Impairment in recognition memory may be associated with near-term risk for suicide attempt in a high-risk sample.

INTRODUCTION: Prior work has implicated several neurocognitive domains, including memory, in patients with a history of prior suicide attempt. The current study evaluated whether a delayed recognition test could enhance prospective prediction of near-term suicide outcomes in a sample of patients at high-risk for suicide. METHODS: 132 Veterans at high-risk for suicide completed a computer-based recognition memory test including semantically-related and -unrelated words. Outcomes were coded as actual suicide attempt (ASA), other suicide-related event (OtherSE) such as aborted/interrupted attempt or preparatory behavior, or neither (noSE), within 90 days after testing. RESULTS: Reduced performance was a significant predictor of upcoming ASA, but not OtherSE, after controlling for standard clinical variables such as current suicidal ideation and history of prior suicide attempt. However, compared to the noSE reference group, the OtherSE group showed a reduction in the expected benefit of semantic relatedness in recognizing familiar words. A computational model, the drift diffusion model (DDM), to explore latent cognitive processes, revealed the OtherSE group had decreased decisional efficiency for semantically-related compared to semantically-unrelated familiar words. LIMITATIONS: This study was a secondary analysis of an existing dataset, involving participants in a treatment trial, and requires replication; ~10 % of the sample was excluded from analysis due to failure to master the practice tasks and/or apparent noncompliance. CONCLUSION: Impairments in recognition memory may be associated with near-term risk for suicide attempt, and may provide a tool to improve prediction of when at-risk individuals may be transitioning into a period of heightened risk for suicide attempt.

Reward and Punishment Learning as Predictors of Cognitive Behavioral Therapy Response in Parkinson's Disease Comorbid with Clinical Depression.

Depression is highly comorbid among individuals with Parkinson's Disease (PD), who often experience unique challenges to accessing and benefitting from empirically supported interventions like Cognitive Behavioral Therapy (CBT). Given the role of reward processing in both depression and PD, this study analyzed a subset (N = 25) of participants who participated in a pilot telemedicine intervention of PD-informed CBT, and also completed a Reward- and Punishment-Learning Task (RPLT) at baseline. At the conclusion of CBT, participants were categorized into treatment responders (n = 14) and non-responders (n = 11). Responders learned more optimally from negative rather than positive feedback on the RPLT, while this pattern was reversed in non-responders. Computational modeling suggested group differences in learning rate to negative feedback may drive the observed differences. Overall, the results suggest that a within-subject bias for punishment-based learning might help to predict response to CBT intervention for depression in those with PD.Plain Language Summary Performance on a Computerized Task may predict which Parkinson's Disease Patients benefit from Cognitive Behavioral Treatment of Clinical DepressionWhy was the study done? Clinical depression regularly arises in individuals with Parkinson's Disease (PD) due to the neurobiological changes with the onset and progression of the disease as well as the unique psychosocial difficulties associated with living with a chronic condition. Nonetheless, psychiatric disorders among individuals with PD are often underdiagnosed and likewise undertreated for a variety of reasons. The results of our study have implications about how to improve the accuracy and specificity of mental health treatment recommendations in the future to maximize benefits for individuals with PD, who often face additional barriers to accessing quality mental health treatment.What did the researchers do? We explored whether performance on a computerized task called the Reward- and Punishment-Learning Task (RPLT) helped to predict response to Cognitive Behavioral Therapy (CBT) for depression better than other predictors identified in previous studies. Twenty-five individuals with PD and clinical depression that completed a 10-week telehealth CBT program were assessed for: Demographics (Age, gender, etc.); Clinical information (PD duration, mental health diagnoses, levels of anxiety/depression, etc.); Neurocognitive performance (Memory, processing speed, impulse control, etc.); and RPLT performance.What did the researchers find? A total of 14 participants significantly benefitted from CBT treatment while 11 did not significantly benefit from treatment.There were no differences before treatment in the demographics, clinical information, and neurocognitive performance of those participants who ended up benefitting from the treatment versus those who did not.There were, however, differences before treatment in RPLT performance so that those individuals that benefitted from CBT seemed to learn better from negative feedback.What do the findings mean? Our results suggest that the CBT program benefitted those PD patients with clinical depression that seemed to overall learn best from avoiding punishment rather than obtaining reward which was targeted in CBT by focusing on increasing engagement in rewarding activities. The Reward- and Punishment-Learning Task hence may be a useful tool to help predict treatment response and provide more individualized recommendations on how to best maximize the benefits of psychotherapy for individuals with PD that may struggle to connect to mental health care. Caution is recommended about interpretating these results beyond this study as the overall number of participants was small and the data for this study were collected as part of a previous study so there was no opportunity to include additional measurements of interest.

Altered learning and transfer abilities in Korsakoff's syndrome depending on task complexity.

Korsakoff's syndrome (KS) is characterized by episodic memory impairment due to damage to the medial diencephalic structures. Although commonly associated with chronic alcoholism, starvation due to the hunger strike is one of its nonalcoholic causes. Learning the stimulus-response associations and transferring the just-learned associations to novel combinations were previously tested by specific tasks in memory-impaired patients with hippocampal, basal forebrain, and basal ganglia damage. To add to this previous research, we aimed to use the same tasks in a group of patients with hunger strike-related KS presenting a stable isolated amnestic profile. Twelve patients with hunger strike-related KS and matched healthy controls were tested in two tasks varying in task complexity. Each task included two phases: the initial phase is feedback-based learning of (simple vs. complex) stimulus-response associations, and the following phase is transfer generalization (in the presence vs. absence of feedback). On a task involving simple associations, five patients with KS failed to learn the associations, while the other seven patients showed intact learning and transfer. On the other task involving more complex associations, seven patients showed slower learning and failed at transfer generalization, whereas the other five patients failed even at the acquisition phase. These findings of a task-complexity-related impairment on associative learning and transfer represent a distinct pattern from the spared learning but impaired transfer previously observed on these tasks in patients with medial temporal lobe amnesia.

Prefrontal transcranial magnetic stimulation boosts response vigour during reinforcement learning in healthy adults.

A 10-Hz repetitive transcranial magnetic stimulation to the left dorsal lateral prefrontal cortex has been shown to increase dopaminergic activity in the dorsal striatum, a region strongly implicated in reinforcement learning. However, the behavioural influence of this effect remains largely unknown. We tested the causal effects of 10-Hz stimulation on behavioural and computational characteristics of reinforcement learning. A total of 40 healthy individuals were randomized into active and sham (placebo) stimulation groups. Each participant underwent one stimulation session (1500 pulses) in which stimulation was applied over the left dorsal lateral prefrontal cortex using a robotic arm. Participants then completed a reinforcement learning task sensitive to striatal dopamine functioning. Participants' choices were modelled using a reinforcement learning model (Q-learning) that calculates separate learning rates associated with positive and negative reward prediction errors. Subjects receiving active stimulation exhibited increased reward rate (number of correct responses per second of task activity) compared with those in sham. Computationally, although no group differences were observed, the active group displayed a higher learning rate for correct trials (αG) compared with incorrect trials (αL). Finally, when tested with novel pairs of stimuli, the active group displayed extremely fast reaction times, and a trend towards a higher reward rate. This study provided specific behavioural and computational accounts of altered striatal-mediated behaviour, particularly response vigour, induced by a proposed increase of dopamine activity by 10-Hz stimulation to the left dorsal lateral prefrontal cortex. Together, these findings bolster the use of repetitive transcranial magnetic stimulation to target neurocognitive disturbances attributed to the dysregulation of dopaminergic-striatal circuits.

Improving the prospective prediction of a near-term suicide attempt in veterans at risk for suicide, using a go/no-go task.

BACKGROUND: Neurocognitive testing may advance the goal of predicting near-term suicide risk. The current study examined whether performance on a Go/No-go (GNG) task, and computational modeling to extract latent cognitive variables, could enhance prediction of suicide attempts within next 90 days, among individuals at high-risk for suicide. METHOD: 136 Veterans at high-risk for suicide previously completed a computer-based GNG task requiring rapid responding (Go) to target stimuli, while withholding responses (No-go) to infrequent foil stimuli; behavioral variables included false alarms to foils (failure to inhibit) and missed responses to targets. We conducted a secondary analysis of these data, with outcomes defined as actual suicide attempt (ASA), other suicide-related event (OtherSE) such as interrupted/aborted attempt or preparatory behavior, or neither (noSE), within 90-days after GNG testing, to examine whether GNG variables could improve ASA prediction over standard clinical variables. A computational model (linear ballistic accumulator, LBA) was also applied, to elucidate cognitive mechanisms underlying group differences. RESULTS: On GNG, increased miss rate selectively predicted ASA, while increased false alarm rate predicted OtherSE (without ASA) within the 90-day follow-up window. In LBA modeling, ASA (but not OtherSE) was associated with decreases in decisional efficiency to targets, suggesting differences in the evidence accumulation process were specifically associated with upcoming ASA. CONCLUSIONS: These findings suggest that GNG may improve prediction of near-term suicide risk, with distinct behavioral patterns in those who will attempt suicide within the next 90 days. Computational modeling suggests qualitative differences in cognition in individuals at near-term risk of suicide attempt.

Plasma Serotonin 2A Receptor Autoantibodies Predict Rapid, Substantial Decline in Neurocognitive Performance in Older Adult Veterans with TBI.

Aim: Traumatic brain injury (TBI) was associated with increased plasma serotonin 2A receptor (5-HT2AR) autoantibodies in adults who experienced neurodegenerative complications. We tested whether the baseline presence of plasma serotonin 2A receptor (5-HT2AR) autoantibodies was a significant predictor of the two-year rate of cognitive decline in middle-aged and older adult TBI. Methods: Plasma from thirty-five middle-aged and older adult veterans (mean 65 years old) who had suffered traumatic brain injury was subjected to protein-A affinity chromatography. One-fortieth dilution of the resulting immunoglobulin (Ig) G fraction was tested for binding (in ELISA) to a linear synthetic peptide corresponding to the second extracellular loop region of the human 5-HT2A receptor. All available patients completed baseline and two-year follow-up neurocognitive tests of memory (St Louis University Mental Status), processing speed (Digit Symbol Substitution Test) and executive function (Trails-making Test, Part B). Change in cognitive performance was computed as (two-year - baseline) raw test score. Results: Eighteen patients completed both baseline and two-year follow up neurocognitive tests. TBI patients harboring plasma 5-HT2AR autoantibodies at the baseline examination (n=13) did not differ significantly in their baseline clinical characteristics (age, education level) compared to TBI patients lacking baseline plasma autoantibodies (n=5). Plasma serotonin 2AR antibody-positive patients experienced a significantly greater post-baseline decline in performance on the St Louis University Mental Status test (P=0.0118) and in the Digit Symbol Substitution Test (P=0.011), but not in Trails-making Part B (P=0.129) compared to serotonin 2AR antibody-negative patients. In multivariable linear regression analyses that adjusted for age, baseline presence of plasma 5-HT2AR autoantibody was a significant predictor of the two-year rate of decline in memory, and processing speed. Binding of plasma autoantibody to the serotonin 2A receptor peptide in the enzyme linked immunosorbent assay was also significantly higher (at 1/160th titer of the protein-A eluate= 1 µg/mL IgG) in TBI patients harboring vs. those not harboring baseline plasma 5-HT2AR autoantibodies. Conclusion: These data suggest that plasma 5-hydroxytryptamine 2A receptor autoantibodies which were increased in approximately two-thirds of middle-aged and older adults following traumatic brain injury predicts rapid and substantial declines in cognitive function (memory and processing speed), independent of age.

A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences.

Recent years have seen a rapid increase in the number of studies using evidence-accumulation models (such as the drift diffusion model, DDM) in the fields of psychology and neuroscience. These models go beyond observed behavior to extract descriptions of latent cognitive processes that have been linked to different brain substrates. Accordingly, it is important for psychology and neuroscience researchers to be able to understand published findings based on these models. However, many articles using (and explaining) these models assume that the reader already has a fairly deep understanding of (and interest in) the computational and mathematical underpinnings, which may limit many readers' ability to understand the results and appreciate the implications. The goal of this article is therefore to provide a practical introduction to the DDM and its application to behavioral data - without requiring a deep background in mathematics or computational modeling. The article discusses the basic ideas underpinning the DDM, and explains the way that DDM results are normally presented and evaluated. It also provides a step-by-step example of how the DDM is implemented and used on an example dataset, and discusses methods for model validation and for presenting (and evaluating) model results. Supplementary material provides R code for all examples, along with the sample dataset described in the text, to allow interested readers to replicate the examples themselves. The article is primarily targeted at psychologists, neuroscientists, and health professionals with a background in experimental cognitive psychology and/or cognitive neuroscience, who are interested in understanding how DDMs are used in the literature, as well as some who may to go on to apply these approaches in their own work.

Distressed (Type D) personality is predicted by avoidance: evidence from a computer-based avatar task.

Background: One personality type associated with poor health outcomes is distressed (Type D) personality which involves high levels of both social inhibition (SI) and negative affectivity (NA). Type D is also linked to psychopathologies such as post-traumatic stress disorder (PTSD), anxiety disorders, and depression. One mechanism through which personality temperament may result in these psychopathologies is avoidance. Recently, a computer-based measure designed to assess avoidant behaviors, in which the participant guides the behavior of an avatar interacting with strangers in social situations, has been found to be related to various forms of avoidance. In the current study, we extended this work with the avatar avoidance task to determine its relationship to distressed (Type D) personality. We hypothesized that Type D personality, along with SI, but not NA, would be positively related to avatar avoidance scores. We also hypothesized that avatar avoidance scores would be higher in Type D individuals than non-Type D individuals. Methods: A total of 302 undergraduates completed the Distressed Type D Personality Scale (DS-14), and a computer-based avatar avoidance task. Results: Type D and SI, and NA to a lesser degree, were positively correlated with avoidance scores on the avatar task. Furthermore, regression analyses revealed that Type D and SI scores were best predicted by a model including avoidance scores and education level while NA scores were best predicted by a model including avoidance scores. Standard cut-off scores on the DS-14 scale resulted in four groups (i.e., low SI and NA, high SI, high NA, and Type D) which significantly differed in avoidance scores. Specifically, Type D individuals had higher avoidance scores than the other three groups. Taken together these findings support a role for avoidance in Type D personality. The computer-based avatar avoidance task may be particularly relevant as an ecologically valid measure to identify avoidance in a virtual setting for use with individuals expressing Type D personality who may be unwilling or unable to accurately self-report or describe their own avoidant tendencies.

Pyridostigmine bromide, chlorpyrifos, and DEET combined Gulf War exposure insult depresses mitochondrial function in neuroblastoma cells.

Gulf War Illness (GWI) is defined by the Centers for Disease Control and Prevention (CDC) as a multi-symptom illness having at least one symptom from two of three factors, which include: fatigue, mood-cognition problems, and musculoskeletal disorders. The cluster of long-term symptoms is unique to military personnel from coalition countries including United States, Australia, and the United Kingdom that served in Operation Desert Storm from 1990 to 1991. Reporting of these symptoms is much lower among soldiers deployed in other parts of the world like Bosnia during the same time period. The exact cause of GWI is unknown, but combined exposure to N,N-diethyl-m-toluamide (DEET), organophosphates like chlorpyrifos (CPF), and pyridostigmine bromide (PB), has been hypothesized as a potential mechanism. Mitochondrial dysfunction is known to occur in most neurodegenerative diseases that share symptoms with GWI and has therefore been implicated in GWI. Although exposure to these and other toxicants continues to be investigated as potential causes of GWI, their combined impact on mitochondrial physiology remains unknown. In this study, the effects of combined GWI toxicant exposure on mitochondrial function were determined in a commonly used and readily available immortalized cell line (N2a), whose higher rate of oxygen consumption resembles that of highly metabolic neurons in vivo. We report that combined exposure containing pesticide CPF 71 µM, insect repellants DEET 78 µM, and antitoxins PB 19 µM, causes profound mitochondrial dysfunction after a 4-h incubation resulting in decreased mitochondrial respiratory states in the absence of proapoptotic signaling, proton leak, or significant increase in reactive oxygen species production.

Dataset of active avoidance in Wistar-Kyoto and Sprague Dawley rats: Experimental data and reinforcement learning model code and output.

Data were collected from 40 Wistar-Kyoto (WKY) and 40 Sprague Dawley (SD) rats during an active escape-avoidance experiment. Footshock could be avoided by pressing a lever during a danger period prior to onset of shock. If avoidance did not occur, a series of footshocks was administered, and the rat could press a lever to escape (terminate shocks). For each animal, data were simplified to the presence or absence of lever press and stimuli in each 12-second time frame. Using the pre-processed dataset, a reinforcement learning (RL) model, based on an actor-critic architecture, was utilized to estimate several different model parameters that best characterized each rat's behaviour during the experiment. Once individual model parameters were determined for all 80 rats, behavioural recovery simulations were run using the RL model with each animal's "best-fit" parameters; the simulated behaviour generated avoidance data (percent of trials avoided during a given experimental session) that could be compared across simulated rats, as is customarily done with empirical data. The datasets representing both the experimental data and the model-generated data can be interpreted in various ways to gain further insight into rat behaviour during avoidance and escape learning. Furthermore, the estimated parameters for each individual rat can be compared across groups. Thus, possible between-strain differences in model parameters can be detected, which might provide insights into strain differences in learning. The software implementing the RL model can also be applied to or serve as a template for other experiments involving acquisition learning. Reference for Co-Submission: K.M. Spiegler, J. Palmieri, K.C.H. Pang, C.E. Myers, A reinforcement-learning model of active avoidance behavior: Differences between Sprague-Dawley and Wistar-Kyoto rats. Behav. Brain Res. (2020 Jun 22[epub ahead of print])  doi: 10.1016/j.bbr.2020.112784.

Serotonin 2A Receptor Autoantibodies Increase in Adult Traumatic Brain Injury In Association with Neurodegeneration.

OBJECTIVE: Traumatic brain injury (TBI) is associated with an increased risk of late neurodegenerative complications via unknown mechanisms. Circulating neurotoxic 5-hydroxytryptamine 2A receptor (5-HT2AR) autoantibodies were reported to increase in subsets of obese type 2 diabetes having microvascular complications. We tested whether 5-HT2AR autoantibodies increase in adults following traumatic brain injury in association with neurodegenerative complications. METHODS: Plasma from thirty-five middle-aged and older adult veterans (mean 65 years old) who had suffered traumatic brain injury was subjected to protein-A affinity chromatography. The resulting immunoglobulin (Ig) G fraction was tested for neurotoxicity (acute neurite retraction, and accelerated cell death) in mouse N2A neuroblastoma cells or for binding to a linear synthetic peptide corresponding to the second extracellular loop region of the human 5-HT2A receptor. RESULTS: Nearly two-thirds of traumatic brain injured-patients harbored 5-HT2AR autoantibodies in their circulation. Active TBI autoantibodies caused neurite retraction in mouse N2A neuroblastoma cells and accelerated N2A cell loss which was substantially prevented by co-incubation with a two hundred and fifty nanomolar concentration of M100907, a highly selective 5-HT2AR antagonist. Antagonists of RhoA/Rho kinase and Gq11/phospholipase C/inositol triphosphate receptor signaling pathways blocked TBI autoantibody-induced neurite retraction. Following traumatic brain injury, autoantibody binding to a 5-HT2A receptor peptide was significantly increased in patients having co-morbid Parkinson's disease (n=3), dementia (n=5), and painful neuropathy (n=8) compared to TBI subsets without neurologic or microvascular complication (n=20). Autoantibody titer was significantly elevated in TBI subsets experiencing multiple neurotraumatic exposures vs. single TBI. Plasma white blood cell, a marker of systemic inflammation, correlated significantly (correlation coefficient r =0.52; P < 0.01) with, 5-HT2A receptor peptide binding of the TBI-autoantibody. CONCLUSION: These data suggest that circulating neurotoxic 5-hydroxytryptamine 2A receptor agonist autoantibodies increase in adults following traumatic brain injury in association with late neurodegenerative complications.

A reinforcement-learning model of active avoidance behavior: Differences between Sprague Dawley and Wistar-Kyoto rats.

Avoidance behavior is a typically adaptive response performed by an organism to avert harmful situations. Individuals differ remarkably in their tendency to acquire and perform new avoidance behaviors, as seen in anxiety disorders where avoidance becomes pervasive and inappropriate. In rodent models of avoidance, the inbred Wistar-Kyoto (WKY) rat demonstrates increased learning and expression of avoidance compared to the outbred Sprague Dawley (SD) rat. However, underlying mechanisms that contribute to these differences are unclear. Computational modeling techniques can help identify factors that may not be easily decipherable from behavioral data alone. Here, we utilize a reinforcement learning (RL) model approach to better understand strain differences in avoidance behavior. An actor-critic model, with separate learning rates for action selection (in the actor) and state evaluation (in the critic), was applied to individual data of avoidance acquisition from a large cohort of WKY and SD rats. Latent parameters were extracted, such as learning rate and subjective reinforcement value of foot shock, that were then compared across groups. The RL model was able to accurately represent WKY and SD avoidance behavior, demonstrating that the model could simulate individual performance. The model determined that the perceived negative value of foot shock was significantly higher in WKY than SD rats, whereas learning rate in the actor was lower in WKY than SD rats. These findings demonstrate the utility of computational modeling in identifying underlying processes that could promote strain differences in behavioral performance.

Demonstrating and disrupting well-learned habits.

Researchers have exerted tremendous efforts to empirically study how habits form and dominate at the expense of deliberation, yet we know very little about breaking these rigid habits to restore goal-directed control. In a three-experiment study, we first illustrate a novel approach of studying well-learned habits, in order to effectively demonstrate habit disruption. In Experiment 1, we use a Go/NoGo task with familiar color-response associations to demonstrate outcome-insensitivity when compared to novel, more flexible associations. Specifically, subjects perform more accurately when the required mapping is the familiar association of green-Go/red-NoGo than when it is red-Go/green-NoGo, confirming outcome-insensitive, habitual control. As a control condition, subjects show equivalent performance with unfamiliar color-response mappings (using the colors blue and purple mapped to Go and NoGo responses). Next, in Experiments 2 and 3, we test a motivation-based feedback manipulation in varying magnitudes (i.e., performance feedback with and without monetary incentives) to break the well-established habits elicited by our familiar stimuli. We find that although performance feedback prior to the contingency reversal test is insufficient to disrupt outcome-insensitivity in Experiment 2, a combination of performance feedback and monetary incentive is able to restore goal-directed control in Experiment 3, effectively breaking the habits. As the first successful demonstration of well-learned habit disruption in the laboratory, these findings provide new insights into how we execute and modify habits, while fostering new and translational research avenues that may be applicable to treating habit-based pathologies.

Racial differences in trends of serious hypoglycemia among higher risk older adults in US Veterans Health Administration, 2004-2015: Relationship to comorbid conditions, insulin use, and hemoglobin A1c level.

AIMS: To evaluate temporal trends in racial/ethnic groups in rates of serious hypoglycemia among higher risk patients dually enrolled in Veterans Health Administration and Medicare fee-for-service and assess the relationship(s) between hypoglycemia rates, insulin/secretagogues and comorbid conditions. METHODS: Retrospective observational serial cross-sectional design. Patients were ≥65 years receiving insulin and/or secretagogues. The primary outcome was the annual (period prevalence) rates (2004-2015), per 1000 patient years, of serious hypoglycemic events, defined as hypoglycemic-related emergency department visits or hospitalizations. RESULTS: Subjects were 77-83% White, 7-10% Black, 4-5% Hispanic, <2% women; 38-58% were ≥75 years old; 72-75% had ≥1 comorbidity. In 2004-2015, rates declined from 63.2 to 33.6(-46.9%) in Blacks; 29.7 to 20.3 (-31.6%) in Whites; and 41.8 to 29.6 (-29.3%) in Hispanics. The Black-White rate differences narrowed regardless of insulin use, hemoglobin A1c level, and frequency and various combinations of comorbid conditions. Among insulin users, the Black-White contrast decreased from 34.7 (98.5 vs. 63.8) in 2004 to 13.2 (43.6 vs. 30.4) in 2015; in non-insulin users, the contrast was 25.7 (44.1 vs. 18.4) in 2004 and 10.1 (18.9 vs. 8.8) in 2015. CONCLUSION: Marked declines in serious hypoglycemia events occurred across race, medications, and comorbidities, suggesting significant changes in clinical practice.

Maladaptive avoidance patterns in Parkinson's disease are exacerbated by symptoms of depression.

Parkinson's disease (PD) is a chronic, progressive neurodegenerative disorder, characterized by a loss of dopaminergic neurons in the substantia nigra pars compacta. Given that dopamine is critically involved in learning and other cognitive processes, such as working memory, dopamine loss in PD has been linked both to learning abnormalities and to cognitive dysfunction more generally in the disease. It is unclear, however, whether avoidance behavior is impacted in PD. This is significant, as this type of instrumental behavior plays an important role in both decision-making and emotional (dys) function. Consequently, the aim of the present study was to examine avoidance learning and operant extinction in PD using a computer-based task. On this task, participants control a spaceship and attempt to shoot an enemy spaceship to gain points. They also learn to hide in safe areas to protect from (i.e., avoid) aversive events (on-screen explosions and point loss). The results showed that patients with PD (N = 25) acquired an avoidance response during aversive periods to the same extent as healthy age-matched controls (N = 19); however, patients demonstrated greater hiding during safe periods not associated with aversive events, which could represent maladaptive generalization of the avoidance response. Furthermore, this impairment was more pronounced during the extinction phase, and in patients who reported higher levels of depression. These results demonstrate for the first time that PD is associated with maladaptive avoidance patterns, which could possibly contribute to the emergence of depression in the disease.

Towards the objective assessment of suicidal states: Some neurocognitive deficits may be temporally related to suicide attempt.

Neurocognitive detection of suicidal states has the potential to significantly advance objective risk assessment. This goal requires establishing that neurocognitive deficits fluctuate around the time of a suicide attempt. The current study therefore evaluated whether neurocognitive performance is temporally related to suicide attempt, in a sample at highrisk for suicide (n = 141). Evaluations consisted of a clinician-administered interview, self-report questionnaires, and neurocognitive tasks assessing response inhibition, attentional control, and memory recognition. Analyses examined whether neurocognitive scores significantly differed according to the following temporal suicide attempt categories: (a) past-week attempt; (b) past-year attempt (not in past week); and (c) no past-year attempt. Univariate results showed that response inhibition and memory recognition were significantly related to suicide attempt recency. Post-hoc pairwise tests showed that participants with a past-week suicide attempt showed greater impairments than those without a past-year attempt. Multivariate tests showed the same pattern of results, adjusting for age, suicide attempts prior to past year, mood disturbance, and suicidal ideation. These results show that neurocognitive assessment of response inhibition and memory recognition shows sensitivity to the recency of a suicide attempt. While future prospective studies are needed, results suggest that phasic neurocognitive deficits may serve as objective markers of short-term suicide risk.

Learning functions in short-term cocaine users.

OBJECTIVE: This study examined learning functions in short-term cocaine users and control participants. METHOD: Seventeen active cocaine users (reporting 3.5 mean years of cocaine use) and seventeen non-cocaine-using controls (with similar reported levels of alcohol and marijuana use) were compared on tasks measuring different aspects of learning. RESULTS: The cocaine users performed more poorly on the Weather Prediction and List-Learning tasks, as well as supplementary executive and psychomotor function tasks, than controls. CONCLUSIONS: Individuals with a relatively short duration of cocaine use exhibited moderate weaknesses in probabilistic category learning, verbal learning and psychomotor functions, relative to controls. These weaknesses may underpin difficulty in learning from the probabilistic consequences of behavior and hinder the ability to respond to cognitive-behavioral treatments.

Inhibited Personality Temperaments Translated Through Enhanced Avoidance and Associative Learning Increase Vulnerability for PTSD.

Although many individuals who experience a trauma go on to develop post-traumatic stress disorder (PTSD), the rate of PTSD following trauma is only about 15-24%. There must be some pre-existing conditions that impart increased vulnerability to some individuals and not others. Diathesis models of PTSD theorize that pre-existing vulnerabilities interact with traumatic experiences to produce psychopathology. Recent work has indicated that personality factors such as behavioral inhibition (BI), harm avoidance (HA), and distressed (Type D) personality are vulnerability factors for the development of PTSD and anxiety disorders. These personality temperaments produce enhanced acquisition or maintenance of associations, especially avoidance, which is a criterion symptom of PTSD. In this review, we highlight the evidence for a relationship between these personality types and enhanced avoidance and associative learning, which may increase risk for the development of PTSD. First, we provide the evidence confirming a relationship among BI, HA, distressed (Type D) personality, and PTSD. Second, we present recent findings that BI is associated with enhanced avoidance learning in both humans and animal models. Third, we will review evidence that BI is also associated with enhanced eyeblink conditioning in both humans and animal models. Overall, data from both humans and animals suggest that these personality traits promote enhanced avoidance and associative learning, as well as slowing of extinction in some training protocols, which all support the learning diathesis model. These findings of enhanced learning in vulnerable individuals can be used to develop objective behavioral measures to pre-identify individuals who are more at risk for development of PTSD following traumatic events, allowing for early (possibly preventative) intervention, as well as suggesting possible therapies for PTSD targeted on remediating avoidance or associative learning. Future work should explore the neural substrates of enhanced avoidance and associative learning for behaviorally inhibited individuals in both the animal model and human participants.

A pilot study of escape, avoidance, and approach behaviors in treated alcohol-dependent males.

Alcohol-dependent individuals are often reported to use behavioral strategies both to escape from and avoid negative affective states, and also to approach positive affective states. However, there has been little examination of how these individuals acquire and express these types of behaviors. In this study, male adults meeting the International Classification of Diseases-10th Revision (ICD-10) criteria for alcohol dependence recruited from an outpatient treatment clinic and healthy male controls were given a computer-based task to assess learning and performance of escape, avoidance, and approach behaviors. In this task, participants control a spaceship and can either gain points by shooting an enemy spaceship or hide in safe areas to escape or avoid on-screen aversive events. We found that patients with alcohol dependence exhibited greater escape and approach behaviors, tended to show greater avoidance behavior, and achieved higher total score on the computer task than healthy controls. This is the first demonstration of such behavioral differences in this population, supporting the overactivation of both positive and negative reinforcement systems in alcohol dependence, and suggesting that such behavioral biases are not limited to alcohol-related cues. The contribution of this work to behavioral assessment and therapeutic approaches, as well as possible future directions, are discussed.

ABCA7 risk variant in healthy older African Americans is associated with a functionally isolated entorhinal cortex mediating deficient generalization of prior discrimination training.

Using high-resolution resting state functional magnetic resonance imaging (fMRI), the present study tested the hypothesis that ABCA7 genetic risk differentially affects intra-medial temporal lobe (MTL) functional connectivity between MTL subfields, versus internetwork connectivity of the MTL with the medial prefrontal cortex (mPFC), in nondemented older African Americans. Although the association of ABCA7 risk variants with Alzheimer's disease (AD) has been confirmed worldwide, its effect size on the relative odds of being diagnosed with AD is significantly higher in African Americans. However, little is known about the neural correlates of cognitive function in older African Americans and how they relate to AD risk conferred by ABCA7. In a case-control fMRI study of 36 healthy African Americans, we observed ABCA7 related impairments in behavioral generalization that was mediated by dissociation in entorhinal cortex (EC) resting state functional connectivity. Specifically, ABCA7 risk variant was associated with EC-hippocampus hyper-synchronization and EC-mPFC hypo-synchronization. Carriers of the risk genotype also had a significantly smaller anterolateral EC, despite our finding no group differences on standardized neuropsychological tests. Our findings suggest a model where impaired cortical connectivity leads to a more functionally isolated EC at rest, which translates into aberrant EC-hippocampus hyper-synchronization resulting in generalization deficits. While we cannot identify the exact mechanism underlying the observed alterations in EC structure and network function, considering the relevance of Aß in ABCA7 related AD pathogenesis, the results of our study may reflect the synergistic reinforcement between amyloid and tau pathology in the EC, which significantly increases tau-induced neuronal loss and accelerates synaptic alterations. Finally, our results add to a growing literature suggesting that generalization of learning may be a useful tool for assessing the mild cognitive deficits seen in the earliest phases of prodromal AD, even before the more commonly reported deficits in episodic memory arise.