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).     

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).     

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.     

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

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

Programmable motion of DNA origami mechanisms

DNA origami enables the precise fabrication of nanoscale geometries. We demonstrate an approach to engineer complex and reversible motion of nanoscale DNA origami machine elements. We first design, fabricate, and characterize the mechanical behavior of flexible DNA origami rotational and linear joints that integrate stiff double-stranded DNA components and flexible single-stranded DNA components to constrain motion along a single degree of freedom and demonstrate the ability to tune the flexibility and range of motion. Multiple joints with simple 1D motion were then integrated into higher order mechanisms. One mechanism is a crank–slider that couples rotational and linear motion, and the other is a Bennett linkage that moves between a compacted bundle and an expanded frame configuration with a constrained 3D motion path. Finally, we demonstrate distributed actuation of the linkage using DNA input strands to achieve reversible conformational changes of the entire structure on ∼minute timescales. Our results demonstrate programmable motion of 2D and 3D DNA origami mechanisms constructed following a macroscopic machine design approach.The ability to control, manipulate, and organize matter at the nanoscale has demonstrated immense potential for advancements in industrial technology, medicine, and materials (1–3). Bottom-up self-assembly has become a particularly promising area for nanofabrication (4, 5); however, to date designing complex motion at the nanoscale remains a challenge (6–9). Amino acid polymers exhibit well-defined and complex dynamics in natural systems and have been assembled into designed structures including nanotubes, sheets, and networks (10–12), although the complexity of interactions that govern amino acid folding make designing complex geometries extremely challenging. DNA nanotechnology, on the other hand, has exploited well-understood assembly properties of DNA to create a variety of increasingly complex designed nanostructures (13–15).Scaffolded DNA origami, the process of folding a long single-stranded DNA (ssDNA) strand into a custom structure (16–18), has enabled the fabrication of nanoscale objects with unprecedented geometric complexity that have recently been implemented in applications such as containers for drug delivery (19, 20), nanopores for single-molecule sensing (21–23), and templates for nanoparticles (24, 25) or proteins (26–28). The majority of these and other applications of DNA origami have largely focused on static structures. Natural biomolecular machines, in contrast, have a rich diversity of functionalities that rely on complex but well-defined and reversible conformational changes. Currently, the scope of biomolecular nanotechnology is limited by an inability to achieve similar motion in designed nanosystems.DNA nanotechnology has enabled critical steps toward that goal starting with the work of Mao et al. (29), who developed a DNA nanostructure that took advantage of the B–Z transition of DNA to switch states. Since then, efforts to fabricate dynamic DNA systems have primarily focused on strand displacement approaches (30) mainly on systems comprising a few strands or arrays of strands undergoing ∼nm-scale motions (31–37) in some cases guided by DNA origami templates (38–40). More recently, strand displacement has been used to reconfigure DNA origami nanostructures, for example opening DNA containers (19, 41, 42), controlling molecular binding (43, 44), or reconfiguring structures (45). The largest triggerable structural change was achieved by Han et al. in a DNA origami Möbius strip (one-sided ribbon structure) that could be opened to approximately double in size (45). Constrained motion has been achieved in systems with rotational motion (19, 20, 32, 41, 44, 46, 47) in some cases to open lid-like components (19, 20, 41) or detect molecular binding (44, 48, 49). A few of these systems achieved reversible conformational changes (32, 41, 44, 46), although the motion path and flexibility were not studied. Constrained linear motion has remained largely unexplored. Linear displacements on the scale of a few nanometers have been demonstrated via conformational changes of DNA structure motifs (50–55), strand invasion to open DNA hairpins (36, 55, 56), or the reversible sliding motion of a DNA tile actuator (56); these cases also did not investigate the motion path or flexibility of motion.Building on these prior studies, this work implements concepts from macroscopic machine design to build modular parts with constrained motion. We demonstrate an ability to tune the flexibility and range of motion and then integrate these parts into prototype mechanisms with designed 2D and 3D motion. We further demonstrate reversible actuation of a mechanism with complex conformational changes on minute timescales.     

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.     

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.     

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.     

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.     

Epileptic baboons have lower numbers of neurons in specific areas of cortex

Epilepsy is characterized by recurrent seizure activity that can induce pathological reorganization and alter normal function in neocortical networks. In the present study, we determined the numbers of cells and neurons across the complete extent of the cortex for two epileptic baboons with naturally occurring seizures and two baboons without epilepsy. Overall, the two epileptic baboons had a 37% average reduction in the number of cortical neurons compared with the two nonepileptic baboons. The loss of neurons was variable across cortical areas, with the most pronounced loss in the primary motor cortex, especially in lateral primary motor cortex, representing the hand and face. Less-pronounced reductions of neurons were found in other parts of the frontal cortex and in somatosensory cortex, but no reduction was apparent in the primary visual cortex and little in other visual areas. The results provide clear evidence that epilepsy in the baboon is associated with considerable reduction in the numbers of cortical neurons, especially in frontal areas of the cortex related to motor functions. Whether or not the reduction of neurons is a cause or an effect of seizures needs further investigation.Epilepsy is associated with structural changes in the cerebral cortex (e.g., refs. 1–6), and partial epilepsies (i.e., seizures originating from a brain region) may lead to loss of neurons (7) and altered connectivity (8). The cerebral cortex is a heterogeneous structure comprised of multiple sensory and motor information-processing systems (e.g., refs. 9 and 10) that vary according to their processing demands, connectivity (e.g., refs. 11 and 12), and intrinsic numbers of cells and neurons (13–16). Chronic seizures have been associated with progressive changes in the region of the epileptic focus and in remote but functionally connected cortical or subcortical structures (3, 17). Because areas of the cortex are functionally and structurally different, they may also differ in susceptibility to pathological changes resulting from epilepsy.The relationship between seizure activity and neuron damage can be difficult to study in humans. Seizure-induced neuronal damage can be convincingly demonstrated in animals using electrically or chemically induced status epilepticus (one continuous seizure episode longer than 5 min) to reveal morphometric (e.g., refs. 18 and 19) or histological changes (e.g., refs. 20 and 21). Subcortical brain regions are often studied for vulnerability to seizure-induced injury (21–27); however, a recent study by Karbowski et al. (28) observed reduction of neurons in cortical layers 5 and 6 in the frontal lobes of rats with seizures. Seizure-induced neuronal damage in the cortex has also been previously demonstrated in baboons with convulsive status epilepticus (29).The goal of the present study was to determine if there is a specific pattern of cell or neuron reduction across the functionally divided areas of the neocortex in baboons with epilepsy. Selected strains of baboons have been studied as a natural primate model of generalized epilepsy (30–36) that is analogous to juvenile myoclonic epilepsy in humans. The baboons demonstrate generalized myoclonic and tonic-clonic seizures, and they have generalized interictal and ictal epileptic discharges on scalp EEG. Because of their phylogenetic proximity to humans, baboons and other Old World monkeys share many cortical areas and other features of cortical organization with humans (e.g., refs. 9 and 10). Cortical cell and neuron numbers were determined using the flow fractionator method (37, 38) in epileptic baboon tissue obtained from the Texas Biomedical Research Institute, where a number of individuals develop generalized epilepsy within a pedigreed baboon colony (31–36). Our results reveal a regionally specific neuron reduction in the cortex of baboons with naturally occurring, generalized seizures.     

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).     

The evolution of self-control

Cognition presents evolutionary research with one of its greatest challenges. Cognitive evolution has been explained at the proximate level by shifts in absolute and relative brain volume and at the ultimate level by differences in social and dietary complexity. However, no study has integrated the experimental and phylogenetic approach at the scale required to rigorously test these explanations. Instead, previous research has largely relied on various measures of brain size as proxies for cognitive abilities. We experimentally evaluated these major evolutionary explanations by quantitatively comparing the cognitive performance of 567 individuals representing 36 species on two problem-solving tasks measuring self-control. Phylogenetic analysis revealed that absolute brain volume best predicted performance across species and accounted for considerably more variance than brain volume controlling for body mass. This result corroborates recent advances in evolutionary neurobiology and illustrates the cognitive consequences of cortical reorganization through increases in brain volume. Within primates, dietary breadth but not social group size was a strong predictor of species differences in self-control. Our results implicate robust evolutionary relationships between dietary breadth, absolute brain volume, and self-control. These findings provide a significant first step toward quantifying the primate cognitive phenome and explaining the process of cognitive evolution.Since Darwin, understanding the evolution of cognition has been widely regarded as one of the greatest challenges for evolutionary research (1). Although researchers have identified surprising cognitive flexibility in a range of species (2–40) and potentially derived features of human psychology (41–61), we know much less about the major forces shaping cognitive evolution (62–71). With the notable exception of Bitterman’s landmark studies conducted several decades ago (63, 72–74), most research comparing cognition across species has been limited to small taxonomic samples (70, 75). With limited comparable experimental data on how cognition varies across species, previous research has largely relied on proxies for cognition (e.g., brain size) or metaanalyses when testing hypotheses about cognitive evolution (76–92). The lack of cognitive data collected with similar methods across large samples of species precludes meaningful species comparisons that can reveal the major forces shaping cognitive evolution across species, including humans (48, 70, 89, 93–98).To address these challenges we measured cognitive skills for self-control in 36 species of mammals and birds (Fig. 1 and Tables S1–S4) tested using the same experimental procedures, and evaluated the leading hypotheses for the neuroanatomical underpinnings and ecological drivers of variance in animal cognition. At the proximate level, both absolute (77, 99–107) and relative brain size (108–112) have been proposed as mechanisms supporting cognitive evolution. Evolutionary increases in brain size (both absolute and relative) and cortical reorganization are hallmarks of the human lineage and are believed to index commensurate changes in cognitive abilities (52, 105, 113–115). Further, given the high metabolic costs of brain tissue (116–121) and remarkable variance in brain size across species (108, 122), it is expected that the energetic costs of large brains are offset by the advantages of improved cognition. The cortical reorganization hypothesis suggests that selection for absolutely larger brains—and concomitant cortical reorganization—was the predominant mechanism supporting cognitive evolution (77, 91, 100–106, 120). In contrast, the encephalization hypothesis argues that an increase in brain volume relative to body size was of primary importance (108, 110, 111, 123). Both of these hypotheses have received support through analyses aggregating data from published studies of primate cognition and reports of “intelligent” behavior in nature—both of which correlate with measures of brain size (76, 77, 84, 92, 110, 124).Open in a separate windowFig. 1.A phylogeny of the species included in this study. Branch lengths are proportional to time except where long branches have been truncated by parallel diagonal lines (split between mammals and birds ∼292 Mya).With respect to selective pressures, both social and dietary complexities have been proposed as ultimate causes of cognitive evolution. The social intelligence hypothesis proposes that increased social complexity (frequently indexed by social group size) was the major selective pressure in primate cognitive evolution (6, 44, 48, 50, 87, 115, 120, 125–141). This hypothesis is supported by studies showing a positive correlation between a species’ typical group size and the neocortex ratio (80, 81, 85–87, 129, 142–145), cognitive differences between closely related species with different group sizes (130, 137, 146, 147), and evidence for cognitive convergence between highly social species (26, 31, 148–150). The foraging hypothesis posits that dietary complexity, indexed by field reports of dietary breadth and reliance on fruit (a spatiotemporally distributed resource), was the primary driver of primate cognitive evolution (151–154). This hypothesis is supported by studies linking diet quality and brain size in primates (79, 81, 86, 142, 155), and experimental studies documenting species differences in cognition that relate to feeding ecology (94, 156–166).Although each of these hypotheses has received empirical support, a comparison of the relative contributions of the different proximate and ultimate explanations requires (i) a cognitive dataset covering a large number of species tested using comparable experimental procedures; (ii) cognitive tasks that allow valid measurement across a range of species with differing morphology, perception, and temperament; (iii) a representative sample within each species to obtain accurate estimates of species-typical cognition; (iv) phylogenetic comparative methods appropriate for testing evolutionary hypotheses; and (v) unprecedented collaboration to collect these data from populations of animals around the world (70).Here, we present, to our knowledge, the first large-scale collaborative dataset and comparative analysis of this kind, focusing on the evolution of self-control. We chose to measure self-control—the ability to inhibit a prepotent but ultimately counterproductive behavior—because it is a crucial and well-studied component of executive function and is involved in diverse decision-making processes (167–169). For example, animals require self-control when avoiding feeding or mating in view of a higher-ranking individual, sharing food with kin, or searching for food in a new area rather than a previously rewarding foraging site. In humans, self-control has been linked to health, economic, social, and academic achievement, and is known to be heritable (170–172). In song sparrows, a study using one of the tasks reported here found a correlation between self-control and song repertoire size, a predictor of fitness in this species (173). In primates, performance on a series of nonsocial self-control control tasks was related to variability in social systems (174), illustrating the potential link between these skills and socioecology. Thus, tasks that quantify self-control are ideal for comparison across taxa given its robust behavioral correlates, heritable basis, and potential impact on reproductive success.In this study we tested subjects on two previously implemented self-control tasks. In the A-not-B task (27 species, n = 344), subjects were first familiarized with finding food in one location (container A) for three consecutive trials. In the test trial, subjects initially saw the food hidden in the same location (container A), but then moved to a new location (container B) before they were allowed to search (Movie S1). In the cylinder task (32 species, n = 439), subjects were first familiarized with finding a piece of food hidden inside an opaque cylinder. In the following 10 test trials, a transparent cylinder was substituted for the opaque cylinder. To successfully retrieve the food, subjects needed to inhibit the impulse to reach for the food directly (bumping into the cylinder) in favor of the detour response they had used during the familiarization phase (Movie S2).Thus, the test trials in both tasks required subjects to inhibit a prepotent motor response (searching in the previously rewarded location or reaching directly for the visible food), but the nature of the correct response varied between tasks. Specifically, in the A-not-B task subjects were required to inhibit the response that was previously successful (searching in location A) whereas in the cylinder task subjects were required to perform the same response as in familiarization trials (detour response), but in the context of novel task demands (visible food directly in front of the subject).     

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.     

Kinetics of nucleotide-dependent structural transitions in the kinesin-1 hydrolysis cycle

To dissect the kinetics of structural transitions underlying the stepping cycle of kinesin-1 at physiological ATP, we used interferometric scattering microscopy to track the position of gold nanoparticles attached to individual motor domains in processively stepping dimers. Labeled heads resided stably at positions 16.4 nm apart, corresponding to a microtubule-bound state, and at a previously unseen intermediate position, corresponding to a tethered state. The chemical transitions underlying these structural transitions were identified by varying nucleotide conditions and carrying out parallel stopped-flow kinetics assays. At saturating ATP, kinesin-1 spends half of each stepping cycle with one head bound, specifying a structural state for each of two rate-limiting transitions. Analysis of stepping kinetics in varying nucleotides shows that ATP binding is required to properly enter the one-head–bound state, and hydrolysis is necessary to exit it at a physiological rate. These transitions differ from the standard model in which ATP binding drives full docking of the flexible neck linker domain of the motor. Thus, this work defines a consensus sequence of mechanochemical transitions that can be used to understand functional diversity across the kinesin superfamily.Kinesin-1 is a motor protein that steps processively toward microtubule plus-ends, tracking single protofilaments and hydrolyzing one ATP molecule per step (1–6). Step sizes corresponding to the tubulin dimer spacing of 8.2 nm are observed when the molecule is labeled by its C-terminal tail (7–10) and to a two-dimer spacing of 16.4 nm when a single motor domain is labeled (4, 11, 12), consistent with the motor walking in a hand-over-hand fashion. Kinesin has served as an important model system for advancing single-molecule techniques (7–10) and is clinically relevant for its role in neurodegenerative diseases (13), making dissection of its step a popular ongoing target of study.Despite decades of work, many essential components of the mechanochemical cycle remain disputed, including (i) how much time kinesin-1 spends in a one-head–bound (1HB) state when stepping at physiological ATP concentrations, (ii) whether the motor waits for ATP in a 1HB or two-heads–bound (2HB) state, and (iii) whether ATP hydrolysis occurs before or after tethered head attachment (4, 11, 14–20). These questions are important because they are fundamental to the mechanism by which kinesins harness nucleotide-dependent structural changes to generate mechanical force in a manner optimized for their specific cellular tasks. Addressing these questions requires characterizing a transient 1HB state in the stepping cycle in which the unattached head is located between successive binding sites on the microtubule. This 1HB intermediate is associated with the force-generating powerstroke of the motor and underlies the detachment pathway that limits motor processivity. Optical trapping (7, 19, 21, 22) and single-molecule tracking studies (4, 8–11) have failed to detect this 1HB state during stepping. Single-molecule fluorescence approaches have detected a 1HB intermediate at limiting ATP concentrations (11, 12, 14, 15), but apart from one study that used autocorrelation analysis to detect a 3-ms intermediate (17), the 1HB state has been undetectable at physiological ATP concentrations.Single-molecule microscopy is a powerful tool for studying the kinetics of structural changes in macromolecules (23). Tracking steps and potential substeps for kinesin-1 at saturating ATP has until now been hampered by the high stepping rates of the motor (up to 100 s⁻¹), which necessitates high frame rates, and the small step size (8.2 nm), which necessitates high spatial precision (7). Here, we apply interferometric scattering microscopy (iSCAT), a recently established single-molecule tool with high spatiotemporal resolution (24–27) to directly visualize the structural changes underlying kinesin stepping. By labeling one motor domain in a dimeric motor, we detect a 1HB intermediate state in which the tethered head resides over the bound head for half the duration of the stepping cycle at saturating ATP. We further show that at physiological stepping rates, ATP binding is required to enter this 1HB state and that ATP hydrolysis is required to exit it. This work leads to a significant revision of the sequence and kinetics of mechanochemical transitions that make up the kinesin-1 stepping cycle and provides a framework for understanding functional diversity across the kinesin superfamily.     

From the Cover: Mother’s voice and heartbeat sounds elicit auditory plasticity in the human brain before full gestation

Brain development is largely shaped by early sensory experience. However, it is currently unknown whether, how early, and to what extent the newborn’s brain is shaped by exposure to maternal sounds when the brain is most sensitive to early life programming. The present study examined this question in 40 infants born extremely prematurely (between 25- and 32-wk gestation) in the first month of life. Newborns were randomized to receive auditory enrichment in the form of audio recordings of maternal sounds (including their mother’s voice and heartbeat) or routine exposure to hospital environmental noise. The groups were otherwise medically and demographically comparable. Cranial ultrasonography measurements were obtained at 30 ± 3 d of life. Results show that newborns exposed to maternal sounds had a significantly larger auditory cortex (AC) bilaterally compared with control newborns receiving standard care. The magnitude of the right and left AC thickness was significantly correlated with gestational age but not with the duration of sound exposure. Measurements of head circumference and the widths of the frontal horn (FH) and the corpus callosum (CC) were not significantly different between the two groups. This study provides evidence for experience-dependent plasticity in the primary AC before the brain has reached full-term maturation. Our results demonstrate that despite the immaturity of the auditory pathways, the AC is more adaptive to maternal sounds than environmental noise. Further studies are needed to better understand the neural processes underlying this early brain plasticity and its functional implications for future hearing and language development.One of the first acoustic stimuli we are exposed to before birth is the voice of the mother and the sounds of her heartbeat. As fetuses, we have substantial capacity for auditory learning and memory already in utero (1–5), and we are particularly tuned to acoustic cues from our mother (6–9). Previous research suggests that the innate preference for mother’s voice shapes the developmental trajectory of the brain (10, 11). Prenatal exposure to mother’s voice may therefore provide the brain with the auditory fitness necessary to process and store speech information immediately after birth (12, 13).There is evidence to suggest that prenatal exposure to the maternal voice and heartbeat sounds can pave the neural pathways in the brain for subsequent development of hearing and language skills (14). For example, the periodic perception of the low-frequency maternal heartbeat in the womb provides the fetus with an important rhythmic experience (15, 16) that likely establishes the neural basis for auditory entrainment and synchrony skills necessary for vocal, gestural, and gaze communication during mother–infant interactions (17, 18).Studies examining the neural response to the maternal voice soon after birth have found activation in posterior temporal regions, preferentially on the left side, as well as brain areas involved in emotional processing including the amygdala and orbito-frontal cortex (19). Similarly, Beauchemin et al. have found activation in language-related cortical regions when newborns listened to their mother’s voice, whereas a stranger’s voice seemed to activate more generic regions of the brain (20). In addition, Partanen et al. have shown that the neural response to maternal sounds depends on experience as full-term newborns react differentially to familiar vs. unfamiliar sounds they were exposed to as fetuses, suggesting correlation between the amount of prenatal exposure and brain activity (21). Taken together, the above studies suggest that the mother’s voice plays a special role in the early shaping of auditory and language areas of the brain.Numerous animal studies have shown that brain development relies on developmentally appropriate acoustic stimulation early in life (22–32). Auditory deprivation during critical periods can adversely affect brain maturation and lead to long-lasting neural despecialization in the auditory cortex (AC), whereas auditory enrichment in the early postnatal period can enhance neural sensitivity in the primary AC, as well as improve auditory recognition and discrimination abilities.Preterm infants are born during a critical period for auditory brain development. However, the maternal auditory nursery provided by the womb vanishes after a premature birth as the preterm newborn enters the neonatal intensive care unit (NICU). The abrupt transition of the fetus from the protected environment of the womb to the exposed environment of the hospital imposes significant challenges on the developing brain (33). These challenges have been associated with neuropathologic consequences, including reduction in regional brain volumes, white matter microstructural abnormalities, and poor cognitive and language outcomes in preterm compared with full-term newborns (34–41).Considering the acoustic gap between the NICU environment and the womb, it is not surprising that auditory brain development is compromised in preterm compared with full-term infants (42, 43). Numerous studies have suggested that the auditory environment available for preterm infants in the NICU may not be conducive for their neurodevelopment (44–47). These concerns are derived from the frequent reality that hospitalized preterm newborns are overexposed to loud, toxic, and unpredictable environmental noise generated by ventilators, infusion pumps, fans, telephones, pagers, monitors, and alarms (48–51), whereas at the same time they are also deprived of the low-frequency, patterned, and biologically familiar sounds of their mother’s voice and heartbeat, which they would otherwise be hearing in utero (33, 45). In addition, the hospital environment contains a significant amount of high-frequency electronic sounds (52, 53) that are less likely to be heard in the womb because of the sound attenuation provided by maternal tissues and fluid within the intrauterine cavity (54–56). Efforts to improve the hospital environment for preterm neonates have primarily focused on reducing hospital noise and maintaining a quiet environment. However, exposing medically fragile preterm newborns to low-frequency audio recordings of their mothers on a daily basis has been less acknowledged to be of necessity, and the extent to which such maternal sound exposure can influence brain maturation after an extremely premature birth has been a matter of much debate.The present study aimed to determine whether enriching the auditory environment for preterm newborns with authentic recordings of their mother’s voice and heartbeat sounds in the first month of life would result in structural alterations in the AC. The rationale driving this question lies in the fact that such enriched maternal sound stimulation would otherwise be present had the baby not been born prematurely.     

Kinesin processivity is gated by phosphate release

Kinesin-1 is a dimeric motor protein, central to intracellular transport, that steps hand-over-hand toward the microtubule (MT) plus-end, hydrolyzing one ATP molecule per step. Its remarkable processivity is critical for ferrying cargo within the cell: over 100 successive steps are taken, on average, before dissociation from the MT. Despite considerable work, it is not understood which features coordinate, or “gate,” the mechanochemical cycles of the two motor heads. Here, we show that kinesin dissociation occurs subsequent to, or concomitant with, phosphate (P_i) release following ATP hydrolysis. In optical trapping experiments, we found that increasing the steady-state population of the posthydrolysis ADP·P_i state (by adding free P_i) nearly doubled the kinesin run length, whereas reducing either the ATP binding rate or hydrolysis rate had no effect. The data suggest that, during processive movement, tethered-head binding occurs subsequent to hydrolysis, rather than immediately after ATP binding, as commonly suggested. The structural change driving motility, thought to be neck linker docking, is therefore completed only upon hydrolysis, and not ATP binding. Our results offer additional insights into gating mechanisms and suggest revisions to prevailing models of the kinesin reaction cycle.Since its discovery nearly 30 years ago (1), kinesin-1—the founding member of the kinesin protein superfamily—has emerged as an important model system for studying biological motors (2, 3). During “hand-over-hand” stepping, kinesin dimers alternate between a two–heads-bound (2-HB) state, with both heads attached to the microtubule (MT), and a one–head-bound (1-HB) state, where a single head, termed the tethered head, remains free of the MT (4, 5). The catalytic cycles of the two heads are maintained out of phase by a series of gating mechanisms, thereby enabling the dimer to complete, on average, over 100 steps before dissociating from the MT (6–8). A key structural element for this coordination is the neck linker (NL), a ∼14-aa segment that connects each catalytic head to a common stalk (9). In the 1-HB state, nucleotide binding is thought to induce a structural reconfiguration of the NL, immobilizing it against the MT-bound catalytic domain (2, 3, 10–17). This transition, called “NL docking,” is believed to promote unidirectional motility by biasing the position of the tethered head toward the next MT binding site (2, 3, 10–17). The completion of an 8.2-nm step (18) entails the binding of this tethered head to the MT, ATP hydrolysis, and detachment of the trailing head, thereby returning the motor to the ATP-waiting state (2, 3, 10–17). Prevailing models of the kinesin mechanochemical cycle (2, 3, 10, 14, 15, 17), which invoke NL docking upon ATP binding, explain the highly directional nature of kinesin motility and offer a compelling outline of the sequence of events following ATP binding. Nevertheless, these abstractions do not speak directly to the branching transitions that determine whether kinesin dissociates from the MT (off-pathway) or continues its processive reaction cycle (on-pathway). The distance moved by an individual motor before dissociating—the run length—is limited by unbinding from the MT. The propensity for a dimer to unbind involves a competition among multiple, force-dependent transitions in the two heads, which are not readily characterized by traditional structural or bulk biochemical approaches. Here, we implemented high-resolution single-molecule optical trapping techniques to determine transitions in the kinesin cycle that govern processivity.     

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).     

Early developmental emergence of human amygdala–prefrontal connectivity after maternal deprivation

Under typical conditions, medial prefrontal cortex (mPFC) connections with the amygdala are immature during childhood and become adult-like during adolescence. Rodent models show that maternal deprivation accelerates this development, prompting examination of human amygdala–mPFC phenotypes following maternal deprivation. Previously institutionalized youths, who experienced early maternal deprivation, exhibited atypical amygdala–mPFC connectivity. Specifically, unlike the immature connectivity (positive amygdala–mPFC coupling) of comparison children, children with a history of early adversity evidenced mature connectivity (negative amygdala–mPFC coupling) and thus, resembled the adolescent phenotype. This connectivity pattern was mediated by the hormone cortisol, suggesting that stress-induced modifications of the hypothalamic–pituitary–adrenal axis shape amygdala–mPFC circuitry. Despite being age-atypical, negative amygdala–mPFC coupling conferred some degree of reduced anxiety, although anxiety was still significantly higher in the previously institutionalized group. These findings suggest that accelerated amygdala–mPFC development is an ontogenetic adaptation in response to early adversity.Even brief exposure to stressful experiences early in life can have life-long impact on brain development and socioemotional functioning. Adverse or deprived caregiving is an example of a highly potent early life stressor in altricial species. Animal studies of maternal deprivation have demonstrated long-term effects on socioemotional and brain development (1–5), with particular influences on frontoamygdala circuitry. The timing of stress exposure and cellular properties of this circuitry may render it particularly vulnerable to early adversity (6). Consistently, rodent and nonhuman primate models show that the amygdala is highly susceptible to early environmental adversity due to its early structural development and readiness to respond to stressors (7–9). Human studies of early adverse caregiving have demonstrated structural volume abnormalities in the amygdala that were associated with increased trait anxiety and emotion dysregulation (10, 11) and increased amygdala reactivity to emotional stimuli (12).Abnormally rapid brain development following early adversity may be a response that reprioritizes developmental goals to match the demands of an adverse early environment. Early life caregiving adversity in rodents alters amygdala–medial prefrontal cortex (mPFC) circuits that in adulthood serve to regulate the activity of the amygdala (13, 14), perhaps through accelerating development of the circuitry. For example, maternal deprivation results in the early emergence of adult-like fear learning based in frontoamygdala circuitry (5) and earlier emergence of amygdala function (8, 15) and structural maturation (16). Maternal separation has also been associated with increased development of neurons in mPFC (17). Such changes can lead to adult-like fear extinction learning and mPFC-mediated down-regulation of the amygdala, even though as a group, these stressed animals can be more fearful and anxious (18). Maternal absence acts to accelerate amygdala–prefrontal functional development via premature elevations of glucocorticoids (8, 19), suggesting that maternal absence acts on amygdala-related circuitry through alterations of the hypothalamic–pituitary–adrenal (HPA) axis, consistent with the established close relationship between the amygdala and HPA axis (20). Whether a similar neurohormonal process explains affective development in humans following early maternal absence is currently unknown.Whereas the hypothesis of accelerated development has not been tested in humans, the conservation of frontoamygdala phenotypes across species would predict a common mechanism through which early life stress influences neuroaffective development. Such changes are important to understand given affective behaviors, including emotion regulation and fear learning, that rely on the amygdala and its connections with mPFC (21–27). In typical human development, childhood and adolescence is a period of large change in frontoamygdala phenotypes (28, 29), with amygdala–mPFC connectivity being markedly immature during childhood (28). The transition from childhood into adolescence has been characterized by a developmental shift in amygdala–mPFC functional connectivity. Specifically, adolescents and adults exhibit inverse (negative) correlations between amygdala and mPFC in response to emotional stimuli, a pattern of connectivity that has been characterized as indexing top-down inhibitory connections (24, 27, 28). Children exhibit a positive amygdala–mPFC coupling phenotype (28) that is associated with greater emotional reactivity, which is typically characteristic of young children. This shift from an immature (positive) amygdala–mPFC coupling phenotype to a mature (negative) coupling phenotype parallels age-related attenuation of amygdala signal and mediates maturation of emotional behavior (28). The goal of the current study was to examine childhood amygdala–mPFC phenotypes following early life adversity with the hypothesis that early life adversity would accelerate development of amygdala–mPFC circuitry. We examined the effects of early adversity among previously institutionalized (PI) children who have a history of early life maternal deprivation.