Empirical validation of directed functional connectivity

Evaluating functional brain organization in individuals and identifying contributions to network overlap

Individual differences in the spatial organization of resting-state networks have received increased attention in recent years. Measures of individual-specific spatial organization of brain networks and overlapping network organization have been linked to important behavioral and clinical traits and are therefore potential biomarker targets for personalized psychiatry approaches. To better understand individual-specific spatial brain organization, this paper addressed three key goals. First, we determined whether it is possible to reliably estimate weighted (non-binarized) resting-state network maps using data from only a single individual, while also maintaining maximum spatial correspondence across individuals. Second, we determined the degree of spatial overlap between distinct networks, using test-retest and twin data. Third, we systematically tested multiple hypotheses (spatial mixing, temporal switching, and coupling) as candidate explanations for why networks overlap spatially. To estimate weighted network organization, we adopt the Probabilistic Functional Modes (PROFUMO) algorithm, which implements a Bayesian framework with hemodynamic and connectivity priors to supplement optimization for spatial sparsity/independence. Our findings showed that replicable individual-specific estimates of weighted resting-state networks can be derived using high-quality fMRI data within individual subjects. Network organization estimates using only data from each individual subject closely resembled group-informed network estimates (which was not explicitly modeled in our individual-specific analyses), suggesting that cross-subject correspondence was largely maintained. Furthermore, our results confirmed the presence of spatial overlap in network organization, which was replicable across sessions within individuals and in monozygotic twin pairs. Intriguingly, our findings provide evidence that overlap between 2-network pairs is indicative of coupling. These results suggest that regions of network overlap concurrently process information from both contributing networks, potentially pointing to the role of overlapping network organization in the integration of information across multiple brain systems.

Causal influence of brainstem response to transcutaneous vagus nerve stimulation on cardiovagal outflow

BACKGROUND

The autonomic response to transcutaneous auricular vagus nerve stimulation (taVNS) has been linked to the engagement of brainstem circuitry modulating autonomic outflow. However, the physiological mechanisms supporting such efferent vagal responses are not well understood, particularly in humans.

HYPOTHESIS

We present a paradigm for estimating directional brain-heart interactions in response to taVNS. We propose that our approach is able to identify causal links between the activity of brainstem nuclei involved in autonomic control and cardiovagal outflow.

METHODS

We adopt an approach based on a recent reformulation of Granger causality that includes permutation-based, nonparametric statistics. The method is applied to ultrahigh field (7T) functional magnetic resonance imaging (fMRI) data collected on healthy subjects during taVNS.

RESULTS

Our framework identified taVNS-evoked functional brainstem responses with superior sensitivity compared to prior conventional approaches, confirming causal links between taVNS stimulation and fMRI response in the nucleus tractus solitarii (NTS). Furthermore, our causal approach elucidated potential mechanisms by which information is relayed between brainstem nuclei and cardiovagal, i.e., high-frequency heart rate variability, in response to taVNS. Our findings revealed that key brainstem nuclei, known from animal models to be involved in cardiovascular control, exert a causal influence on taVNS-induced cardiovagal outflow in humans.

CONCLUSION

Our causal approach allowed us to noninvasively evaluate directional interactions between fMRI BOLD signals from brainstem nuclei and cardiovagal outflow.

Evaluating functional brain organization in individuals and identifying contributions to network overlap

Individual differences in the spatial organization of resting state networks have received increased attention in recent years. Measures of individual-specific spatial organization of brain networks and overlapping network organization have been linked to important behavioral and clinical traits and are therefore potential biomarker targets for personalized psychiatry approaches. To better understand individual-specific spatial brain organization, this paper addressed three key goals. First, we determined whether it is possible to reliably estimate weighted (non-binarized) resting state network maps using data from only a single individual, while also maintaining maximum spatial correspondence across individuals. Second, we determined the degree of spatial overlap between distinct networks, using test-retest and twin data. Third, we systematically tested multiple hypotheses (spatial mixing, temporal switching, and coupling) as candidate explanations for why networks overlap spatially. To estimate weighted network organization, we adopt the Probabilistic Functional Modes (PROFUMO) algorithm, which implements a Bayesian framework with hemodynamic and connectivity priors to supplement optimization for spatial sparsity/independence. Our findings showed that replicable individual-specific estimates of weighted resting state networks can be derived using high quality fMRI data within individual subjects. Network organization estimates using only data from each individual subject closely resembled group-informed network estimates (which was not explicitly modeled in our individual-specific analyses), suggesting that cross-subject correspondence was largely maintained. Furthermore, our results confirmed the presence of spatial overlap in network organization, which was replicable across sessions within individuals and in monozygotic twin pairs. Intriguingly, our findings provide evidence that network overlap is indicative of linear additive coupling. These results suggest that regions of network overlap concurrently process information from all contributing networks, potentially pointing to the role of overlapping network organization in the integration of information across multiple brain systems.

Generalizing prediction of task-evoked brain activity across datasets and populations

Predictions of task-based functional magnetic resonance imaging (fMRI) from task-free resting-state (rs) fMRI have gained popularity over the past decade. This method holds a great promise for studying individual variability in brain function without the need to perform highly demanding tasks. However, in order to be broadly used, prediction models must prove to generalize beyond the dataset they were trained on. In this work, we test the generalizability of prediction of task-fMRI from rs-fMRI across sites, MRI vendors and age-groups. Moreover, we investigate the data requirements for successful prediction. We use the Human Connectome Project (HCP) dataset to explore how different combinations of training sample sizes and number of fMRI datapoints affect prediction success in various cognitive tasks. We then apply models trained on HCP data to predict brain activations in data from a different site, a different MRI vendor (Phillips vs. Siemens scanners) and a different age group (children from the HCP-development project). We demonstrate that, depending on the task, a training set of approximately 20 participants with 100 fMRI timepoints each yields the largest gain in model performance. Nevertheless, further increasing sample size and number of timepoints results in significantly improved predictions, until reaching approximately 450-600 training participants and 800-1000 timepoints. Overall, the number of fMRI timepoints influences prediction success more than the sample size. We further show that models trained on adequate amounts of data successfully generalize across sites, vendors and age groups and provide predictions that are both accurate and individual-specific. These findings suggest that large-scale publicly available datasets may be utilized to study brain function in smaller, unique samples.

Forecasting brain activity based on models of spatiotemporal brain dynamics: A comparison of graph neural network architectures

Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph-structured signals like those observed in complex brain networks. In our study we compare different spatiotemporal GNN architectures and study their ability to model neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently often used for directed functional connectivity analysis. We show that by learning localized functional interactions on the anatomical substrate, GNN-based approaches are able to robustly scale to large network studies, even when available data are scarce. By including anatomical connectivity as the physical substrate for information propagation, such GNNs also provide a multimodal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatiotemporal dynamics in brain networks.

Experimental evidence for a child-to-adolescent switch in human amygdala-prefrontal cortex communication: A cross-sectional pilot study

Interactions between the amygdala and prefrontal cortex are fundamental to human emotion. Despite the central role of frontoamygdala communication in adult emotional learning and regulation, little is known about how top-down control emerges during human development. In the present cross-sectional pilot study, we experimentally manipulated prefrontal engagement to test its effects on the amygdala during development. Inducing dorsal anterior cingulate cortex (dACC) activation resulted in developmentally-opposite effects on amygdala reactivity during childhood versus adolescence, such that dACC activation was followed by increased amygdala reactivity in childhood but reduced amygdala reactivity in adolescence. Bayesian network analyses revealed an age-related switch between childhood and adolescence in the nature of amygdala connectivity with the dACC and ventromedial PFC (vmPFC). Whereas adolescence was marked by information flow from dACC and vmPFC to amygdala (consistent with that observed in adults), the reverse information flow, from the amygdala to dACC and vmPFC, was dominant in childhood. The age-related switch in information flow suggests a potential shift from bottom-up co-excitatory to top-down regulatory frontoamygdala connectivity and may indicate a profound change in the circuitry supporting maturation of emotional behavior. These findings provide novel insight into the developmental construction of amygdala-cortical connections and implications for the ways in which childhood experiences may influence subsequent prefrontal function.

Gamma-band synchronisation in a frontotemporal auditory information processing network

Neural oscillations are fundamental mechanisms of the human brain that enable coordinated activity of different brain regions during perceptual and cognitive processes. A frontotemporal network generated by means of gamma oscillations and comprising the auditory cortex (AC) and the anterior cingulate cortex (ACC) has been shown to be involved in the cognitively demanding auditory information processing. This study aims to reveal patterns of functional and effective connectivity within this network in healthy subjects by means of simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). We simultaneously recorded EEG and fMRI in 28 healthy subjects during the performance of a cognitively demanding auditory choice reaction task. Connectivity between the ACC and AC was analysed employing EEG and fMRI connectivity measures. We found a significant BOLD signal correlation between the ACC and AC, a significant task-dependant increase of fMRI connectivity (gPPI) and a significant increase in functional coupling in the gamma frequency range between these regions (LPS), which was increased in top-down direction (granger analysis). EEG and fMRI connectivity measures were positively correlated. The results of these study point to a role of a top-down influence of the ACC on the AC executed by means of gamma synchronisation. The replication of fMRI connectivity patterns in simultaneously recorded EEG data and the correlation between connectivity measures from both domains found in our study show, that brain connectivity based on the synchronisation of gamma oscillations is mirrored in fMRI connectivity patterns.

SEED-G: Simulated EEG Data Generator for Testing Connectivity Algorithms

EEG signals are widely used to estimate brain circuits associated with specific tasks and cognitive processes. The testing of connectivity estimators is still an open issue because of the lack of a ground-truth in real data. Existing solutions such as the generation of simulated data based on a manually imposed connectivity pattern or mass oscillators can model only a few real cases with limited number of signals and spectral properties that do not reflect those of real brain activity. Furthermore, the generation of time series reproducing non-ideal and non-stationary ground-truth models is still missing. In this work, we present the SEED-G toolbox for the generation of pseudo-EEG data with imposed connectivity patterns, overcoming the existing limitations and enabling control of several parameters for data simulation according to the user's needs. We first described the toolbox including guidelines for its correct use and then we tested its performances showing how, in a wide range of conditions, datasets composed by up to 60 time series were successfully generated in less than 5 s and with spectral features similar to real data. Then, SEED-G is employed for studying the effect of inter-trial variability Partial Directed Coherence (PDC) estimates, confirming its robustness.

Structural MRI and functional connectivity features predict current clinical status and persistence behavior in prescription opioid users

Prescription opioid use disorder (POUD) has reached epidemic proportions in the United States, raising an urgent need for diagnostic biological tools that can improve predictions of disease characteristics. The use of neuroimaging methods to develop such biomarkers have yielded promising results when applied to neurodegenerative and psychiatric disorders, yet have not been extended to prescription opioid addiction. With this long-term goal in mind, we conducted a preliminary study in this understudied clinical group. Univariate and multivariate approaches to distinguishing between POUD (n = 26) and healthy controls (n = 21) were investigated, on the basis of structural MRI (sMRI) and resting-state functional connectivity (restFC) features. Univariate approaches revealed reduced structural integrity in the subcortical extent of a previously reported addiction-related network in POUD subjects. No reliable univariate between-group differences in cortical structure or edgewise restFC were observed. Contrasting these mixed univariate results, multivariate machine learning classification approaches recovered more statistically reliable group differences, especially when sMRI and restFC features were combined in a multi-modal model (classification accuracy = 66.7%, p < .001). The same multivariate multi-modal approach also yielded reliable prediction of individual differences in a clinically relevant behavioral measure (persistence behavior; predicted-to-actual overlap r = 0.42, p = .009). Our findings suggest that sMRI and restFC measures can be used to reliably distinguish the neural effects of long-term opioid use, and that this endeavor numerically benefits from multivariate predictive approaches and multi-modal feature sets. This can serve as theoretical proof-of-concept for future longitudinal modeling of prognostic POUD characteristics from neuroimaging features, which would have clearer clinical utility.

Computations and Connectivity Underlying Aversive Counterfactuals

Background: Mentally simulating counterfactuals (scenarios that have not actually occurred) is a sophisticated human cognitive function underlying creativity, planning, and daydreaming. One example is the "would you rather" game, in which forced choices are made between outlandish negative counterfactuals. Materials and Methods: We measured behavioral and neural correlates while participants made "would you rather" choices framed as approaching or avoiding aversive counterfactual scenarios (e.g., illnesses, car accidents). Results: We found in two independent cohorts that participants were highly susceptible to framing effects when making these decisions, taking significantly longer to respond to approach frames compared with avoidance. Brain imaging showed that choices to approach and avoid resulted in a pattern of activation consistent with a network associated with responding to aversive stimuli, identified via a coordinate-based meta-analysis of 238 studies. Bayesian graph connectivity analysis showed that network connectivity differed by choice frame, with significantly stronger connectivity for approach choices compared with avoidance choices among primarily limbic nodes (putamen, insula, caudate, and amygdala). Computational modeling of behavior revealed that approach frames led to significantly longer nondecision times, increased evidence required to make decisions, and faster evidence accumulation than avoidance frames. Stronger network connectivity between corticostriatal and limbic regions was associated with rate of evidence accumulation and length of nondecision time during approach choices. For avoidance choices, prefrontal connectivity was related to nondecision time. Conclusions: These results suggest that "would you rather" decisions about aversive counterfactuals differentially recruit limbic circuit connectivity based on choice frame. Impact statement We measured brain connectivity and latent cognitive variables underlying aversive counterfactual choices. We found a replicable reaction time effect whereby approach decisions were slower than avoidance decisions. Computational modeling identified that the latent cognitive variable of evidence accumulation was related to strength of connectivity between corticostriatal and limbic nodes during approach decisions. Multidimensional scaling (MDS) and clustering revealed a three-dimensional choice structure that differed between individuals, and between approach and avoidance choices within individuals. Our results suggest that cognitive evaluations of aversive counterfactuals involve flexible representations that can be altered by choice framing. These findings have broad implications for prospective decision making.

The boundaries of state-space Granger causality analysis applied to BOLD simulated data: A comparative modelling and simulation approach

BACKGROUND

The analysis of connectivity has become a fundamental tool in human neuroscience. Granger Causality Mapping is a data-driven method that uses Granger Causality (GC) to assess the existence and direction of influence between signals, based on temporal precedence of information. More recently, a theory of Granger causality has been developed for state-space (SS-GC) processes, but little is known about its statistical validation and application on functional magnetic resonance imaging (fMRI) data.

NEW METHOD

We explored different multivariate computational frameworks to define the optimal combination for GC estimation. We hypothesized a new heuristic, combining SS-GC with a distinct statistical validation technique, Time Reversed Testing, validating it on synthetic data. We test its performance with a number of experimental parameters, including block structure, sampling frequency, noise and system mean pairwise correlation, using a statistical framework of binary classification.

RESULTS

We found that SS-GC with time reversed testing outperforms other frameworks. The results validate the application of SS-GC to generative models. When estimating reliable causal relations, SS-GC returns promising results, especially when considering synthetic data with a high impact of noise and sampling rate.

CONCLUSIONS

In this study, we empirically explored the boundaries of SS-GC with time reversed testing, a data-driven causality analysis framework with potential applicability to fMRI data.

Brain-wide resting-state connectivity regulation by the hippocampus and medial prefrontal cortex is associated with fluid intelligence

The connectivity hub property of the hippocampus (HIP) and the medial prefrontal cortex (MPFC) is essential for their widespread involvement in cognition; however, the cooperation mechanism between them is far from clear. Herein, using resting-state functional MRI and Gaussian Bayesian network to describe the directed organizing architecture of the HIP-MPFC pathway with regions in the brain, we demonstrated that the HIP and the MPFC have central roles as the driving hub and aggregating hub, respectively. The status of the HIP and the MPFC is dominant in communications between the HIP and the default-mode network, between the HIP and core neurocognitive networks, including the default-mode, frontoparietal, and salience networks, and between brain-wide representative regions, suggesting a strong and robust central position of the two regions in regulating the dynamics of large-scale brain activity. Furthermore, we found that the directed connectivity and flow from the right HIP to the MPFC is significantly linked to fluid intelligence. Together, these results clarify the different roles of the HIP and the MPFC that jointly contribute to network dynamics and cognitive ability from a data-driven insight via the use of the directed connectivity method.

Advancing functional connectivity research from association to causation

Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures.

Altered connectivity of the right anterior insula drives the pain connectome changes in chronic knee osteoarthritis

Supplemental Digital Content is Available in the Text.

A resting-state functional magnetic resonance imaging study of chronic knee osteoarthritis pain using a range of functional connectivity analyses to better understand brain network changes.

Resting-state functional connectivity (FC) has proven a powerful approach to understand the neural underpinnings of chronic pain, reporting altered connectivity in 3 main networks: the default mode network (DMN), central executive network, and the salience network (SN). The interrelation and possible mechanisms of these changes are less well understood in chronic pain. Based on emerging evidence of its role to drive switches between network states, the right anterior insula (rAI, an SN hub) may play a dominant role in network connectivity changes underpinning chronic pain. To test this hypothesis, we used seed-based resting-state FC analysis including dynamic and effective connectivity metrics in 25 people with chronic osteoarthritis (OA) pain and 19 matched healthy volunteers. Compared with controls, participants with painful knee OA presented with increased anticorrelation between the rAI (SN) and DMN regions. Also, the left dorsal prefrontal cortex (central executive network hub) showed more negative FC with the right temporal gyrus. Granger causality analysis revealed increased negative influence of the rAI on the posterior cingulate (DMN) in patients with OA in line with the observed enhanced anticorrelation. Moreover, dynamic FC was lower in the DMN of patients and thus more similar to temporal dynamics of the SN. Together, these findings evidence a widespread network disruption in patients with persistent OA pain and point toward a driving role of the rAI.

Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging (fMRI) has been used to relate neuronal network activity to cognitive processing and behavior. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, Linear Non-Gaussian Acyclic Models, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.

Task activations produce spurious but systematic inflation of task functional connectivity estimates

Most neuroscientific studies have focused on task-evoked activations (activity amplitudes at specific brain locations), providing limited insight into the functional relationships between separate brain locations. Task-state functional connectivity (FC) - statistical association between brain activity time series during task performance - moves beyond task-evoked activations by quantifying functional interactions during tasks. However, many task-state FC studies do not remove the first-order effect of task-evoked activations prior to estimating task-state FC. It has been argued that this results in the ambiguous inference "likely active or interacting during the task", rather than the intended inference "likely interacting during the task". Utilizing a neural mass computational model, we verified that task-evoked activations substantially and inappropriately inflate task-state FC estimates, especially in functional MRI (fMRI) data. Various methods attempting to address this problem have been developed, yet the efficacies of these approaches have not been systematically assessed. We found that most standard approaches for fitting and removing mean task-evoked activations were unable to correct these inflated correlations. In contrast, methods that flexibly fit mean task-evoked response shapes effectively corrected the inflated correlations without reducing effects of interest. Results with empirical fMRI data confirmed the model's predictions, revealing activation-induced task-state FC inflation for both Pearson correlation and psychophysiological interaction (PPI) approaches. These results demonstrate that removal of mean task-evoked activations using an approach that flexibly models task-evoked response shape is an important preprocessing step for valid estimation of task-state FC.

Brain network segregation and integration during an epoch-related working memory fMRI experiment

The characterization of brain subnetwork segregation and integration has previously focused on changes that are detectable at the level of entire sessions or epochs of imaging data. In this study, we applied time-varying functional connectivity analysis together with temporal network theory to calculate point-by-point estimates in subnetwork segregation and integration during an epoch-based (2-back, 0-back, baseline) working memory fMRI experiment as well as during resting-state. This approach allowed us to follow task-related changes in subnetwork segregation and integration at a high temporal resolution. At a global level, the cognitively more taxing 2-back epochs elicited an overall stronger response of integration between subnetworks compared to the 0-back epochs. Moreover, the visual, sensorimotor and fronto-parietal subnetworks displayed characteristic and distinct temporal profiles of segregation and integration during the 0- and 2-back epochs. During the interspersed epochs of baseline, several subnetworks, including the visual, fronto-parietal, cingulo-opercular and dorsal attention subnetworks showed pronounced increases in segregation. Using a drift diffusion model we show that the response time for the 2-back trials are correlated with integration for the fronto-parietal subnetwork and correlated with segregation for the visual subnetwork. Our results elucidate the fast-evolving events with regard to subnetwork integration and segregation that occur in an epoch-related task fMRI experiment. Our findings suggest that minute changes in subnetwork integration are of importance for task performance.

Mutual connectivity analysis of resting-state functional MRI data with local models

Functional connectivity analysis of functional MRI (fMRI) can represent brain networks and reveal insights into interactions amongst different brain regions. However, most connectivity analysis approaches adopted in practice are linear and non-directional. In this paper, we demonstrate the advantage of a data-driven, directed connectivity analysis approach called Mutual Connectivity Analysis using Local Models (MCA-LM) that approximates connectivity by modeling nonlinear dependencies of signal interaction, over more conventionally used approaches, such as Pearson's and partial correlation, Patel's conditional dependence measures, etcetera. We demonstrate on realistic simulations of fMRI data that, at long sampling intervals, i.e. high repetition time (TR) of fMRI signals, MCA-LM performs better than or comparable to correlation-based methods and Patel's measures. However, at fast image acquisition rates corresponding to low TR, MCA-LM significantly outperforms these methods. This insight is particularly useful in the light of recent advances in fast fMRI acquisition techniques. Methods that can capture the complex dynamics of brain activity, such as MCA-LM, should be adopted to extract as much information as possible from the improved representation. Furthermore, MCA-LM works very well for simulations generated at weak neuronal interaction strengths, and simulations modeling inhibitory and excitatory connections as it disentangles the two opposing effects between pairs of regions/voxels. Additionally, we demonstrate that MCA-LM is capable of capturing meaningful directed connectivity on experimental fMRI data. Such results suggest that it introduces sufficient complexity into modeling fMRI time-series interactions that simple, linear approaches cannot, while being data-driven, computationally practical and easy to use. In conclusion, MCA-LM can provide valuable insights towards better understanding brain activity.

Solving the Credit Assignment Problem With the Prefrontal Cortex

In naturalistic multi-cue and multi-step learning tasks, where outcomes of behavior are delayed in time, discovering which choices are responsible for rewards can present a challenge, known as the credit assignment problem. In this review, I summarize recent work that highlighted a critical role for the prefrontal cortex (PFC) in assigning credit where it is due in tasks where only a few of the multitude of cues or choices are relevant to the final outcome of behavior. Collectively, these investigations have provided compelling support for specialized roles of the orbitofrontal (OFC), anterior cingulate (ACC), and dorsolateral prefrontal (dlPFC) cortices in contingent learning. However, recent work has similarly revealed shared contributions and emphasized rich and heterogeneous response properties of neurons in these brain regions. Such functional overlap is not surprising given the complexity of reciprocal projections spanning the PFC. In the concluding section, I overview the evidence suggesting that the OFC, ACC and dlPFC communicate extensively, sharing the information about presented options, executed decisions and received rewards, which enables them to assign credit for outcomes to choices on which they are contingent. This account suggests that lesion or inactivation/inhibition experiments targeting a localized PFC subregion will be insufficient to gain a fine-grained understanding of credit assignment during learning and instead poses refined questions for future research, shifting the focus from focal manipulations to experimental techniques targeting cortico-cortical projections.

Top-down cortical interactions in visuospatial attention

The voluntary allocation of visuospatial attention depends upon top-down influences from the frontal eye field (FEF) and intraparietal sulcus (IPS)-the core regions of the dorsal attention network (DAN)-to visual occipital cortex (VOC), and has been further associated with within-DAN influences, particularly from the FEF to IPS. However, the degree to which these influences manifest at rest and are then modulated during anticipatory visuospatial attention tasks remains poorly understood. Here, we measured both undirected and directed functional connectivity (UFC, DFC) between the FEF, IPS, and VOC at rest and during an anticipatory visuospatial attention task, using a slow event-related design. Whereas the comparison between rest and task indicated FC modulations that persisted throughout the task duration, the large number of task trials we collected further enabled us to measure shorter timescale modulations of FC across the trial. Relative to rest, task engagement induced enhancement of both top-down influences from the DAN to VOC, as well as bidirectional influences between the FEF and IPS. These results suggest that task performance induces enhanced interaction within the DAN and a greater top-down influence on VOC. While resting FC generally showed right hemisphere dominance, task-related enhancement favored the left hemisphere, effectively balancing a resting hemispheric asymmetry, particularly within the DAN. On a shorter (within-trial) timescale, VOC-to-DAN and bidirectional FEF-IPS influences were transiently elevated during the anticipatory period of the trial, evincing phasic modulations related to changing attentional demands. In contrast to these task-specific effects, resting and task-related influence patterns were highly correlated, suggesting a predisposing role for resting organization, which requires minimal tonic and phasic modulations for control of visuospatial attention.

From connectome to cognition: The search for mechanism in human functional brain networks

Recent developments in functional connectivity research have expanded the scope of human neuroimaging, from identifying changes in regional activation amplitudes to detailed mapping of large-scale brain networks. However, linking network processes to a clear role in cognition demands advances in the theoretical frameworks, algorithms, and experimental approaches applied. This would help evolve the field from a descriptive to an explanatory state, by targeting network interactions that can mechanistically account for cognitive effects. In the present review, we provide an explicit framework to aid this search for "network mechanisms", which anchors recent methodological advances in functional connectivity estimation to a renewed emphasis on careful experimental design. We emphasize how this framework can address specific questions in network neuroscience. These span ambiguity over the cognitive relevance of resting-state networks, how to characterize task-evoked and spontaneous network dynamics, how to identify directed or "effective" connections, and how to apply multivariate pattern analysis at the network level. In parallel, we apply the framework to highlight the mechanistic interaction of network components that remain "stable" across task domains and more "flexible" components associated with on-task reconfiguration. By emphasizing the need to structure the use of diverse analytic approaches with sound experimentation, our framework promotes an explanatory mapping between the workings of the cognitive mind and the large-scale network mechanisms of the human brain.

Impaired Coupling between the Dorsomedial Prefrontal Cortex and the Amygdala in Schizophrenia Smokers Viewing Anti-smoking Images

BACKGROUND

Cigarette smoking is highly prevalent in schizophrenia and is one of the main factors contributing to the significantly decreased life expectancy in this population. Schizophrenia smokers, compared to their counterparts with no comorbid psychiatric disorder, are largely unaware and indifferent to the long-term negative consequences of cigarette smoking. The objective of this study was to determine, for the first time, if these meta-cognitive deficits are associated with neuro-functional alterations in schizophrenia smokers.

METHODS

Twenty-four smokers with no psychiatric disorder and 21 smokers with schizophrenia (DSM-IV criteria) were scanned using functional magnetic resonance imaging and exposed to anti-smoking images. Granger causality analyses were used to examine the effective connectivity between brain regions found to be significantly activated.

RESULTS

Across groups, potent activations were observed in the left ventro-lateral prefrontal cortex, the left amygdala (AMG), and the dorsomedial prefrontal cortex (dmPFC). Using the dmPFC as a seed region, we found an abnormal negative connectivity from the dmPFC to the AMG in schizophrenia smokers during the viewing of anti-smoking stimuli. This abnormal connectivity was not present during the viewing of aversive stimuli unrelated to tobacco.

DISCUSSION

Given the well-established roles of the dmPFC in social cognition and of the AMG in emotional processing, our results suggest that the relative indifference of schizophrenia smokers regarding the negative consequences of tobacco smoking could be explained by a cognitive-affective dissonance.