Reward Reinforcement Creates Enduring Facilitation of Goal-directed Behavior.

Stimulus-response habits benefit behavior by automatizing the selection of rewarding actions. However, this automaticity can come at the cost of reduced flexibility to adapt behavior when circumstances change. The goal-directed system is thought to counteract the habit system by providing the flexibility to pursue context-appropriate behaviors. The dichotomy between habitual action selection and flexible goal-directed behavior has recently been challenged by findings showing that rewards bias both action and goal selection. Here, we test whether reward reinforcement can give rise to habitual goal selection much as it gives rise to habitual action selection. We designed a rewarded, context-based perceptual discrimination task in which performance on one rule was reinforced. Using drift-diffusion models and psychometric analyses, we found that reward facilitates the initiation and execution of rules. Strikingly, we found that these biases persisted in a test phase in which rewards were no longer available. Although this facilitation is consistent with the habitual goal selection hypothesis, we did not find evidence that reward reinforcement reduced cognitive flexibility to implement alternative rules. Together, the findings suggest that reward creates a lasting impact on the selection and execution of goals but may not lead to the inflexibility characteristic of habits. Our findings demonstrate the role of the reward learning system in influencing how the goal-directed system selects and implements goals.

Stress Impairs Episodic Retrieval by Disrupting Hippocampal and Cortical Mechanisms of Remembering.

Despite decades of science investigating the neural underpinnings of episodic memory retrieval, a critical question remains: how does stress influence remembering and the neural mechanisms of recollection in humans? Here, we used functional magnetic resonance imaging and multivariate pattern analyses to examine the effects of acute stress during retrieval. We report that stress reduced the probability of recollecting the details of past experience, and that this impairment was driven, in part, by a disruption of the relationship between hippocampal activation, cortical reinstatement, and memory performance. Moreover, even memories expressed with high confidence were less accurate under stress, and this stress-induced decline in accuracy was explained by reduced posterior hippocampal engagement despite similar levels of category-level cortical reinstatement. Finally, stress degraded the relationship between the engagement of frontoparietal control networks and retrieval decision uncertainty. Collectively, these findings demonstrate the widespread consequences of acute stress on the neural systems of remembering.

Distributed representation of context by intrinsic subnetworks in prefrontal cortex.

Human prefrontal cortex supports goal-directed behavior by representing abstract information about task context. The organizational basis of these context representations, and of representations underlying other higher-order processes, is unknown. Here, we use multivariate decoding and analyses of spontaneous correlations to show that context representations are distributed across subnetworks within prefrontal cortex. Examining targeted prefrontal regions, we found that pairs of voxels with similar context preferences exhibited spontaneous correlations that were approximately twice as large as those between pairs with opposite context preferences. This subnetwork organization was stable across task-engaged and resting states, suggesting that abstract context representations are constrained by an intrinsic functional architecture. These results reveal a principle of fine-scaled functional organization in association cortex.

Adaptive Engagement of Cognitive Control in Context-Dependent Decision Making.

Many decisions require a context-dependent mapping from sensory evidence to action. The capacity for flexible information processing of this sort is thought to depend on a cognitive control system in frontoparietal cortex, but the costs and limitations of control entail that its engagement should be minimized. Here, we show that humans reduce demands on control by exploiting statistical structure in their environment. Using a context-dependent perceptual discrimination task and model-based analyses of behavioral and neuroimaging data, we found that predictions about task context facilitated decision making and that a quantitative measure of context prediction error accounted for graded engagement of the frontoparietal control network. Within this network, multivariate analyses further showed that context prediction error enhanced the representation of task context. These results indicate that decision making is adaptively tuned by experience to minimize costs while maintaining flexibility.

Perceptual insensitivity to higher-order statistical moments of coherent random dot motion.

When the visual system analyzes distributed patterns of sensory inputs, what features of those distributions does it use? It has been previously demonstrated that higher-order statistical moments of luminance distributions influence perception of static surfaces and textures. Here, we tested whether the brain also represents higher-order moments of dynamic stimuli. We constructed random dot kinematograms, where dots moved according to probability distributions that selectively differed in terms of their mean, variance, skewness, or kurtosis. When viewing these stimuli, human observers were sensitive to the mean direction of coherent motion and to the variance of dot displacement angles, but they were insensitive to skewness and kurtosis. Observer behavior accorded with a model of directional motion energy, suggesting that information about higher-order moments is discarded early in the visual processing hierarchy. These results demonstrate that use of higher-order moments is not a general property of visual perception.

Intensive Working Memory Training Produces Functional Changes in Large-scale Frontoparietal Networks.

Working memory is central to human cognition, and intensive cognitive training has been shown to expand working memory capacity in a given domain. It remains unknown, however, how the neural systems that support working memory are altered through intensive training to enable the expansion of working memory capacity. We used fMRI to measure plasticity in activations associated with complex working memory before and after 20 days of training. Healthy young adults were randomly assigned to train on either a dual n-back working memory task or a demanding visuospatial attention task. Training resulted in substantial and task-specific expansion of dual n-back abilities accompanied by changes in the relationship between working memory load and activation. Training differentially affected activations in two large-scale frontoparietal networks thought to underlie working memory: the executive control network and the dorsal attention network. Activations in both networks linearly scaled with working memory load before training, but training dissociated the role of the two networks and eliminated this relationship in the executive control network. Load-dependent functional connectivity both within and between these two networks increased following training, and the magnitudes of increased connectivity were positively correlated with improvements in task performance. These results provide insight into the adaptive neural systems that underlie large gains in working memory capacity through training.

Frontoparietal representations of task context support the flexible control of goal-directed cognition.

Cognitive control allows stimulus-response processing to be aligned with internal goals and is thus central to intelligent, purposeful behavior. Control is thought to depend in part on the active representation of task information in prefrontal cortex (PFC), which provides a source of contextual bias on perception, decision making, and action. In the present study, we investigated the organization, influences, and consequences of context representation as human subjects performed a cued sorting task that required them to flexibly judge the relationship between pairs of multivalent stimuli. Using a connectivity-based parcellation of PFC and multivariate decoding analyses, we determined that context is specifically and transiently represented in a region spanning the inferior frontal sulcus during context-dependent decision making. We also found strong evidence that decision context is represented within the intraparietal sulcus, an area previously shown to be functionally networked with the inferior frontal sulcus at rest and during task performance. Rule-guided allocation of attention to different stimulus dimensions produced discriminable patterns of activation in visual cortex, providing a signature of top-down bias over perception. Furthermore, demands on cognitive control arising from the task structure modulated context representation, which was found to be strongest after a shift in task rules. When context representation in frontoparietal areas increased in strength, as measured by the discriminability of high-dimensional activation patterns, the bias on attended stimulus features was enhanced. These results provide novel evidence that illuminates the mechanisms by which humans flexibly guide behavior in complex environments.

Approach to machine learning for extraction of real-world data variables from electronic health records.

Background: As artificial intelligence (AI) continues to advance with breakthroughs in natural language processing (NLP) and machine learning (ML), such as the development of models like OpenAI's ChatGPT, new opportunities are emerging for efficient curation of electronic health records (EHR) into real-world data (RWD) for evidence generation in oncology. Our objective is to describe the research and development of industry methods to promote transparency and explainability. Methods: We applied NLP with ML techniques to train, validate, and test the extraction of information from unstructured documents (e.g., clinician notes, radiology reports, lab reports, etc.) to output a set of structured variables required for RWD analysis. This research used a nationwide electronic health record (EHR)-derived database. Models were selected based on performance. Variables curated with an approach using ML extraction are those where the value is determined solely based on an ML model (i.e. not confirmed by abstraction), which identifies key information from visit notes and documents. These models do not predict future events or infer missing information. Results: We developed an approach using NLP and ML for extraction of clinically meaningful information from unstructured EHR documents and found high performance of output variables compared with variables curated by manually abstracted data. These extraction methods resulted in research-ready variables including initial cancer diagnosis with date, advanced/metastatic diagnosis with date, disease stage, histology, smoking status, surgery status with date, biomarker test results with dates, and oral treatments with dates. Conclusion: NLP and ML enable the extraction of retrospective clinical data in EHR with speed and scalability to help researchers learn from the experience of every person with cancer.

Designing and Interpreting Psychophysical Investigations of Cognition.

Scientific experimentation depends on the artificial control of natural phenomena. The inaccessibility of cognitive processes to direct manipulation can make such control difficult to realize. Here, we discuss approaches for overcoming this challenge. We advocate the incorporation of experimental techniques from sensory psychophysics into the study of cognitive processes such as decision making and executive control. These techniques include the use of simple parameterized stimuli to precisely manipulate available information and computational models to jointly quantify behavior and neural responses. We illustrate the potential for such techniques to drive theoretical development, and we examine important practical details of how to conduct controlled experiments when using them. Finally, we highlight principles guiding the use of computational models in studying the neural basis of cognition.

Decision Making through Integration of Sensory Evidence at Prolonged Timescales.

When multiple pieces of information bear on a decision, the best approach is to combine the evidence provided by each one. Evidence integration models formalize the computations underlying this process [1-3], explain human perceptual discrimination behavior [4-9], and correspond to neuronal responses elicited by discrimination tasks [10-14]. These findings suggest that evidence integration is key to understanding the neural basis of decision making [15-18]. But while evidence integration has most often been studied with simple tasks that limit deliberation to relatively brief periods, many natural decisions unfold over much longer durations. Neural network models imply acute limitations on the timescale of evidence integration [19-23], and it is currently unknown whether existing computational insights can generalize beyond rapid judgments. Here, we introduce a new psychophysical task and report model-based analyses of human behavior that demonstrate evidence integration at long timescales. Our task requires probabilistic inference using brief samples of visual evidence that are separated in time by long and unpredictable gaps. We show through several quantitative assays how decision making can approximate a normative integration process that extends over tens of seconds without accruing significant memory leak or noise. These results support the generalization of evidence integration models to a broader class of behaviors while posing new challenges for models of how these computations are implemented in biological networks.

Illness progression, recent stress, and morphometry of hippocampal subfields and medial prefrontal cortex in major depression.

BACKGROUND: Longitudinal studies of illness progression in patients with major depressive disorder (MDD) indicate that the onset of subsequent depressive episodes becomes increasingly decoupled from external stressors. A possible mechanism underlying this phenomenon is that multiple episodes induce long-lasting neurobiological changes that confer increased risk for recurrence. Prior morphometric studies have frequently reported volumetric reductions in patients with MDD--especially in medial prefrontal cortex (mPFC) and the hippocampus--but few studies have investigated whether these changes are exacerbated by prior episodes. METHODS: In a sample of 103 medication-free patients with depression and control subjects with no history of depression, structural magnetic resonance imaging was performed to examine relationships between number of prior episodes, current stress, hippocampal subfield volume and cortical thickness. Volumetric analyses of the hippocampus were performed using a recently validated subfield segmentation approach, and cortical thickness estimates were obtained using vertex-based methods. Participants were grouped on the basis of the number of prior depressive episodes and current depressive diagnosis. RESULTS: Number of prior episodes was associated with both lower reported stress levels and reduced volume in the dentate gyrus. Cortical thinning of the left mPFC was associated with a greater number of prior depressive episodes but not current depressive diagnosis. CONCLUSIONS: Collectively, these findings are consistent with preclinical models suggesting that the dentate gyrus and mPFC are especially vulnerable to stress exposure and provide evidence for morphometric changes that are consistent with stress-sensitization models of recurrence in MDD.

Failure of working memory training to enhance cognition or intelligence.

Fluid intelligence is important for successful functioning in the modern world, but much evidence suggests that fluid intelligence is largely immutable after childhood. Recently, however, researchers have reported gains in fluid intelligence after multiple sessions of adaptive working memory training in adults. The current study attempted to replicate and expand those results by administering a broad assessment of cognitive abilities and personality traits to young adults who underwent 20 sessions of an adaptive dual n-back working memory training program and comparing their post-training performance on those tests to a matched set of young adults who underwent 20 sessions of an adaptive attentional tracking program. Pre- and post-training measurements of fluid intelligence, standardized intelligence tests, speed of processing, reading skills, and other tests of working memory were assessed. Both training groups exhibited substantial and specific improvements on the trained tasks that persisted for at least 6 months post-training, but no transfer of improvement was observed to any of the non-trained measurements when compared to a third untrained group serving as a passive control. These findings fail to support the idea that adaptive working memory training in healthy young adults enhances working memory capacity in non-trained tasks, fluid intelligence, or other measures of cognitive abilities.

Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python.

Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.