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Several imaging modalities, including T1-weighted structural imaging, diffusion tensor imaging, and functional MRI can show chronological age related changes. Employing machine learning algorithms, an individual's imaging data can predict their age with reasonable accuracy. While details vary according to modality, the general strategy is to: (1) extract image-related features, (2) build a model on a training set that uses those features to predict an individual's age, (3) validate the model on a test dataset, producing a predicted age for each individual, (4) define the \"Brain Age Gap Estimate\" (BrainAGE) as the difference between an individual's predicted age and his/her chronological age, (5) estimate the relationship between BrainAGE and other variables of interest, and (6) make inferences about those variables and accelerated or delayed brain aging. For example, a group of individuals with overall positive BrainAGE may show signs of accelerated aging in other variables as well. There is inevitably an overestimation of the age of younger individuals and an underestimation of the age of older individuals due to \"regression to the mean.\" The correlation between chronological age and BrainAGE may significantly impact the relationship between BrainAGE and other variables of interest when they are also related to age. In this study, we examine the detectability of variable effects under different assumptions. We use empirical results from two separate datasets [training = 475 healthy volunteers, aged 18-60 years (259 female); testing = 489 participants including people with mood/anxiety, substance use, eating disorders and healthy controls, aged 18-56 years (312 female)] to inform simulation parameter selection. Outcomes in simulated and empirical data strongly support the proposal that models incorporating BrainAGE should include chronological age as a covariate. We propose either including age as a covariate in step 5 of the above framework, or employing a multistep procedure where age is regressed on BrainAGE prior to step 5, producing BrainAGE Residualized (BrainAGER) scores.

IntroductionAlthough neuroscience has made tremendous progress towards understanding the basic neural circuitry underlying important processes such as attention, memory and emotion, little progress has been made in applying these insights to psychiatric populations to make clinically meaningful treatment predictions. The overall aim of the Tulsa 1000 (T-1000) study is to use the NIMH Research Domain Criteria framework in order to establish a robust and reliable dimensional set of variables that quantifies the positive and negative valence, cognition and arousal domains, including interoception, to generate clinically useful treatment predictions.Methods and analysisThe T-1000 is a naturalistic study that will recruit, assess and longitudinally follow 1000 participants, including healthy controls and treatment-seeking individuals with mood, anxiety, substance use and eating disorders. Each participant will undergo interview, behavioural, biomarker and neuroimaging assessments over the course of 1 year. The study goal is to determine how disorders of affect, substance use and eating behaviour organise across different levels of analysis (molecules, genes, cells, neural circuits, physiology, behaviour and self-report) to predict symptom severity, treatment outcome and long-term prognosis. The data will be used to generate computational models based on Bayesian statistics. The final end point of this multilevel latent variable analysis will be standardised assessments that can be developed into clinical tools to help clinicians predict outcomes and select the best intervention for each individual, thereby reducing the burden of mental disorders, and taking psychiatry a step closer towards personalised medicine.Ethics and disseminationEthical approval was obtained from Western Institutional Review Board screening protocol #20101611. The dissemination plan includes informing health professionals of results for clinical practice, submitting results to journals for peer-reviewed publication, presenting results at national and international conferences and making the dataset available to researchers and mental health professionals.Trial registration number NCT02450240; Pre-results.

Electroencephalography (EEG) measures the brain's electrophysiological spatio-temporal activities with high temporal resolution. Multichannel and broadband analysis of EEG signals is referred to as EEG microstates (EEG-ms) and can characterize such dynamic neuronal activity. EEG-ms have gained much attention due to the increasing evidence of their association with mental activities and large-scale brain networks identified by functional magnetic resonance imaging (fMRI). Spatially independent EEG-ms are quasi-stationary topographies (e.g., stable, lasting a few dozen milliseconds) typically classified into four canonical classes (microstates A through D). They can be identified by clustering EEG signals around EEG global field power (GFP) maxima points. We examined the EEG-ms properties and the dynamics of cohorts of mood and anxiety (MA) disorders subjects ( = 61) and healthy controls (HCs; = 52). In both groups, we found four distinct classes of EEG-ms (A through D), which did not differ among cohorts. This suggests a lack of significant structural cortical abnormalities among cohorts, which would otherwise affect the EEG-ms topographies. However, both cohorts' brain network dynamics significantly varied, as reflected in EEG-ms properties. Compared to HC, the MA cohort features a lower transition probability between EEG-ms B and D and higher transition probability from A to D and from B to C, with a trend towards significance in the average duration of microstate C. Furthermore, we harnessed a recently introduced theoretical approach to analyze the temporal dependencies in EEG-ms. The results revealed that the transition matrices of MA group exhibit higher symmetrical and stationarity properties as compared to HC ones. In addition, we found an elevation in the temporal dependencies among microstates, especially in microstate B for the MA group. The determined alteration in EEG-ms temporal dependencies among the cohorts suggests that brain abnormalities in mood and anxiety disorders reflect aberrant neural dynamics and a temporal dwelling among ceratin brain states (i.e., mood and anxiety disorders subjects have a less dynamicity in switching between different brain states).

Major depressive disorder (MDD) is associated with immunologic and metabolic alterations linked to central processing dysfunctions, including attenuated reward processing. This study investigated the associations between inflammation, metabolic hormones (leptin, insulin, adiponectin), and reward-related brain processing in MDD patients with high (MDD-High) and low (MDD-Low) C-reactive protein (CRP) levels compared to healthy comparison subjects (HC). Participants completed a blood draw and a monetary incentive delay task during functional magnetic resonance imaging. Although groups did not differ in insulin or adiponectin concentrations, both MDD-High (Wilcoxon p  = 0.004, d  = 0.65) and MDD-Low (Wilcoxon p  = 0.046, d  = 0.53) showed higher leptin concentrations than HC but did not differ from each other. Across MDD participants, higher leptin levels were associated with lower brain activation during reward anticipation in the left insula ( r  = − 0.30, p  = 0.004) and left dorsolateral putamen ( r  = -− 0.24, p  = 0.025). In contrast, within HC, higher leptin concentrations were associated with higher activation during reward anticipation in the same regions (insula: r  = 0.40, p  = 0.007; putamen: r  = 0.37, p  = 0.014). Depression may be characterized by elevated pro-inflammatory signaling via leptin concentrations through alternate inflammatory pathways distinct to CRP.

Psychological theories of suicide suggest that certain traits may reduce aversion to physical threat and increase the probability of transitioning from suicidal ideation to action. Here, we investigated whether blunted sensitivity to bodily signals is associated with suicidal action by comparing individuals with a history of attempted suicide to a matched psychiatric reference sample without suicide attempts. We examined interoceptive processing across a panel of tasks: breath-hold challenge, cold-pressor challenge, and heartbeat perception during and outside of functional magnetic resonance imaging. Suicide attempters tolerated the breath-hold and cold-pressor challenges for significantly longer and displayed lower heartbeat perception accuracy than non-attempters. These differences were mirrored by reduced activation of the mid/posterior insula during attention to heartbeat sensations. Our findings suggest that suicide attempters exhibit an ‘interoceptive numbing’ characterized by increased tolerance for aversive sensations and decreased awareness of non-aversive sensations. We conclude that blunted interoception may be implicated in suicidal behavior. The human brain closely monitors body signals essential for our survival, including our heartbeat, our breathing and even the temperature of our skin. This mostly unconscious process is called interoception. It helps people perceive potential or actual threats and helps them to respond appropriately. For example, a person charged by a wild animal will act instinctively to run, fight or freeze. Unlike most creatures, humans show an ability to counteract these survival instincts, and are capable of intentionally engaging in behaviors that result in physical harm. Recent increases in the rate of suicide have made it more urgent to try to understand what leads to this behavior in humans. Now, DeVille et al. show that people with psychiatric disorders who have survived a suicide attempt have blunted interoception. In four experiments, people with a history of suicide attempts were compared to another group of individuals without a history of suicide attempts. The groups were carefully matched such that there were no significant differences in the demographic and clinical characteristics of the two groups, including in terms of their age, sex, body mass index and psychiatric symptoms. Both groups completed uncomfortable tasks like holding their breath or keeping their hand in icy cold water. The participants also completed two tasks that required them to focus on their own heartbeat, one of which was paired with functional magnetic resonance imaging. Those with a history of suicide attempts held their breath and kept their hand in cold water for longer, and also were less in tune with their heart rate. This “interoceptive numbing” was associated with less activity in part of the brain called the insular cortex. These differences could not be explained by the individuals having a psychiatric disorder or a history of considering suicide, or by them taking psychiatric medications. Instead, the interoceptive numbing was most often seen in individuals who made an attempt on their own life. The experiments identify physical characteristics that may differentiate people who attempt suicide from those who do not. This lays the groundwork for future research aimed at identifying biological indicators of suicide risk. More studies are needed to verify the results. If the results are verified, the next step would be prospective studies to determine whether measuring interoception can help clinicians predict who is at risk of a suicide attempt. If it does, it might give clinicians a new tool to try to prevent suicide by ensuring those at greatest risk receive appropriate care.

Major depressive disorder (MDD) is associated with interoceptive processing dysfunctions, but the molecular mechanisms underlying this dysfunction are poorly understood. This study combined brain neuronal-enriched extracellular vesicle (NEEV) technology and serum markers of inflammation and metabolism with Functional Magnetic Resonance Imaging (fMRI) to identify the contribution of gene regulatory pathways, in particular micro-RNA (miR) 93, to interoceptive dysfunction in MDD. Individuals with MDD ( n  = 41) and healthy comparisons (HC; n  = 35) provided blood samples and completed an interoceptive attention task during fMRI. EVs were separated from plasma using a precipitation method. NEEVs were enriched by magnetic streptavidin bead immunocapture utilizing a neural adhesion marker (L1CAM/CD171) biotinylated antibody. The origin of NEEVs was validated with two other neuronal markers - neuronal cell adhesion molecule (NCAM) and ATPase Na+/K+ transporting subunit alpha 3 (ATP1A3). NEEV specificities were confirmed by flow cytometry, western blot, particle size analyzer, and transmission electron microscopy. NEEV small RNAs were purified and sequenced. Results showed that: (1) MDD exhibited lower NEEV miR-93 expression than HC; (2) within MDD but not HC, those individuals with the lowest NEEV miR-93 expression had the highest serum concentrations of interleukin (IL)-1 receptor antagonist, IL-6, tumor necrosis factor, and leptin; and (3) within HC but not MDD, those participants with the highest miR-93 expression showed the strongest bilateral dorsal mid-insula activation during interoceptive versus exteroceptive attention. Since miR-93 is regulated by stress and affects epigenetic modulation by chromatin re-organization, these results suggest that healthy individuals but not MDD participants show an adaptive epigenetic regulation of insular function during interoceptive processing. Future investigations will need to delineate how specific internal and external environmental conditions contribute to miR-93 expression in MDD and what molecular mechanisms alter brain responsivity to body-relevant signals.

Background Adherence to treatment, i.e. the extent to which a patient’s therapeutic engagement coincides with the prescribed treatment, is among the most important problems in mental health care. The current study investigated the influence of pairing an acute positive reinforcing dopaminergic/noradrenergic effect (methylphenidate, MPH) with a standard antidepressant on the rates of adherence to medication treatment. The primary objective of this study was to determine whether MPH + escitalopram resulted in higher rates of medication adherence relative to placebo + escitalopram. Methods Twenty participants with moderate to severe depression were 1–1 randomized to either (1) 5 mg MPH + 10 mg escitalopram or (2) placebo + 10 mg escitalopram with the possibility for a dose increase at 4 weeks. A Bayesian analysis was conducted to evaluate the outcomes. Results First, neither percent Pill count nor Medication Electronic Monitoring System adherence showed that MPH was superior to placebo. In fact, placebo showed slightly higher adherence rates on the primary (7.82% better than MPH) and secondary (7.07% better than MPH) outcomes. There was a less than 25% chance of MPH augmentation showing at least as good or better adherence than placebo. Second, both groups showed a significant effect of treatment on the QIDS-SR with a median effect of an 8.6-point score reduction. Third, neither subjective measures of adherence attitudes nor socio-demographic covariates had a significant influence on the primary or secondary outcome variables. Conclusions These data do not support the use of MPH to increase adherence to antidepressant medication in individuals with moderate to severe depression. ClinicalTrials.gov identifier NCT03388164 , registered on 01/02/2018.

[...]the spatial components extracted through individual or group-level ICA are never identical between studies and are often labeled by visual inspection and expert opinion. The Critique on ICA Performing ICA-based rsfMRI studies involves: data preprocessing and clean-up (sometimes through subject-level ICA or SICA), group-level ICA (GICA) on the entire dataset (usually with temporal concatenation), separating signal from noise independent components (ICs), network labeling, and time-series and spatial map extraction based on selected ICs for all subjects (Nickerson et al., 2017). Because ICA is a time-consuming and computationally resource-demanding procedure, a significant reduction in runtime may be worthwhile, especially in large-scale studies. A Solution We propose to use the ICs resulting from prior studies, such as the UK Biobank and HCP, in an “atlas-like” manner. Because such ICs are already published (Miller et al., 2016), they could be well-studied and agreed upon by the experts across the field. Similar to other studies that use a common atlas, the subject population should be a reasonable representation of the subject group recruited in the reference studies. Since most large public datasets are from normal populations, one might question

An emerging hypothesis regarding the mechanisms underlying antidepressant pharmacotherapy suggests that these agents benefit depressed patients by reversing negative emotional processing biases (Harmer, 2008). Neuropsychological indices and functional neuroimaging measures of the amygdala response show that antidepressant drugs shift implicit and explicit processing biases away from the negative valence and toward the positive valence. However, few studies have explored such biases in regions extensively connected with the amygdala, such as the pregenual anterior cingulate cortex (pgACC) area, where pre-treatment activity consistently has predicted clinical outcome during antidepressant treatment. We used functional magnetic resonance imaging (fMRI) to investigate changes in haemodynamic response patterns to positive vs. negative stimuli in patients with major depressive disorder (MDD) under antidepressant treatment. Participants with MDD (n = 10) underwent fMRI before and after 8 wk sertraline treatment; healthy controls (n = 10) were imaged across an equivalent interval. A backward masking task was used to elicit non-conscious neural responses to sad, happy and neutral face expressions. Haemodynamic responses to emotional face stimuli were compared between conditions and groups in the pgACC. The response to masked-sad vs. masked-happy faces (SN-HN) in pgACC in the depressed subjects was higher in the pre-treatment condition than in the post-treatment condition and this difference was significantly greater than the corresponding change across time in the controls. The treatment-associated difference was attributable to an attenuated response to sad faces and an enhanced response to happy faces. Pre-treatment pgACC responses to SN-HN correlated positively with clinical improvement during treatment. The pgACC participates with the amygdala in processing the salience of emotional stimuli. Treatment-associated functional changes in this limbic network may influence the non-conscious processing of such stimuli by reversing the negative processing bias extant in MDD.

Major depressive disorder (MDD) is associated with a mood-congruent processing bias in the amygdala toward face stimuli portraying sad expressions that is evident even when such stimuli are presented below the level of conscious awareness. The extended functional anatomical network that maintains this response bias has not been established, however. To identify neural network differences in the hemodynamic response to implicitly presented facial expressions between depressed and healthy control participants. Unmedicated-depressed participants with MDD (n=22) and healthy controls (HC; n=25) underwent functional MRI as they viewed face stimuli showing sad, happy or neutral face expressions, presented using a backward masking design. The blood-oxygen-level dependent (BOLD) signal was measured to identify regions where the hemodynamic response to the emotionally valenced stimuli differed between groups. The MDD subjects showed greater BOLD responses than the controls to masked-sad versus masked-happy faces in the hippocampus, amygdala and anterior inferotemporal cortex. While viewing both masked-sad and masked-happy faces relative to masked-neutral faces, the depressed subjects showed greater hemodynamic responses than the controls in a network that included the medial and orbital prefrontal cortices and anterior temporal cortex. Depressed and healthy participants showed distinct hemodynamic responses to masked-sad and masked-happy faces in neural circuits known to support the processing of emotionally valenced stimuli and to integrate the sensory and visceromotor aspects of emotional behavior. Altered function within these networks in MDD may establish and maintain illness-associated differences in the salience of sensory/social stimuli, such that attention is biased toward negative and away from positive stimuli.