Reinforcement learning processes as forecasters of depression remission.

BACKGROUND: Aspects of reinforcement learning have been associated with specific depression symptoms and may inform the course of depressive illness. METHODS: We applied support vector machines to investigate whether blood­oxygen-level dependent (BOLD) responses linked with neural prediction error (nPE) and neural expected value (nEV) from a probabilistic learning task could forecast depression remission. We investigated whether predictions were moderated by treatment use or symptoms. Participants included 55 individuals (n = 39 female) with a depression diagnosis at baseline; 36 of these individuals completed standard cognitive behavioral therapy and 19 were followed during naturalistic course of illness. All participants were assessed for depression diagnosis at a follow-up visit. RESULTS: Both nPE and nEV classifiers forecasted remission significantly better than null classifiers. The nEV classifier performed significantly better than the nPE classifier. We found no main or interaction effects of treatment status on nPE or nEV accuracy. We found a significant interaction between nPE-forecasted remission status and anhedonia, but not for negative affect or anxious arousal, when controlling for nEV-forecasted remission status. LIMITATIONS: Our sample size, while comparable to that of other studies, limits options for maximizing and evaluating model performance. We addressed this with two standard methods for optimizing model performance (90:10 train and test scheme and bootstrapped sampling). CONCLUSIONS: Results support nEV and nPE as relevant biobehavioral signals for understanding depression outcome independent of treatment status, with nEV being stronger than nPE as a predictor of remission. Reinforcement learning variables may be useful components of an individualized medicine framework for depression healthcare.

Anxiety as a disorder of uncertainty: implications for understanding maladaptive anxiety, anxious avoidance, and exposure therapy.

In cognitive-behavioral conceptualizations of anxiety, exaggerated threat expectancies underlie maladaptive anxiety. This view has led to successful treatments, notably exposure therapy, but is not consistent with the empirical literature on learning and choice alterations in anxiety. Empirically, anxiety is better described as a disorder of uncertainty learning. How disruptions in uncertainty lead to impairing avoidance and are treated with exposure-based methods, however, is unclear. Here, we integrate concepts from neurocomputational learning models with clinical literature on exposure therapy to propose a new framework for understanding maladaptive uncertainty functioning in anxiety. Specifically, we propose that anxiety disorders are fundamentally disorders of uncertainty learning and that successful treatments, particularly exposure therapy, work by remediating maladaptive avoidance from dysfunctional explore/exploit decisions in uncertain, potentially aversive situations. This framework reconciles several inconsistencies in the literature and provides a path forward to better understand and treat anxiety.

Using smartphones to optimise and scale-up the assessment of model-based planning.

Model-based planning is thought to protect against over-reliance on habits. It is reduced in individuals high in compulsivity, but effect sizes are small and may depend on subtle features of the tasks used to assess it. We developed a diamond-shooting smartphone game that measures model-based planning in an at-home setting, and varied the game's structure within and across participants to assess how it affects measurement reliability and validity with respect to previously established correlates of model-based planning, with a focus on compulsivity. Increasing the number of trials used to estimate model-based planning did remarkably little to affect the association with compulsivity, because the greatest signal was in earlier trials. Associations with compulsivity were higher when transition ratios were less deterministic and depending on the reward drift utilised. These findings suggest that model-based planning can be measured at home via an app, can be estimated in relatively few trials using certain design features, and can be optimised for sensitivity to compulsive symptoms in the general population.

Humans adaptively resolve the explore-exploit dilemma under cognitive constraints: Evidence from a multi-armed bandit task.

When navigating uncertain worlds, humans must balance exploring new options versus exploiting known rewards. Longer horizons and spatially structured option values encourage humans to explore, but the impact of real-world cognitive constraints such as environment size and memory demands on explore-exploit decisions is unclear. In the present study, humans chose between options varying in uncertainty during a multi-armed bandit task with varying environment size and memory demands. Regression and cognitive computational models of choice behavior showed that with a lower cognitive load, humans are more exploratory than a simulated value-maximizing learner, but under cognitive constraints, they adaptively scale down exploration to maintain exploitation. Thus, while humans are curious, cognitive constraints force people to decrease their strategic exploration in a resource-rational-like manner to focus on harvesting known rewards.

A double-blind study assessing the impact of orbitofrontal theta burst stimulation on goal-directed behavior.

Patients with disorders of compulsivity show impairments in goal-directed behavior, which have been linked to orbitofrontal cortex (OFC) dysfunction. We recently showed that continuous theta burst stimulation (cTBS), which reduces OFC activity, had a beneficial effect on compulsive behaviors both immediately and at 1 week follow-up compared with inhibitory TBS (iTBS). In this same sample, we investigated whether two behavioral measures of goal-directed control (devaluation success on a habit override task; model-based planning on the two-step task) were also affected by acute modulation of OFC activity. Overall, model-based planning and devaluation success were significantly related to each other and (for devaluation success) to symptoms in our transdiagnostic clinical sample. These measures were moderately to highly stable across time. In individuals with low levels of model-based planning, active cTBS improved devaluation success. Analogous to previously reported clinical effects, this effect was specific to cTBS and not iTBS. Overall, results suggested that measures of goal directed behavior are reliable but less affected by cTBS than clinical self-report. Future research should continue to examine longitudinal changes in behavioral measures to determine their temporal relationship with symptom improvement after treatment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Delay discounting in suicidal behavior: Myopic preference or inconsistent valuation?

Prior studies sought to explain the predisposition to suicidal behavior in terms of myopic preference for immediate versus delayed reward, generating mixed evidence. Data from gambling and bandit tasks, however, suggest that suboptimal decisions in suicidal individuals are explained by inconsistent valuation rather than myopic preferences. We tested these two alternative hypotheses using a delay discounting task in 622 adults (suicide attempters with depression, suicide ideators with depression, nonsuicidal participants with depression, and healthy controls) recruited across three sites through inpatient psychiatric units, mood disorders clinics, primary care, and advertisements. Multilevel models revealed group differences in valuation consistencies in all three samples, with high-lethality suicide attempters exhibiting less consistent valuation than all other groups in Samples 1 and 3 and less consistent valuation than the healthy controls or participants with depression in Sample 2. In contrast, group differences in preference for immediate versus delayed reward were observed only in Sample 1 and were due to the high-lethality suicide attempters displaying a weaker preference for immediate reward than low-lethality suicide attempters. The findings were robust to confounds such as cognitive functioning and comorbidities. Seemingly impulsive choices in suicidal behavior are explained by inconsistent reward valuation rather than a true preference for immediate reward. In a suicidal crisis, this inconsistency may result in a misestimation of the value of suicide relative to constructive alternatives and deterrents. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Reinforcement Learning Disruptions in Individuals With Depression and Sensitivity to Symptom Change Following Cognitive Behavioral Therapy.

Importance: Major depressive disorder is prevalent and impairing. Parsing neurocomputational substrates of reinforcement learning in individuals with depression may facilitate a mechanistic understanding of the disorder and suggest new cognitive therapeutic targets. Objective: To determine associations among computational model-derived reinforcement learning parameters, depression symptoms, and symptom changes after treatment. Design, Setting, and Participants: In this mixed cross-sectional-cohort study, individuals performed reward and loss variants of a probabilistic learning task during functional magnetic resonance imaging at baseline and follow-up. A volunteer sample with and without a depression diagnosis was recruited from the community. Participants were assessed from July 2011 to February 2017, and data were analyzed from May 2017 to May 2021. Main Outcomes and Measures: Computational model-based analyses of participants' choices assessed a priori hypotheses about associations between components of reward-based and loss-based learning with depression symptoms. Changes in both learning parameters and symptoms were then assessed in a subset of participants who received cognitive behavioral therapy (CBT). Results: Of 101 included adults, 69 (68.3%) were female, and the mean (SD) age was 34.4 (11.2) years. A total of 69 participants with a depression diagnosis and 32 participants without a depression diagnosis were included at baseline; 48 participants (28 with depression who received CBT and 20 without depression) were included at follow-up (mean [SD] of 115.1 [15.6] days). Computational model-based analyses of behavioral choices and neural data identified associations of learning with symptoms during reward learning and loss learning, respectively. During reward learning only, anhedonia (and not negative affect or arousal) was associated with model-derived learning parameters (learning rate: posterior mean regression ß = -0.14; 95% credible interval [CrI], -0.12 to -0.03; outcome sensitivity: posterior mean regression ß = 0.18; 95% CrI, 0.02 to 0.37) and neural learning signals (moderation of association between striatal prediction error and expected value signals: t97 = -2.10; P = .04). During loss learning only, negative affect (and not anhedonia or arousal) was associated with learning parameters (outcome shift: posterior mean regression ß = -0.11; 95% CrI, -0.20 to -0.01) and disrupted neural encoding of learning signals (association with subgenual anterior cingulate prediction error signals: r = -0.28; P = .005). Symptom improvement following CBT was associated with normalization of learning parameters that were disrupted at baseline (reward learning rate: posterior mean regression ß = 0.15; 90% CrI, 0.001 to 0.41; loss outcome shift: posterior mean regression ß = 0.42; 90% CrI, 0.09 to 0.77). Conclusions and Relevance: In this study, the mapping of reinforcement learning components to symptoms of major depression revealed mechanistic features associated with these symptoms and points to possible learning-based therapeutic processes and targets.

Improving the Reliability of Computational Analyses: Model-Based Planning and Its Relationship With Compulsivity.

BACKGROUND: Computational models show great promise in mapping latent decision-making processes onto dissociable neural substrates and clinical phenotypes. One prominent example in reinforcement learning is model-based planning, which specifically relates to transdiagnostic compulsivity. However, the reliability of computational model-derived measures such as model-based planning is unclear. Establishing reliability is necessary to ensure that such models measure stable, traitlike processes, as assumed in computational psychiatry. Although analysis approaches affect validity of reinforcement learning models and reliability of other task-based measures, their effect on reliability of reinforcement learning models of empirical data has not been systematically studied. METHODS: We first assessed within- and across-session reliability and effects of analysis approaches (model estimation, parameterization, and data cleaning) of measures of model-based planning in patients with compulsive disorders (n = 38). The analysis approaches affecting test-retest reliability were tested in 3 large generalization samples (healthy participants: n = 541 and 111; people with a range of compulsivity: n = 1413). RESULTS: Analysis approaches greatly influenced reliability: reliability of model-based planning measures ranged from 0 (no concordance) to above 0.9 (acceptable for clinical applications). The largest influence on reliability was whether model-estimation approaches were robust and accounted for the hierarchical structure of estimated parameters. Improvements in reliability generalized to other datasets and greatly reduced the sample size needed to find a relationship between model-based planning and compulsivity in an independent dataset. CONCLUSIONS: These results indicate that computational psychiatry measures such as model-based planning can reliably measure latent decision-making processes, but when doing so must assess the ability of methods to estimate complex models from limited data.

Threat-induced anxiety during goal pursuit disrupts amygdala-prefrontal cortex connectivity in posttraumatic stress disorder.

To investigate how unpredictable threat during goal pursuit impacts fronto-limbic activity and functional connectivity in posttraumatic stress disorder (PTSD), we compared military veterans with PTSD (n = 25) vs. trauma-exposed control (n = 25). Participants underwent functional magnetic resonance imaging (fMRI) while engaged in a computerized chase-and-capture game task that involved optimizing monetary rewards obtained from capturing virtual prey while simultaneously avoiding capture by virtual predators. The game was played under two alternating contexts-one involving exposure to unpredictable task-irrelevant threat from randomly occurring electrical shocks, and a nonthreat control condition. Activation in and functional connectivity between the amygdala and ventromedial prefrontal cortex (vmPFC) was tested across threat and nonthreat task contexts with generalized psychophysiological interaction (gPPI) analyses. PTSD patients reported higher anxiety than controls across contexts. Better task performance represented by successfully avoiding capture by predators under threat compared with nonthreat contexts was associated with stronger left amygdala-vmPFC functional connectivity in controls and greater vmPFC activation in PTSD patients. PTSD symptom severity was negatively correlated with vmPFC activation in trauma-exposed controls and with right amygdala-vmPFC functional connectivity across all participants in the threat relative to nonthreat contexts. The findings showed that veterans with PTSD have disrupted amygdala-vmPFC functional connectivity and greater localized vmPFC processing under threat modulation of goal-directed behavior, specifically related to successfully avoiding loss of monetary rewards. In contrast, trauma survivors without PTSD relied on stronger threat-modulated left amygdala-vmPFC functional connectivity during goal-directed behavior, which may represent a resilience-related functional adaptation.

Ventromedial prefrontal value signals and functional connectivity during decision-making in suicidal behavior and impulsivity.

Suicide is linked to impaired value-based decision-making and impulsivity, but whether these risk factors share neural underpinnings is unclear. Disrupted ventromedial prefrontal cortex (vmPFC) value signals may underlie this behavioral phenotype. We investigated vmPFC value signals, vmPFC-frontoparietal connectivity, and the impact of impulsivity during decision-making in depressed individuals with and without suicidal behavior. Middle-aged and older adults (n = 116; 35 with a history of suicide attempts, 25 with ideation only, 25 depressed controls with no ideation, and 31 nonpsychiatric controls) completed a decision-making task with drifting reward probabilities during fMRI. Values of choices, estimated by a reinforcement learning model, were regressed against BOLD signal. VmPFC value activation was compared between groups. Moderating effects of impulsivity on vmPFC-frontoparietal connectivity were assessed in nonpsychiatric controls and compared among patient groups. VmPFC value responses in participants with a history of suicide attempts were reduced relative to nonpsychiatric controls (p < 0.05). In nonpsychiatric controls, vmPFC-frontoparietal connectivity was negatively moderated by impulsivity (pFWE corrected < 0.05). This effect was preserved in comparison patient groups but abolished in suicide attempters (p < 0.001). This change in neural connectivity patterns also affected behavior: people with a history of suicide attempts showed a disrupted effect of vmPFC-frontoparietal connectivity, impulsivity, and reinforcement on choice quality (p < 0.001). These effects were specific to vmPFC and not to striatum. In summary, findings from this study largely support disrupted vmPFC value signals in suicidal behavior. In addition, it uncovers an altered pattern of vmPFC-frontoparietal connectivity in impulsive people with suicidal behavior, which may underlie disrupted choice processes in a suicidal crisis.

In Cocaine Dependence, Neural Prediction Errors During Loss Avoidance Are Increased With Cocaine Deprivation and Predict Drug Use.

BACKGROUND: In substance-dependent individuals, drug deprivation and drug use trigger divergent behavioral responses to environmental cues. These divergent responses are consonant with data showing that short- and long-term adaptations in dopamine signaling are similarly sensitive to state of drug use. The literature suggests a drug state-dependent role of learning in maintaining substance use; evidence linking dopamine to both reinforcement learning and addiction provides a framework to test this possibility. METHODS: In a randomized crossover design, 22 participants with current cocaine use disorder completed a probabilistic loss-learning task during functional magnetic resonance imaging while on and off cocaine (44 sessions). Another 54 participants without Axis I psychopathology served as a secondary reference group. Within-drug state and paired-subjects' learning effects were assessed with computational model-derived individual learning parameters. Model-based neuroimaging analyses evaluated effects of drug use state on neural learning signals. Relationships among model-derived behavioral learning rates (α+, α-), neural prediction error signals (δ+, δ-), cocaine use, and desire to use were assessed. RESULTS: During cocaine deprivation, cocaine-dependent individuals exhibited heightened positive learning rates (α+), heightened neural positive prediction error (δ+) responses, and heightened association of α+ with neural δ+ responses. The deprivation-enhanced neural learning signals were specific to successful loss avoidance, comparable to participants without psychiatric conditions, and mediated a relationship between chronicity of drug use and desire to use cocaine. CONCLUSIONS: Neurocomputational learning signals are sensitive to drug use status and suggest that heightened reinforcement by successful avoidance of negative outcomes may contribute to drug seeking during deprivation. More generally, attention to drug use state is important for delineating substrates of addiction.

Value-Based Choice, Contingency Learning, and Suicidal Behavior in Mid- and Late-Life Depression.

BACKGROUND: Suicidal behavior is associated with impaired decision making in contexts of uncertainty. Existing studies, however, do not definitively address whether suidice attempers have 1) impairment in learning from experience or 2) impairment in choice based on comparison of estimated option values. Our reinforcement learning model-based behavioral study tested these hypotheses directly in middle-aged and older suicide attempters representative of those who die by suicide. METHODS: Two samples (sample 1, n = 135; sample 2, n = 125) of suicide attempters with depression (nattempters = 54 and 39, respectively), suicide ideators, nonsuicidal patients with depression, and healthy control participants completed a probabilistic three-choice decision-making task. A second experiment in sample 2 experimentally dissociated long-term learned value from reward magnitude. Analyses combined computational reinforcement learning and mixed-effects models of decision times and choices. RESULTS: With regard to learning, suicide attempters (vs. all comparison groups) were less sensitive to one-back reinforcement, as indicated by a reduced effect on both choices and decision times. Learning deficits scaled with attempt lethality and were partially explained by poor cognitive control. With regard to value-based choice, suicide attempters (vs. all comparison groups) displayed abnormally long decision times when choosing between similarly valued options and were less able to distinguish between the best and second-best options. Group differences in value-based choice were robust to controlling for cognitive performance, comorbidities, impulsivity, psychotropic exposure, and possible brain damage from attempts. CONCLUSIONS: Serious suicidal behavior is associated with impaired reward learning, likely undermining the search for alternative solutions. Attempted suicide is associated with impaired value comparison during the choice process, potentially interfering with the consideration of deterrents and alternatives in a crisis.

Associability-modulated loss learning is increased in posttraumatic stress disorder.

Disproportionate reactions to unexpected stimuli in the environment are a cardinal symptom of posttraumatic stress disorder (PTSD). Here, we test whether these heightened responses are associated with disruptions in distinct components of reinforcement learning. Specifically, using functional neuroimaging, a loss-learning task, and a computational model-based approach, we assessed the mechanistic hypothesis that overreactions to stimuli in PTSD arise from anomalous gating of attention during learning (i.e., associability). Behavioral choices of combat-deployed veterans with and without PTSD were fit to a reinforcement learning model, generating trial-by-trial prediction errors (signaling unexpected outcomes) and associability values (signaling attention allocation to the unexpected outcomes). Neural substrates of associability value and behavioral parameter estimates of associability updating, but not prediction error, increased with PTSD during loss learning. Moreover, the interaction of PTSD severity with neural markers of associability value predicted behavioral choices. These results indicate that increased attention-based learning may underlie aspects of PTSD and suggest potential neuromechanistic treatment targets.

Altered resting-state functional connectivity of basolateral and centromedial amygdala complexes in posttraumatic stress disorder.

The amygdala is a major structure that orchestrates defensive reactions to environmental threats and is implicated in hypervigilance and symptoms of heightened arousal in posttraumatic stress disorder (PTSD). The basolateral and centromedial amygdala (CMA) complexes are functionally heterogeneous, with distinct roles in learning and expressing fear behaviors. PTSD differences in amygdala-complex function and functional connectivity with cortical and subcortical structures remain unclear. Recent military veterans with PTSD (n=20) and matched trauma-exposed controls (n=22) underwent a resting-state fMRI scan to measure task-free synchronous blood-oxygen level dependent activity. Whole-brain voxel-wise functional connectivity of basolateral and CMA seeds was compared between groups. The PTSD group had stronger functional connectivity of the basolateral amygdala (BLA) complex with the pregenual anterior cingulate cortex (ACC), dorsomedial prefrontal cortex, and dorsal ACC than the trauma-exposed control group (p<0.05; corrected). The trauma-exposed control group had stronger functional connectivity of the BLA complex with the left inferior frontal gyrus than the PTSD group (p<0.05; corrected). The CMA complex lacked connectivity differences between groups. We found PTSD modulates BLA complex connectivity with prefrontal cortical targets implicated in cognitive control of emotional information, which are central to explanations of core PTSD symptoms. PTSD differences in resting-state connectivity of BLA complex could be biasing processes in target regions that support behaviors central to prevailing laboratory models of PTSD such as associative fear learning. Further research is needed to investigate how differences in functional connectivity of amygdala complexes affect target regions that govern behavior, cognition, and affect in PTSD.

Acute effects of trauma-focused research procedures on participant safety and distress.

The ethical conduct of research on posttraumatic stress disorder (PTSD) requires assessing the risks to study participants. Some previous findings suggest that patients with PTSD report higher distress compared to non-PTSD participants after trauma-focused research. However, the impact of study participation on participant risk, such as suicidal/homicidal ideation and increased desire to use drugs or alcohol, has not been adequately investigated. Furthermore, systematic evaluation of distress using pre- and post-study assessments, and the effects of study procedures involving exposure to aversive stimuli, are lacking. Individuals with a history of PTSD (n=68) and trauma-exposed non-PTSD controls (n=68) responded to five questions about risk and distress before and after participating in research procedures including a PTSD diagnostic interview and a behavioral task with aversive stimuli consisting of mild electrical shock. The desire to use alcohol or drugs increased modestly with study participation among the subgroup (n=48) of participants with current PTSD. Participation in these research procedures was not associated with increased distress or participant risk, nor did study participation interact with lifetime PTSD diagnosis. These results suggest some increase in distress with active PTSD but a participant risk profile that supports a favorable risk-benefit ratio for conducting research in individuals with PTSD.

Neural systems for cognitive and emotional processing in posttraumatic stress disorder.

Individuals with posttraumatic stress disorder (PTSD) show altered cognition when trauma-related material is present. PTSD may lead to enhanced processing of trauma-related material, or it may cause impaired processing of trauma-unrelated information. However, other forms of emotional information may also alter cognition in PTSD. In this review, we discuss the behavioral and neural effects of emotion processing on cognition in PTSD, with a focus on neuroimaging results. We propose a model of emotion-cognition interaction based on evidence of two network models of altered brain activation in PTSD. The first is a trauma-disrupted network made up of ventrolateral PFC, dorsal anterior cingulate cortex (ACC), hippocampus, insula, and dorsomedial PFC that are differentially modulated by trauma content relative to emotional trauma-unrelated information. The trauma-disrupted network forms a subnetwork of regions within a larger, widely recognized network organized into ventral and dorsal streams for processing emotional and cognitive information that converge in the medial PFC and cingulate cortex. Models of fear learning, while not a cognitive process in the conventional sense, provide important insights into the maintenance of the core symptom clusters of PTSD such as re-experiencing and hypervigilance. Fear processing takes place within the limbic corticostriatal loop composed of threat-alerting and threat-assessing components. Understanding the disruptions in these two networks, and their effect on individuals with PTSD, will lead to an improved knowledge of the etiopathogenesis of PTSD and potential targets for both psychotherapeutic and pharmacotherapeutic interventions.

Amygdala volume changes in posttraumatic stress disorder in a large case-controlled veterans group.

CONTEXT: Smaller hippocampal volumes are well established in posttraumatic stress disorder (PTSD), but the relatively few studies of amygdala volume in PTSD have produced equivocal results. OBJECTIVE: To assess a large cohort of recent military veterans with PTSD and trauma-exposed control subjects, with sufficient power to perform a definitive assessment of the effect of PTSD on volumetric changes in the amygdala and hippocampus and of the contribution of illness duration, trauma load, and depressive symptoms. DESIGN: Case-controlled design with structural magnetic resonance imaging and clinical diagnostic assessments. We controlled statistically for the important potential confounds of alcohol use, depression, and medication use. SETTING: Durham Veterans Affairs Medical Center, which is located in proximity to major military bases. PATIENTS: Ambulatory patients (n = 200) recruited from a registry of military service members and veterans serving after September 11, 2001, including a group with current PTSD (n = 99) and a trauma-exposed comparison group without PTSD (n = 101). MAIN OUTCOME MEASURE: Amygdala and hippocampal volumes computed from automated segmentation of high-resolution structural 3-T magnetic resonance imaging. RESULTS: Smaller volume was demonstrated in the PTSD group compared with the non-PTSD group for the left amygdala (P = .002), right amygdala (P = .01), and left hippocampus (P = .02) but not for the right hippocampus (P = .25). Amygdala volumes were not associated with PTSD chronicity, trauma load, or severity of depressive symptoms. CONCLUSIONS: These results provide clear evidence of an association between a smaller amygdala volume and PTSD. The lack of correlation between trauma load or illness chronicity and amygdala volume suggests that a smaller amygdala represents a vulnerability to developing PTSD or the lack of a dose-response relationship with amygdala volume. Our results may trigger a renewed impetus for investigating structural differences in the amygdala, its genetic determinants, its environmental modulators, and the possibility that it reflects an intrinsic vulnerability to PTSD.

Nonparametric spirometry reference values for Hispanic Americans.

Recent literature sites ethnic origin as a major factor in developing pulmonary function reference values. Extensive studies established reference values for European and African Americans, but not for Hispanic Americans. The Third National Health and Nutrition Examination Survey defines Hispanic as individuals of Spanish speaking cultures. While no group was excluded from the target population, sample size requirements only allowed inclusion of individuals who identified themselves as Mexican Americans. This research constructs nonparametric reference value confidence intervals for Hispanic American pulmonary function. The method is applicable to all ethnicities. We use empirical likelihood confidence intervals to establish normal ranges for reference values. Its major advantage: it is model free, but shares asymptotic properties of model based methods. Statistical comparisons indicate that empirical likelihood interval lengths are comparable to normal theory intervals. Power and efficiency studies agree with previously published theoretical results.

High-resolution voxelation mapping of human and rodent brain gene expression.

Voxelation allows high-throughput acquisition of multiple volumetric images of brain gene expression, similar to those obtained from biomedical imaging systems. To obtain these images, the method employs analysis of spatially registered voxels (cubes). For creation of high-resolution maps using voxelation, relatively small voxel sizes are necessary and instruments will be required for semiautomated harvesting of such voxels. Here, we describe two devices that allow spatially registered harvesting of voxels from the human and rodent brain, giving linear resolutions of 3.3 and 1 mm, respectively. Gene expression patterns obtained using these devices showed good agreement with known expression patterns. The voxelation instruments and their future iterations represent a valuable approach to the genome scale acquisition of gene expression patterns in the human and rodent brain.