Orbitofrontal cortex and dorsal striatum functional connectivity predicts incubation of opioid craving after voluntary abstinence

We recently introduced a rat model of incubation of opioid craving after voluntary abstinence induced by negative consequences of drug seeking. Here, we used resting-state functional MRI to determine whether longitudinal functional connectivity changes in orbitofrontal cortex (OFC) circuits predict incubation of opioid craving after voluntary abstinence. We trained rats to self-administer for 14 d either intravenous oxycodone or palatable food. After 3 d, we introduced an electric barrier for 12 d that caused cessation of reward self-administration. We tested the rats for oxycodone or food seeking under extinction conditions immediately after self-administration training (early abstinence) and after electric barrier exposure (late abstinence). We imaged their brains before self-administration and during early and late abstinence. We analyzed changes in OFC functional connectivity induced by reward self-administration and electric barrier–induced abstinence. Oxycodone seeking was greater during late than early abstinence (incubation of oxycodone craving). Oxycodone self-administration experience increased OFC functional connectivity with dorsal striatum and related circuits that was positively correlated with incubated oxycodone seeking. In contrast, electric barrier–induced abstinence decreased OFC functional connectivity with dorsal striatum and related circuits that was negatively correlated with incubated oxycodone seeking. Food seeking was greater during early than late abstinence (abatement of food craving). Food self-administration experience and electric barrier–induced abstinence decreased or maintained functional connectivity in these circuits that were not correlated with abated food seeking. Opposing functional connectivity changes in OFC with dorsal striatum and related circuits induced by opioid self-administration versus voluntary abstinence predicted individual differences in incubation of opioid craving.

High rates of relapse perpetuate opioid addiction and are a major obstacle in addressing the US opioid crisis (1, 2). In humans, relapse and craving are often triggered by reexposure to cues and contexts previously associated with drug use (3, 4). In rats with a history of opioid (heroin or oxycodone) self-administration, opioid seeking progressively increases or incubates during homecage forced abstinence (5, 6). However, a main limitation of most current animal models of incubation of drug craving and relapse is that prior to relapse testing, abstinence is experimenter-imposed or forced (6, 7). This contrasts with the human condition where abstinence is often self-imposed due to adverse consequences of drug seeking (8).Based on these considerations, we recently introduced a rat model of incubation of oxycodone craving after “voluntary abstinence” induced by adverse consequences of drug seeking (9). The model is based on the electric barrier conflict model (10) that was more recently adapted to study relapse to drug seeking (11). In our modified model, we induce abstinence by introducing an electric barrier near the drug-paired lever that rats must cross to gain access to oxycodone. As shock intensity increases over days, rats decrease their oxycodone intake and eventually stop self-administering oxycodone. We then assess relapse to drug seeking during early and late abstinence in the absence of oxycodone or shock. We found that oxycodone seeking in the relapse tests is greater after 15 and 30 abstinence days than after 1 d, demonstrating incubation of oxycodone craving after electric barrier–induced abstinence (9). Unexpectedly, in both sexes, the incubation effect was stronger after electric barrier–induced abstinence than after homecage forced abstinence (9).In the present study, we determined whether functional connectivity changes that develop during oxycodone self-administration and subsequent electric barrier–induced abstinence would predict individual differences in incubation of oxycodone craving. We measured functional connectivity of orbitofrontal cortex (OFC)–related circuits using resting-state functional MRI (fMRI) (12, 13), a noninvasive brain imaging technique that longitudinally measures synchronous activity between brain regions. Resting-state fMRI has been used to characterize the relationship between brain activity and behaviors in both humans (14, 15) and laboratory animals (16, 17). Our group developed a rat resting-state fMRI protocol (18) and used it to investigate neural mechanisms in rodent models of neurological and psychiatric disorders (17, 19). Here, we compared the fMRI measures of rats with a history of oxycodone self-administration to drug-naïve rats trained to self-administer palatable food pellets that we use in our studies on relapse to drug seeking after food choice-induced abstinence (20).We focused on the OFC as the seed region for assessing longitudinal functional connectivity changes because previous human imaging studies reported that craving induced by heroin cues is associated with increased OFC activity (21, 22). In rats, cue-induced reinstatement of heroin seeking after extinction (23), incubation of heroin seeking after forced abstinence (24, 25), and relapse to fentanyl seeking after food choice-induced abstinence (26) are associated with increased Fos expression and other immediate early genes in OFC. Additionally, reversible inactivation of OFC decreases incubated heroin and oxycodone seeking after forced abstinence (25, 27) and relapse to fentanyl seeking after food choice-induced abstinence (26). Finally, heroin self-administration causes long-lasting impairments of OFC-mediated decision-making processes (28).     

Prestimulus functional connectivity determines pain perception in humans

Pain is a highly subjective experience that can be substantially influenced by differences in individual susceptibility as well as personality. How susceptibility to pain and personality translate to brain activity is largely unknown. Here, we report that the functional connectivity of two key brain areas before a sensory event reflects the susceptibility to a subsequent noxious stimulus being perceived as painful. Specifically, the prestimulus connectivity among brain areas related to the subjective perception of the body and to the modulation of pain (anterior insular cortex and brainstem, respectively) determines whether a noxious event is perceived as painful. Further, these effects of prestimulus connectivity on pain perception covary with pain-relevant personality traits. More anxious and pain-attentive individuals display weaker descending connectivity to pain modulatory brain areas. We conclude that variations in functional connectivity underlie personality-related differences in individual susceptibility to pain.     

Predicting human resting-state functional connectivity from structural connectivity

In the cerebral cortex, the activity levels of neuronal populations are continuously fluctuating. When neuronal activity, as measured using functional MRI (fMRI), is temporally coherent across 2 populations, those populations are said to be functionally connected. Functional connectivity has previously been shown to correlate with structural (anatomical) connectivity patterns at an aggregate level. In the present study we investigate, with the aid of computational modeling, whether systems-level properties of functional networks—including their spatial statistics and their persistence across time—can be accounted for by properties of the underlying anatomical network. We measured resting state functional connectivity (using fMRI) and structural connectivity (using diffusion spectrum imaging tractography) in the same individuals at high resolution. Structural connectivity then provided the couplings for a model of macroscopic cortical dynamics. In both model and data, we observed (i) that strong functional connections commonly exist between regions with no direct structural connection, rendering the inference of structural connectivity from functional connectivity impractical; (ii) that indirect connections and interregional distance accounted for some of the variance in functional connectivity that was unexplained by direct structural connectivity; and (iii) that resting-state functional connectivity exhibits variability within and across both scanning sessions and model runs. These empirical and modeling results demonstrate that although resting state functional connectivity is variable and is frequently present between regions without direct structural linkage, its strength, persistence, and spatial statistics are nevertheless constrained by the large-scale anatomical structure of the human cerebral cortex.     

Similarity in functional brain connectivity at rest predicts interpersonal closeness in the social network of an entire village

People often have the intuition that they are similar to their friends, yet evidence for homophily (being friends with similar others) based on self-reported personality is inconsistent. Functional connectomes—patterns of spontaneous synchronization across the brain—are stable within individuals and predict how people tend to think and behave. Thus, they may capture interindividual variability in latent traits that are particularly similar among friends but that might elude self-report. Here, we examined interpersonal similarity in functional connectivity at rest—that is, in the absence of external stimuli—and tested if functional connectome similarity is associated with proximity in a real-world social network. The social network of a remote village was reconstructed; a subset of residents underwent functional magnetic resonance imaging. Similarity in functional connectomes was positively related to social network proximity, particularly in the default mode network. Controlling for similarities in demographic and personality data (the Big Five personality traits) yielded similar results. Thus, functional connectomes may capture latent interpersonal similarities between friends that are not fully captured by commonly used demographic or personality measures. The localization of these results suggests how friends may be particularly similar to one another. Additionally, geographic proximity moderated the relationship between neural similarity and social network proximity, suggesting that such associations are particularly strong among people who live particularly close to one another. These findings suggest that social connectivity is reflected in signatures of brain functional connectivity, consistent with the common intuition that friends share similarities that go beyond, for example, demographic similarities.

Human social networks exhibit a high degree of homophily, such that individuals who are close together in their social network (i.e., friends or friends of friends, rather than people further removed from one another in social ties) tend to be exceptionally similar to one another with respect to physical and demographic traits, such as age, gender, and ethnicity (1). Yet, a common intuition is that friends are similar to each other in ways that go beyond readily observable and relatively coarse characteristics, such as demographics. The most common method to assess such similarities is the administration of self-report surveys measuring how people tend to think and behave (i.e., personality). However, past research has found no evidence, or only relatively weak evidence, for a relationship between similarity in personality and social network proximity (e.g., refs. 2 and 3).A separate body of research using functional MRI (fMRI) has shown that patterns of functional brain connectivity at rest comprise person-specific “fingerprints” that capture interindividual variability in a wide range of social, cognitive, and behavioral tendencies and capacities (4–10). These resting-state “functional connectomes” have also been shown to be predictive of individual differences in self-reported personality (11). Given that functional connectomes are predictive of an array of cognitive and behavioral phenotypes, interindividual similarities in functional connectomes may reflect similarities in how friends, and more generally people close to one another in their social network, think and behave. Such similarities may include those that are not sufficiently captured by widely used self-report surveys, such as measures of personality. Thus, fMRI can provide a window into the types of latent similarities that are associated with friendship. This approach is particularly promising given recent research integrating task-based fMRI and social network analysis, which has shown, for example, that when viewing videos, friends, and more generally, people closer together in their real-world social network, have exceptionally similar neural responses, which could be indicative of similarities in how friends attend to (12), understand (13), and interpret (14) the world (15, 16). Taken together with other recent work (17), these findings highlight the promise of integrating social network analysis and tools from cognitive neuroscience to improve our understanding of how individuals shape and are shaped by the real-world social networks in which they are embedded.Here, we tested if patterns of neural responding at rest (e.g., individuals’ functional connectomes) are associated with proximity between individuals in the social network of an entire village (Fig. 1). Specifically, we tested the hypothesis that greater similarity in individuals’ functional connectomes would be associated with greater proximity between those individuals in the social network. Given the large body of research demonstrating that links between interpersonal similarity in a number of cognitive, affective, and behavioral outcomes and social network proximity disappear beyond three or four “degrees of separation” (18–26), we focused our analyses on people four or fewer “degrees of separation” from one another in the village’s social network (Materials and Methods). We also tested if such relationships would persist after controlling not only for similarities in demographic characteristics but also for similarities in self-reported personality (i.e., the Big Five personality traits: extraversion, neuroticism, agreeableness, conscientiousness, and openness/intellect), which are thought to capture stable individual differences in people’s cognitive, affective, and behavioral tendencies (27). Although self-report personality questionnaires capture much variation in how people tend to think and behave, there is considerable variance in such tendencies that is unaccounted for by such questionnaires (28) and that may be encoded in individuals’ functional connectomes. Here, we tested if similarity in such latent traits is associated with proximity in a friendship network. Additionally, we examined which brain networks were particularly strongly associated with social network proximity to inform interpretations of the psychological significance of these results, as well as predictions for future research. Finally, given the well-established relationship between the physical distance between people and their distance from one another in social ties, we tested if geographic distance moderates the relationship between neural similarity and social network proximity.Open in a separate windowFig. 1.Social network characterization. Residents of a rural village located on a small island completed a survey in which they indicated their social ties with other individuals in their community. The complete social network (n = 798) of the village was reconstructed using this data, and a subset of residents (red nodes; n = 64) participated in the fMRI study. Lines (“edges”) indicate the existence of a reciprocated or unreciprocated social tie between individuals. For visualization purposes, unweighted edges were used to depict social ties. However, in our analyses, edges were weighted by individuals’ ratings of emotional closeness with one another (Materials and Methods).     

Reduced fronto-cerebellar functional connectivity in chronic alcoholic patients

Background: Alcohol dependence is associated with neurocognitive deficits related to neuropathological changes in structure, metabolism, and function of the brain. Impairments of motor functioning in alcoholics have been attributed to well‐characterized neuropathological brain abnormalities in cerebellum. Methods: Using functional magnetic resonance imaging (fMRI), we studied in vivo the functional connectivity between cerebellar and cortical brain regions. Participants were 10 uncomplicated chronic alcoholic patients studied after 5 to 7 days of abstinence when signs of withdrawal had abated and 10 matched healthy controls. We focused on regions of prefrontal, frontal, temporal, and parietal cortex that exhibited an fMRI response associated with nondominant hand finger tapping in the patients but not in the controls. We predicted that fronto‐cerebellar functional connectivity would be diminished in alcoholics compared with controls. Results: Functional connectivity in a circuit involving premotor areas (Brodmann Area 6) and Lobule VI of the superior cerebellum was reduced in the patients compared with the controls. Functional connectivity was also reduced in a circuit involving prefrontal cortex (Brodmann Area 9) and Lobule VIII of the inferior cerebellum. Reductions in connectivity were specific to fronto‐cerebellar circuits and were not found in other regions examined. Conclusions: Our findings show a pattern in recently abstinent alcoholic patients of specific deficits in functional connectivity and recruitment of additional brain regions for the performance of a simple finger‐tapping task. A small sample, differences in smoking, and a brief abstinence period preclude definitive conclusions, but this pattern of diminished fronto‐cerebellar functional connectivity is highly compatible with the characteristic neuropathological lesions documented in alcoholics and may reflect brain dysfunction associated with alcoholism.     

Functional connectivity density mapping

Brain networks with energy-efficient hubs might support the high cognitive performance of humans and a better understanding of their organization is likely of relevance for studying not only brain development and plasticity but also neuropsychiatric disorders. However, the distribution of hubs in the human brain is largely unknown due to the high computational demands of comprehensive analytical methods. Here we propose a 10³ times faster method to map the distribution of the local functional connectivity density (lFCD) in the human brain. The robustness of this method was tested in 979 subjects from a large repository of MRI time series collected in resting conditions. Consistently across research sites, a region located in the posterior cingulate/ventral precuneus (BA 23/31) was the area with the highest lFCD, which suggest that this is the most prominent functional hub in the brain. In addition, regions located in the inferior parietal cortex (BA 18) and cuneus (BA 18) had high lFCD. The variability of this pattern across subjects was <36% and within subjects was 12%. The power scaling of the lFCD was consistent across research centers, suggesting that that brain networks have a “scale-free” organization.     

Individual variability in functional connectivity predicts performance of a perceptual task

People differ in their ability to perform novel perceptual tasks, both during initial exposure and in the rate of improvement with practice. It is also known that regions of the brain recruited by particular tasks change their activity during learning. Here we investigate neural signals predictive of individual variability in performance. We used resting-state functional MRI to assess functional connectivity before training on a novel visual discrimination task. Subsequent task performance was related to functional connectivity measures within portions of visual cortex and between visual cortex and prefrontal association areas. Our results indicate that individual differences in performing novel perceptual tasks can be related to individual differences in spontaneous cortical activity.     

Ongoing dynamics in large-scale functional connectivity predict perception

Most brain activity occurs in an ongoing manner not directly locked to external events or stimuli. Regional ongoing activity fluctuates in unison with some brain regions but not others, and the degree of long-range coupling is called functional connectivity, often measured with correlation. Strength and spatial distributions of functional connectivity dynamically change in an ongoing manner over seconds to minutes, even when the external environment is held constant. Direct evidence for any behavioral relevance of these continuous large-scale dynamics has been limited. Here, we investigated whether ongoing changes in baseline functional connectivity correlate with perception. In a continuous auditory detection task, participants perceived the target sound in roughly one-half of the trials. Very long (22–40 s) interstimulus intervals permitted investigation of baseline connectivity unaffected by preceding evoked responses. Using multivariate classification, we observed that functional connectivity before the target predicted whether it was heard or missed. Using graph theoretical measures, we characterized the difference in functional connectivity between states that lead to hits vs. misses. Before misses compared with hits and task-free rest, connectivity showed reduced modularity, a measure of integrity of modular network structure. This effect was strongest in the default mode and visual networks and caused by both reduced within-network connectivity and enhanced across-network connections before misses. The relation of behavior to prestimulus connectivity was dissociable from that of prestimulus activity amplitudes. In conclusion, moment to moment dynamic changes in baseline functional connectivity may shape subsequent behavioral performance. A highly modular network structure seems beneficial to perceptual efficiency.The brain is highly active in a continuous manner, and much of neural activity is not directly locked to external events or stimuli. This continuous brain activity is spatiotemporally organized into a functional connectivity architecture that comprises several large-scale networks. Large-scale networks span different cerebral lobes and include subcortical structures (1). The regions comprised in such networks commonly coactivate together in response to task demands (2), but they also show correlated and spontaneous activity fluctuations when no changes occur in the external environment. The functional connectivity architecture ensuing from these activity cofluctuations largely persists across all mental states, including various tasks, resting wakefulness, and sleep, albeit showing some degree of modulation across these states (3, 4).Strength and spatial distributions of functional connectivity within this architecture are not, however, stationary across time. At the spatial level of large-scale networks, functional connectivity shows prominent changes over the range of seconds to minutes (5). These so-called infraslow timescales and the spatial distribution of large-scale networks can be particularly well-investigated using functional MRI (fMRI). We refer to the nonstationarity of functional connectivity as ongoing dynamics. The notion of ongoing refers to dynamics that are not brought about by particular external events, such as stimuli or cues. Such large-scale ongoing network dynamics are thought to be crucial for the brain to explore a large space of dynamic functional capabilities (6). This potential functional importance has recently sparked interest in ongoing connectivity dynamics (5). The most common approach to studying ongoing dynamics in connectivity with fMRI has been to measure connectivity in time windows sliding through a prolonged task-free resting state (reviewed in ref. 5). These investigations have established the nonstationarity of large-scale functional connectivity that previously had been largely neglected. Some studies have shown characteristic changes of dynamic connectivity in different patient populations (7). However, none of these resting-state studies have directly investigated the functional consequences of ongoing dynamics during performance (in other words, how moment to moment changes in baseline functional connectivity relate to cognition and behavior). Furthermore, the ongoing dynamics of functional connectivity may be modulated by cognitive task context, further motivating investigation of the behavioral importance during task beyond the resting state.Several lines of research call for a dedicated investigation of this question. Slow ongoing fluctuations in regional prestimulus baseline activity amplitudes (in fMRI or electrophysiological recordings) in task-relevant sensory or motor regions (8–12) as well as entire large-scale brain networks (12–14) correlate with evoked neural response strength and subsequent behavior. It is not clear whether, beyond fluctuations in regional ongoing activity amplitude, the correlation of such amplitude fluctuations across large-scale network regions (i.e., connectivity) relates to behavioral variability. Some prior work seems to suggest that ongoing connectivity dynamics between two task-relevant regions may turn behaviorally relevant (15). Also, in addition to these effects in infraslow timescales, some electrophysiological recordings indicate a behavioral relevance for phase synchrony between individual regions of interest and the rest of the brain at faster timescales (16). Building on this so-far rather sparse evidence, we here sought to investigate behavioral effects from ongoing dynamics in large-scale functional connectivity, calling on tools of multivariate classification for trial by trial prediction of behavior and graph theory for a more detailed and spatially comprehensive characterization.We analyzed behavioral outcome on a trial by trial basis as a function of dynamic connectivity states before stimulus presentation. We tested whether large-scale functional connectivity states predict perception of a sparse and irregularly appearing stimulus. To investigate ongoing nontask-locked changes in baseline connectivity, we minimized contributions from stimulus-evoked activity. This study, therefore, extends beyond important previous investigations of connectivity dynamics that occur after changes in the external environment (such as stimulation, cues, instructions, or feedback) at infraslow (17–20) and fast electrophysiological timescales (21, 22). Specifically, we asked (i) whether ongoing dynamics of large-scale functional connectivity relate to perceptual performance and (ii) which properties of baseline functional connectivity distinguish brain states that support perceptual accuracy from those that do not.Eleven blindfolded participants performed a detection task on an auditory broadband stimulus (500 ms). They pressed a button whenever they heard the sound during two to three 20-min-long fMRI runs. The stimulus was presented at the individually determined detection threshold and repeated very sparsely at highly variable interstimulus intervals ranging from 20 to 40 s (Fig. 1A). This dataset has previously been used to investigate behavioral correlates of baseline activity amplitudes and enables this investigation to directly compare effects from prestimulus activity amplitudes with those from prestimulus connectivity (12). The unusually long interstimulus interval design allowed us to focus all analyses on the prestimulus time after excluding the hemodynamic response evoked by the previous stimulus. To answer our first question of whether prestimulus baseline connectivity predicts behavior, we applied trial by trial classification of subsequent perceptual outcome based on patterns of functional connectivity before stimulus presentation. To address our second question and determine what characterizes the difference across these brain states, we modeled connectivity before hits and before misses as separate graphs using tools of complex network theory and compared graph metrics between these states.Open in a separate windowFig. 1.(A) Experimental design of a threshold-level auditory stimulus presented on top of background scanner noise at very long, unpredictable interstimulus intervals (ISI, 20–40 s). Participants listened for the faint target sound continuously throughout 20-min runs and pressed a response button whenever they perceived the target. (B) Illustration of baseline time segments unaffected by evoked responses that were defined as appropriate for analysis of ongoing functional connectivity (marked in gray). The illustrated blood-oxygen-level-dependent (BOLD) hemodynamic response peaks at 6 s, reaches maximum poststimulus undershoot at 12 s, and returns to baseline before 16 s relative to stimulus onset according to finite impulse response estimation of the brain response to this stimulus in the same data in 10 bilateral brain areas (12). Baseline segments started after 16 s poststimulus and ended 1 s after the next stimulus onset. Baseline segments shorter than 6 s in length (interstimulus interval <22 s) were excluded. For classification analyses requiring trial by trial data, data were further restricted to baseline segments that were at least 15 s long (10 image volumes and interstimulus intervals ≥31 s). In this exemplary 2-min period of the task, four stimuli (marked as solid vertical lines) occur at interstimulus intervals of 40 (maximum in this design), 20 (minimum), and 32 s. Note that, for simplification, this illustration does not depict the spontaneous signal fluctuations during the baseline period and the ensuing variability in stimulus-evoked hemodynamic responses that are at the heart of this study.     

Intrinsic functional architecture predicts electrically evoked responses in the human brain

Adaptive brain function is characterized by dynamic interactions within and between neuronal circuits, often occurring at the time scale of milliseconds. These complex interactions between adjacent and noncontiguous brain areas depend on a functional architecture that is maintained even in the absence of input. Functional MRI studies carried out during rest (R-fMRI) suggest that this architecture is represented in low-frequency (<0.1 Hz) spontaneous fluctuations in the blood oxygen level-dependent signal that are correlated within spatially distributed networks of brain areas. These networks, collectively referred to as the brain's intrinsic functional architecture, exhibit a remarkable correspondence with patterns of task-evoked coactivation as well as maps of anatomical connectivity. Despite this striking correspondence, there is no direct evidence that this intrinsic architecture forms the scaffold that gives rise to faster processes relevant to information processing and seizure spread. Here, we demonstrate that the spatial distribution and magnitude of temporally correlated low-frequency fluctuations observed with R-fMRI during rest predict the pattern and magnitude of corticocortical evoked potentials elicited within 500 ms after single-pulse electrical stimulation of the cerebral cortex with intracranial electrodes. Across individuals, this relationship was found to be independent of the specific regions and functional systems probed. Our findings bridge the immense divide between the temporal resolutions of these distinct measures of brain function and provide strong support for the idea that the low-frequency signal fluctuations observed with R-fMRI maintain and update the intrinsic architecture underlying the brain's repertoire of functional responses.     

Estimated functional capacity predicts mortality in older adults

OBJECTIVES: To examine associations between functional capacity estimated from cardiorespiratory fitness (CRF) and mortality risks in adults aged 60 and older.
DESIGN: Prospective study, averaging 13.6 years follow-up.
SETTING: Preventive medical clinic.
PARTICIPANTS: Four thousand sixty adults who completed preventive medical examinations between 1971 and 2001; 24.7% women, mean age±standard deviation 64.6±4.9, body mass index (BMI) 25.9±3.8 kg/m².
MEASUREMENTS: CRF was quantified as metabolic equivalents (METs) achieved during maximal treadmill exercise. The lowest 20% of the age- and sex-specific MET distribution was defined as having low CRF, the middle 40% moderate CRF, and the upper 40% high CRF. Cox regression was used to estimate death rates (per 1,000 person-years), hazard ratios (HRs), and their 95% confidence intervals (CIs).
RESULTS: Nine hundred eighty-nine deaths occurred during follow-up. Death rates adjusted for age, sex, and examination year were 30.9, 18.3, and 13.4 for all causes ( P <.001); 15.9, 8.6, and 5.4 for cardiovascular disease (CVD) ( P <.001); and 6.1, 4.9, and 4.2 for cancer ( P =.04) for subjects with low, moderate, and high CRF, respectively. After adjusting for smoking, abnormal electrocardiograms at rest or while exercising, percentage of age-predicted maximal heart rate achieved during exercise testing, baseline medical conditions, BMI, hypercholesterolemia, and family CVD and cancer history, subjects with high CRF had notably lower mortality risk than those with low CRF from all causes (HR=0.59, 95% CI=0.47–0.74) and from CVD (HR=0.57, 95% CI=0.41–0.80).
CONCLUSION: CRF is an important independent predictor of death in older adults. The results add to the existing evidence that promoting physical activity in older adults provides substantial health benefits, even in the oldest old.     

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.     

Specialization and integration of functional thalamocortical connectivity in the human infant

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

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.     

Inter-hemispheric functional connectivity disruption in children with prenatal alcohol exposure

Background: MRI studies, including recent diffusion tensor imaging (DTI) studies, have shown corpus callosum abnormalities in children prenatally exposed to alcohol, especially in the posterior regions. These abnormalities appear across the range of fetal alcohol spectrum disorders (FASD). Several studies have demonstrated cognitive correlates of callosal abnormalities in FASD including deficits in visual‐motor skill, verbal learning, and executive functioning. The goal of this study was to determine whether inter‐hemispheric structural connectivity abnormalities in FASD are associated with disrupted inter‐hemispheric functional connectivity and disrupted cognition. Methods: Twenty‐one children with FASD and 23 matched controls underwent a 6‐minute resting‐state functional MRI scan as well as anatomical imaging and DTI. Using a semi‐automated method, we parsed the corpus callosum and delineated 7 inter‐hemispheric white matter tracts with DTI tractography. Cortical regions of interest (ROIs) at the distal ends of these tracts were identified. Right–left correlations in resting fMRI signal were computed for these sets of ROIs, and group comparisons were made. Correlations with facial dysmorphology, cognition, and DTI measures were computed. Results: A significant group difference in inter‐hemispheric functional connectivity was seen in a posterior set of ROIs, the para‐central region. Children with FASD had functional connectivity that was 12% lower than in controls in this region. Subgroup analyses were not possible owing to small sample size, but the data suggest that there were effects across the FASD spectrum. No significant association with facial dysmorphology was found. Para‐central functional connectivity was significantly correlated with DTI mean diffusivity, a measure of microstructural integrity, in posterior callosal tracts in controls but not in FASD. Significant correlations were seen between these structural and functional measures, and Wechsler perceptual reasoning ability. Conclusions: Inter‐hemispheric functional connectivity disturbances were observed in children with FASD relative to controls. The disruption was measured in medial parietal regions (para‐central) that are connected by posterior callosal fiber projections. We have previously shown microstructural abnormalities in these same posterior callosal regions, and the current study suggests a possible relationship between the two. These measures have clinical relevance as they are associated with cognitive functioning.     

Impaired brain networks functional connectivity after acute mild hypoxia

This study aimed to analyze the changes in brain networks functional connectivity of pilots exposed to simulated hypoxia using resting-state functional magnetic resonance imaging (fMRI). A total of 35 healthy male pilots exposed to 14.5% oxygen concentration (corresponding to an altitude of 3000 m) underwent resting-state fMRI scans. The independent component analysis (ICA) approach was used to analyze changes in the resting-state brain networks functional connectivity of pilots after hypoxic exposure, and 9 common components in brain functional networks were identified. In the functional connections that showed significant group differences, linear regression was used to examine the association between functional connectivity and clinical characteristics. The brain networks functional connectivity after hypoxia exposure decreased significantly, including the left frontoparietal network and visual network 1-area, left frontoparietal network and visual network 2-area, right frontoparietal network and visual network 2-area, dorsal attention network and ventral attention network, dorsal attention network and auditory network, and ventral attention network and visual network 1-area. We found no correlation between the altered functional connectivity and arterial oxygen saturation level. Our findings provide insights into the mechanisms underlying hypoxia-induced cognitive impairment in pilots.     

Disrupted hypothalamic functional connectivity related to cognitive impairment after diffuse axonal injury

This study aims to investigate whether there is imaging evidence of disrupted hypothalamic functional connectivity (FC) in patients with diffuse axonal injury (DAI) and relationships with cognitive impairment.Resting-state functional magnetic resonance imaging (fMRI) data were acquired from acute patients with diagnosed DAI (n = 30) and healthy controls (HC) (n = 30). We first assessed hypothalamic FC with seed-based analysis. Furthermore, the lateral and medial hypothalamic seed was selected to show distinct functional connectivity in DAI. In addition, partial correlation was used to measure the clinical associations with the altered hypothalamic FC in DAI patients.Compared with HC, DAI group showed significantly increased hypothalamic FC with superior temporal gyrus, and the regions around the operculum. Furthermore, there was a significant negative correlation between the connectivity coefficient of hypothalamus to right and left superior temporal gyrus and the disability rating scale scores in DAI group. When the seed regions were divided into lateral and medial hypothalamus, except for increased connectivity of medial hypothalamus (P < .01 with correction), we more observed that decreased left lateral hypothalamic connectivity was positively correlated with mini-mental state examination (MMSE) scores.Our results suggest that there are alterations of hypothalamic FC in DAI and offer further understanding of clinical symptoms including related cognitive impairment.