The robustness of persistent homology of brain networks to data acquisition-related non-neural variability in resting state fMRI.

There is increasing interest in investigating brain function based on functional connectivity networks (FCN) obtained from resting-state functional magnetic resonance imaging (fMRI). FCNs, typically obtained using measures of time series association such as Pearson's correlation, are sensitive to data acquisition parameters such as sampling period. This introduces non-neural variability in data pooled from different acquisition protocols and MRI scanners, negating the advantages of larger sample sizes in pooled data. To address this, we hypothesize that the topology or shape of brain networks must be preserved irrespective of how densely it is sampled, and metrics which capture this topology may be statistically similar across sampling periods, thereby alleviating this source of non-neural variability. Accordingly, we present an end-to-end pipeline that uses persistent homology (PH), a branch of topological data analysis, to demonstrate similarity across FCNs acquired at different temporal sampling periods. PH, as a technique, extracts topological features by capturing the network organization across all continuous threshold values, as opposed to graph theoretic methods, which fix a discrete network topology by thresholding the connectivity matrix. The extracted topological features are encoded in the form of persistent diagrams that can be compared against one another using the earth-moving metric, also popularly known as the Wasserstein distance. We extract topological features from three data cohorts, each acquired at different temporal sampling periods and demonstrate that these features are statistically the same, hence, empirically showing that PH may be robust to changes in data acquisition parameters such as sampling period.

Functional parcellation of the hippocampus based on its layer-specific connectivity with default mode and dorsal attention networks.

Recent neuroimaging evidence suggests that there might be an anterior-posterior functional differentiation of the hippocampus along the long-axis. The HERNET (hippocampal encoding/retrieval and network) model proposed an encoding/retrieval dichotomy with the anterior hippocampus more connected to the dorsal attention network (DAN) during memory encoding, and the posterior portions more connected to the default mode network (DMN) during retrieval. Evidence both for and against the HERNET model has been reported. In this study, we test the validity of the HERNET model non-invasively in humans by computing functional connectivity (FC) in layer-specific cortico-hippocampal microcircuits. This was achieved by acquiring sub-millimeter functional magnetic resonance imaging (fMRI) data during encoding/retrieval tasks at 7T. Specifically, FC between infra-granular output layers of DAN with hippocampus during encoding and FC between supra-granular input layers of DMN with hippocampus during retrieval were computed to test the predictions of the HERNET model. Our results support some predictions of the HERNET model including anterior-posterior gradient along the long axis of the hippocampus. While preferential relationships between the entire hippocampus and DAN/DMN during encoding/retrieval, respectively, were observed as predicted, anterior-posterior specificity in these network relationships could not be confirmed. The strength and clarity of evidence for/against the HERNET model were superior with layer-specific data compared to conventional volume data.

Intrinsic functional connectivity of the frontoparietal network predicts inter-individual differences in the propensity for costly third-party punishment.

Humans are motivated to give norm violators their just deserts through costly punishment even as unaffected third parties (i.e., third-party punishment, TPP). A great deal of individual variability exists in costly punishment; however, how this variability particularly in TPP is represented by the brain's intrinsic network architecture remains elusive. Here, we examined whether inter-individual differences in the propensity for TPP can be predicted based on resting-state functional connectivity (RSFC) combining an economic TPP game with task-free functional neuroimaging and a multivariate prediction framework. Our behavioral results revealed that TPP punishment increased with the severity of unfairness for offers. People with higher TPP propensity punished more harshly across norm-violating scenarios. Our neuroimaging findings showed RSFC within the frontoparietal network predicted individual differences in TPP propensity. Our findings contribute to understanding the neural fingerprint for an individual's propensity to costly punish strangers, and shed some light on how social norm enforcement behaviors are represented by the brain's intrinsic network architecture.

Human-dog relationships as a working framework for exploring human-robot attachment: a multidisciplinary review.

Robotic agents will be life-long companions of humans in the foreseeable future. To achieve such successful relationships, people will likely attribute emotions and personality, assign social competencies, and develop a long-lasting attachment to robots. However, without a clear theoretical framework-building on biological, psychological, and technological knowledge-current societal demands for establishing successful human-robot attachment (HRA) as a new form of inter-species interactions might fail. The study of evolutionarily adaptive animal behavior (i.e., ethology) suggests that human-animal behaviors can be considered as a plausible solution in designing and building models of ethorobots-including modeling the inter-species bond between domesticated animals and humans. Evidence shows that people assign emotional feelings and personality characteristics to animal species leading to cooperation and communication-crucial for designing social robots such as companion robots. Because dogs have excellent social skills with humans, current research applies human-dog relationships as a template to understand HRA. Our goal of this article is twofold. First, we overview the research on how human-dog interactions are implemented as prototypes of non-human social companions in HRA. Second, we review research about attitudes that humans have for interacting with robotic dogs based on their appearance and behavior, the implications for forming attachments, and human-animal interactions in the rising sphere of robot-assisted therapy. The rationale for this review is to provide a new perspective to facilitate future research among biologists, psychologists, and engineers-contributing to the creation of innovative research practices for studying social behaviors and its implications for society addressing HRA.

Dog-human social relationship: representation of human face familiarity and emotions in the dog brain.

This study investigated the behavioral and neural indices of detecting facial familiarity and facial emotions in human faces by dogs. Awake canine fMRI was used to evaluate dogs' neural response to pictures and videos of familiar and unfamiliar human faces, which contained positive, neutral, and negative emotional expressions. The dog-human relationship was behaviorally characterized out-of-scanner using an unsolvable task. The caudate, hippocampus, and amygdala, mainly implicated in reward, familiarity and emotion processing, respectively, were activated in dogs when viewing familiar and emotionally salient human faces. Further, the magnitude of activation in these regions correlated with the duration for which dogs showed human-oriented behavior towards a familiar (as opposed to unfamiliar) person in the unsolvable task. These findings provide a bio-behavioral basis for the underlying markers and functions of human-dog interaction as they relate to familiarity and emotion in human faces.

Comparing pet and detection dogs (Canis familiaris) on two aspects of social cognition.

Interspecific communication between dogs and humans enables dogs to occupy significant roles in human society, both in companion and working roles. Dogs excel at using human communicative signals in problem-solving tasks, and solicit human contact when unable to solve a problem. Dogs' sociocognitive behavior likely results from a selection for attention to humans during domestication, but is highly susceptible to environmental factors. Training for particular tasks appears to enhance dog-human communication, but effects may depend on the nature of the relationship determined by their role. Our aim was to examine two types of social cognition (responsiveness to human gestures, and human-directed communicative behavior in an unsolvable task) in pet dogs (n = 29) and detection dogs (n = 35). The groups did not differ in their ability to follow human signals, but pets were less responsive to signals given by a stranger than by their owner. Pets also exhibited more human-directed gazing in the unsolvable task, showing a bias for gazing at their owner compared with the stranger, whereas detection dogs showed greater persistence in attempting to solve the task compared with pets. Thus, different aspects of dogs' sociocognitive behavior may differentially vary as a function of selection or training for particular roles.

Functional connectivity of specific resting-state networks predicts trust and reciprocity in the trust game.

Economic games are used to elicit a social, conflictual situation in which people have to make decisions weighing self-related and collective interests. Combining these games with task-based fMRI has been shown to be successful in investigating the neural underpinnings of cooperative behaviors. However, it remains elusive to which extent resting-state functional connectivity (RSFC) represents an individual's propensity to prosocial behaviors in the context of economic games. Here, we investigated whether task-free RSFC predicts individual differences in the propensity to trust and reciprocate in a one-round trust game (TG) employing a prediction-analytics framework. Our results demonstrated that individual differences in the propensity to trust and reciprocity could be predicted by individual differences in the RSFC. Different subnetworks of the default-mode network associated with mentalizing exclusively predicted trust and reciprocity. Moreover, reciprocity was further predicted by the frontoparietal and cingulo-opercular networks associated with cognitive control and saliency, respectively. Our results contribute to a better understanding of how complex social behaviors are enrooted in large-scale intrinsic brain dynamics, which may represent neuromarkers for impairment of prosocial behavior in mental health disorders.

Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma.

Brain connectivity studies report group differences in pairwise connection strengths. While informative, such results are difficult to interpret since our understanding of the brain relies on region-based properties, rather than on connection information. Given that large disruptions in the brain are often caused by a few pivotal sources, we propose a novel framework to identify the sources of functional disruption from effective connectivity networks. Our approach integrates static and time-varying effective connectivity modeling in a probabilistic framework, to identify aberrant foci and the corresponding aberrant connectomics network. Using resting-state fMRI, we illustrate the utility of this novel approach in U.S. Army soldiers (N = 87) with posttraumatic stress disorder (PTSD), mild traumatic brain injury (mTBI) and combat controls. Additionally, we employed machine-learning classification to identify those significant connectivity features that possessed high predictive ability. We identified three disrupted foci (middle frontal gyrus [MFG], insula, hippocampus), and an aberrant prefrontal-subcortical-parietal network of information flow. We found the MFG to be the pivotal focus of network disruption, with aberrant strength and temporal-variability of effective connectivity to the insula, amygdala and hippocampus. These connectivities also possessed high predictive ability (giving a classification accuracy of 81%); and they exhibited significant associations with symptom severity and neurocognitive functioning. In summary, dysregulation originating in the MFG caused elevated and temporally less-variable connectivity in subcortical regions, followed by a similar effect on parietal memory-related regions. This mechanism likely contributes to the reduced control over traumatic memories leading to re-experiencing, hyperarousal and flashbacks observed in soldiers with PTSD and mTBI. Hum Brain Mapp 39:264-287, 2018. © 2017 Wiley Periodicals, Inc.

Hemodynamic response function (HRF) variability confounds resting-state fMRI functional connectivity.

PURPOSE: fMRI is the convolution of the hemodynamic response function (HRF) and unmeasured neural activity. HRF variability (HRFv) across the brain could, in principle, alter functional connectivity (FC) estimates from resting-state fMRI (rs-fMRI). Given that HRFv is driven by both neural and non-neural factors, it is problematic when it confounds FC. However, this aspect has remained largely unexplored even though FC studies have grown exponentially. We hypothesized that HRFv confounds FC estimates in the brain's default-mode-network. METHODS: We tested this hypothesis using both simulations (where the ground truth is known and modulated) as well as rs-fMRI data obtained in a 7T MRI scanner (N = 47, healthy). FC was obtained using 2 pipelines: data with hemodynamic deconvolution (DC) to estimate the HRF and minimize HRFv, and data with no deconvolution (NDC, HRFv-ignored). DC and NDC FC networks were compared, along with regional HRF differences, revealing potential false connectivities that resulted from HRFv. RESULTS: We found evidence supporting our hypothesis using both simulations and experimental data. With simulations, we found that HRFv could cause a change of up to 50% in FC. With rs-fMRI, several potential false connectivities attributable to HRFv, with majority connections being between different lobes, were identified. We found a double exponential relationship between the magnitude of HRFv and its impact on FC, with a mean/median error of 30.5/11.5% caused in FC by HRF confounds. CONCLUSION: HRFv, if ignored, could cause identification of false FC. FC findings from HRFv-ignored data should be interpreted cautiously. We suggest deconvolution to minimize HRFv.

Separate brain areas for processing human and dog faces as revealed by awake fMRI in dogs (Canis familiaris).

Functional magnetic resonance imaging (fMRI) has emerged as a viable method to study the neural processing underlying cognition in awake dogs. Working dogs were presented with pictures of dog and human faces. The human faces varied in familiarity (familiar trainers and unfamiliar individuals) and emotional valence (negative, neutral, and positive). Dog faces were familiar (kennel mates) or unfamiliar. The findings revealed adjacent but separate brain areas in the left temporal cortex for processing human and dog faces in the dog brain. The human face area (HFA) and dog face area (DFA) were both parametrically modulated by valence indicating emotion was not the basis for the separation. The HFA and DFA were not influenced by familiarity. Using resting state fMRI data, functional connectivity networks (connectivity fingerprints) were compared and matched across dogs and humans. These network analyses found that the HFA mapped onto the human fusiform area and the DFA mapped onto the human superior temporal gyrus, both core areas in the human face processing system. The findings provide insight into the evolution of face processing.

Dynamic brain connectivity is a better predictor of PTSD than static connectivity.

Using resting-state functional magnetic resonance imaging, we test the hypothesis that subjects with post-traumatic stress disorder (PTSD) are characterized by reduced temporal variability of brain connectivity compared to matched healthy controls. Specifically, we test whether PTSD is characterized by elevated static connectivity, coupled with decreased temporal variability of those connections, with the latter providing greater sensitivity toward the pathology than the former. Static functional connectivity (FC; nondirectional zero-lag correlation) and static effective connectivity (EC; directional time-lagged relationships) were obtained over the entire brain using conventional models. Dynamic FC and dynamic EC were estimated by letting the conventional models to vary as a function of time. Statistical separation and discriminability of these metrics between the groups and their ability to accurately predict the diagnostic label of a novel subject were ascertained using separate support vector machine classifiers. Our findings support our hypothesis that PTSD subjects have stronger static connectivity, but reduced temporal variability of connectivity. Further, machine learning classification accuracy obtained with dynamic FC and dynamic EC was significantly higher than that obtained with static FC and static EC, respectively. Furthermore, results also indicate that the ease with which brain regions engage or disengage with other regions may be more sensitive to underlying pathology than the strength with which they are engaged. Future studies must examine whether this is true only in the case of PTSD or is a general organizing principle in the human brain. Hum Brain Mapp 38:4479-4496, 2017. © 2017 Wiley Periodicals, Inc.

Compromised hippocampus-striatum pathway as a potential imaging biomarker of mild-traumatic brain injury and posttraumatic stress disorder.

OBJECTIVES: Military service members risk acquiring posttraumatic stress disorder (PTSD) and mild-traumatic brain injury (mTBI), with high comorbidity. Owing to overlapping symptomatology in chronic mTBI or postconcussion syndrome (PCS) and PTSD, it is difficult to assess the etiology of a patient's condition without objective measures. Using resting-state functional MRI in a novel framework, we tested the hypothesis that their neural signatures are characterized by functionally hyperconnected brain regions which are less variable over time. Additionally, we predicted that such connectivities possessed the highest ability in predicting the diagnostic membership of a novel subject (top-predictors) in addition to being statistically significant. METHODS: U.S. Army Soldiers (N = 87) with PTSD and comorbid PCS + PTSD were recruited along with combat controls. Static and dynamic functional connectivities were evaluated. Group differences were obtained in accordance with our hypothesis. Machine learning classification (MLC) was employed to determine top predictors. RESULTS: From whole-brain connectivity, we identified the hippocampus-striatum connectivity to be significantly altered in accordance with our hypothesis. Diffusion tractography revealed compromised white-matter integrity between aforementioned regions only in the PCS + PTSD group, suggesting a structural etiology for the PCS + PTSD group rather than being an extreme subset of PTSD. Employing MLC, connectivities provided worst-case accuracy of 84% (9% more than psychological measures). Additionally, the hippocampus-striatum connectivities were found to be top predictors and thus a potential biomarker of PTSD/mTBI. CONCLUSIONS: PTSD/mTBI are associated with hippocampal-striatal hyperconnectivity from which it is difficult to disengage, leading to a habit-like response toward episodic traumatic memories, which fits well with behavioral manifestations of combat-related PTSD/mTBI. Hum Brain Mapp 38:2843-2864, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Can Patel's τ accurately estimate directionality of connections in brain networks from fMRI?

PURPOSE: Investigating directional interactions between brain regions plays a critical role in fully understanding brain function. Consequently, multiple methods have been developed for noninvasively inferring directional connectivity in human brain networks using functional MRI (fMRI). Recent simulations by Smith et al. showed that Patel's τ, a method based on higher-order statistics, was the best approach for inferring directional interactions from fMRI. Because simulations make restrictive assumptions about reality, we set out to verify this finding using experimental fMRI data obtained from a three-region network in a rat model with electrophysiological validation. METHODS: Previous studies have shown that dynamic causal modeling can correctly estimate the directionality of this three-region network; Granger causality can also work after the deconvolution of the hemodynamic response. Therefore, we set out to test the hypothesis that Patel's τ obtained from either raw or deconvolved fMRI data should correctly estimate the directionality of neuronal influences obtained from intracerebral electroencephalogram in this network. RESULTS: Our results indicate that the accuracy of network directionality estimated using Patel's τ was not better than chance. CONCLUSION: First, our results highlight the necessity of experimental validation of simulation results. Second, it is unclear which brain mechanism is modeled by a directionality inferred from Patel's τ. Third, this study shows that a directional connection ascertained by different methods may mean different things and more experimental studies are needed for investigating the neuronal mechanisms underlying the direction of a connection in the brain ascertained by fMRI using different methods. M Magn Reson Med 78:2003-2010, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Functional connectivity of the left and right hippocampi: Evidence for functional lateralization along the long-axis using meta-analytic approaches and ultra-high field functional neuroimaging.

Theories regarding the functional specialization of the hippocampus date back to over a century ago. Two main theories have dominated the field. First, evidence has supported the notion of hemispheric specialization, which appears to be preserved across species. Second, an emergent and mounting set of data has suggested an anterior-posterior neurofunctional gradient. However, no study has examined these theories, and their potential interaction, using objective, robust methodological approaches. Here, we employed an established meta-analytic technique and use ultra-high field, high-resolution functional and structural neuroimaging to examine hippocampal lateralization with consideration for a long-axis differentiation. Data revealed strong support for an evolutionarily preserved hemispheric specialization. Specifically, we found intra- and interhemispheric differences with regard to anterior and posterior functional and structural connectivity, between the right and left hippocampi. For task-independent functional connectivity, we found the right anterior hippocampus to have functional connectivity with a large, distributed network, whereas the left anterior hippocampus demonstrated primarily fronto-limbic connectivity. These patterns were reversed for the posterior segmentations. Not surprisingly, for task-dependent connectivity, we found interhemispheric differences within key ipsilateral structures (i.e., parahippocampal gyrus) for both anterior and posterior segmentations. Furthermore, we identified pivotal neural hubs that share connectivity across behavioral domains, and are supported by structural connectivity (i.e., posterior cingulate cortex). Thus, our data provide evidence for a hemisphere-specific, anterior-posterior specialization of the hippocampal formation.

Diffusion of responsibility attenuates altruistic punishment: A functional magnetic resonance imaging effective connectivity study.

Humans altruistically punish violators of social norms to enforce cooperation and pro-social behaviors. However, such altruistic behaviors diminish when others are present, due to a diffusion of responsibility. We investigated the neural signatures underlying the modulations of diffusion of responsibility on altruistic punishment, conjoining a third-party punishment task with event-related functional magnetic resonance imaging and multivariate Granger causality mapping. In our study, participants acted as impartial third-party decision-makers and decided how to punish norm violations under two different social contexts: alone (i.e., full responsibility) or in the presence of putative other third-party decision makers (i.e., diffused responsibility). Our behavioral results demonstrated that the diffusion of responsibility served as a mediator of context-dependent punishment. In the presence of putative others, participants who felt less responsible also punished less severely in response to norm violations. Our neural results revealed that underlying this behavioral effect was a network of interconnected brain regions. For unfair relative to fair splits, the presence of others led to attenuated responses in brain regions implicated in signaling norm violations (e.g., AI) and to increased responses in brain regions implicated in calculating values of norm violations (e.g., vmPFC, precuneus) and mentalizing about others (dmPFC). The dmPFC acted as the driver of the punishment network, modulating target regions, such as AI, vmPFC, and precuneus, to adjust altruistic punishment behavior. Our results uncovered the neural basis of the influence of diffusion of responsibility on altruistic punishment and highlighted the role of the mentalizing network in this important phenomenon. Hum Brain Mapp 37:663-677, 2016. © 2015 Wiley Periodicals, Inc.

Enhancement of Odor-Induced Activity in the Canine Brain by Zinc Nanoparticles: A Functional MRI Study in Fully Unrestrained Conscious Dogs.

Using noninvasive in vivo functional magnetic resonance imaging (fMRI), we demonstrate that the enhancement of odorant response of olfactory receptor neurons by zinc nanoparticles leads to increase in activity in olfaction-related and higher order areas of the dog brain. To study conscious dogs, we employed behavioral training and optical motion tracking for reducing head motion artifacts. We obtained brain activation maps from dogs in both anesthetized state and fully conscious and unrestrained state. The enhancement effect of zinc nanoparticles was higher in conscious dogs with more activation in higher order areas as compared with anesthetized dogs. In conscious dogs, voxels in the olfactory bulb and hippocampus showed higher activity to odorants mixed with zinc nanoparticles as compared with pure odorants, odorants mixed with gold nanoparticles as well as zinc nanoparticles alone. These regions have been implicated in odor intensity processing in other species including humans. If the enhancement effect of zinc nanoparticles observed in vivo are confirmed by future behavioral studies, zinc nanoparticles may provide a way for enhancing the olfactory sensitivity of canines for detection of target substances such as explosives and contraband substances at very low concentrations, which would otherwise go undetected.

Effective connectivity during episodic memory retrieval in schizophrenia participants before and after antipsychotic medication.

BACKGROUND: Impairment in episodic memory is one of the most robust findings in schizophrenia. Disruptions of fronto-temporal functional connectivity that could explain some aspects of these deficits have been reported. Recent work has identified abnormal hippocampal function in unmedicated patients with schizophrenia (SZ), such as increased metabolism and glutamate content that are not always seen in medicated SZ. For these reasons, we hypothesized that altered fronto-temporal connectivity might originate from the hippocampus and might be partially restored by antipsychotic medication. METHODS: Granger causality methods were used to evaluate the effective connectivity between frontal and temporal regions in 21 unmedicated SZ and 20 matched healthy controls (HC) during performance of an episodic memory retrieval task. In 16 SZ, effective connectivity between these regions was evaluated before and after 1-week of antipsychotic treatment. RESULTS: In HC, significant effective connectivity originating from the right hippocampus to frontal regions was identified. Compared to HC, unmedicated SZ showed significant altered fronto-temporal effective connectivity, including reduced right hippocampal to right medial frontal connectivity. After 1-week of antipsychotic treatment, connectivity more closely resembled the patterns observed in HC, including increased effective connectivity from the right hippocampus to frontal regions. CONCLUSIONS: These results support the notion that memory disruption in schizophrenia might originate from hippocampal dysfunction and that medication restores some aspects of fronto-temporal dysconnectivity. Patterns of fronto-temporal connectivity could provide valuable biomarkers to identify new treatments for the symptoms of schizophrenia, including memory deficits.

Neural changes with tactile learning reflect decision-level reweighting of perceptual readout.

Despite considerable work, the neural basis of perceptual learning remains uncertain. For visual learning, although some studies suggested that changes in early sensory representations are responsible, other studies point to decision-level reweighting of perceptual readout. These competing possibilities have not been examined in other sensory systems, investigating which could help resolve the issue. Here we report a study of human tactile microspatial learning in which participants achieved >six-fold decline in acuity threshold after multiple training sessions. Functional magnetic resonance imaging was performed during performance of the tactile microspatial task and a control, tactile temporal task. Effective connectivity between relevant brain regions was estimated using multivariate, autoregressive models of hidden neuronal variables obtained by deconvolution of the hemodynamic response. Training-specific increases in task-selective activation assessed using the task × session interaction and associated changes in effective connectivity primarily involved subcortical and anterior neocortical regions implicated in motor and/or decision processes, rather than somatosensory cortical regions. A control group of participants tested twice, without intervening training, exhibited neither threshold improvement nor increases in task-selective activation. Our observations argue that neuroplasticity mediating perceptual learning occurs at the stage of perceptual readout by decision networks. This is consonant with the growing shift away from strictly modular conceptualization of the brain toward the idea that complex network interactions underlie even simple tasks. The convergence of our findings on tactile learning with recent studies of visual learning reconciles earlier discrepancies in the literature on perceptual learning.

Threat-related learning relies on distinct dorsal prefrontal cortex network connectivity.

Conditioned changes in the emotional response to threat (e.g. aversive unconditioned stimulus; UCS) are mediated in part by the prefrontal cortex (PFC). Unpredictable threats elicit large emotional responses, while the response is diminished when the threat is predictable. A better understanding of how PFC connectivity to other brain regions varies with threat predictability would provide important insights into the neural processes that mediate conditioned diminution of the emotional response to threat. The present study examined brain connectivity during predictable and unpredictable threat exposure using a fear conditioning paradigm (previously published in Wood et al., 2012) in which unconditioned functional magnetic resonance imaging data were reanalyzed to assess effective connectivity. Granger causality analysis was performed using the time series data from 15 activated regions of interest after hemodynamic deconvolution, to determine regional effective connectivity. In addition, connectivity path weights were correlated with trait anxiety measures to assess the relationship between negative affect and brain connectivity. Results indicate the dorsomedial PFC (dmPFC) serves as a neural hub that influences activity in other brain regions when threats are unpredictable. In contrast, the dorsolateral PFC (dlPFC) serves as a neural hub that influences the activity of other brain regions when threats are predictable. These findings are consistent with the view that the dmPFC coordinates brain activity to take action, perhaps in a reactive manner, when an unpredicted threat is encountered, while the dlPFC coordinates brain regions to take action, in what may be a more proactive manner, to respond to predictable threats. Further, dlPFC connectivity to other brain regions (e.g. ventromedial PFC, amygdala, and insula) varied with negative affect (i.e. trait anxiety) when the UCS was predictable, suggesting that stronger connectivity may be required for emotion regulation in individuals with higher levels of negative affect.

Early life trauma and directional brain connectivity within major depression.

OBJECTIVE: Early life trauma (ELT) is a significant risk factor for the onset of depression. Emerging findings indicate ELT is associated with enhanced amygdala reactivity to aversive stimuli in never-depressed healthy controls as well as those with acute depression but may be absent in non-ELT exposed depressed. The precise mechanism mediating these differences in amygdala reactivity remains unclear. METHOD: The authors used Granger causality methods to evaluate task-based directional connectivity between medial or lateral prefrontal cortex (PFC) and amygdala in 20 unmedicated patients with current major depressive disorder (MDD) and 19 healthy matched controls while participants engaged in an affective variant of the flanker task comparing response to sad and neutral faces. These data were correlated with childhood trauma history. RESULTS: Exposure to ELT was associated with failure of inhibition within the MDD group based on medial PFC-amygdala connectivity. In contrast, non-ELT exposed MDD was associated with a negative causal pathway from medial prefrontal cortex to amygdala, despite reduced dorsolateral PFC input in comparison to healthy controls. Neither MDD group demonstrated significant lateral PFC-amygdala connectivity in comparison to healthy controls. CONCLUSIONS: Failure of the circuit implicated in emotion regulation was associated with a significant history of ELT but not with MDD more broadly. Non-ELT related depression was associated with intact regulation of emotion despite the absence of difference in severity of illness. These findings indicate opposing system-level differences within depression relative to ELT are expressed as differential amygdala reactivity.