Self-regulation of the posterior cingulate cortex with real-time fMRI neurofeedback augmented mindfulness training in healthy adolescents: A nonrandomized feasibility study.

Mindfulness training (MT) promotes the development of one's ability to observe and attend to internal and external experiences with objectivity and nonjudgment with evidence to improve psychological well-being. Real-time functional MRI neurofeedback (rtfMRI-nf) is a noninvasive method of modulating activity of a brain region or circuit. The posterior cingulate cortex (PCC) has been hypothesized to be an important hub instantiating a mindful state. This nonrandomized, single-arm study examined the feasibility and tolerability of training typically developing adolescents to self-regulate the posterior cingulate cortex (PCC) using rtfMRI-nf during MT. Thirty-four adolescents (mean age: 15 years; 14 females) completed the neurofeedback augmented mindfulness training task, including Focus-on-Breath (MT), Describe (self-referential thinking), and Rest conditions, across three neurofeedback and two non-neurofeedback runs (Observe, Transfer). Self-report assessments demonstrated the feasibility and tolerability of the task. Neurofeedback runs differed significantly from non-neurofeedback runs for the Focus-on-Breath versus Describe contrast, characterized by decreased activity in the PCC during the Focus-on-Breath condition (z = -2.38 to -6.27). MT neurofeedback neural representation further involved the medial prefrontal cortex, anterior cingulate cortex, dorsolateral prefrontal cortex, posterior insula, hippocampus, and amygdala. State awareness of physical sensations increased following rtfMRI-nf and was maintained at 1-week follow-up (Cohens' d = 0.69). Findings demonstrate feasibility and tolerability of rtfMRI-nf in healthy adolescents, replicates the role of PCC in MT, and demonstrate a potential neuromodulatory mechanism to leverage and streamline the learning of mindfulness practice. ( ClinicalTrials.gov identifier #NCT04053582; August 12, 2019).

A hidden menace? Cytomegalovirus infection is associated with reduced cortical gray matter volume in major depressive disorder.

Human cytomegalovirus (HCMV) infection is associated with neuropathology in patients with impaired immunity and/or inflammatory diseases. However, the association between gray matter volume (GMV) and HCMV has never been examined in major depressive disorder (MDD) despite the presence of inflammation and impaired viral immunity in a subset of patients. We tested this relationship in two independent samples consisting of 179 individuals with MDD and 41 healthy controls (HC) (sample 1) and 124 MDD participants and 148 HCs (sample 2). HCMV positive (HCMV+) and HCMV negative (HCMV-) groups within each sample were balanced on up to 11 different clinical/demographic variables using inverse probability of treatment weighting. GMV of 87 regions was measured with FreeSurfer. There was a main effect of HCMV serostatus but not diagnosis that replicated across samples. Relative to HCMV- subjects, HCMV+ subjects in sample 1 showed a significant reduction of volume in six regions (puncorrected < 0.05). The reductions in GMV of the right supramarginal gyrus (standardized beta coefficient (SBC) = -0.26) and left fusiform gyrus (SBC = -0.25) in sample 1 were replicated in sample 2: right supramarginal gyrus (puncorrected < 0.05, SBC = -0.32), left fusiform gyrus (PFDR < 0.01, SBC = -0.51). Posthoc tests revealed that the effect of HCMV was driven by differences between the HCMV+ and HCMV- MDD subgroups. HCMV IgG level, a surrogate marker of viral activity, was correlated with GMV in the left fusiform gyrus (r = -0.19, Puncorrected = 0.049) and right supramarginal gyrus (r = -0.19, puncorrected = 0.043) in the HCMV+ group of sample 1. Conceivably, HCMV infection may be a treatable source of neuropathology in vulnerable MDD patients.

Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.

Prevent breaking bad: A proof of concept study of rebalancing the brain's rumination circuit with real-time fMRI functional connectivity neurofeedback.

Rumination, repetitively thinking about the causes, consequences, and one's negative affect, has been considered as an important factor of depression. The intrusion of ruminative thoughts is not easily controlled, and it may be useful to visualize one's neural activity related to rumination and to use that information to facilitate one's self-control. Real-time fMRI neurofeedback (rtfMRI-nf) enables one to see and regulate the fMRI signal from their own brain. This proof-of concept study utilized connectivity-based rtfMRI-nf (cnf) to normalize brain functional connectivity (FC) associated with rumination. Healthy participants were instructed to brake or decrease FC between the precuneus and the right temporoparietal junction (rTPJ), associated with high levels of rumination, while engaging in a self-referential task. The cnf group (n = 14) showed a linear decrease in the precuneus-rTPJ FC across neurofeedback training (trend [112] = -0.180, 95% confidence interval [CI] -0.330 to -0.031, while the sham group (n = 14) showed a linear increase in the target FC (trend [112] = 0.151, 95% CI 0.017 to 0.299). Although the cnf group showed a greater reduction in state-rumination compared to the sham group after neurofeedback training (p < .05), decoupled precuneus-rTPJ FC did not predict attenuated state-rumination. We did not find any significant aversive effects of rtfMRI-nf in all study participants. These results suggest that cnf has the capacity to influence FC among precuneus and rTPJ of a ruminative brain circuit. This approach can be applied to mood and anxiety patients to determine the clinical benefits of reduction in maladaptive rumination.

Taking the body off the mind: Decreased functional connectivity between somatomotor and default-mode networks following Floatation-REST.

Floatation-Reduced Environmental Stimulation Therapy (REST) is a procedure that reduces stimulation of the human nervous system by minimizing sensory signals from visual, auditory, olfactory, gustatory, thermal, tactile, vestibular, gravitational, and proprioceptive channels, in addition to minimizing musculoskeletal movement and speech. Initial research has found that Floatation-REST can elicit short-term reductions in anxiety, depression, and pain, yet little is known about the brain networks impacted by the intervention. This study represents the first functional neuroimaging investigation of Floatation-REST, and we utilized a data-driven exploratory analysis to determine whether the intervention leads to altered patterns of resting-state functional connectivity (rsFC). Healthy participants underwent functional magnetic resonance imaging (fMRI) before and after 90 min of Floatation-REST or a control condition that entailed resting supine in a zero-gravity chair for an equivalent amount of time. Multivariate Distance Matrix Regression (MDMR), a statistically-stringent whole-brain searchlight approach, guided subsequent seed-based connectivity analyses of the resting-state fMRI data. MDMR identified peak clusters of rsFC change between the pre- and post-float fMRI, revealing significant decreases in rsFC both within and between posterior hubs of the default-mode network (DMN) and a large swath of cortical tissue encompassing the primary and secondary somatomotor cortices extending into the posterior insula. The control condition, an active form of REST, showed a similar pattern of reduced rsFC. Thus, reduced stimulation of the nervous system appears to be reflected by reduced rsFC within the brain networks most responsible for creating and mapping our sense of self.

Appetite changes reveal depression subgroups with distinct endocrine, metabolic, and immune states.

There exists little human neuroscience research to explain why some individuals lose their appetite when they become depressed, while others eat more. Answering this question may reveal much about the various pathophysiologies underlying depression. The present study combined neuroimaging, salivary cortisol, and blood markers of inflammation and metabolism collected prior to scanning. We compared the relationships between peripheral endocrine, metabolic, and immune signaling and brain activity to food cues between depressed participants experiencing increased (N = 23) or decreased (N = 31) appetite and weight in their current depressive episode and healthy control participants (N = 42). The two depression subgroups were unmedicated and did not differ in depression severity, anxiety, anhedonia, or body mass index. Depressed participants experiencing decreased appetite had higher cortisol levels than subjects in the other two groups, and their cortisol values correlated inversely with the ventral striatal response to food cues. In contrast, depressed participants experiencing increased appetite exhibited marked immunometabolic dysregulation, with higher insulin, insulin resistance, leptin, CRP, IL-1RA, and IL-6, and lower ghrelin than subjects in other groups, and the magnitude of their insulin resistance correlated positively with the insula response to food cues. These findings provide novel evidence linking aberrations in homeostatic signaling pathways within depression subtypes to the activity of neural systems that respond to food cues and select when, what, and how much to eat. In conjunction with prior work, the present findings strongly support the existence of pathophysiologically distinct depression subtypes for which the direction of appetite change may be an easily measured behavioral marker.

Into the Unknown: Examining Neural Representations of Parent-Adolescent Interactions.

The parent-adolescent relationship is important for adolescents' emotion regulation (ER), yet little is known regarding the neural patterns of dyadic ER that occur during parent-adolescent interactions. A novel measure that can be used to examine such patterns is cross-brain connectivity (CBC)-concurrent and time-lagged connectivity between two individuals' brain regions. This study sought to provide evidence of CBC and explore associations between CBC, parenting, and adolescent internalizing symptoms. Thirty-five adolescents (mean age = 15 years, 69% female, 72% Non-Hispanic White, 17% Black, 11% Hispanic or Latino) and one biological parent (94% female) completed an fMRI hyperscanning conflict discussion task. Results revealed CBC between emotion-related brain regions. Exploratory analyses indicated CBC is associated with parenting and adolescent depressive symptoms.

Self-regulation of ventromedial prefrontal cortex activation using real-time fMRI neurofeedback-Influence of default mode network.

The ventromedial prefrontal cortex (vmPFC) is involved in regulation of negative emotion and decision-making, emotional and behavioral control, and active resilient coping. This pilot study examined the feasibility of training healthy subjects (n = 27) to self-regulate the vmPFC activity using a real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf). Participants in the experimental group (EG, n = 18) were provided with an ongoing vmPFC hemodynamic activity (rtfMRI-nf signal represented as variable-height bar). Individuals were instructed to raise the bar by self-relevant value-based thinking. Participants in the control group (CG, n = 9) performed the same task; however, they were provided with computer-generated sham neurofeedback signal. Results demonstrate that (a) both the CG and the EG show a higher vmPFC fMRI signal at the baseline than during neurofeedback training; (b) no significant positive training effect was seen in the vmPFC across neurofeedback runs; however, the medial prefrontal cortex, middle temporal gyri, inferior frontal gyri, and precuneus showed significant decreasing trends across the training runs only for the EG; (c) the vmPFC rtfMRI-nf signal associated with the fMRI signal across the default mode network (DMN). These findings suggest that it may be difficult to modulate a single DMN region without affecting other DMN regions. Observed decreased vmPFC activity during the neurofeedback task could be due to interference from the fMRI signal within other DMN network regions, as well as interaction with task-positive networks. Even though participants in the EG did not show significant positive increase in the vmPFC activity among neurofeedback runs, they were able to learn to accommodate the demand of self-regulation task to maintain the vmPFC activity with the help of a neurofeedback signal.

Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

Improved autoregressive model for correction of noise serial correlation in fast fMRI.

PURPOSE: In rapidly acquired functional MRI (fast fMRI) data, the noise serial correlations (SC) can produce problematically overestimated T-statistics which lead to invalid statistical inferences. This study aims to evaluate and improve the accuracy of high-order autoregressive model (AR(p), where p is the model order) based prewhitening method in the SC correction. METHODS: Fast fMRI images were acquired at rest (null data) using a multiband simultaneous multi-slice echo planar imaging pulse sequence with repetition time (TR) = 300 and 500 ms. The SC effect in the fast fMRI data was corrected using the prewhitening method based on two AR(p) models: (1) the conventional model (fixed AR(p)) which preselects a constant p for all the image voxels; (2) an improved model (ARAICc ) that employs the corrected Akaike information criterion voxel-wise to automatically select the model orders for each voxel. To evaluate accuracy of SC correction, false positive characteristics were measured by assuming the presence of block and event-related tasks in the null data without image smoothing. The performance of prewhitening was also examined in smoothed images by adding pseudo task fMRI signals into the null data and comparing the detected to simulated activations (ground truth). RESULTS: The measured false positive characteristics agreed well with the theoretical curve when using the ARAICc , and the activation maps in the smoothed data matched the ground truth. The ARAICc showed improved performance than the fixed AR(p) method. CONCLUSION: The ARAICc can effectively remove noise SC, and accurate statistical analysis results can be obtained with the ARAICc correction in fast fMRI.

Diagnosis-independent loss of T-cell costimulatory molecules in individuals with cytomegalovirus infection.

Major depressive disorder (MDD) is associated with physiological changes commonly observed with increasing age, such as inflammation and impaired immune function. Age-related impaired adaptive immunity is characterized by the loss of naive T-cells and the reciprocal accumulation of memory T-cells together with the loss of T-cell co-stimulatory molecules. Additionally, the presence and activity of cytomegalovirus (CMV) alters the architecture of the T-cell compartment in a manner consistent with premature aging. Because CMV is also thought to reactivate with psychological stress, this study tested whether MDD influences age-related phenotypes of T-cell populations in the context of CMV infection in young and middle-aged adults. Morning blood samples from volunteers with a DSM-IV diagnosis of MDD (n = 98, mean age(SD) = 36(10) years, 74.5% female, 57.1% CMV+) and comparison controls (n = 98, mean age(SD) = 34(10) years, 68.4% female, 51.0% CMV+) were evaluated for CMV IgG antibody status and the distribution of late differentiated (CD27-CD28-) cells within CD4+ and CD8+ T-cell subsets, i.e. naive (CCR7+CD45RA+), effector memory (EM, CCR7-CD45RA-), central memory (CM, CCR7+CD45RA-) and effector memory cells re-expressing CD45RA (EMRA, CCR7-CD45RA+). Mixed linear regression models controlling for age, sex, ethnicity and flow cytometry batch showed that CMV seropositivity was associated with a reduction in naive T-cells, expansion of EMRA T-cells, and a greater percent distribution of CD27-CD28- cells within CD4+ and CD8+ memory T-cell subsets (p's < 0.004), but there was no significant effect of MDD, nor any significant interaction between CMV and diagnosis. Unexpectedly, depressed men were less likely to be CMV+ and depressed women were more likely to be CMV+ than sex-matched controls suggesting a possible interaction between sex and MDD on CMV susceptibility, but this three-way interaction did not significantly affect the T-cell subtypes. Our findings suggest that depression in young and middle-aged adults does not prematurely advance aging of the T-cell compartment independently of CMV, but there may be significant sex-specific effects on adaptive immunity that warrant further investigation.

Screen media activity and brain structure in youth: Evidence for diverse structural correlation networks from the ABCD study.

The adolescent brain undergoes profound structural changes which is influenced by many factors. Screen media activity (SMA; e.g., watching television or videos, playing video games, or using social media) is a common recreational activity in children and adolescents; however, its effect on brain structure is not well understood. A multivariate approach with the first cross-sectional data release from the Adolescent Brain Cognitive Development (ABCD) study was used to test the maturational coupling hypothesis, i.e. the notion that coordinated patterns of structural change related to specific behaviors. Moreover, the utility of this approach was tested by determining the association between these structural correlation networks and psychopathology or cognition. ABCD participants with usable structural imaging and SMA data (N = 4277 of 4524) were subjected to a Group Factor Analysis (GFA) to identify latent variables that relate SMA to cortical thickness, sulcal depth, and gray matter volume. Subject scores from these latent variables were used in generalized linear mixed-effect models to investigate associations between SMA and internalizing and externalizing psychopathology, as well as fluid and crystalized intelligence. Four SMA-related GFAs explained 37% of the variance between SMA and structural brain indices. SMA-related GFAs correlated with brain areas that support homologous functions. Some but not all SMA-related factors corresponded with higher externalizing (Cohen's d effect size (ES) 0.06-0.1) but not internalizing psychopathology and lower crystalized (ES: 0.08-0.1) and fluid intelligence (ES: 0.04-0.09). Taken together, these findings support the notion of SMA related maturational coupling or structural correlation networks in the brain and provides evidence that individual differences of these networks have mixed consequences for psychopathology and cognitive performance.

Image processing and analysis methods for the Adolescent Brain Cognitive Development Study.

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study.

Real-time fMRI neurofeedback of the mediodorsal and anterior thalamus enhances correlation between thalamic BOLD activity and alpha EEG rhythm.

Real-time fMRI neurofeedback (rtfMRI-nf) with simultaneous EEG allows volitional modulation of BOLD activity of target brain regions and investigation of related electrophysiological activity. We applied this approach to study correlations between thalamic BOLD activity and alpha EEG rhythm. Healthy volunteers in the experimental group (EG, n = 15) learned to upregulate BOLD activity of the target region consisting of the mediodorsal (MD) and anterior (AN) thalamic nuclei using rtfMRI-nf during retrieval of happy autobiographical memories. Healthy subjects in the control group (CG, n = 14) were provided with a sham feedback. The EG participants were able to significantly increase BOLD activities of the MD and AN. Functional connectivity between the MD and the inferior precuneus was significantly enhanced during the rtfMRI-nf task. Average individual changes in the occipital alpha EEG power significantly correlated with the average MD BOLD activity levels for the EG. Temporal correlations between the occipital alpha EEG power and BOLD activities of the MD and AN were significantly enhanced, during the rtfMRI-nf task, for the EG compared to the CG. Temporal correlations with the alpha power were also significantly enhanced for the posterior nodes of the default mode network, including the precuneus/posterior cingulate, and for the dorsal striatum. Our findings suggest that the temporal correlation between the MD BOLD activity and posterior alpha EEG power is modulated by the interaction between the MD and the inferior precuneus, reflected in their functional connectivity. Our results demonstrate the potential of the rtfMRI-nf with simultaneous EEG for noninvasive neuromodulation studies of human brain function.

Cardiorespiratory noise correction improves the ASL signal.

Cardiorespiratory fluctuations such as changes in heart rate or respiration volume influence the temporal dynamics of cerebral blood flow (CBF) measurements during arterial spin labeling (ASL) fMRI. This "physiological noise" can confound estimates of resting state network activity, and it may lower the signal-to-noise ratio of ASL during task-related experiments. In this study we examined several methods for minimizing the contributions of both synchronized and non-synchronized physiological noise in ASL measures of CBF, by combining the RETROICOR approach with different linear deconvolution models. We evaluated the amount of variance in CBF that could be explained by each method during physiological rest, in both resting state and task performance conditions. To further demonstrate the feasibility of this approach, we induced low-frequency cardiorespiratory deviations via peripheral adrenergic stimulation with isoproterenol, and determined how these fluctuations influenced CBF, before and after applying noise correction. By suppressing physiological noise, we observed substantial improvements in the signal-to-noise ratio at the individual and group activation levels. Our results suggest that variations in cardiac and respiratory parameters can account for a large proportion of the variance in resting and task-based CBF, and indicate that regressing out these non-neuronal signal variations improves the intrinsically low signal-to-noise ratio of ASL. This approach may help to better identify and control physiologically driven activations in ASL resting state and task-based analyses.

Differential privacy-based evaporative cooling feature selection and classification with relief-F and random forests.

MOTIVATION: Classification of individuals into disease or clinical categories from high-dimensional biological data with low prediction error is an important challenge of statistical learning in bioinformatics. Feature selection can improve classification accuracy but must be incorporated carefully into cross-validation to avoid overfitting. Recently, feature selection methods based on differential privacy, such as differentially private random forests and reusable holdout sets, have been proposed. However, for domains such as bioinformatics, where the number of features is much larger than the number of observations p≫n , these differential privacy methods are susceptible to overfitting. METHODS: We introduce private Evaporative Cooling, a stochastic privacy-preserving machine learning algorithm that uses Relief-F for feature selection and random forest for privacy preserving classification that also prevents overfitting. We relate the privacy-preserving threshold mechanism to a thermodynamic Maxwell-Boltzmann distribution, where the temperature represents the privacy threshold. We use the thermal statistical physics concept of Evaporative Cooling of atomic gases to perform backward stepwise privacy-preserving feature selection. RESULTS: On simulated data with main effects and statistical interactions, we compare accuracies on holdout and validation sets for three privacy-preserving methods: the reusable holdout, reusable holdout with random forest, and private Evaporative Cooling, which uses Relief-F feature selection and random forest classification. In simulations where interactions exist between attributes, private Evaporative Cooling provides higher classification accuracy without overfitting based on an independent validation set. In simulations without interactions, thresholdout with random forest and private Evaporative Cooling give comparable accuracies. We also apply these privacy methods to human brain resting-state fMRI data from a study of major depressive disorder. AVAILABILITY AND IMPLEMENTATION: Code available at http://insilico.utulsa.edu/software/privateEC . CONTACT: brett-mckinney@utulsa.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Kynurenic acid is reduced in females and oral contraceptive users: Implications for depression.

The incidence of depression is approximately 2-fold greater in women than men but the biological mechanisms underlying this phenomenon remain unclear. One potential mechanism that has been understudied is immune function, which is modulated by sex hormones and differs considerably between males and females. The immune-regulating kynurenine pathway previously has been implicated in the pathogenesis of mood disorders. In particular, a decreased ratio of neuroprotective (kynurenic acid; KynA) to neurotoxic (3-hydroxykynurenine; 3HK and quinolinic acid; QA) kynurenine pathway metabolites has been reported in several mood disorder subtypes. Yet there is a paucity of research investigating sex differences in the kynurenine pathway in the context of depression. Similarly, oral contraceptive (OC) use has been shown to be a risk factor for depression but to our knowledge this epidemiological relationship has not been considered within the framework of immune dysfunction. Here, we compared the concentrations of c-reactive protein (CRP) and kynurenine pathway metabolites in a combined sample of subjects with major depressive disorder (MDD), bipolar disorder (BD), and healthy controls (HC) comprising 130 men and 350 women. CRP was measured in a CLIA-certified hospital laboratory. Kynurenine metabolites were quantified using high performance liquid chromatography with tandem mass spectrometry. Estradiol and progesterone were quantified with the Mesoscale Discovery (MSD) platform. After controlling for diagnosis, age, sex, BMI, analysis batch, and self-reported childhood trauma we found that women had significantly lower KynA/3HK and KynA/QA ratios than men, and that these results were driven by a decrease in KynA. There was no significant difference between males and females in the concentration of CRP. Further, women taking OC showed significantly higher levels of CRP and lower ratios of KynA/3HK and KynA/QA compared with women on no form of contraception. Moreover, among women using OC, progesterone concentrations were positively correlated with KynA, KynA/3HK, and KynA/QA. Although preliminary, our results indicate that on average, healthy women show the same pattern of kynurenine pathway metabolism as that observed in subjects with depression. This finding raises the possibility that a reduction in KynA concentrations in women may constitute a vulnerability factor that partly explains the higher incidence of depression in females. Further, the significant association between OC use and reduced KynA as well as increased CRP, could conceivably partially account for the epidemiological association between OC use and depression. Nonetheless, because of the cross-sectional nature of this study, these hypotheses need to be more rigorously tested with longitudinal designs and/or large epidemiological studies.

Amygdala real-time functional magnetic resonance imaging neurofeedback for major depressive disorder: A review.

Advances in imaging technologies have allowed for the analysis of functional magnetic resonance imaging data in real-time (rtfMRI), leading to the development of neurofeedback (nf) training. This rtfMRI-nf training utilizes functional magnetic resonance imaging (fMRI) tomographic localization capacity to allow a person to see and regulate the localized hemodynamic signal from his or her own brain. In this review, we summarize the results of several studies that have developed and applied neurofeedback training to healthy and depressed individuals with the amygdala as the neurofeedback target and the goal to increase the hemodynamic response during positive autobiographical memory recall. We review these studies and highlight some of the challenges and advances in developing an rtfMRI-nf paradigm for broader use in psychiatric populations. The work described focuses on our line of research aiming to develop the rtfMRI-nf into an intervention, and includes a discussion of the selection of a region of interest for feedback, selecting a control condition, behavioral and cognitive effects of training, and predicting which participants are most likely to respond well to training. While the results of these studies are encouraging and suggest the clinical potential of amygdala rtfMRI-nf in alleviating symptoms of major depressive disorder, larger studies are warranted to confirm its efficacy.

Convergent gustatory and viscerosensory processing in the human dorsal mid-insula.

The homeostatic regulation of feeding behavior requires an organism to be able to integrate information from its internal environment, including peripheral visceral signals about the body's current energy needs, with information from its external environment, such as the palatability of energy-rich food stimuli. The insula, which serves as the brain's primary sensory cortex for representing both visceral signals from the body and taste signals from the mouth and tongue, is a likely candidate region in which this integration might occur. However, to date it has been unclear whether information from these two homeostatically critical faculties is merely co-represented in the human insula, or actually integrated there. Recent functional neuroimaging evidence of a common substrate for visceral interoception and taste perception within the human dorsal mid-insula suggests a model whereby a single population of neurons may integrate viscerosensory and gustatory signals. To test this model, we used fMRI-Adaptation to identify whether insula regions that exhibit repetition suppression following repeated interoception trials would then also exhibit adapted responses to subsequent gustatory stimuli. Multiple mid and anterior regions of the insula exhibited adaptation to interoceptive trials specifically, but only the dorsal mid-insula regions exhibited an adapted gustatory response following interoception. The discovery of this gustatory-interoceptive convergence within the neurons of the human insula supports the existence of a heretofore-undocumented neural pathway by which visceral signals from the periphery modulate the activity of brain regions involved in feeding behavior. Hum Brain Mapp 38:2150-2164, 2017. © 2017 Wiley Periodicals, Inc.

Sex differences in neural responses to subliminal sad and happy faces in healthy individuals: Implications for depression.

Twice as many women as men suffer from mood and anxiety disorders, yet the biological underpinnings of this phenomenon have been understudied and remain unclear. We and others have shown that the hemodynamic response to subliminally presented sad or happy faces during functional MRI (fMRI) is a robust biomarker for the attentional bias toward negative information classically observed in major depression. Here we used fMRI to compare the performance of healthy females (n = 28) and healthy males (n = 28) on a backward masking task using a fast event-related design with gradient-recalled, echoplanar imaging with sensitivity encoding. The image data were compared across groups using a region-of-interest analysis with small-volume correction to control for multiple testing (Pcorrected < 0.05, cluster size ≥ 20 voxels). Notably, compared with males, females showed greater BOLD activity in the subgenual anterior cingulate cortex (sgACC) and the right hippocampus when viewing masked sad vs. masked happy faces. Furthermore, females displayed reduced BOLD activity in the right pregenual ACC and left amygdala when viewing masked happy vs. masked neutral faces. Given that we have previously reported similar findings for depressed participants compared with healthy controls (regardless of gender), our results raise the possibility that on average healthy females show subtle emotional processing biases that conceivably reflect a subgroup of women predisposed to depression. Nevertheless, we note that the differences between males and females were small and derived from region-of-interest rather than voxelwise analyses. © 2016 Wiley Periodicals, Inc.