability (7, 13, 17, 18). Age, IQ, and language ability accounted for as much as 45% of the variance in outcome measures in a sample composed of predominantly individuals with both ASD and intellectual disability (11). Others reported more modest numbers for these predictors, with IQ predicting 3% of variance in outcome and language ability predicting 32% of variance in outcome (14). A study that included only individuals with ASD without cooccurring intellectual disability found age and IQ as weaker predictors, predicting 6–28% of various adaptive behavior subscales (6). Although these previous studies have been successful in predicting these outcomes using behavioral measures, in most cases, the majority of the variance in outcomes remains unexplained. Thus, it remains difficult to identify individuals with ASD who may struggle to achieve independence during adulthood and who may benefit from additional intervention.In the present study, we explored whether a functional neuroimaging-based measure of brain connectivity, termed resting-state functional connectivity MRI (rs-fcMRI), can predict variance in behavioral outcomes in young adults with ASD beyond that explained by cognitive or behavioral measures. Functional connectivity strength in individuals with ASD has been found to predict an ASD diagnosis (19–21) and to correlate with many aspects of cognition and behavior that also predict outcome, including IQ (21, 22), and ASD symptomatology using the Autism Diagnostic Observation Schedule (21–23), the Autism Diagnostic Interview-Revised (20), and the Social Responsiveness Scale (SRS) (19, 22, 24). As such, brain measures may explain additional variance in behavioral outcomes. One previous study has shown that combining functional MRI (fMRI) data with behavioral data increased predictive power for categorical language outcomes in early developing ASD (25). Brain-derived data has also added explanatory power to predictive models of depression (26), dyslexia (27), alcoholism (28), and reading and math ability (29, 30).We tested whether rs-fcMRI data acquired in late adolescence and early adulthood [time 1 (T1)] could predict behavioral outcomes at least 1 y after the imaging data were acquired [time 2 (T2)]. We defined behavioral outcomes with a measure of predominantly social autistic traits (SRS) and a measure of adaptive functioning [Adaptive Behavior Assessment System-Second Edition (ABAS-II)]. Using an approach that controlled for nuisance variables (e.g., variable duration between time 1 and time 2) and variables known to strongly predict outcome (e.g., age and baseline score on outcome measure), we performed regressions to investigate whether and to what extent the remaining variance in outcome could be predicted by baseline functional connectivity in networks known to be involved in ASD.     

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.     

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.     

Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases

Brain stimulation, a therapy increasingly used for neurological and psychiatric disease, traditionally is divided into invasive approaches, such as deep brain stimulation (DBS), and noninvasive approaches, such as transcranial magnetic stimulation. The relationship between these approaches is unknown, therapeutic mechanisms remain unclear, and the ideal stimulation site for a given technique is often ambiguous, limiting optimization of the stimulation and its application in further disorders. In this article, we identify diseases treated with both types of stimulation, list the stimulation sites thought to be most effective in each disease, and test the hypothesis that these sites are different nodes within the same brain network as defined by resting-state functional-connectivity MRI. Sites where DBS was effective were functionally connected to sites where noninvasive brain stimulation was effective across diseases including depression, Parkinson''s disease, obsessive-compulsive disorder, essential tremor, addiction, pain, minimally conscious states, and Alzheimer’s disease. A lack of functional connectivity identified sites where stimulation was ineffective, and the sign of the correlation related to whether excitatory or inhibitory noninvasive stimulation was found clinically effective. These results suggest that resting-state functional connectivity may be useful for translating therapy between stimulation modalities, optimizing treatment, and identifying new stimulation targets. More broadly, this work supports a network perspective toward understanding and treating neuropsychiatric disease, highlighting the therapeutic potential of targeted brain network modulation.A promising treatment approach for many psychiatric and neurological diseases is focal brain stimulation, traditionally divided into invasive approaches requiring neurosurgery and noninvasive approaches that stimulate the brain from outside the skull. The dominant invasive treatment is deep brain stimulation (DBS) in which an electrode is surgically implanted deep in the brain and used to deliver electrical pulses at high frequency (generally 120–160 Hz) (1, 2). In some instances, the therapeutic effects of DBS resemble those of structural lesions at the same site, but in other cases DBS appears to activate the stimulated region or adjacent white matter fibers (1, 2). DBS systems are approved by the US Food and Drug Administration (FDA) for treatment of essential tremor and Parkinson''s disease, have humanitarian device exemptions for dystonia and obsessive compulsive disorder, and are being explored as a therapy for many other diseases including depression, Alzheimer’s disease, and even minimally conscious states (1, 3–6).Although DBS can result in dramatic therapeutic benefit, the risk inherent in neurosurgery has motivated research into noninvasive alternatives (7–9). Transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) have received the most investigation (10–13). TMS uses a rapidly changing magnetic field to induce currents and action potentials in underlying brain tissue, whereas tDCS involves the application of weak (1–2 mA) electrical currents to modulate neuronal membrane potential. Depending on the stimulation parameters, both TMS and tDCS can be used to excite (>5 Hz TMS, anodal tDCS) or inhibit (<1 Hz TMS, cathodal tDCS) the underlying cortical tissue (10). These neurophysiological effects are well validated only for the primary motor cortex (M1) and can vary across subjects; however the terms “excitatory” and “inhibitory” stimulation are used often and are used here as a shorthand to refer to TMS or tDCS at these parameters. The primary clinical application and FDA-approved indication is high-frequency (i.e., excitatory) TMS to the left dorsolateral prefrontal cortex (DLPFC) for treatment of medication-refractory depression (14–19). However, TMS and tDCS have shown evidence of efficacy in a number of other neurological and psychiatric disorders (10–13).How invasive and noninvasive brain stimulation relate to one another has received relatively little attention. Because of the different FDA-approved indications, patient populations, sites of administration, and presumed mechanisms of action, they have remained largely separate clinical and scientific fields. However, these boundaries are beginning to erode. First, the patient populations treated with invasive or noninvasive brain stimulation are starting to converge. For example, the primary indication for TMS is depression, and the primary indication for DBS is Parkinson''s disease, but DBS is being investigated as a treatment for depression, and TMS is being investigated as a treatment for Parkinson''s disease (4, 20–25). Second, although therapeutic mechanisms remain unknown, invasive and noninvasive brain stimulation share important properties. In both cases, the effects of stimulation propagate beyond the stimulation site to impact a distributed set of connected brain regions (i.e., a brain network) (4, 10, 26–33). Given increasing evidence that these network effects are relevant to therapeutic response (4, 34–36), it is possible that invasive and noninvasive stimulation of different brain regions actually modify the same brain network to provide therapeutic benefit.Linking invasive and noninvasive brain stimulation and identifying the relevant brain networks is important for several reasons. First, findings could be used to improve treatments. For example, TMS treatment of depression is limited by the inability to identify the optimal stimulation site in the left DLPFC (15, 18, 37–39). Using resting-state functional-connectivity MRI (rs-fcMRI), a technique used to visualize brain networks based on correlated fluctuations in blood oxygenation (40–42), the efficacy of different DLPFC TMS sites has been related to their correlation with the subgenual cingulate, a DBS target for depression (43). rs-fcMRI maps with the subgenual cingulate thus might be used to select an optimal TMS site in the DLPFC, perhaps even individualized to specific patients (44). Because identification of the ideal stimulation site is a ubiquitous problem across diseases and brain-stimulation modalities (1, 15, 18, 37–39), such an approach could prove valuable across disorders. Second, although the primary goal of therapeutic brain stimulation is to help patients, it also can provide unique and fundamental insight into human brain function. Investigating how different types of stimulation to different brain regions could impart similar behavioral effects is relevant to understanding the functional role of brain networks.Here we investigate all neurological and psychiatric diseases treated with both invasive and noninvasive brain stimulation. We list the stimulation sites that have evidence of efficacy in each disease and test the hypothesis that these sites represent different nodes in the same brain network as visualized with rs-fcMRI. Further, we determine whether this approach can identify sites where stimulation is ineffective and determine which type of noninvasive brain stimulation (excitatory or inhibitory) will prove effective. To test these hypotheses, we take advantage of a unique rs-fcMRI dataset collected from 1,000 normal subjects, processed to allow precise subcortical and cortical alignment between subjects and with anatomical brain atlases (45–47).     

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?     

Age-dependent changes in prefrontal intrinsic connectivity

The prefrontal cortex continues to mature after puberty and into early adulthood, mirroring the time course of maturation of cognitive abilities. However, the way in which prefrontal activity changes during peri- and postpubertal cortical maturation is largely unknown. To address this question, we evaluated the developmental stage of peripubertal rhesus monkeys with a series of morphometric, hormonal, and radiographic measures, and conducted behavioral and neurophysiological tests as the monkeys performed working memory tasks. We compared firing rate and the strength of intrinsic functional connectivity between neurons in peripubertal vs. adult monkeys. Notably, analyses of spike train cross-correlations demonstrated that the average magnitude of functional connections measured between neurons was lower overall in the prefrontal cortex of peripubertal monkeys compared with adults. The difference resulted because negative functional connections (indicative of inhibitory interactions) were stronger and more prevalent in peripubertal compared with adult monkeys, whereas the positive connections showed similar distributions in the two groups. Our results identify changes in the intrinsic connectivity of prefrontal neurons, particularly that mediated by inhibition, as a possible substrate for peri- and postpubertal advances in cognitive capacity.The prefrontal cortex, the brain area associated with the highest-level cognitive operations, is known to undergo a protracted period of development (1–3). A virtually linear increase in performance with age has been observed in tasks that assess visuospatial working memory, executive control, and resistance to distraction, a process that continues well after puberty and into early adulthood (4, 5). The accrual of cognitive capacities during this period parallels structural changes of the prefrontal cortex in humans and nonhuman primates (6–10). Imaging studies in humans suggest that patterns of brain activation associated with working memory tasks undergo distinct changes between childhood and adulthood, supporting the idea of prolonged prefrontal maturation (11–14). However, how the patterns of prefrontal activation change during cortical maturation remains unclear. A possible mechanism that could account for variations in prefrontal responses—and which could have a significant functional impact (15)—is an overall change in the distribution of intrinsic functional connections, i.e., those between neurons within the prefrontal cortex. The intrinsic connectivity of a network is directly related to the correlation structure of neuronal responses, and this determines in a fundamental way the information-coding properties of the network and its ability to sustain activity on its own (16–18). In this study, we sought to determine if the strengths of functional connections inferred from multisite neurophysiological recordings differ between peripubertal and adult monkeys.     

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.     

Injury alters intrinsic functional connectivity within the primate spinal cord

Recent demonstrations of correlated low-frequency MRI signal variations between subregions of the spinal cord at rest in humans, similar to those found in the brain, suggest that such resting-state functional connectivity constitutes a common feature of the intrinsic organization of the entire central nervous system. We report our detection of functional connectivity within the spinal cords of anesthetized squirrel monkeys at rest and show that the strength of connectivity within these networks is altered by the effects of injuries. By quantifying the low-frequency MRI signal correlations between different horns within spinal cord gray matter, we found distinct functional connectivity relationships between the different sensory and motor horns, a pattern that was similar to activation patterns evoked by nociceptive heat or tactile stimulation of digits. All horns within a single spinal segment were functionally connected, with the strongest connectivity occurring between ipsilateral dorsal and ventral horns. Each horn was strongly connected to the same horn on neighboring segments, but this connectivity reduced drastically along the spinal cord. Unilateral injury to the spinal cord significantly weakened the strength of the intrasegment horn-to-horn connectivity only on the injury side and in slices below the lesion. These findings suggest resting-state functional connectivity may be a useful biomarker of functional integrity in injured and recovering spinal cords.Resting-state functional connectivity (rsFC) has been widely used to identify and characterize neural circuits in the brain (1–3), and its presentation at various spatial scales and its changes with specific physiological conditions confirm its fundamental role in maintaining normal brain function (4, 5). More importantly, alterations of rsFC networks in various disease conditions have altered our view about the functional significance of spontaneous baseline neural activity (3). Two very recent reports of success in detecting intrinsic functional circuits in human spines using resting-state fMRI once more suggest that rsFC is a fundamental, common feature of the entire nervous system (6, 7).Despite these exciting findings in human subjects, the functional and behavioral relevance of the intrinsic functional networks within the spine gray matter remains largely obscure, and there have been no previous reports attempting to understand their significance. One way to address this question is to manipulate the network and then examine how the network reacts to the manipulation. This type of approach is impossible to execute in humans but can be performed in nonhuman primates in a very well-controlled manner. Thus, this study aimed to better understand the functional and behavioral relevance of newly identified rsFC in the spinal cord by first determining whether similar intrinsic rsFC networks can be detected in the spinal cords of anesthetized monkeys. We also sought to determine the relationship between stimulus-evoked fMRI activation patterns and the intrinsic rsFC networks in the spine, and whether and how unilateral injury to the cord changes the pattern or strength of rsFC. To address the above-mentioned critical questions, we conducted our research in a well-established unilateral spinal cord injury (SCI) model (8, 9) and used high-resolution fMRI at high field (10, 11) and tracer histology to quantify resting-state networks.Traumatic SCI is a devastating medical condition that disrupts neural pathways, can lead to severe sensory impairment and motor deficits, and generally severely impairs the quality of life of SCI patients (12). Considerable research efforts and clinical trials have been devoted to attempts to restore impaired spinal cord function by promoting regeneration of disrupted neural pathways (13) or developing effective functional electrical stimulations (14) that assist the regaining of sensation and motor control. However, there has long existed a need for objective, noninvasive metrics of the effects of interventions. To date, behavioral assessments have been the gold-standard outcome measures of interventions, in both animals and humans studies (15). The strength or pattern of intrinsic rsFC within the spine may be a more sensitive noninvasive indicator of the states of local circuits both within and across spinal segments. Quantification of the intrinsic functional connectivity within the spine before and after injury has considerable potential to be a more objective imaging biomarker of spinal cord functional integrity. The knowledge gained in this fMRI study should be valuable for directing the use of fMRI for evaluating clinical prognosis and the effectiveness of therapeutic interventions.