Biology subjects

Pine, D. S.

Publications and source records attributed to Pine, D. S..

6 recordsLinked to original sources

bioRxiv2023

BOLD response is more than just magnitude: improving detection sensitivity through capturing hemodynamic profiles

Typical FMRI analyses assume a canonical hemodynamic response function (HRF) with a focus on the overshoot peak height, while other morphological aspects are largely ignored. Thus, in most reported analyses, the overall effect is reduced from a curve to a single scalar. Here, we adopt a data-driven approach to HRF estimation at the whole-brain voxel level, without assuming a response profile at the individual level. Then, we estimate the response in its entirety with a roughness penalty at the population level to improve predictive accuracy, inferential efficiency, and cross-study reproducibility. Using a fast event-related FMRI dataset, we demonstrate the extent of under-fitting and information loss that occurs when adopting the canonical approach. We also address the following questions: O_LIHow much does the HRF shape vary across regions, conditions, and groups? C_LIO_LIDoes an agnostic approach improve sensitivity to detect an effect compared to an assumed HRF? C_LIO_LICan examining HRF shape help validate the presence of an effect complementing statistical evidence? C_LIO_LICould the HRF shape provide evidence for whole-brain BOLD response during a simple task? C_LI

neuroscience↗

bioRxiv2021

Instructions and experiential learning have similar impacts on pain and pain-related brain responses but produce dissociations in value-based reversal learning

Recent data suggest that interactions between systems involved in higher order knowledge and associative learning drive responses during appetitive and aversive learning. However, it is unknown how these systems impact subjective responses, such as pain. We tested how instructions and reversal learning influence pain and pain-evoked brain activation. Healthy volunteers (n = 40) were either instructed about contingencies between cues and aversive outcomes or learned through experience in a paradigm where contingencies reversed three times. We measured predictive cue effects on pain and heat-evoked brain responses using functional magnetic resonance imaging. Predictive cues dynamically modulated pain perception as contingencies changed, regardless of whether participants received contingency instructions. Heat-evoked responses in the insula, anterior cingulate, and putamen updated as contingencies changed, whereas the periaqueductal gray and thalamus responded to initial contingencies throughout the task. Quantitative modeling revealed that expected value was shaped purely by instructions in the Instructed Group, whereas expected value updated dynamically in the Uninstructed Group as a function of error-based learning. These differences were accompanied by dissociations in the neural correlates of value-based learning in the rostral anterior cingulate, medial prefrontal cortex, and orbitofrontal cortex. These results show how predictions impact subjective pain. Moreover, imaging data delineate three types of networks involved in pain generation and value-based learning: those that respond to initial contingencies, those that update dynamically during feedback-driven learning as contingencies change, and those that are sensitive to instruction. Together, these findings provide multiple points of entry for therapies designs to impact pain.

neuroscience↗

bioRxiv2021

Hyperbolic trade-off: the importance of balancing trial and subject sample sizes in neuroimaging

Here we investigate the crucial role of trials in task-based neuroimaging from the perspectives of statistical efficiency and condition-level generalizability. Big data initiatives have gained popularity for leveraging a large sample of subjects to study a wide range of effect magnitudes in the brain. On the other hand, most taskbased FMRI designs feature a relatively small number of subjects, so that resulting parameter estimates may be associated with compromised precision. Nevertheless, little attention has been given to another important dimension of experimental design, which can equally boost a studys statistical efficiency: the trial sample size. The common practice of condition-level modeling implicitly assumes no cross-trial variability. Here, we systematically explore the different factors that impact effect uncertainty, drawing on evidence from hierarchical modeling, simulations and an FMRI dataset of 42 subjects who completed a large number of trials of cognitive control task. We find that, due to the hyperbolic relationship between trial and subject sample sizes and the presence of relatively large cross-trial variability, 1) trial sample size has nearly the same impact as subject sample size on statistical efficiency; 2) increasing both the number of trials and subjects improves statistical efficiency more effectively than focusing on subjects alone; 3) trial sample size can be leveraged alongside subject sample size to improve the cost-effectiveness of an experimental design; 4) for small trial sample sizes, trial-level modeling, rather than condition-level modeling through summary statistics, may be necessary to accurately assess the standard error of an effect estimate. We close by making practical suggestions for improving experimental designs across neuroimaging and behavioral studies.

neuroscience↗

bioRxiv2021

Beyond the intraclass correlation: A hierarchical modeling approach to test-retest assessment

The concept of test-retest reliability indexes the consistency of a measurement across time. High reliability is critical for any scientific study, but specifically for the study of individual differences. Evidence of poor reliability of commonly used behavioral and functional neuroimaging tasks is mounting. Reports on low reliability of task-based fMRI have called into question the adequacy of using even the most common, well-characterized cognitive tasks with robust population-level effects, to measure individual differences. Here, we lay out a hierarchical framework that estimates reliability as a correlation divorced from trial-level variability, and show that reliability estimates tend to be higher compared to the conventional framework that adopts condition-level modeling and ignores across-trial variability. We examine how estimates from the two frameworks diverge and assess how different factors (e.g., trial and subject sample sizes, relative magnitude of cross-trial variability) impact reliability estimates. We also show that, under specific circumstances, the two statistical frameworks converge. Results from the two approaches are approximately equivalent if (a) the trial sample size is sufficiently large, or (b) cross-trial variability is in the same order of magnitude as, or less than, cross-subject variability. As empirical data indicate that cross-trial variability is large in most tasks, this work highlights that a large number of trials (e.g., greater than 100) may be required to achieve precise reliability estimates. We reference the tools TRR and 3dLMEr for the community to apply trial-level models to behavior and neuroimaging data and discuss how to make these new measurements most useful for current studies.

neuroscience↗

bioRxiv2018

Adolescent Cognitive Control, Theta Oscillations, and Social Motivation

Theta oscillations (4-8 Hz) provide an organizing principle of cognitive control, allowing goal-directed behavior that is conserved across species. In human adults, theta power over medial-frontal cortex (MFC) underlies monitoring, whereas theta synchrony between MFC and lateral-frontal regions reflects control recruitment. Prior work has not separated theta before/after motor responses, nor explained how medial-lateral synchrony drives different kinds of control behaviors. Thetas role during adolescence, a developmental window characterized by a motivation-control mismatch also remains unclear, preventing possible cross-species work. Here, adolescents performed a flanker task alone or under observation to increase social motivation. We separated theta dynamics immediately before/after motor responses, identifying functional dissociations. We also dissociate MFC connectivity with rostral/caudal frontal cortex and distinct forms of behavioral control, which further differed before/after response. Finally, social motivation was found to exclusively upregulate post-response error monitoring and changes in control to prevent future errors, as opposed to pre-response theta dynamics.

neuroscience↗

bioRxiv2017

Intraclass correlation: improved modeling approaches and applications for neuroimaging

Intraclass correlation (ICC) is a reliability metric that gauges similarity when, for example, entities are measured under similar, or even the same, well-controlled conditions, which in MRI applications include runs/sessions, twins, parent/child, scanners, sites, etc. The popular definition and interpretation of ICC are usually framed statistically under the conventional ANOVA platform. Here, we show that the ANOVA frame-work is often limited, rigid, and inflexible in modeling capabilities. Additionally we provide a comprehensive overview of ICC analysis in its prior usage in neuroimaging. These intrinsic limitations motivate several novel improvements. Specifically, we start with the conventional ICC model under the ANOVA platform, and extend it along two dimensions: first, fixing the failure in ICC estimation when negative values occur under degen-erative circumstance, and second, incorporating reliability information of effect estimates into the ICC model. These endeavors lead to four modeling strategies: linear mixed-effects (LME), Bayesian mixed-effects (BME), multilevel mixed-effects (MME), and Bayesian multilevel mixed-effects (BMME). Compared to ANOVA, each of these four models directly provides estimates for fixed effects as well as their statistical significances, in addition to the ICC estimate. These new modeling approaches can also accommodate missing data as well as fixed effects for confounding variables. More importantly, we show that the novel MME and BMME approaches offer more accurate characterization and decomposition among the variance components, leading to more robust ICC computation. Based on these theoretical considerations and model performance comparisons with a real experimental dataset, we offer the following general-purpose recommendations. First, ICC estimation through MME or BMME is preferable when precision information is available for the effect estimate; precision information provides weights that more accurately allocate the variances in the data. When precision information is unavailable, ICC estimation through LME or the novel BME is the preferred option. Second, even though the absolute agreement version, ICC(2,1), is presently more popular in the field, the consistency version, ICC(3,1), is a practical and informative choice for whole-brain ICC analysis that achieves a well-balanced compromise when all potential fixed effects are accounted for. Third, approaches for clear, meaningful, and useful result reporting in ICC analysis are discussed. All models, ICC formulations, and related statistical testing methods have been implemented in an open source program 3dICC, which is publicly available as part of the AFNI suite. Even though our work here focuses on the whole brain level, the modeling strategy and recommendations can be equivalently applied to other situations such as voxel, region, and network levels.

neuroscience↗