Brainstem glucose metabolism predicts reward dependence scores in treatment-resistant major depression

BACKGROUND: It has been suggested that individual differences in temperament could be involved in the (non-)response to antidepressant (AD) treatment. However, how neurobiological processes such as brain glucose metabolism may relate to personality features in the treatment-resistant depressed (TRD) state remains largely unclear. METHODS: To examine how brainstem metabolism in the TRD state may predict Cloninger's temperament dimensions Harm Avoidance (HA), Novelty Seeking (NS), and Reward Dependence (RD), we collected (18)fluorodeoxyglucose positron emission tomography ((18)FDG PET) scans in 40 AD-free TRD patients. All participants were assessed with the Temperament and Character Inventory (TCI). We applied a multiple kernel learning (MKL) regression to predict the HA, NS, and RD from brainstem metabolic activity, the origin of respectively serotonergic, dopaminergic, and noradrenergic neurotransmitter (NT) systems. RESULTS: The MKL model was able to significantly predict RD but not HA and NS from the brainstem metabolic activity. The MKL pattern regression model identified increased metabolic activity in the pontine nuclei and locus coeruleus, the medial reticular formation, the dorsal/median raphe, and the ventral tegmental area that contributed to the predictions of RD. CONCLUSIONS: The MKL algorithm identified a likely metabolic marker in the brainstem for RD in major depression. Although (18)FDG PET does not investigate specific NT systems, the predictive value of brainstem glucose metabolism on RD scores however indicates that this temperament dimension in the TRD state could be mediated by different monoaminergic systems, all involved in higher order reward-related behavior

Decoding negative affect personality trait from patterns of brain activation to threat stimuli

INTRODUCTION: Pattern recognition analysis (PRA) applied to functional magnetic resonance imaging (fMRI) has been used to decode cognitive processes and identify possible biomarkers for mental illness. In the present study, we investigated whether the positive affect (PA) or negative affect (NA) personality traits could be decoded from patterns of brain activation in response to a human threat using a healthy sample. METHODS: fMRI data from 34 volunteers (15 women) were acquired during a simple motor task while the volunteers viewed a set of threat stimuli that were directed either toward them or away from them and matched neutral pictures. For each participant, contrast images from a General Linear Model (GLM) between the threat versus neutral stimuli defined the spatial patterns used as input to the regression model. We applied a multiple kernel learning (MKL) regression combining information from different brain regions hierarchically in a whole brain model to decode the NA and PA from patterns of brain activation in response to threat stimuli. RESULTS: The MKL model was able to decode NA but not PA from the contrast images between threat stimuli directed away versus neutral with a significance above chance. The correlation and the mean squared error (MSE) between predicted and actual NA were 0.52 (p-value=0.01) and 24.43 (p-value=0.01), respectively. The MKL pattern regression model identified a network with 37 regions that contributed to the predictions. Some of the regions were related to perception (e.g., occipital and temporal regions) while others were related to emotional evaluation (e.g., caudate and prefrontal regions). CONCLUSION: These results suggest that there was an interaction between the individuals' NA and the brain response to the threat stimuli directed away, which enabled the MKL model to decode NA from the brain patterns. To our knowledge, this is the first evidence that PRA can be used to decode a personality trait from patterns of brain activation during emotional contexts

Can Emotional and Behavioral Dysregulation in Youth Be Decoded from Functional Neuroimaging?

High comorbidity among pediatric disorders characterized by behavioral and emotional dysregulation poses problems for diagnosis and treatment, and suggests that these disorders may be better conceptualized as dimensions of abnormal behaviors. Furthermore, identifying neuroimaging biomarkers related to dimensional measures of behavior may provide targets to guide individualized treatment. We aimed to use functional neuroimaging and pattern regression techniques to determine whether patterns of brain activity could accurately decode individual-level severity on a dimensional scale measuring behavioural and emotional dysregulation at two different time points

Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach

Background: Healthcare workers are at high risk for developing mental health problems during the COVID-19 pandemic. There is an urgent need to identify vulnerability and protective factors related to the severity of psychiatric symptoms among healthcare workers to implement targeted prevention and intervention programs to reduce the mental health burden worldwide during COVID-19. // Objective: The present study aimed to apply a machine learning approach to predict depression and PTSD symptoms based on psychometric questions that assessed: (1) the level of stress due to being isolated from one's family; (2) professional recognition before and during the pandemic; and (3) altruistic acceptance of risk during the COVID-19 pandemic among healthcare workers. // Methods: A total of 437 healthcare workers who experienced some level of isolation at the time of the pandemic participated in the study. Data were collected using a web survey conducted between June 12, 2020, and September 19, 2020. We trained two regression models to predict PTSD and depression symptoms. Pattern regression analyses consisted of a linear epsilon-insensitive support vector machine (ε-SVM). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r), the coefficient of determination (r2), and the normalized mean squared error (NMSE) to evaluate the model performance. A permutation test was applied to estimate significance levels. // Results: Results were significant using two different cross-validation strategies to significantly decode both PTSD and depression symptoms. For all of the models, the stress due to social isolation and professional recognition were the variables with the greatest contributions to the predictive function. Interestingly, professional recognition had a negative predictive value, indicating an inverse relationship with PTSD and depression symptoms. // Conclusions: Our findings emphasize the protective role of professional recognition and the vulnerability role of the level of stress due to social isolation in the severity of posttraumatic stress and depression symptoms. The insights gleaned from the current study will advance efforts in terms of intervention programs and public health messaging

Evolving patterns of sterodynamic sea-level rise under mitigation scenarios and insights from linear system theory

Long-term behaviour of sea-level rise is an important factor in assessing the impact of climate change on multi-century timescales. Under the stabilisation scenario RCP4.5, Sterodynamic Sea-Level (SdynSL) and ocean density change in the CMIP5 models exhibit distinct patterns over the periods before and after Radiative Forcing (RF) stabilisation (2000–2070 vs. 2100–2300). The stabilisation pattern is more geographically uniform and involves deeper penetration of density change than the transient pattern. In RCP2.6, 4.5 and 8.5, the spatiotemporal evolution of SdynSL change can be approximated as a linear combination of the transient and stabilisation patterns. Specifically, SdynSL change is dominated by the transient pattern when RF increases rapidly, but it is increasingly affected by the stabilisation pattern once RF starts to stabilise. The growth of the stabilisation pattern could persist for centuries after RF ceases increasing. The evolving patterns of SdynSL change can also be approximated as a linear system's responses (characterised by its Green’s function) to time-dependent boundary conditions. By examining SdynSL change simulated in linear system models with different estimates of Green's functions, we find that both the climatological ocean circulation and the ocean's dynamical response to RF play a role in shaping the patterns of SdynSL change. The linear system model is more accurate than the univariate pattern scaling in emulating the CMIP5 SdynSL change beyond 2100. The emergence of the stabilisation pattern leads to a 1–10% decrease in the ocean's expansion efficiency of heat over 2000–2300 in RCP2.6 and 4.5

Can emotional and behavioral dysregulation in youth be decoded from functional neuroimaging?

Introduction High comorbidity among pediatric disorders characterized by behavioral and emotional dysregulation poses problems for diagnosis and treatment, and suggests that these disorders may be better conceptualized as dimensions of abnormal behaviors. Furthermore, identifying neuroimaging biomarkers related to dimensional measures of behavior may provide targets to guide individualized treatment. We aimed to use functional neuroimaging and pattern regression techniques to determine whether patterns of brain activity could accurately decode individual-level severity on a dimensional scale measuring behavioural and emotional dysregulation at two different time points. Methods A sample of fifty-seven youth (mean age: 14.5 years; 32 males) was selected from a multisite study of youth with parent-reported behavioral and emotional dysregulation. Participants performed a block-design reward paradigm during functional Magnetic Resonance Imaging (fMRI). Pattern regression analyses consisted of Relevance Vector Regression (RVR) and two cross-validation strategies implemented in the Pattern Recognition for Neuroimaging toolbox (PRoNTo). Medication was treated as a binary confounding variable. Decoded and actual clinical scores were compared using Pearson's correlation coefficient (r) and mean squared error (MSE) to evaluate the models. Permutation test was applied to estimate significance levels. Results Relevance Vector Regression identified patterns of neural activity associated with symptoms of behavioral and emotional dysregulation at the initial study screen and close to the fMRI scanning session. The correlation and the mean squared error between actual and decoded symptoms were significant at the initial study screen and close to the fMRI scanning session. However, after controlling for potential medication effects, results remained significant only for decoding symptoms at the initial study screen. Neural regions with the highest contribution to the pattern regression model included cerebellum, sensory-motor and fronto-limbic areas. Conclusions The combination of pattern regression models and neuroimaging can help to determine the severity of behavioral and emotional dysregulation in youth at different time points. Copyright: © 2016 Portugal et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Evaluation of behavioral variance/covariance explained by the neuroimaging data through a pattern‐based regression

Neuroimaging data have been widely used to understand the neural bases of human behaviors. However, most studies were either based on a few predefined regions of interest or only able to reveal limited vital regions, hence not providing an overarching description of the relationship between neuroimaging and behaviors. Here, we proposed a voxel‐based pattern regression that not only could investigate the overall brain‐associated variance (BAV) for a given behavioral measure but could also evaluate the shared neural bases between different behaviors across multiple neuroimaging data. The proposed method demonstrated consistently high reliability and accuracy through comprehensive simulations. We further implemented this approach on real data of adolescents (IMAGEN project, n = 2089) and adults (HCP project, n = 808) to investigate brain‐based variances of multiple behavioral measures, for instance, cognitive behaviors, substance use, and psychiatric disorders. Notably, intelligence‐related scores showed similar high BAVs with the gray matter volume across both datasets. Further, our approach allows us to reveal the latent brain‐based correlation across multiple behavioral measures, which are challenging to obtain otherwise. For instance, we observed a shared brain architecture underlying depression and externalizing problems in adolescents, while the symptom comorbidity may only emerge later in adults. Overall, our approach will provide an important statistical tool for understanding human behaviors using neuroimaging data

Illustration of a pattern regression model with hypothetical 2D data.

<p>The numbers in the squares correspond to activation levels in different brain voxels. Each brain scan corresponds to a point in a 2D voxel space. The goal is to “train” a predictive function that given a new brain scan can accurately decode the correspondent clinical score.</p