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

The Brain: A Panel

Papers Presented: The Brain\u27s Power on Personality Development by Ashmima Brown, Jamila James, & Cara Clark The Brain\u27s Highway by Mikayla Rolda & Anna Le Abstract: The peripheral nerves, which are made out of fibers or bundles of axons, include all the nerves beyond the brain and the spinal cord. They start from the edges of the central nerves, or spinal cord, and extend outwards to the periphery of the body. The main function of the peripheral nerves is to carry information from the central nervous system to the muscles and to important organs and then relay the sensory information back to the brain. There are three types of nerves in the peripheral nervous system, and the first to be introduced would be the sensory nerves, also known as afferent nerves. These sensory neurons carry information about the visual environment from the eyes to the brain. Another type of peripheral nerves is the motor nerves, also called the efferent nerves. These efferent nerves contain special axons of motor neurons to help control glands and muscles. The third type of peripheral nerves is the autonomic nerves that regulate the internal organs such as heart, intestines, and stomach. Let\u27s Split! How the Effects of a Hemispherectomy of the Right Side of the Brain Compares to the Removal of the Left Side: by Jourdan Lawrence & Brylea Huitt Abstract: The “let’s split” project explores how a hemispherectomy of the left side of the brain affects personality and development, compared to removal of the right side of the brain. As a secondary research project, the project will explain the importance of each side of the brain and the common results of a hemispherectomy on either side. A hemispherectomy is a rare treatment in which a cerebral hemisphere of the brain is removed as a result of frequent seizures or epilepsy. While the prognosis of this treatment is good, it remains a last resort. Young patients are able to improve dramatically after the treatment, however there are some complications. Primarily, as a result of the hemispherectomy, patients always suffer from paralysis on the side of the body opposite from the removed hemisphere. Furthermore, the older the patient is, the less likely the brain can transfer over the information from one side of the brain to the other effectively. By studying these issues, however, this project can discover how the issues correlate with the right or left side of the brain respectively. With this information, neurologists and other healthcare professionals may be able to increase the quality of healthcare delivery for these patients

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

Predicting Bipolar Disorder Risk Factors in Distressed Young Adults From Patterns of Brain Activation to Reward: A Machine Learning Approach

BACKGROUND: The aim of this study was to apply multivariate pattern recognition to predict the severity of behavioral traits and symptoms associated with risk for bipolar spectrum disorder from patterns of whole-brain activation during reward expectancy to facilitate the identification of individual-level neural biomarkers of bipolar disorder risk. METHODS: We acquired functional neuroimaging data from two independent samples of transdiagnostically recruited adults (18-25 years of age; n = 56, mean age 21.9 ± 2.2 years, 42 women; n = 36, mean age 21.2 ± 2.2 years, 24 women) during reward expectancy task performance. Pattern recognition model performance in each sample was measured using correlation and mean squared error between actual and whole-brain activation-predicted scores on behavioral traits and symptoms. RESULTS: In the first sample, the model significantly predicted severity of a specific hypo/mania-related symptom, heightened energy, measured by the energy manic subdomain of the Mood Spectrum Structured Interviews (r = .42, p = .001; mean squared error = 9.93, p = .001). The region with the highest contribution to the model was the left ventrolateral prefrontal cortex. Results were confirmed in the second sample (r = .33, p = .01; mean squared error = 8.61, p = .01), in which the severity of this symptom was predicted using a bilateral ventrolateral prefrontal cortical mask (r = .33, p = .009, mean squared error = 9.37, p = .04). CONCLUSIONS: The severity of a specific hypo/mania-related symptom was predicted from patterns of whole-brain activation in two independent samples. Given that emerging manic symptoms predispose to bipolar disorders, these findings could provide neural biomarkers to aid early identification of individual-level bipolar disorder risk in young adults

Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important

Combining neuroimaging and clinical information for diagnosis, as for example behavioral tasks and genetics characteristics, is potentially beneficial but presents challenges in terms of finding the best data representation for the different sources of information. Their simple combination usually does not provide an improvement if compared with using the best source alone. In this paper, we proposed a framework based on a recent multiple kernel learning algorithm called EasyMKL and we investigated the benefits of this approach for diagnosing two different mental health diseases. The well known Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset tackling the Alzheimer Disease (AD) patients versus healthy controls classification task, and a second dataset tackling the task of classifying an heterogeneous group of depressed patients versus healthy controls. We used EasyMKL to combine a huge amount of basic kernels alongside a feature selection methodology, pursuing an optimal and sparse solution to facilitate interpretability. Our results show that the proposed approach, called EasyMKLFS, outperforms baselines (e.g. SVM and SimpleMKL), state-of-the-art random forests (RF) and feature selection (FS) methods

The Neural Core of Fear and Anxiety – Commonalities and Differences of Fear and Anxiety Circuits

The main goal of the present PhD project was to investigate the neural commonalities and differences of fear and anxiety responses in the human brain. Anxiety disorders are a group of mental disorders characterized by excessive anxiety (a worry about future events) and fear (a reaction to current events). The neural patterns and underlying mechanisms of transient (fear) and sustained (anxiety) responses are not yet fully understood. Identifying a neural biosignature of fear and anxiety, i.e. identifying their differences and commonalities irrespective of modality of aversive events is an important goal in psychiatric neuroimaging and may have major future implications in precision psychiatry in terms of better diagnostics and predicting treatment outcome. As a prerequisite for investigating these neural representations with neuroimaging, I developed a standardized and fast method for assessing individual stimulus intensity at pain threshold and demonstrated in a behavioral experiment (N = 40) that the new method produced reliable intensity estimates that were stable over time. In a subsequent fMRI study, the main experiment of this thesis, 35 healthy participants underwent an experimental paradigm that consisted of different conditions for evoking fear and anxiety responses. During the experiment, behavioral, psychological (trait and state variables), physiological (heart and respiratory rate) variables as well as brain activity were acquired. Fear- and anxiety related responses were evoked within a fully factorial within-subjects design with predictable and unpredictable stimuli from two sensory modalities (visual, somatosensory), which had negative or neutral valence. While some brain areas showed modality-specific processing, neuroimaging results revealed modality-general activation patterns coding for fear (in brain stem and paracingulate cortex) and anxiety (in middle and superior frontal gyri) hinting at multisensory or abstract processing of threat