Radiogenomics Map-Based Molecular and Imaging Phenotypical Characterization in Localised Prostate Cancer Using Pre-Biopsy Biparametric MR Imaging.

To create a radiogenomics map and evaluate the correlation between molecular and imaging phenotypes in localized prostate cancer (PCa), using radical prostatectomy histopathology as a reference standard. Radiomic features were extracted from T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) images of clinically localized PCa patients (n = 15) across different Gleason score-based risk categories. DNA extraction was performed on formalin-fixed, paraffin-embedded (FFPE) samples. Gene expression analysis of androgen receptor expression, apoptosis, and hypoxia was conducted using the Chromosome Analysis Suite (ChAS) application and OSCHIP files. The relationship between gene expression alterations and textural features was assessed using Pearson's correlation analysis. Receiver operating characteristic (ROC) analysis was utilized to evaluate the predictive accuracy of the model. A significant correlation was observed between radiomic texture features and copy number variation (CNV) of genes associated with apoptosis, hypoxia, and androgen receptor (p-value ≤ 0.05). The identified radiomic features, including Sum Entropy ADC, Inverse Difference ADC, Sum Variance T2WI, Entropy T2WI, Difference Variance T2WI, and Angular Secondary Moment T2WI, exhibited potential for predicting cancer grade and biological processes such as apoptosis and hypoxia. Incorporating radiomics and genomics into a prediction model significantly improved the prediction of prostate cancer grade (clinically significant prostate cancer), yielding an AUC of 0.95. Radiomic texture features significantly correlate with genotypes for apoptosis, hypoxia, and androgen receptor expression in localised prostate cancer. Integration of these into the prediction model improved prediction accuracy of clinically significant prostate cancer.

Radiomics Machine Learning Analysis of Clear Cell Renal Cell Carcinoma for Tumour Grade Prediction Based on Intra-Tumoural Sub-Region Heterogeneity.

BACKGROUND: Renal cancers are among the top ten causes of cancer-specific mortality, of which the ccRCC subtype is responsible for most cases. The grading of ccRCC is important in determining tumour aggressiveness and clinical management. OBJECTIVES: The objectives of this research were to predict the WHO/ISUP grade of ccRCC pre-operatively and characterise the heterogeneity of tumour sub-regions using radiomics and ML models, including comparison with pre-operative biopsy-determined grading in a sub-group. METHODS: Data were obtained from multiple institutions across two countries, including 391 patients with pathologically proven ccRCC. For analysis, the data were separated into four cohorts. Cohorts 1 and 2 included data from the respective institutions from the two countries, cohort 3 was the combined data from both cohort 1 and 2, and cohort 4 was a subset of cohort 1, for which both the biopsy and subsequent histology from resection (partial or total nephrectomy) were available. 3D image segmentation was carried out to derive a voxel of interest (VOI) mask. Radiomics features were then extracted from the contrast-enhanced images, and the data were normalised. The Pearson correlation coefficient and the XGBoost model were used to reduce the dimensionality of the features. Thereafter, 11 ML algorithms were implemented for the purpose of predicting the ccRCC grade and characterising the heterogeneity of sub-regions in the tumours. RESULTS: For cohort 1, the 50% tumour core and 25% tumour periphery exhibited the best performance, with an average AUC of 77.9% and 78.6%, respectively. The 50% tumour core presented the highest performance in cohorts 2 and 3, with average AUC values of 87.6% and 76.9%, respectively. With the 25% periphery, cohort 4 showed AUC values of 95.0% and 80.0% for grade prediction when using internal and external validation, respectively, while biopsy histology had an AUC of 31.0% for the classification with the final grade of resection histology as a reference standard. The CatBoost classifier was the best for each of the four cohorts with an average AUC of 80.0%, 86.5%, 77.0% and 90.3% for cohorts 1, 2, 3 and 4 respectively. CONCLUSIONS: Radiomics signatures combined with ML have the potential to predict the WHO/ISUP grade of ccRCC with superior performance, when compared to pre-operative biopsy. Moreover, tumour sub-regions contain useful information that should be analysed independently when determining the tumour grade. Therefore, it is possible to distinguish the grade of ccRCC pre-operatively to improve patient care and management.

Impaired value-based decision-making in Parkinson's disease apathy.

Apathy is a common and disabling complication of Parkinson's disease characterized by reduced goal-directed behaviour. Several studies have reported dysfunction within prefrontal cortical regions and projections from brainstem nuclei whose neuromodulators include dopamine, serotonin and noradrenaline. Work in animal and human neuroscience have confirmed contributions of these neuromodulators on aspects of motivated decision-making. Specifically, these neuromodulators have overlapping contributions to encoding the value of decisions, and influence whether to explore alternative courses of action or persist in an existing strategy to achieve a rewarding goal. Building upon this work, we hypothesized that apathy in Parkinson's disease should be associated with an impairment in value-based learning. Using a four-armed restless bandit reinforcement learning task, we studied decision-making in 75 volunteers; 53 patients with Parkinson's disease, with and without clinical apathy, and 22 age-matched healthy control subjects. Patients with apathy exhibited impaired ability to choose the highest value bandit. Task performance predicted an individual patient's apathy severity measured using the Lille Apathy Rating Scale (R = -0.46, P < 0.001). Computational modelling of the patient's choices confirmed the apathy group made decisions that were indifferent to the learnt value of the options, consistent with previous reports of reward insensitivity. Further analysis demonstrated a shift away from exploiting the highest value option and a reduction in perseveration, which also correlated with apathy scores (R = -0.5, P < 0.001). We went on to acquire functional MRI in 59 volunteers; a group of 19 patients with and 20 without apathy and 20 age-matched controls performing the Restless Bandit Task. Analysis of the functional MRI signal at the point of reward feedback confirmed diminished signal within ventromedial prefrontal cortex in Parkinson's disease, which was more marked in apathy, but not predictive of their individual apathy severity. Using a model-based categorization of choice type, decisions to explore lower value bandits in the apathy group activated prefrontal cortex to a similar degree to the age-matched controls. In contrast, Parkinson's patients without apathy demonstrated significantly increased activation across a distributed thalamo-cortical network. Enhanced activity in the thalamus predicted individual apathy severity across both patient groups and exhibited functional connectivity with dorsal anterior cingulate cortex and anterior insula. Given that task performance in patients without apathy was no different to the age-matched control subjects, we interpret the recruitment of this network as a possible compensatory mechanism, which compensates against symptomatic manifestation of apathy in Parkinson's disease.

Disrupted limbic-prefrontal effective connectivity in response to fearful faces in lifetime depression.

BACKGROUND: Multiple brain imaging studies of negative emotional bias in major depressive disorder (MDD) have used images of fearful facial expressions and focused on the amygdala and the prefrontal cortex. The results have, however, been inconsistent, potentially due to small sample sizes (typically N<50). It remains unclear if any alterations are a characteristic of current depression or of past experience of depression, and whether there are MDD-related changes in effective connectivity between the two brain regions. METHODS: Activations and effective connectivity between the amygdala and dorsolateral prefrontal cortex (DLPFC) in response to fearful face stimuli were studied in a large population-based sample from Generation Scotland. Participants either had no history of MDD (N=664 in activation analyses, N=474 in connectivity analyses) or had a diagnosis of MDD during their lifetime (LMDD, N=290 in activation analyses, N=214 in connectivity analyses). The within-scanner task involved implicit facial emotion processing of neutral and fearful faces. RESULTS: Compared to controls, LMDD was associated with increased activations in left amygdala (PFWE=0.031,kE=4) and left DLPFC (PFWE=0.002,kE=33), increased mean bilateral amygdala activation (ß=0.0715,P=0.0314), and increased inhibition from left amygdala to left DLPFC, all in response to fearful faces contrasted to baseline. Results did not appear to be attributable to depressive illness severity or antidepressant medication status at scan time. LIMITATIONS: Most studied participants had past rather than current depression, average severity of ongoing depression symptoms was low, and a substantial proportion of participants were receiving medication. The study was not longitudinal and the participants were only assessed a single time. CONCLUSIONS: LMDD is associated with hyperactivity of the amygdala and DLPFC, and with stronger amygdala to DLPFC inhibitory connectivity, all in response to fearful faces, unrelated to depression severity at scan time. These results help reduce inconsistency in past literature and suggest disruption of 'bottom-up' limbic-prefrontal effective connectivity in depression.

Functional Magnetic Resonance Imaging Signatures of Pavlovian and Instrumental Valuation Systems during a Modified Orthogonalized Go/No-go Task.

Motivational (i.e., Pavlovian) values interfere with instrumental responding and can lead to suboptimal decision-making. In humans, task-based neuroimaging studies have only recently started illuminating the functional neuroanatomy of Pavlovian biasing of instrumental control. To provide a mechanistic understanding of the neural dynamics underlying the Pavlovian and instrumental valuation systems, analysis of neuroimaging data has been informed by computational modeling of conditioned behavior. Nonetheless, because of collinearities in Pavlovian and instrumental predictions, previous research failed to tease out hemodynamic activity that is parametrically and dynamically modulated by coexistent Pavlovian and instrumental value expectations. Moreover, neural correlates of Pavlovian to instrumental transfer effects have so far only been identified in extinction (i.e., in the absence of learning). In this study, we devised a modified version of the orthogonalized go/no-go paradigm, which introduced Pavlovian-only catch trials to better disambiguate trial-by-trial Pavlovian and instrumental predictions in both sexes. We found that hemodynamic activity in the ventromedial pFC covaried uniquely with the model-derived Pavlovian value expectations. Notably, modulation of neural activity encoding for instrumental predictions in the supplementary motor cortex was linked to successful action selection in conflict conditions. Furthermore, hemodynamic activity in regions pertaining to the limbic system and medial pFC was correlated with synergistic Pavlovian and instrumental predictions and improved conditioned behavior during congruent trials. Altogether, our results provide new insights into the functional neuroanatomy of decision-making and corroborate the validity of our variant of the orthogonalized go/no-go task as a behavioral assay of the Pavlovian and instrumental valuation systems.

The effect of adverse childhood experiences on chronic pain and major depression in adulthood: a systematic review and meta-analysis.

BACKGROUND: Adverse childhood experiences have been linked to increased multimorbidity, with physical and mental health consequences throughout life. Chronic pain is often associated with mood disorders, such as major depressive disorder (MDD); both have been linked to adverse childhood experiences. It is unclear how the effect of adverse childhood experiences on neural processing impacts on vulnerability to chronic pain, MDD, or both, and whether there are shared mechanisms. We aimed to assess evidence for central neural changes associated with adverse childhood experiences in subjects with chronic pain, MDD, or both using systematic review and meta-analysis. METHODS: Electronic databases were systematically searched for neuroimaging studies of adverse childhood experiences, with chronic pain, MDD, or both. Two independent reviewers screened title, abstracts, and full text, and assessed quality. After extraction of neuroimaging data, activation likelihood estimate meta-analysis was performed to identify significant brain regions associated with these comorbidities. RESULTS: Forty-nine of 2414 studies were eligible, of which 43 investigated adverse childhood experiences and MDD and six investigated adverse childhood experiences and chronic pain. None investigated adverse childhood experiences, chronic pain, and MDD together. Functional and structural brain abnormalities were identified in the superior frontal, lingual gyrus, hippocampus, insula, putamen, superior temporal, inferior temporal gyrus, and anterior cerebellum in patients with MDD exposed to adverse childhood experiences. In addition, brain function abnormalities were identified for patients with MDD or chronic pain and exposure to adverse childhood experiences in the cingulate gyrus, inferior parietal lobule, and precuneus in task-based functional MRI studies. CONCLUSIONS: We found that adverse childhood experiences exposure can result in different functional and structural brain alterations in adults with MDD or chronic pain compared with those without adverse childhood experiences. SYSTEMATIC REVIEW PROTOCOL: PROSPERO CRD42021233989.

Blunted Expected Reward Value Signals in Binge Alcohol Drinkers.

Alcohol-related morbidities and mortality are highly prevalent, increasing the burden to societies and health systems with 3 million deaths globally each year in young adults directly attributable to alcohol. Cue-induced alcohol craving has been formulated as a type of aberrant associative learning, modeled using temporal difference theory with an expected reward value (ERV) linked to craving. Clinically, although harmful use of alcohol is associated with increased time spent obtaining and using alcohol, it is also associated with self-neglect. The latter implies that the motivational aspects of nonalcohol stimuli are blunted. Using an instrumental learning task with non-alcohol-related stimuli, here, we tested hypotheses that the encoding of cue signals (ERV) predicting reward delivery would be blunted in binge alcohol drinkers in both sexes. We also predicted that for the binge drinking group alone, ratings of problematic alcohol use would correlate with abnormal ERV signals consistent with between groups (i.e., binge drinkers vs controls) abnormalities. Our results support our hypotheses with the ERV (nonalcohol cue) signal blunted in binge drinkers and with the magnitude of the abnormality correlating with ratings of problematic alcohol use. This implies that consistent with hypotheses, the motivational aspects of non-alcohol-related stimuli are blunted in binge drinkers. A better understanding of the mechanisms of harmful alcohol use will, in time, facilitate the development of more effective interventions, which should aim to decrease the motivational value of alcohol and increase the motivational value of non-alcohol-related stimuli.SIGNIFICANCE STATEMENT Allostasis theory predicts specific abnormalities in brain function and subjective experiences that occur when people develop drug problems including addiction. Cue-induced alcohol craving has been formulated as a type of aberrant associative learning, modeled using temporal difference theory with ERV linked to craving. Here, we used an instrumental learning task with non-alcohol-associated stimuli to test hypotheses that the encoding of nonalcohol cue signals (ERV) and reward prediction error signals showed blunting in binge alcohol drinkers. We conclude that fMRI can be used to noninvasively test allostasis and associative learning theory predictions in binge drinkers.

Associations of negative affective biases and depressive symptoms in a community-based sample.

BACKGROUND: Major depressive disorder (MDD) was previously associated with negative affective biases. Evidence from larger population-based studies, however, is lacking, including whether biases normalise with remission. We investigated associations between affective bias measures and depressive symptom severity across a large community-based sample, followed by examining differences between remitted individuals and controls. METHODS: Participants from Generation Scotland (N = 1109) completed the: (i) Bristol Emotion Recognition Task (BERT), (ii) Face Affective Go/No-go (FAGN), and (iii) Cambridge Gambling Task (CGT). Individuals were classified as MDD-current (n = 43), MDD-remitted (n = 282), or controls (n = 784). Analyses included using affective bias summary measures (primary analyses), followed by detailed emotion/condition analyses of BERT and FAGN (secondary analyses). RESULTS: For summary measures, the only significant finding was an association between greater symptoms and lower risk adjustment for CGT across the sample (individuals with greater symptoms were less likely to bet more, despite increasingly favourable conditions). This was no longer significant when controlling for non-affective cognition. No differences were found for remitted-MDD v. controls. Detailed analysis of BERT and FAGN indicated subtle negative biases across multiple measures of affective cognition with increasing symptom severity, that were independent of non-effective cognition [e.g. greater tendency to rate faces as angry (BERT), and lower accuracy for happy/neutral conditions (FAGN)]. Results for remitted-MDD were inconsistent. CONCLUSIONS: This suggests the presence of subtle negative affective biases at the level of emotion/condition in association with depressive symptoms across the sample, over and above those accounted for by non-affective cognition, with no evidence for affective biases in remitted individuals.

Fronto-medial electrode placement for electroconvulsive treatment of depression.

Electroconvulsive therapy (ECT) is the most effective treatment for severe treatment-resistant depression but concern about cognitive side-effects, particularly memory loss, limits its use. Recent observational studies on large groups of patients who have received ECT report that cognitive side-effects were associated with electric field (EF) induced increases in hippocampal volume, whereas therapeutic efficacy was associated with EF induced increases in sagittal brain structures. The aim in the present study was to determine whether a novel fronto-medial (FM) ECT electrode placement would minimize electric fields in bilateral hippocampi (HIP) whilst maximizing electric fields in dorsal sagittal cortical regions. An anatomically detailed computational head model was used with finite element analysis, to calculate ECT-induced electric fields in specific brain regions identified by translational neuroimaging studies of treatment-resistant depressive illness, for a range of electrode placements. As hypothesized, compared to traditional bitemporal (BT) electrode placement, a specific FM electrode placement reduced bilateral hippocampal electric fields two-to-three-fold, whilst the electric fields in the dorsal anterior cingulate (dAC) were increased by approximately the same amount. We highlight the clinical relevance of this specific FM electrode placement for ECT, which may significantly reduce cognitive and non-cognitive side-effects and suggest a clinical trial is indicated.

Local CpG density affects the trajectory and variance of age-associated DNA methylation changes.

BACKGROUND: DNA methylation is an epigenetic mark associated with the repression of gene promoters. Its pattern in the genome is disrupted with age and these changes can be used to statistically predict age with epigenetic clocks. Altered rates of aging inferred from these clocks are observed in human disease. However, the molecular mechanisms underpinning age-associated DNA methylation changes remain unknown. Local DNA sequence can program steady-state DNA methylation levels, but how it influences age-associated methylation changes is unknown. RESULTS: We analyze longitudinal human DNA methylation trajectories at 345,895 CpGs from 600 individuals aged between 67 and 80 to understand the factors responsible for age-associated epigenetic changes at individual CpGs. We show that changes in methylation with age occur at 182,760 loci largely independently of variation in cell type proportions. These changes are especially apparent at 8322 low CpG density loci. Using SNP data from the same individuals, we demonstrate that methylation trajectories are affected by local sequence polymorphisms at 1487 low CpG density loci. More generally, we find that low CpG density regions are particularly prone to change and do so variably between individuals in people aged over 65. This differs from the behavior of these regions in younger individuals where they predominantly lose methylation. CONCLUSIONS: Our results, which we reproduce in two independent groups of individuals, demonstrate that local DNA sequence influences age-associated DNA methylation changes in humans in vivo. We suggest that this occurs because interactions between CpGs reinforce maintenance of methylation patterns in CpG dense regions.

Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health.

Characterising associations between the methylome, proteome and phenome may provide insight into biological pathways governing brain health. Here, we report an integrated DNA methylation and phenotypic study of the circulating proteome in relation to brain health. Methylome-wide association studies of 4058 plasma proteins are performed (N = 774), identifying 2928 CpG-protein associations after adjustment for multiple testing. These are independent of known genetic protein quantitative trait loci (pQTLs) and common lifestyle effects. Phenome-wide association studies of each protein are then performed in relation to 15 neurological traits (N = 1,065), identifying 405 associations between the levels of 191 proteins and cognitive scores, brain imaging measures or APOE e4 status. We uncover 35 previously unreported DNA methylation signatures for 17 protein markers of brain health. The epigenetic and proteomic markers we identify are pertinent to understanding and stratifying brain health.

Comparative Analysis for the Distinction of Chromophobe Renal Cell Carcinoma from Renal Oncocytoma in Computed Tomography Imaging Using Machine Learning Radiomics Analysis.

Background: ChRCC and RO are two types of rarely occurring renal tumors that are difficult to distinguish from one another based on morphological features alone. They differ in prognosis, with ChRCC capable of progressing and metastasizing, but RO is benign. This means discrimination of the two tumors is of crucial importance. Objectives: The purpose of this research was to develop and comprehensively evaluate predictive models that can discriminate between ChRCC and RO tumors using Computed Tomography (CT) scans and ML-Radiomics texture analysis methods. Methods: Data were obtained from 78 pathologically confirmed renal masses, scanned at two institutions. Data from the two institutions were combined to form a third set resulting in three data cohorts, i.e., cohort 1, 2 and combined. Contrast-enhanced scans were used and the axial cross-sectional slices of each tumor were extracted from the 3D data using a semi-automatic segmentation technique for both 2D and 3D scans. Radiomics features were extracted before and after applying filters and the dimensions of the radiomic features reduced using the least absolute shrinkage and selection operator (LASSO) method. Synthetic minority oversampling technique (SMOTE) was applied to avoid class imbalance. Five ML algorithms were used to train models for predictive classification and evaluated using 5-fold cross-validation. Results: The number of selected features with good model performance was 20, 40 and 6 for cohorts 1, 2 and combined, respectively. The best model performance in cohorts 1, 2 and combined had an excellent Area Under the Curve (AUC) of 1.00 ± 0.000, 1.00 ± 0.000 and 0.87 ± 0.073, respectively. Conclusions: ML-based radiomics signatures are potentially useful for distinguishing ChRCC and RO tumors, with a reliable level of performance for both 2D and 3D scanning.

Automated brain tumor identification using magnetic resonance imaging: A systematic review and meta-analysis.

Background: Automated brain tumor identification facilitates diagnosis and treatment planning. We evaluate the performance of traditional machine learning (TML) and deep learning (DL) in brain tumor detection and segmentation, using MRI. Methods: A systematic literature search from January 2000 to May 8, 2021 was conducted. Study quality was assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Detection meta-analysis was performed using a unified hierarchical model. Segmentation studies were evaluated using a random effects model. Sensitivity analysis was performed for externally validated studies. Results: Of 224 studies included in the systematic review, 46 segmentation and 38 detection studies were eligible for meta-analysis. In detection, DL achieved a lower false positive rate compared to TML; 0.018 (95% CI, 0.011 to 0.028) and 0.048 (0.032 to 0.072) (P < .001), respectively. In segmentation, DL had a higher dice similarity coefficient (DSC), particularly for tumor core (TC); 0.80 (0.77 to 0.83) and 0.63 (0.56 to 0.71) (P < .001), persisting on sensitivity analysis. Both manual and automated whole tumor (WT) segmentation had "good" (DSC ≥ 0.70) performance. Manual TC segmentation was superior to automated; 0.78 (0.69 to 0.86) and 0.64 (0.53 to 0.74) (P = .014), respectively. Only 30% of studies reported external validation. Conclusions: The comparable performance of automated to manual WT segmentation supports its integration into clinical practice. However, manual outperformance for sub-compartmental segmentation highlights the need for further development of automated methods in this area. Compared to TML, DL provided superior performance for detection and sub-compartmental segmentation. Improvements in the quality and design of studies, including external validation, are required for the interpretability and generalizability of automated models.

Protracted abstinence in males with an opioid use disorder: partial recovery of nucleus accumbens function.

Opioid use disorder (OUD) affects more than 27 million people globally accounting for more than 300,000 deaths annually. Protracted abstinence among individuals with OUD is rare due to a high relapse rate among those not receiving medications for OUD. Extensive preclinical studies form the basis of the allostasis theory, which proposes long-lasting functional brain abnormalities that persist after opioid withdrawal and contribute to relapse. Few studies have tested the allostasis theory in humans using neuroimaging. Here, we used fMRI and an instrumental learning task to test allostasis theory predictions (ATP) of functional abnormalities in both positive valence (PVS) and negative valence (NVS) accumbens systems in OUD patients with protracted abstinence (n = 15), comparing them with OUD patients receiving methadone treatment (MT) (n = 33), and with healthy controls (n = 23). As hypothesized, protracted abstinence OUD patients showed incomplete recovery of nucleus accumbens function, as evidenced by the blunted response to aversive events (NVS) during negative reinforcement, as observed in MT patients. In contrast, their accumbens response to rewarding events (PVS) during positive reinforcement was similar to that of controls and different from that in MT patients whose response was blunted. Protracted abstinence OUD patients also showed improvements in depression symptoms compared to MT patients. Residual depressive symptoms and pre-MT intravenous drug measures were associated with worse accumbens function in protracted abstinence. These results support the ATP of long-lasting dysfunction of NVS after withdrawal and show preliminary evidence of recovery of PVS function with protracted withdrawal. Therapeutic strategies that target NVS may facilitate recovery.

Prediction of depression symptoms in individual subjects with face and eye movement tracking.

BACKGROUND: Depression is a challenge to diagnose reliably and the current gold standard for trials of DSM-5 has been in agreement between two or more medical specialists. Research studies aiming to objectively predict depression have typically used brain scanning. Less expensive methods from cognitive neuroscience may allow quicker and more reliable diagnoses, and contribute to reducing the costs of managing the condition. In the current study we aimed to develop a novel inexpensive system for detecting elevated symptoms of depression based on tracking face and eye movements during the performance of cognitive tasks. METHODS: In total, 75 participants performed two novel cognitive tasks with verbal affective distraction elements while their face and eye movements were recorded using inexpensive cameras. Data from 48 participants (mean age 25.5 years, standard deviation of 6.1 years, 25 with elevated symptoms of depression) passed quality control and were included in a case-control classification analysis with machine learning. RESULTS: Classification accuracy using cross-validation (within-study replication) reached 79% (sensitivity 76%, specificity 82%), when face and eye movement measures were combined. Symptomatic participants were characterised by less intense mouth and eyelid movements during different stages of the two tasks, and by differences in frequencies and durations of fixations on affectively salient distraction words. CONCLUSIONS: Elevated symptoms of depression can be detected with face and eye movement tracking during the cognitive performance, with a close to clinically-relevant accuracy (~80%). Future studies should validate these results in larger samples and in clinical populations.

Hair glucocorticoids are associated with childhood adversity, depressive symptoms and reduced global and lobar grey matter in Generation Scotland.

Hypothalamic-pituitary-adrenal (HPA) axis dysregulation has been commonly reported in major depressive disorder (MDD), but with considerable heterogeneity of results; potentially due to the predominant use of acute measures of an inherently variable/phasic system. Chronic longer-term measures of HPA-axis activity have yet to be systematically examined in MDD, particularly in relation to brain phenotypes, and in the context of early-life/contemporaneous stress. Here, we utilise a temporally stable measure of cumulative HPA-axis function (hair glucocorticoids) to investigate associations between cortisol, cortisone and total glucocorticoids with concurrent measures of (i) lifetime-MDD case/control status and current symptom severity, (ii) early/current-life stress and (iii) structural neuroimaging phenotypes, in N = 993 individuals from Generation Scotland (mean age = 59.1 yrs). Increased levels of hair cortisol were significantly associated with reduced global and lobar brain volumes with reductions in the frontal, temporal and cingulate regions (ßrange = -0.057 to -0.104, all PFDR < 0.05). Increased levels of hair cortisone were significantly associated with MDD (lifetime-MDD status, current symptoms, and severity; ßrange = 0.071 to 0.115, all PFDR = < 0.05), with early-life adversity (ß = 0.083, P = 0.017), and with reduced global and regional brain volumes (global: ß = -0.059, P = 0.043; nucleus accumbens: ß = -0.075, PFDR = 0.044). Associations with total glucocorticoids followed a similar pattern to the cortisol findings. In this large community-based sample, elevated glucocorticoids were significantly associated with MDD, with early, but not later-life stress, and with reduced global and regional brain phenotypes. These findings provide important foundations for future mechanistic studies to formally explore causal relationships between early adversity, chronic rather than acute measures of glucocorticoids, and neurobiological associations relevant to the aetiology of MDD.

Compulsivity and impulsivity in opioid dependence.

OBJECTIVE: Chronic exposure to illicit opioid drugs can cause serious health and social problems. However, less is known about the differential effect of various opioid treatments, such as methadone and buprenorphine, on neurocognitive domains such as compulsivity and impulsivity, despite their relevance to the treatment of opioid dependence. METHODS: A total of 186 participants were recruited with a cross-sectional design: i) illicit heroin users (n = 27), ii) former heroin users stabilized on methadone MMT (n = 48), iii) a buprenorphine maintenance treatment (BMT) group (n = 18), iv) an abstinent (ABS) group with a history of opioid dependence who were previously stabilized on MMT or BMT (n = 29) and v) healthy controls (HC) (n = 64). We used the Intra-Extra Dimensional Shift (IED) and Cambridge Gambling Task (CGT) paradigms for measuring compulsivity and impulsivity constructs respectively. RESULTS: Heightened compulsivity persisted in the heroin, buprenorphine and abstinent groups. Heroin, methadone and buprenorphine groups exhibited impaired behavioral responses to feedback, consisting of increased deliberation time and poorer risk adjustment. Higher compulsivity measures were negatively associated with opioid dose which may reflect sedation effects. CONCLUSIONS: Our results suggest that compulsivity and impulsivity are core neurocognitive dimensions for opioid dependence which differ in their presentation according to the stage of treatment. Participants taking higher morphine equivalent doses performed better in compulsivity measures. These findings have implications for the treatment of opioid dependence and longitudinal studies are warranted.

Spectral clustering based on structural magnetic resonance imaging and its relationship with major depressive disorder and cognitive ability.

There is increasing interest in using data-driven unsupervised methods to identify structural underpinnings of common mental illnesses, including major depressive disorder (MDD) and associated traits such as cognition. However, studies are often limited to severe clinical cases with small sample sizes and most do not include replication. Here, we examine two relatively large samples with structural magnetic resonance imaging (MRI), measures of lifetime MDD and cognitive variables: Generation Scotland (GS subsample, N = 980) and UK Biobank (UKB, N = 8,900), for discovery and replication, using an exploratory approach. Regional measures of FreeSurfer derived cortical thickness (CT), cortical surface area (CSA), cortical volume (CV) and subcortical volume (subCV) were input into a clustering process, controlling for common covariates. The main analysis steps involved constructing participant K-nearest neighbour graphs and graph partitioning with Markov stability to determine optimal clustering of participants. Resultant clusters were (1) checked whether they were replicated in an independent cohort and (2) tested for associations with depression status and cognitive measures. Participants separated into two clusters based on structural brain measurements in GS subsample, with large Cohen's d effect sizes between clusters in higher order cortical regions, commonly associated with executive function and decision making. Clustering was replicated in the UKB sample, with high correlations of cluster effect sizes for CT, CSA, CV and subCV between cohorts across regions. The identified clusters were not significantly different with respect to MDD case-control status in either cohort (GS subsample: pFDR = .2239-.6585; UKB: pFDR = .2003-.7690). Significant differences in general cognitive ability were, however, found between the clusters for both datasets, for CSA, CV and subCV (GS subsample: d = 0.2529-.3490, pFDR  < .005; UKB: d = 0.0868-0.1070, pFDR  < .005). Our results suggest that there are replicable natural groupings of participants based on cortical and subcortical brain measures, which may be related to differences in cognitive performance, but not to the MDD case-control status.

Alcohol Binge Drinking: Negative and Positive Valence System Abnormalities.

BACKGROUND: Each year, 3 million deaths occur owing to alcohol misuse. Translational studies are crucial to translate preclinical findings to patients. Preclinical studies have highlighted abnormalities in specific brain systems, with these forming the basis of allostasis theory. However, few studies have tested predictions in humans using neuroimaging. METHODS: We used a Research Domain Criteria approach to test allostasis theory predictions of blunted positive valence system (PVS) and abnormally increased negative valence system (NVS) responses in 57 binge alcohol drinking subjects and healthy control subjects who completed an instrumental task during functional magnetic resonance imaging. RESULTS: As hypothesized, binge alcohol drinkers showed abnormally increased activity in NVS-linked regions, such as the hippocampus and dorsal cingulate, and abnormally blunted activity in PVS-linked regions, such as the striatum, compared with control subjects. Higher measures of problematic alcohol use were associated with more abnormal brain activity only for binge drinkers who had been most recently drinking. CONCLUSIONS: These results support allostasis theory predictions of abnormally increased NVS and blunted PVS responses in binge alcohol drinkers. Further similar translational neuroimaging studies are indicated, particularly focusing on the NVS.

Structural brain correlates of serum and epigenetic markers of inflammation in major depressive disorder.

Inflammatory processes are implicated in the aetiology of Major Depressive Disorder (MDD); however, the relationship between peripheral inflammation, brain structure and depression remains unclear, partly due to complexities around the use of acute/phasic inflammatory biomarkers. Here, we report the first large-scale study of both serological and methylomic signatures of CRP (considered to represent acute and chronic measures of inflammation respectively) and their associations with depression status/symptoms, and structural neuroimaging phenotypes (T1 and diffusion MRI) in a large community-based sample (Generation Scotland; NMDD cases = 271, Ncontrols = 609). Serum CRP was associated with overall MDD severity, and specifically with current somatic symptoms- general interest (ß = 0.145, PFDR = 6 × 10-4) and energy levels (ß = 0.101, PFDR = 0.027), along with reduced entorhinal cortex thickness (ß = -0.095, PFDR = 0.037). DNAm CRP was significantly associated with reduced global grey matter/cortical volume and widespread reductions in integrity of 16/24 white matter tracts (with greatest regional effects in the external and internal capsules, ßFA= -0.12 to -0.14). In general, the methylation-based measures showed stronger associations with imaging metrics than serum-based CRP measures (ßaverage = -0.15 versus ßaverage = 0.01 respectively). These findings provide evidence for central effects of peripheral inflammation from both serological and epigenetic markers of inflammation, including in brain regions previously implicated in depression. This suggests that these imaging measures may be involved in the relationship between peripheral inflammation and somatic/depressive symptoms. Notably, greater effects on brain morphology were seen for methylation-based rather than serum-based measures of inflammation, indicating the importance of such measures for future studies.