Callousness, exploitativeness, and tracking of cooperation incentives in the human default network.

Human cognitive capacities that enable flexible cooperation may have evolved in parallel with the expansion of frontoparietal cortical networks, particularly the default network. Conversely, human antisocial behavior and trait antagonism are broadly associated with reduced activity, impaired connectivity, and altered structure of the default network. Yet, behaviors like interpersonal manipulation and exploitation may require intact or even superior social cognition. Using a reinforcement learning model of decision-making on a modified trust game, we examined how individuals adjusted their cooperation rate based on a counterpart's cooperation and social reputation. We observed that learning signals in the default network updated the predicted utility of cooperation or defection and scaled with reciprocal cooperation. These signals were weaker in callous (vs. compassionate) individuals but stronger in those who were more exploitative (vs. honest and humble). Further, they accounted for associations between exploitativeness, callousness, and reciprocal cooperation. Separately, behavioral sensitivity to prior reputation was reduced in callous but not exploitative individuals and selectively scaled with responses of the medial temporal subsystem of the default network. Overall, callousness was characterized by blunted behavioral and default network sensitivity to cooperation incentives. Exploitativeness predicted heightened sensitivity to others' cooperation but not social reputation. We speculate that both compassion and exploitativeness may reflect cognitive adaptations to social living, enabled by expansion of the default network in anthropogenesis.

Reproducibility of intravoxel incoherent motion quantification in the liver across field strengths and gradient hardware.

PURPOSE: To evaluate reproducibility and interlobar agreement of intravoxel incoherent motion (IVIM) quantification in the liver across field strengths and MR scanners with different gradient hardware. METHODS: Cramer-Rao lower bound optimization was performed to determine optimized monopolar and motion-robust 2D (b-value and first-order motion moment [M1]) IVIM-DWI acquisitions. Eleven healthy volunteers underwent diffusion MRI of the liver, where each optimized acquisition was obtained five times across three MRI scanners. For each data set, IVIM estimates (diffusion coefficient (D), pseudo-diffusion coefficients ( d 1 * $$ {d}_1^{\ast } $$ and d 2 * $$ {d}_2^{\ast } $$ ), blood velocity SDs (Vb1 and Vb2), and perfusion fractions [f1 and f2]) were obtained in the right and left liver lobes using two signal models (pseudo-diffusion and M1-dependent physical) with and without T2 correction (fc1 and fc2) and three fitting techniques (tri-exponential region of interest-based full and segmented fitting and blood velocity SD distribution fitting). Reproducibility and interlobar agreement were compared across methods using within-subject and pairwise coefficients of variation (CVw and CVp), paired sample t-tests, and Bland-Altman analysis. RESULTS: Using a combination of motion-robust 2D (b-M1) data acquisition, M1-dependent physical signal modeling with T2 correction, and blood velocity SD distribution fitting, multiscanner reproducibility with median CVw = 5.09%, 11.3%, 9.20%, 14.2%, and 12.6% for D, Vb1, Vb2, fc1, and fc2, respectively, and interlobar agreement with CVp = 8.14%, 11.9%, 8.50%, 49.9%, and 42.0%, respectively, was achieved. CONCLUSION: Recently proposed advanced IVIM acquisition, signal modeling, and fitting techniques may facilitate reproducible IVIM quantification in the liver, as needed for establishment of IVIM-based quantitative biomarkers for detection, staging, and treatment monitoring of diseases.

Optimizing molecular testing of lung cancer needle biopsy specimens: potential solutions from an interdisciplinary qualitative study.

BACKGROUND: Molecular testing can detect actionable genomic alterations and tumor cell surface proteins in patients with non-small cell lung cancer (NSCLC). However, utilization remains suboptimal, representing missed treatment opportunities. This study aimed to identify challenges and potential solutions to obtaining percutaneous lung needle biopsy specimens for successful molecular testing in patients with advanced NSCLC. METHODS: This interdisciplinary qualitative study included ten radiologists and four pathologists from academic and community settings across the United States who routinely perform and analyze percutaneous lung needle biopsies. Participants underwent semi-structured one-on-one interviews (Phase 1). Interview questionnaires were constructed based on a literature review of key lines of inquiry and conducted by professional market researchers using the theoretical domains framework. Primary barriers to molecular testing were identified using thematic analysis. Subsequently, multidisciplinary focus groups were convened to identify potential solutions (Phase 2). RESULTS: Four themes emerged as barriers to molecular testing and were matched to the clinical workflow: (1) biopsy request, (2) biopsy procedure, (3) specimen analysis, and (4) communication. The nineteen potential solutions included adding a "checkbox" to indicate molecular testing in the biopsy request, leveraging pre-procedural imaging to guide biopsies, conserving tissue through appropriate allocation strategies and next generation sequencing panels instead of sequential single-gene assays, instituting reflex-molecular testing upon NSCLC diagnosis, tracking and communicating biopsy outcomes at multidisciplinary tumor boards, and improving integration of radiologists and pathologists into oncology care teams. CONCLUSIONS: Potential solutions exist to increase successful molecular testing of lung needle biopsy specimens in patients with advanced NSCLC.

Machine Learning Informs RNA-Binding Chemical Space.

Small molecule targeting of RNA has emerged as a new frontier in medicinal chemistry, but compared to the protein targeting literature our understanding of chemical matter that binds to RNA is limited. In this study, we reported Repository Of BInders to Nucleic acids (ROBIN), a new library of nucleic acid binders identified by small molecule microarray (SMM) screening. The complete results of 36 individual nucleic acid SMM screens against a library of 24 572 small molecules were reported (including a total of 1 627 072 interactions assayed). A set of 2 003 RNA-binding small molecules was identified, representing the largest fully public, experimentally derived library of its kind to date. Machine learning was used to develop highly predictive and interpretable models to characterize RNA-binding molecules. This work demonstrates that machine learning algorithms applied to experimentally derived sets of RNA binders are a powerful method to inform RNA-targeted chemical space.

Dual projecting cells linking thalamic and cortical communication routes between the medial prefrontal cortex and hippocampus.

The interactions between the medial prefrontal cortex (mPFC) and the hippocampus (HC) are critical for memory and decision making and have been specifically implicated in several neurological disorders including schizophrenia, epilepsy, frontotemporal dementia, and Alzheimer's disease. The ventral midline thalamus (vmThal), and lateral entorhinal cortex and perirhinal cortex (LEC/PER) constitute major communication pathways that facilitate mPFC-HC interactions in memory. Although vmThal and LEC/PER circuits have been delineated separately we sought to determine whether these two regions share cell-specific inputs that could influence both routes simultaneously. To do this we used a dual fluorescent retrograde tracing approach using cholera toxin subunit-B (CTB-488 and CTB-594) with injections targeting vmThal and the LEC/PER in rats. Retrograde cell body labeling was examined in key regions of interest within the mPFC-HC system including: (1) mPFC, specifically anterior cingulate cortex (ACC), dorsal and ventral prelimbic cortex (dPL, vPL), and infralimbic cortex (IL); (2) medial and lateral septum (MS, LS); (3) subiculum (Sub) along the dorsal-ventral and proximal-distal axes; and (4) LEC and medial entorhinal cortex (MEC). Results showed that dual vmThal-LEC/PER-projecting cell populations are found in MS, vSub, and the shallow layers II/III of LEC and MEC. We did not find any dual projecting cells in mPFC or in the cornu ammonis (CA) subfields of the HC. Thus, mPFC and HC activity is sent to vmThal and LEC/PER via non-overlapping projection cell populations. Importantly, the dual projecting cell populations in MS, vSub, and EC are in a unique position to simultaneously influence both cortical and thalamic mPFC-HC pathways critical to memory. SIGNIFICANCE STATEMENT: The interactions between mPFC and HC are critical for learning and memory, and dysfunction within this circuit is implicated in various neurodegenerative and psychiatric diseases. mPFC-HC interactions are mediated through multiple communication pathways including a thalamic hub through the vmThal and a cortical hub through lateral entorhinal cortex and perirhinal cortex. Our data highlight newly identified dual projecting cell populations in the septum, Sub, and EC of the rat brain. These dual projecting cells may have the ability to modify the information flow within the mPFC-HC circuit through synchronous activity, and thus offer new cell-specific circuit targets for basic and translational studies in memory.

Borderline personality disorder: stress reactivity or stress generation? A prospective dimensional study.

BACKGROUND: Individuals diagnosed with borderline personality disorder (BPD) often describe their lives as stressful and unpredictable. However, it is unclear whether the adversity faced by those with BPD is a product of stress reactivity or stress generation. Here, we examined the dynamic, prospective associations between BPD and stressful life events over 3 years. Given the heterogeneity present in BPD, we sought to understand which empirically derived dimensions of this heterogeneous disorder explain stress reactivity v. stress generation. METHODS: Participants included 355 individuals diagnosed with BPD and followed longitudinally at three annual assessments. Auto-regressive cross-lagged panel models were used to examine prospective associations between stressful life events and three latent dimensions implicated in BPD: negative affect, disinhibition, and antagonism. RESULTS: Antagonism and disinhibition, but not negative affect, prospectively predicted dependent stressful life events (events the individual may have some role in). Evidence for decompensation under stress was more tenuous, with independent stressful life events (those presumably outside the individual's control) predicting increases in negative affect. CONCLUSIONS: Our longitudinal study of a well-characterized clinical sample found more evidence for stress generation than for stress-induced decompensation in BPD. Stress generation in BPD is driven by externalizing dimensions: antagonism and disinhibition. These results highlight the utility of empirically derived dimensions for parsing heterogeneity present in BPD, leading to improvements in diagnostic evaluation, clinical prediction, and individualized approaches to treatment planning.

The College of American Pathologists Foundation's See, Test & Treat Program®: an Evaluation of a One-Day Cancer Screening Program Implemented in Mississippi.

Women in Mississippi experience significant barriers to healthcare that results in high incidence rates of late-stage breast, cervical, and oropharyngeal cancer. We implemented See, Test, & Treat, a cancer screening and education program, that was aimed at increasing access to cancer screening for underserved women in the Jackson Metropolitan Area. During the event, 103 women between the ages of 21 and 69 years old received breast, cervical, and/or oral cancer screenings. Quantitative and qualitative data were collected to evaluate the effect of the program on the participants' cancer screening knowledge, self-efficacy to obtain medical check-ups, and intentions to engage in health-enhancing behaviors. Of the 57 women who received a mammogram, 18 had abnormal results that required follow-up care. None of the women who received a Pap test had abnormal results, but 8 women were diagnosed with trichomoniasis. One woman was diagnosed with stage 4 oral cancer. The evaluation data indicated that participants found that free cancer screenings and receipt of results on the same day were primary benefits of the program.

Medial prefrontal cortex and hippocampal activity differentially contribute to ordinal and temporal context retrieval during sequence memory.

Remembering sequences of events defines episodic memory, but retrieval can be driven by both ordinality and temporal contexts. Whether these modes of retrieval operate at the same time or not remains unclear. Theoretically, medial prefrontal cortex (mPFC) confers ordinality, while the hippocampus (HC) associates events in gradually changing temporal contexts. Here, we looked for evidence of each with BOLD fMRI in a sequence task that taxes both retrieval modes. To test ordinal modes, items were transferred between sequences but retained their position (e.g., AB3). Ordinal modes activated mPFC, but not HC. To test temporal contexts, we examined items that skipped ahead across lag distances (e.g., ABD). HC, but not mPFC, tracked temporal contexts. There was a mPFC and HC by retrieval mode interaction. These current results suggest that the mPFC and HC are concurrently engaged in different retrieval modes in support of remembering when an event occurred.

Calretinin and calbindin architecture of the midline thalamus associated with prefrontal-hippocampal circuitry.

The midline thalamus bidirectionally connects the medial prefrontal cortex (mPFC) and hippocampus (HC) creating a unique cortico-thalamo-cortical circuit fundamental to memory and executive function. While the anatomical connectivity of midline thalamus has been thoroughly investigated, little is known about its cellular organization within each nucleus. Here we used immunohistological techniques to examine cellular distributions in the midline thalamus based on the calcium binding proteins parvalbumin (PV), calretinin (CR), and calbindin (CB). We also examined these calcium binding proteins in a population of reuniens cells known to project to both mPFC and HC using a dual fluorescence retrograde adenoassociated virus-based tracing approach. These dual reuniens mPFC-HC projecting cells, in particular, are thought to be important for synchronizing mPFC and HC activity. First, we confirmed the absence of PV+ neurons in the midline thalamus. Second, we found a common pattern of CR+ and CB+ cells throughout midline thalamus with CR+ cells running along the nearby third ventricle (3V) and penetrating the midline. CB+ cells were consistently more lateral and toward the middle of the dorsal-ventral extent of the midline thalamus. Notably, single-labeled CR+ and CB+ zones were partially overlapping and included dual-labeled CR+ /CB+ cells. Within RE, we also observed a CR and CB subzone specific diversity. Interestingly, dual mPFC-HC projecting neurons in RE expressed none of the calcium binding proteins examined, but were contained in nests of CR+ and CB+ cells. Overall, the midline thalamus was well organized into CR+ and CB+ rich zones distributed throughout the region, with dual mPFC-HC projecting cells in reuniens representing a unique cell population. These results provide a cytoarchitectural organization in the midline thalamus based on calcium binding protein expression, and set the stage for future cell-type specific interrogations of the functional role of these different cell populations in mPFC-HC interactions.

Adherence to National Comprehensive Cancer Network ALK Testing Guidelines for Patients with Advanced Non-Small Cell Lung Cancer in U.S. Community Medical Centers.

BACKGROUND: National Comprehensive Cancer Network (NCCN) guidelines recommend biomarker testing as the first step in the management of patients with advanced non-small cell lung cancer (aNSCLC). We assessed anaplastic lymphoma kinase (ALK) testing rates and factors related to underuse in community medical systems between 2012 and 2019 to understand guideline adoption. METHODS: A retrospective observational study using a nationwide electronic health record (EHR)-derived deidentified database was conducted. Patients with aNSCLC diagnosed in community medical centers from January 2012 to May 2019 were included to describe the ALK testing trend. This cohort was further restricted to patients diagnosed after 2015 to understand factors associated with testing underuse using mixed-effects multivariable logistic regression models. RESULTS: Trends for increased ALK testing rates by year were observed in both NCCN guideline-eligible patients (59.5% in 2012 to 84.1% in 2019) and -ineligible patients (15.6% to 50.8%) in a cohort of 41,728 patients. Histology type and smoking status had the greatest impact on test use. Compared with patients with nonsquamous histology and no smoking history, patients with squamous histology and no smoking history (adjusted odds ratio [aOR], 7.6; 95% confidence interval [CI], 5.6-10.4), NSCLC histology not otherwise specified (NOS) with smoking history (aOR, 3.4; 95% CI, 2.8-4.2); NSCLC NOS/nonsmoker (aOR, 1.8; 95% CI, 1.1-3.2), and nonsquamous/smoker (aOR, 1.5; 95% CI, 1.3-1.7) were less likely to be tested. Factors related to underuse also included Eastern Cooperative Oncology Group performance status, stage at initial diagnosis, and demographics. CONCLUSION: This analysis of real-world data shows increasing test use by year; however, one fifth of patients eligible for ALK testing still remain untested and potentially missing therapeutic options. IMPLICATIONS FOR PRACTICE: Advancement in treatment of lung cancer is accompanied by an increasing number of tests that should be run to determine potential therapy options for each patient. This study assessed adoption of testing recommendations for anaplastic lymphoma kinase rearrangements in a national database. Although test use increased over the time period studied (2012-2019), there is still room for improvement. Efforts are needed to increase test use in undertested groups, thus enabling eligible patients to benefit from novel lung cancer therapies.

Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models.

In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro methods together with other computational methods such as quantitative structure-activity relationship modeling and absorption, distribution, metabolism, and excretion calculations are being used. An overview of machine learning and its applications in predictive toxicology is presented here, including support vector machines (SVMs), random forest (RF) and decision trees (DTs), neural networks, regression models, naïve Bayes, k-nearest neighbors, and ensemble learning. The recent successes of these machine learning methods in predictive toxicology are summarized, and a comparison of some models used in predictive toxicology is presented. In predictive toxicology, SVMs, RF, and DTs are the dominant machine learning methods due to the characteristics of the data available. Lastly, this review describes the current challenges facing the use of machine learning in predictive toxicology and offers insights into the possible areas of improvement in the field.

Disentangling cognitive processes in externalizing psychopathology using drift diffusion modeling: Antagonism, but not disinhibition, is associated with poor cognitive control.

Although externalizing psychopathology has been linked to deficits in cognitive control, the cognitive processes underlying this association are unclear. Here, we provide a theoretical account of how research on cognitive processes can help to integrate and distinguish personality and psychopathology. We then apply this account to connect the two major subcomponents of externalizing, Antagonism and Disinhibition, with specific control processes using a battery of inhibitory control tasks and corresponding computational modeling. Participants (final N = 104) completed the flanker, go/no-go, and recent probes tasks, as well as normal and maladaptive personality inventories and measures of psychological distress. We fit participants' task behavior using a hierarchical drift diffusion model (DDM) to decompose their responses into specific cognitive processes. Using multilevel structural equation models, we found that Antagonism was associated with faster RTs on the flanker task and lower accuracy on flanker and go/no-go tasks. These results were complemented by DDM parameter associations: Antagonism was linked to decreased threshold and drift rate parameter estimates in the flanker task and a decreased drift rate on no-go trials. Altogether, our findings indicate that Antagonism is associated with specific impairments in fast (sub-second) inhibitory control processes involved in withholding prepared/prepotent responses and filtering distracting information. Disinhibition and momentary distress, however, were not associated with task performance.

Quantitative Predictions for Molecular Initiating Events Using Three-Dimensional Quantitative Structure-Activity Relationships.

The aim of human toxicity risk assessment is to determine a safe dose or exposure to a chemical for humans. This requires an understanding of the exposure of a person to a chemical and how much of the chemical is required to cause an adverse effect. To do this computationally, we need to understand how much of a chemical is required to perturb normal biological function in an adverse outcome pathway (AOP). The molecular initiating event (MIE) is the first step in an adverse outcome pathway and can be considered as a chemical interaction between a chemical toxicant and a biological molecule. Key chemical characteristics can be identified and used to model the chemistry of these MIEs. In this study, we do just this by using chemical substructures to categorize chemicals and 3D quantitative structure-activity relationships (QSARs) based on comparative molecular field analysis (CoMFA) to calculate molecular activity. Models have been constructed across a variety of human biological targets, the glucocorticoid receptor, mu opioid receptor, cyclooxygenase-2 enzyme, human ether-à-go-go related gene channel, and dopamine transporter. These models tend to provide molecular activity estimation well within one log unit and electronic and steric fields that can be visualized to better understand the MIE and biological target of interest. The outputs of these fields can be used to identify key aspects of a chemical's chemistry which can be changed to reduce its ability to activate a given MIE. With this methodology, the quantitative chemical activity can be predicted for a wide variety of MIEs, which can feed into AOP-based chemical risk assessments, and understanding of the chemistry behind the MIE can be gained.

Structural Alerts and Random Forest Models in a Consensus Approach for Receptor Binding Molecular Initiating Events.

A molecular initiating event (MIE) is the gateway to an adverse outcome pathway (AOP), a sequence of events ending in an adverse effect. In silico predictions of MIEs are a vital tool in a modern, mechanism-focused approach to chemical risk assessment. For 90 biological targets representing important human MIEs, structural alert-based models have been constructed with an automated procedure that uses Bayesian statistics to iteratively select substructures. These models give impressive average performance statistics (an average of 92% correct predictions across targets), significantly improving on previous models. Random Forest models have been constructed from physicochemical features for the same targets, giving similarly impressive performance statistics (93% correct predictions). A key difference between the models is interpretation of predictions-the structural alert models are transparent and easy to interpret, while Random Forest models can only identify the most important physicochemical features for making predictions. The two complementary models have been combined in a consensus model, improving performance compared to each individual model (94% correct predictions) and increasing confidence in predictions. Variation in model performance has been explained by calculating a modelability index (MODI), using Tanimoto coefficient between Morgan fingerprints to identify nearest neighbor chemicals. This work is an important step toward building confidence in the use of in silico tools for assessment of toxicity.

Confidence in Inactive and Active Predictions from Structural Alerts.

Having a measure of confidence in computational predictions of biological activity from in silico tools is vital when making predictions for new chemicals, for example, in chemical risk assessment. Where predictions of biological activity are used as an indicator of a potential hazard, false-negative predictions are the most concerning prediction; however, assigning confidence in inactive predictions is particularly challenging. How can one confidently identify the absence of activating features? In this study, we present methods for assigning confidence to both active and inactive predictions from structural alerts for protein-binding molecular initiating events (MIEs). Structural alerts were derived through an iterative statistical method. Confidence in the activity predictions is assigned by measuring the Tanimoto similarity between Morgan fingerprints of chemicals in the test set to relevant chemicals in the training set, and suitable cutoff values have been defined to give different confidence categories. To avoid a potential compound series bias in the test set and hence overestimate the performance of the method, we measured the biological activity of 27 compounds with 24 proteins, which gave us an additional 648 experimental measurements; many of the measurements are currently nonexistent in the literature and databases. This data set was complemented with newly measured biological activities published in ChEMBL25 and formed a combined independent validation data set. Applying the confidence categories to the computational predictions for the new data leads to the identification of chemicals for which one should be confident of either an inactive or active prediction, allowing model predictions to be used responsibly.

In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays.

Molecular initiating events (MIEs) are key events in adverse outcome pathways that link molecular chemistry to target biology. As they are based on chemistry, these interactions are excellent targets for computational chemistry approaches to in silico modeling. In this work, we aim to link ligand chemical structures to MIEs for androgen receptor (AR) and glucocorticoid receptor (GR) binding using ToxCast data. This has been done using an automated computational algorithm to perform maximal common substructure searches on chemical binders for each target from the ToxCast dataset. The models developed show a high level of accuracy, correctly assigning 87.20% of AR binders and 96.81% of GR binders in a 25% test set using holdout cross-validation. The 2D structural alerts developed can be used as in silico models to predict these MIEs and as guidance for in vitro ToxCast assays to confirm hits. These models can target such experimental work, reducing the number of assays to be performed to gain required toxicological insight. Development of these models has also allowed some structural alerts to be identified as predictors for agonist or antagonist behavior at the receptor target. This work represents a first step in using computational methods to guide and target experimental approaches.

The use of adverse outcome pathways in the safety evaluation of food additives.

In the last decade, adverse outcome pathways have been introduced in the fields of toxicology and risk assessment of chemicals as pragmatic tools with broad application potential. While their use in the pharmaceutical and cosmetics sectors has been well documented, their application in the food area remains largely unexplored. In this respect, an expert group of the International Life Sciences Institute Europe has recently explored the use of adverse outcome pathways in the safety evaluation of food additives. A key activity was the organization of a workshop, gathering delegates from the regulatory, industrial and academic areas, to discuss the potentials and challenges related to the application of adverse outcome pathways in the safety assessment of food additives. The present paper describes the outcome of this workshop followed by a number of critical considerations and perspectives defined by the International Life Sciences Institute Europe expert group.

Disinhibition and Detachment in Adolescence: A Developmental Cognitive Neuroscience Perspective on the Alternative Model for Personality Disorders.

Personality pathology often emerges during adolescence, but attempts to understand its neurocognitive basis have traditionally been undermined by problems associated with the categorical classification of personality disorders. In contrast, dimensional models of personality pathology, such as the Alternative Model for Personality Disorders (AMPD) in DSM-5, may provide a stronger foundation for neurobiological investigations of maladaptive individual differences in personality. As an example, we review studies of the adolescent development of reward processing and cognitive control and connect these systems to the normal personality hierarchy and to two dimensions included in the AMPD - Detachment and Disinhibition. We argue that by linking developmental changes in these systems to the AMPD, researchers will be better positioned to understand the relationship between neurocognitive development and the expression of personality pathology in adolescence and early adulthood.

The nucleus reuniens of the thalamus sits at the nexus of a hippocampus and medial prefrontal cortex circuit enabling memory and behavior.

The nucleus reuniens of the thalamus (RE) is a key component of an extensive network of hippocampal and cortical structures and is a fundamental substrate for cognition. A common misconception is that RE is a simple relay structure. Instead, a better conceptualization is that RE is a critical component of a canonical higher-order cortico-thalamo-cortical circuit that supports communication between the medial prefrontal cortex (mPFC) and the hippocampus (HC). RE dysfunction is implicated in several clinical disorders including, but not limited to Alzheimer's disease, schizophrenia, and epilepsy. Here, we review key anatomical and physiological features of the RE based primarily on studies in rodents. We present a conceptual model of RE circuitry within the mPFC-RE-HC system and speculate on the computations RE enables. We review the rapidly growing literature demonstrating that RE is critical to, and its neurons represent, aspects of behavioral tasks that place demands on memory focusing on its role in navigation, spatial working memory, the temporal organization of memory, and executive functions.

Early change in reward and punishment sensitivity as a predictor of response to antidepressant treatment for major depressive disorder: a CAN-BIND-1 report.

BACKGROUND: In an effort to optimize patient outcomes, considerable attention is being devoted to identifying patient characteristics associated with major depressive disorder (MDD) and its responsiveness to treatment. In the current study, we extend this work by evaluating whether early change in these sensitivities is associated with response to antidepressant treatment for MDD. METHODS: Participants included 210 patients with MDD who were treated with 8 weeks of escitalopram and 112 healthy comparison participants. Of the original 210 patients, 90 non-responders received adjunctive aripiprazole for an additional 8 weeks. Symptoms of depression and anhedonia were assessed at the beginning of treatment and 8 weeks later in both samples. Reward and punishment sensitivity were assessed using the BIS/BAS scales measured at the initiation of treatment and 2 weeks later. RESULTS: Individuals with MDD exhibited higher punishment sensitivity and lower reward sensitivity compared with healthy comparison participants. Change in reward sensitivity during the first 2 weeks of treatment was associated with improved depressive symptoms and anhedonia following 8 weeks of treatment with escitalopram. Similarly, improvement in reward responsiveness during the first 2 weeks of adjunctive therapy with aripiprazole was associated with fewer symptoms of depression at post-treatment. CONCLUSIONS: Findings highlight the predictive utility of early change in reward sensitivity during antidepressant treatment for major depression. In a clinical setting, a lack of change in early reward processing may signal a need to modify a patient's treatment plan with alternative or augmented treatment approaches.