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Poldrack, R. A.

Publications and source records attributed to Poldrack, R. A..

5 recordsLinked to original sources

Spacing of Cue-approach Training Leads to Better Maintenance of Behavioral Change

The maintenance of behavioral change over the long term is essential to achieve public health goals such as combatting obesity and drug use. Previous work by our group has demonstrated a reliable shift in preferences for appetitive foods following a novel non-reinforced training paradigm. In the current studies, we tested whether distributing training trials over two consecutive days would affect preferences immediately after training as well as over time at a one-month follow-up. In four studies, three different designs and an additional pre-registered replication of one sample, we found that spacing of cue-approach training induced a shift in food choice preferences over one month. The spacing and massing schedule employed governed the long-term changes in choice behavior. Applying spacing strategies to training paradigms that target automatic processes could prove a useful tool for the long-term maintenance of health improvement goals with the development of real-world behavioral change paradigms that incorporate distributed practice principles.

animal behavior and cognition

The modulation of neural gain facilitates a transition between functional segregation and integration in the brain

Cognitive function relies on a dynamic, context-sensitive balance between functional integration and segregation in the brain. Previous work has proposed that this balance is mediated by global fluctuations in neural gain by projections from ascending neuromodulatory nuclei. To test this hypothesis in silico, we studied the effects of neural gain on network dynamics in a model of large-scale neuronal dynamics. We found that increases in neural gain pushed the network through an abrupt dynamical transition, leading to an integrated network topology that was maximal in frontoparietal rich club regions. This gain-mediated transition was also associated with increased topological complexity, as well as increased variability in time-resolved topological structure, further highlighting the potential computational benefits of the gain-mediated network transition. These results support the hypothesis that neural gain modulation has the computational capacity to mediate the balance between integration and segregation in the brain.

neuroscience

Large-Scale cognitive GWAS Meta-analysis Reveals Tissue-Specific Neural Expression and Potential Nootopic Drug Targets

Neurocognitive ability is a fundamental readout of brain function, and cognitive deficits are a critical component of neuropsychiatric disorders, yet neurocognition is poorly understood at the molecular level. In the present report, we present the largest genome-wide association studies (GWAS) of cognitive ability to date (N=107,207), and further enhance signal by combining results with a large-scale GWAS of educational attainment. We identified 70 independent genomic loci associated with cognitive ability, 34 of which were novel. A total of 350 genes were implicated, and this list showed significant enrichment for genes associated with Mendelian disorders with an intellectual disability phenotype. Competitive pathway analysis of gene results implicated the biological process of neurogenesis, as well as the gene targets of two pharmacologic agents: cinnarizine, a T-type calcium channel blocker; and LY97241, a potassium channel inhibitor. Transcriptome-wide analysis revealed that the implicated genes were strongly expressed in neurons, but not astrocytes or oligodendrocytes, and were more strongly associated with fetal brain expression than adult brain expression. Several tissue-specific gene expression relationships to cognitive ability were observed (for example, DAG1 levels in the hippocampus). Finally, we report novel genetic correlations between cognitive ability and disparate phenotypes such as maternal age at first birth and number of children, as well as several autoimmune disorders.

genomics

Reinforcement learning over time: spaced versus massed training establishes stronger value associations

Over the past few decades, neuroscience research has illuminated the neural mechanisms supporting learning from reward feedback, demonstrating a critical role for the striatum and midbrain dopamine system. Learning paradigms are increasingly being extended to understand learning dysfunctions in mood and psychiatric disorders as well as addiction in the area of computational psychiatry. However, one potentially critical characteristic that this research ignores is the effect of time on learning: human feedback learning paradigms are conducted in a single rapidly paced session, while learning experiences in ecologically relevant circumstances and in animal research are almost always separated by longer periods of time. Event spacing is known to have strong positive effects on item memory across species and in reward learning in animals. Remarkably, the effect of spaced training on human reinforcement learning has not been investigated. In our experiments, we examined reward learning distributed across weeks vs. learning completed in a traditionally-paced or \"massed\" single session. Participants learned to make the best response for landscape stimuli that were either associated with a positive or negative value. In our first study, as expected, we found that after equal amounts of extensive training, accuracy was high and equivalent between the spaced and massed conditions. However, in a final online test 3 weeks later, we found that participants exhibited significantly greater memory for the value of spaced-trained stimuli. In our second study, our methods allowed for a direct comparison of maintenance of conditioning. We found that spaced training again had a beneficial effect: more than 87% of conditioning was maintained for spaced-trained stimuli, while only 30% was maintained for massed-trained stimuli. In addition, supporting a role for working memory in massed learning, across both studies we found a significant positive correlation between initial learning and working memory capacity. Our results indicate that single-session learning tasks may not lead to the kind of robust and lasting value associations that are characteristic of \"habitual\" value associations. Overall, these studies begin to address a large gap in our knowledge of fundamental processes of human reinforcement learning, with potentially broad implications for our understanding of learning in mood disorders and addiction.

neuroscience

MRIQC: Predicting Quality in Manual MRI Assessment Protocols Using No-Reference Image Quality Measures

Quality control of MRI is essential for excluding problematic acquisitions and avoiding bias in subsequent image processing and analysis. Visual inspection is subjective and impractical for large scale datasets. Although automated quality assessments have been demonstrated on single-site datasets, it is unclear that solutions can generalize to unseen data acquired at new sites. Here, we introduce the MRI Quality Control tool (MRIQC), a tool for extracting quality measures and fitting a binary (accept/exclude) classifier. Our tool can be run both locally and as a free online service via the OpenNeuro.org portal. The classifier is trained on a publicly available, multi-site dataset (17 sites, N=1102). We perform model selection evaluating different normalization and feature exclusion approaches aimed at maximizing across-site generalization and estimate an accuracy of 76%{+/-}13% on new sites, using leave-one-site-out cross-validation. We confirm that result on a held-out dataset (2 sites, N=265) also obtaining a 76% accuracy. Even though the performance of the trained classifier is statistically above chance, we show that it is susceptible to site effects and unable to account for artifacts specific to new sites. MRIQC performs with high accuracy in intra-site prediction, but performance on unseen sites leaves space for improvement which might require more labeled data and new approaches to the between-site variability. Overcoming these limitations is crucial for a more objective quality assessment of neuroimaging data, and to enable the analysis of extremely large and multi-site samples.

bioinformatics