Connectivity reveals relationship of brain areas for reward-guided learning and decision making in human and monkey frontal cortex

Reward-guided decision-making depends on a network of brain regions. Among these are the orbitofrontal and the anterior cingulate cortex. However, it is difficult to ascertain if these areas constitute anatomical and functional unities, and how these areas correspond between monkeys and humans. To address these questions we looked at connectivity profiles of these areas using resting-state functional MRI in 38 humans and 25 macaque monkeys. We sought brain regions in the macaque that resembled 10 human areas identified with decision making and brain regions in the human that resembled six macaque areas identified with decision making. We also used diffusion-weighted MRI to delineate key human orbital and medial frontal brain regions. We identified 21 different regions, many of which could be linked to particular aspects of reward-guided learning, valuation, and decision making, and in many cases we identified areas in the macaque with similar coupling profiles.As humans we make decisions by taking into account different types of information, weighing our options carefully, and eventually coming to a conclusion. We then learn from witnessing the outcome of our decisions. Human functional MRI (fMRI) has had a major impact on elucidating the neural networks mediating decision making and learning, but key insights can only be obtained in neural recording, stimulation, and focal lesion studies conducted in animal models, such as the macaque. Combining insights from human fMRI and animal studies is, however, not straightforward because there is uncertainty about basic issues, such as anatomical and functional correspondences between species (1). For example, although there are many reports of decision value-related activity in the human ventromedial prefrontal cortex (vmPFC) (2, 3), it is unclear whether they can be related to reports of reward-related activity either on the ventromedial surface of the frontal lobe (4, 5), in the adjacent medial orbitofrontal sulcus (6), or indeed to any macaque brain area. It is claimed that some areas implicated in reward-guided decision making and learning, such as parts of anterior cingulate cortex (ACC), are not found in macaques (7), but such theories have never been formally tested.In addition, there is uncertainty about the basic constituent components of decision-making and learning circuits. To return to the example of the vmPFC, although this region is often contrasted with similarly large subdivisions of the frontal cortex, such as the lateral orbitofrontal cortex (lOFC) and ACC (8), it is unclear whether, and if so how, it should be decomposed into further subdivisions. Moreover, there are sometimes fundamental disagreements about how brain areas contribute to decision making and learning. For example, it has been claimed both that the ACC does (9–11) and does not (12) contribute to reward-based decision making and that it is concerned with distinct processes for task control, error detection, and conflict resolution (13, 14). Reliable identification and location of ACC subcomponent regions could assist the resolution of such debates.In the present study we formally compared brain regions implicated in reward-guided decision making and learning in humans and monkeys, and attempted to identify their key subdivisions in relation to function (Fig. 1). We used fMRI in 25 monkeys and 38 humans to delineate the functional interactions of “decision-making regions” with other areas in the brain while subjects were at rest. Such interactions are reliant on anatomical connections between areas (15) and determine the information an area has access to and the way it can influence other areas, and thereby behavior. Each region of the brain has a defining set of interactions, a connectional or interactional “finger-print” (16), that can be compared across species (17–19). We focused on areas throughout the entire medial and orbital frontal lobe, including the ACC, lOFC, vmPFC, and frontal pole (FP) that have been related to decision making in humans and monkeys. The results suggested areal correspondences between species, as well as finer functional fractionations within regions than previously assumed. In a second step we used a complementary technique, diffusion-weighted (DW) MRI, to confirm the existence of 21 distinct component regions within the human medial and orbitofrontal cortex. The results suggest that every day human decision making capitalizes on a neural apparatus similar to that supporting decision making in monkeys.Open in a separate windowFig. 1.(A) Overall approach of the study. fMRI analyses in 38 humans and 25 macaques were used to establish the whole-brain functional connectivity of regions in medial and orbital frontal cortex identified with reward-guided learning and decision making in the two species. The example shows the macaque brain regions that have a similar coupling profile to a human vmPFC region identified in a decision-making study (27). Reproduced from ref. 27, with permission from Macmillan Publishers Ltd, Nature Neuroscience. (B) Each region’s functional connectivity with 23 key regions was then determined and (C) summarized as a functional connectivity fingerprint. (D) Once the functional connectivity fingerprint of a human brain area was established it was compared with the functional connectivity fingerprints of 380 ROIs in macaque orbital and medial frontal cortex (one example is shown here) by calculating the summed absolute difference [the “Manhattan” or “city-block” distance (17–19) of the coupling scores]. (E) Examples of the functional connectivity fingerprints for a human (blue) and a monkey (red) brain area. Most monkey ROIs matched human areas relatively poorly and extremely good and extremely bad matches were relatively rare. We used two SDs below the mean of this distribution of summed absolute differences as a cut-off to look for “significantly” good human to monkey matches. (F) A heat map summarizing the degree of correspondence between the functional connectivity patterns of each voxel in the macaque and the human brain region shown in A. Warm red areas indicate macaque voxels that correspond most strongly. (G) Complementary parts of the investigation started with the functional connectivity fingerprints of both human (Upper) and macaque (Lower) brain areas involved in reward-guided learning and decision making and then compared them with the functional connectivity fingerprints of areas in the other species. (Top Left) Reproduced from ref. 27, with permission from Macmillan Publishers Ltd, Nature Neuroscience. (Bottom Left) Reproduced from ref. 11, with permission from Macmillan Publishers Ltd, Nature Neuroscience.     

From the Cover: Learning sculpts the spontaneous activity of the resting human brain

The brain is not a passive sensory-motor analyzer driven by environmental stimuli, but actively maintains ongoing representations that may be involved in the coding of expected sensory stimuli, prospective motor responses, and prior experience. Spontaneous cortical activity has been proposed to play an important part in maintaining these ongoing, internal representations, although its functional role is not well understood. One spontaneous signal being intensely investigated in the human brain is the interregional temporal correlation of the blood-oxygen level-dependent (BOLD) signal recorded at rest by functional MRI (functional connectivity-by-MRI, fcMRI, or BOLD connectivity). This signal is intrinsic and coherent within a number of distributed networks whose topography closely resembles that of functional networks recruited during tasks. While it is apparent that fcMRI networks reflect anatomical connectivity, it is less clear whether they have any dynamic functional importance. Here, we demonstrate that visual perceptual learning, an example of adult neural plasticity, modifies the resting covariance structure of spontaneous activity between networks engaged by the task. Specifically, after intense training on a shape-identification task constrained to one visual quadrant, resting BOLD functional connectivity and directed mutual interaction between trained visual cortex and frontal-parietal areas involved in the control of spatial attention were significantly modified. Critically, these changes correlated with the degree of perceptual learning. We conclude that functional connectivity serves a dynamic role in brain function, supporting the consolidation of previous experience.     

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.     

Precuneus shares intrinsic functional architecture in humans and monkeys

Evidence from macaque monkey tracing studies suggests connectivity-based subdivisions within the precuneus, offering predictions for similar subdivisions in the human. Here we present functional connectivity analyses of this region using resting-state functional MRI data collected from both humans and macaque monkeys. Three distinct patterns of functional connectivity were demonstrated within the precuneus of both species, with each subdivision suggesting a discrete functional role: (i) the anterior precuneus, functionally connected with the superior parietal cortex, paracentral lobule, and motor cortex, suggesting a sensorimotor region; (ii) the central precuneus, functionally connected to the dorsolateral prefrontal, dorsomedial prefrontal, and multimodal lateral inferior parietal cortex, suggesting a cognitive/associative region; and (iii) the posterior precuneus, displaying functional connectivity with adjacent visual cortical regions. These functional connectivity patterns were differentiated from the more ventral networks associated with the posterior cingulate, which connected with limbic structures such as the medial temporal cortex, dorsal and ventromedial prefrontal regions, posterior lateral inferior parietal regions, and the lateral temporal cortex. Our findings are consistent with predictions from anatomical tracer studies in the monkey, and provide support that resting-state functional connectivity (RSFC) may in part reflect underlying anatomy. These subdivisions within the precuneus suggest that neuroimaging studies will benefit from treating this region as anatomically (and thus functionally) heterogeneous. Furthermore, the consistency between functional connectivity networks in monkeys and humans provides support for RSFC as a viable tool for addressing cross-species comparisons of functional neuroanatomy.     

Double dissociation of two cognitive control networks in patients with focal brain lesions

Neuroimaging studies of cognitive control have identified two distinct networks with dissociable resting state connectivity patterns. This study, in patients with heterogeneous damage to these networks, demonstrates network independence through a double dissociation of lesion location on two different measures of network integrity: functional correlations among network nodes and within-node graph theory network properties. The degree of network damage correlates with a decrease in functional connectivity within that network while sparing the nonlesioned network. Graph theory properties of intact nodes within the damaged network show evidence of dysfunction compared with the undamaged network. The effect of anatomical damage thus extends beyond the lesioned area, but remains within the bounds of the existing network connections. Together this evidence suggests that networks defined by their role in cognitive control processes exhibit independence in resting data.     

Increased functional connectivity indicates the severity of cognitive impairment in multiple sclerosis

Correlations in spontaneous brain activity provide powerful access to large-scale organizational principles of the CNS. However, making inferences about cognitive processes requires a detailed understanding of the link between these couplings and the structural integrity of the CNS. We studied the impact of multiple sclerosis, which leads to the severe disintegration of the central white matter, on functional connectivity patterns in spontaneous cortical activity. Using a data driven approach based on the strength of a salient pattern of cognitive pathology, we identified distinct networks that exhibit increases in functional connectivity despite the presence of strong and diffuse reductions of the central white-matter integrity. The default mode network emerged as a core target of these connectivity modulations, showing enhanced functional coupling in bilateral inferior parietal cortex, posterior cingulate, and medial prefrontal cortex. These findings imply a complex and diverging relation of anatomical and functional connectivity in early multiple sclerosis and, thus, add an important observation for understanding how cognitive abilities and CNS integrity may be reflected in the intrinsic covariance of functional signals.     

Resting state functional connectivity as a predictor of brief intervention response in adults with alcohol use disorder: A preliminary study

Background

Brief interventions for alcohol use disorder (AUD) are generally efficacious, albeit with variability in response. Resting state functional connectivity (rsFC) may characterize neurobiological indicators that predict the response to brief interventions and is the focus of the current investigation.

Materials and Methods

Forty-six individuals with AUD (65.2% female) completed a resting state functional magnetic resonance imaging (fMRI) scan immediately followed by a brief intervention aimed at reducing alcohol consumption. Positive clinical response was defined as a reduction in alcohol consumption by at least one World Health Organization (WHO) risk drinking level at 3-month follow-up. rsFC was analyzed using seed-to-voxel analysis with seed regions from four networks: salience network, reward network, frontoparietal network, and default mode network.

Results

At baseline, responders had greater rsFC between the following seed regions in relation to voxel-based clusters than non-responders: (i) anterior cingulate cortex (ACC) in relation to left postcentral gyrus and right supramarginal gyrus (salience network); (ii) right posterior parietal cortex in relation to right ventral ACC (salience network); (iii) right interior frontal gyrus (IFG) pars opercularis in relation to right cerebellum and right occipital fusiform gyrus (frontoparietal); and (iv) right primary motor cortex in relation to left thalamus (default mode). Lower rsFC in responders vs. nonresponders was seen between the (i) right rostral prefrontal cortex in relation to left IFG pars triangularis (frontoparietal); (ii) right IFG pars triangularis in relation to right cerebellum (frontoparietal); (iii) right IFG pars triangularis in relation to right frontal eye fields and right angular gyrus (frontoparietal); and (iv) right nucleus accumbens in relation to right orbital frontal cortex and right insula (reward).

Conclusions

Resting state functional connectivity in the frontoparietal, salience, and reward networks predicts the response to a brief intervention in individuals with AUD and could reflect greater receptivity or motivation for behavior change.

     

Functional brain imaging of episodic memory decline in ageing

The episodic long‐term memory system supports remembering of events. It is considered to be the most age‐sensitive system, with an average onset of decline around 60 years of age. However, there is marked interindividual variability, such that some individuals show faster than average change and others show no or very little change. This variability may be related to the risk of developing dementia, with elevated risk for individuals with accelerated episodic memory decline. Brain imaging with functional magnetic resonance imaging (MRI) of blood oxygen level‐dependent (BOLD) signalling or positron emission tomography (PET) has been used to reveal the brain bases of declining episodic memory in ageing. Several studies have demonstrated a link between age‐related episodic memory decline and the hippocampus during active mnemonic processing, which is further supported by studies of hippocampal functional connectivity in the resting state. The hippocampus interacts with anterior and posterior neocortical regions to support episodic memory, and alterations in hippocampus–neocortex connectivity have been shown to contribute to impaired episodic memory. Multimodal MRI studies and more recently hybrid MRI/PET studies allow consideration of various factors that can influence the association between the hippocampal BOLD signal and memory performance. These include neurovascular factors, grey and white matter structural alterations, dopaminergic neurotransmission, amyloid‐Β and glucose metabolism. Knowledge about the brain bases of episodic memory decline can guide interventions to strengthen memory in older adults, particularly in those with an elevated risk of developing dementia, with promising results for combinations of cognitive and physical stimulation.     

Functional connectivity and cannabis use in high-risk adolescents

Abstract

Background: Adolescence is a unique neurodevelopmental period when regions of the brain most able to assess risk and reward are still in development. Cannabis use during adolescence has been associated with persistent negative outcomes. Although measures of resting brain activity are useful in assessing functional connectivity, such measures have not been broadly applied in adolescent cannabis-users. Objectives: The goal of the present study was to analyze the associations between cannabis use and resting brain activity in a sample of high-risk adolescents. Methods: Eighty-two high-risk youth between 14–18 years old were recruited from a juvenile justice day program. Youth completed a brief neurocognitive battery including assessments of cannabis use and a 5-minute resting functional magnetic resonance imaging (fMRI) scan. Intrinsic connectivity networks were extracted using the GIFT toolbox. Brain activity in a fronto-temporal network was compared in youth with high cannabis use vs. low cannabis use using an independent-samples t-test with alcohol use entered as a covariate. Results: Analysis revealed two elements within the fronto-temporal network related to cannabis use: one in middle frontal gyrus related to high cannabis use, and one in middle temporal gyrus related to low cannabis use. Only the frontal source survived application of a cluster size threshold and was significant at p?<?0.005. Conclusions: These results are consistent with patterns of activity in adult cannabis-users. The observed effect may reflect either pre-existing risk factors or near-term consequences of cannabis use. Prevention and intervention strategies that address fronto-temporal functioning may be particularly helpful in this population.     

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).     

Decoupling of the brain's default mode network during deep sleep

The recent discovery of a circuit of brain regions that is highly active in the absence of overt behavior has led to a quest for revealing the possible function of this so-called default-mode network (DMN). A very recent study, finding similarities in awake humans and anesthetized primates, has suggested that DMN activity might not simply reflect ongoing conscious mentation but rather a more general form of network dynamics typical of complex systems. Here, by performing functional MRI in humans, it is shown that a natural, sleep-induced reduction of consciousness is reflected in altered correlation between DMN network components, most notably a reduced involvement of frontal cortex. This suggests that DMN may play an important role in the sustenance of conscious awareness.     

Metabolic connectivity mapping reveals effective connectivity in the resting human brain

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

Rat brains also have a default mode network

The default mode network (DMN) in humans has been suggested to support a variety of cognitive functions and has been implicated in an array of neuropsychological disorders. However, its function(s) remains poorly understood. We show that rats possess a DMN that is broadly similar to the DMNs of nonhuman primates and humans. Our data suggest that, despite the distinct evolutionary paths between rodent and primate brain, a well-organized, intrinsically coherent DMN appears to be a fundamental feature in the mammalian brain whose primary functions might be to integrate multimodal sensory and affective information to guide behavior in anticipation of changing environmental contingencies.     

Neurophysiological origin of human brain asymmetry for speech and language

The physiological basis of human cerebral asymmetry for language remains mysterious. We have used simultaneous physiological and anatomical measurements to investigate the issue. Concentrating on neural oscillatory activity in speech-specific frequency bands and exploring interactions between gestural (motor) and auditory-evoked activity, we find, in the absence of language-related processing, that left auditory, somatosensory, articulatory motor, and inferior parietal cortices show specific, lateralized, speech-related physiological properties. With the addition of ecologically valid audiovisual stimulation, activity in auditory cortex synchronizes with left-dominant input from the motor cortex at frequencies corresponding to syllabic, but not phonemic, speech rhythms. Our results support theories of language lateralization that posit a major role for intrinsic, hardwired perceptuomotor processing in syllabic parsing and are compatible both with the evolutionary view that speech arose from a combination of syllable-sized vocalizations and meaningful hand gestures and with developmental observations suggesting phonemic analysis is a developmentally acquired process.     

Equitable decision making is associated with neural markers of intrinsic value

Standard economic and evolutionary models assume that humans are fundamentally selfish. On this view, any acts of prosociality--such as cooperation, giving, and other forms of altruism--result from covert attempts to avoid social injunctions against selfishness. However, even in the absence of social pressure, individuals routinely forego personal gain to share resources with others. Such anomalous giving cannot be accounted for by standard models of social behavior. Recent observations have suggested that, instead, prosocial behavior may reflect an intrinsic value placed on social ideals such as equity and charity. Here, we show that, consistent with this alternative account, making equitable interpersonal decisions engaged neural structures involved in computing subjective value, even when doing so required foregoing material resources. By contrast, making inequitable decisions produced activity in the anterior insula, a region linked to the experience of subjective disutility. Moreover, inequity-related insula response predicted individuals' unwillingness to make inequitable choices. Together, these data suggest that prosocial behavior is not simply a response to external pressure, but instead represents an intrinsic, and intrinsically social, class of reward.     

Evidence from intrinsic activity that asymmetry of the human brain is controlled by multiple factors

Cerebral lateralization is a fundamental property of the human brain and a marker of successful development. Here we provide evidence that multiple mechanisms control asymmetry for distinct brain systems. Using intrinsic activity to measure asymmetry in 300 adults, we mapped the most strongly lateralized brain regions. Both men and women showed strong asymmetries with a significant, but small, group difference. Factor analysis on the asymmetric regions revealed 4 separate factors that each accounted for significant variation across subjects. The factors were associated with brain systems involved in vision, internal thought (the default network), attention, and language. An independent sample of right- and left-handed individuals showed that hand dominance affects brain asymmetry but differentially across the 4 factors supporting their independence. These findings show the feasibility of measuring brain asymmetry using intrinsic activity fluctuations and suggest that multiple genetic or environmental mechanisms control cerebral lateralization.     

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.     

Physical exercise keeps the brain connected by increasing white matter integrity in healthy controls

Physical exercise leads to structural changes in the brain. However, it is unclear whether the initiation or continuous practice of physical exercise causes this effect and whether brain connectivity benefits from exercise. We examined the effect of 6 months of exercise on the brain in participants who exercise regularly (n = 25) and in matched healthy controls (n = 20). Diffusion tensor imaging brain scans were obtained from both groups. Our findings demonstrate that regular physical exercise significantly increases the integrity of white matter fiber tracts, especially those related to frontal function. This implies that exercise improves brain connectivity in healthy individuals, which has important implications for understanding the effect of fitness programs on the brains of healthy subjects.     

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.