Biology subjects

Nickerson, L. D.

Publications and source records attributed to Nickerson, L. D..

9 recordsLinked to original sources

bioRxiv2026

Δ⁹-Tetrahydrocannabinol-induced enhancement of reward responsivity via mesocorticolimbic modulation in squirrel monkeys

{Delta}9-tetrahydrocannabinol (THC)-containing products are widely used recreationally, partly due to THCs ability to enhance the appetitive (i.e., rewarding) properties of diverse stimuli. However, the neural mechanisms through which THC modulates reward-related processing remain poorly understood. Here, we used a Pavlovian paradigm in adult squirrel monkeys (3males, 1female) to associate a visual conditioned stimulus (CS+) with appetitive food delivery. The modulatory effects of acute THC (1-10g/kg, i.m.) on behavioral and brain responses to CS+ were evaluated. Event-related functional MRI (fMRI) was employed to characterize the neural correlates of conditioned responding to the CS+, both in the absence and presence of THC treatment, with preconditioning scans serving as control. Behaviorally, THC (3g/kg) selectively enhanced conditioned responding to the CS+ without altering responses to the control stimulus (CS-) or increasing baseline consummatory responding, underscoring the specificity of THCs action on reward-associated processes. Consistently, fMRI analyses revealed that THC amplified CS+-evoked activation within mesocorticolimbic regions, including the anterior cingulate cortex (ACC), striatum, hippocampus, and substantia nigra-ventral tegmental area (SN-VTA), without affecting activity in visual and motor cortices. This finding underscores the selectivity of THCs neuromodulatory effects on reward-related circuitry. Independent of CS exposure, resting-state functional connectivity analyses indicate that THC enhanced mesocorticolimbic network integration, as evident in strengthened SN-VTA-centered connectivity with the ACC, striatum, and hippocampus. Collectively, these findings demonstrate that THC enhances the responses to appetitive stimuli, through selective modulation of mesocorticolimbic circuitry, highlighting the SN-VTA as a pivotal hub for cannabinoid-mediated regulation of incentive salience and motivational drive toward reward-associated stimuli. One-sentence summariesTHC enhances behavioral and neural responses to rewards through mesocorticolimbic modulation centered on the SN-VTA.

neuroscience↗

bioRxiv2025

QuNex Recipes: Executable, Human-Readable Workflows for Reproducible Neuroimaging Research

Preprocessing and analysis of neuroimaging data are technically demanding, often requiring a combination of multiple software tools, modality-specific pipelines, and extensive parameter tuning to match dataset characteristics. These complexities make it difficult to document workflows in sufficient detail to ensure complete transparency and reproducibility. To address these challenges, we introduce QuNex recipes, a framework for defining and executing complete neuroimaging workflows - encompassing data onboarding, preprocessing, and analysis - in a transparent, machine- and human-readable format. Recipes are implemented as an integrated feature of the Quantitative Neuroimaging Environment & Toolbox (QuNex), a containerized, open-source platform for end-to-end multimodal and multi-species neuroimaging processing. The recipes framework enables seamless integration of QuNex commands with custom scripts and external tools, capturing every processing step and parameter setting. A fully reproducible study can thus be shared and replicated by providing only (a) the QuNex version used, (b) the recipe file, and (c) the data. This approach standardizes workflow specification, enhances transparency, and enables one-command replication of complex neuroimaging analyses. By providing a standardized way to describe and share workflows, recipes facilitate open exchange of best practices and reproducible methods within the neuroimaging community.

neuroscience↗

bioRxiv2025

Cognition-centric brain activity across diverse imaging tasks constrains the representation of mental health in task-evoked brain function

Large-scale neuroimaging studies best powered to investigate mental health biomarkers continue to administer a set of in-scanner tasks that, to date, yield weak brain-mental health associations. Triangulating the source of these weak associations is critical to directing the focus of future work. Using these tasks strong brain-cognition associations as a positive control, we analyzed potential sources of weak brain-mental health associations across five popular fMRI tasks and dozens of task-evoked brain networks. Across tasks and networks, latent mental health factors showed substantially weaker brain associations than cognition despite comparable measurement reliability and greater interindividual variability. Moreover, increasing brain-feature dimensionality improved cognitive but not mental health signal, and task-evoked network profiles were 1.8-2.3 times more homogeneous and 3.5 times lower-dimensional than mental health profiles. Findings implicate a signal evocation bottleneck: common fMRI tasks elicit little interindividual variation in mental health-related brain activity relative to the diversity of mental health itself. Overall, these findings motivate a shift in focus to the development and use of tasks in large-scale studies more explicitly designed to evoke mental health-relevant brain activity. Significance StatementIdentification of robust individual-specific mental health-related brain signals from fMRI remains elusive, limiting the clinical utility of brain imaging. To investigate why, we systematically evaluated brain signals evoked by tasks used by the Human Connectome Project-Young Adult study, which are now commonly included in many large-scale neuroimaging studies. Our findings suggest that, more so than analytical or measurement constraints on detecting these signals, a primary limitation is a failure of these prevalent fMRI tasks to evoke mental health-relevant brain signals in the first place. These findings suggest that progress in psychiatric neuroimaging may require tasks more explicitly designed to evoke mental health-relevant brain activity.

neuroscience↗

bioRxiv2024

Concurrent large-scale brain dynamics during the emotional face matching task and their relation to behavior and mental health

Prior investigations of emotion processings neural underpinnings rely on a priori models of brain response, obscuring detection of task-relevant neurobiological processes with complex temporal dynamics. To overcome this limitation, we applied unsupervised machine learning to functional magnetic resonance imaging data acquired during the emotional face matching task (EFMT) in healthy young adults from the Human Connectome Project (n=413; n=416 replication). Tensorial independent component analysis showed that the EFMT engages 10 large-scale brain networks - each recruiting visual association cortex in distinct temporal fashions and in tandem with diverse non-visual regions - that collectively recruit 74% of cortex, posterior cerebellum, and amygdala. Despite prominent use of the EFMT to probe negative affect and related psychopathology, EFMT-recruited networks strongly reflected individual differences in cognition but not internalizing/negative affect. Overall, we characterize a richer-than-expected tapestry of concurrent EFMT-recruited brain processes, their diverse activation dynamics, and their relations to task performance and latent mental health phenotypes.

neuroscience↗

bioRxiv2024

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results

Decades of neuroscience research has shown that macroscale brain dynamics can be reliably decomposed into a subset of large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them can vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. To address this problem, we have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and sixteen widely used functional brain atlases, consistent with recommended reporting standards developed by the Organization for Human Brain Mapping. The atlases included in the toolbox show some topographical convergence for specific networks, such as those labeled as "default" or "visual". Network naming varies across atlases, particularly for networks spanning frontoparietal association cortices. For this reason, quantitative comparison with multiple atlases is recommended to benchmark novel neuroimaging findings. We provide several exemplar demonstrations using the Human Connectome Project task fMRI results and UK Biobank independent component analysis maps to illustrate how researchers can use the NCT to report their own findings through quantitative evaluation against multiple published atlases. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The NCT also includes functionality to incorporate additional atlases in the future. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.

neuroscience↗

bioRxiv2023

Site effects depth denoising and signal enhancement using dual-projection based ICA model

Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the scanner/site variability is a significant confound that complicates the interpretation of the results, so effective and complete removal of the scanner/site variability is necessary to realize the full advantages of pooling multi-site datasets. Independent component analysis (ICA) and general linear model (GLM) based denoising methods are the two primary methods used to denoise scanner/site-related effects. Unfortunately, there are challenges with both ICA-based and GLM-based denoising methods to remove site effects completely when the signals of interest and scanner/site-related noises are correlated, which may occur in neuroscience studies. In this study, we propose an effective and powerful denoising strategy that implements dual-projection (DP) theory based on ICA to remove the scanner/site-related effects more completely. This method can separate the signal effects correlated with noise variables from the identified noise effects for removal without losing signals of interest. Both simulations and vivo structural MRI datasets, including a dataset from Autism Brain Imaging Data Exchange II and a traveling subject dataset from the Strategic Research Program for Brain Sciences, were used to test the proposed GLM- and ICA-based denoising methods and our DP-based ICA denoising method. Results show that DP-based ICA

neuroscience↗

bioRxiv2021

Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data

The study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. Intersubject correlation (ISC) analysis of functional magnetic resonance imaging (fMRI) data is a widely used method that can measure neural responses to naturalistic stimuli that are consistent across subjects. However, interdependent correlation values in ISC artificially inflated the degrees of freedom, which hinders the investigation of individual differences. Besides, the existing ISC model mainly focus on similarities between subjects but fails to distinguish neural responses to different stimuli features. To estimate large-scale brain networks evoked with naturalistic stimuli, we propose a novel analytic framework to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In the framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants. Tensor component analysis (TCA) will then reveal spatially and temporally shared components, i.e., naturalistic stimuli evoked networks, their temporal courses of activity and subject loadings of each component. To enhance the reproducibility of the estimation with TCA, a novel spectral clustering method, tensor spectral clustering, was proposed and applied to evaluate the stability of TCA algorithm. We demonstrate the effectiveness of the proposed framework via simulations and real fMRI data collected during a motor task with a traditional fMRI study design. We also apply the proposed framework to fMRI data collected during passive movie watching to illustrate how reproducible brain networks are identified evoked by naturalistic movie viewing.

neuroscience↗

bioRxiv2018

Combining Multi-Site/Multi-Study MRI Data: Linked-ICA Denoising for Removing Scanner and Site Variability from Multimodal MRI Data

Large multi-site studies that pool magnetic resonance imaging (MRI) data across research sites or studies, or that utilize shared data from imaging repositories, present exceptional opportunities to advance neuroscience and enhance reproducibility of neuroimaging research. However, both scanner and site variability are confounds that hinder pooling data collected across different sites or across different operating systems on the same scanner, even when all acquisition protocols are harmonized. These confounds degrade statistical analyses and can lead to spurious findings. Unfortunately, methods to address this problem are scant. In this study, we propose a novel denoising approach for multi-site multimodal MRI data that implements a data-driven linked independent component analysis (LICA) to efficiently identify scanner/site-related effects for removal. Removing these effects results in denoised data that can then be combined across sites/studies to improve modality-specific statistical processing. We use data from six different studies collected on the same scanner across major hardware (gradient and head coil) and software upgrades to demonstrate our LICA-based denoising approach. The proposed method is superior compared to the existing methods we tested and has great potential for large-scale multi-site studies to produce combined data free from study/site confounds.

neuroscience↗

bioRxiv2017

Correspondence of Resting State and Task Brain Networks: Reproducibility and New Insights

After a series of reports uncovered various methodological problems with functional magnetic resonance imaging (fMRI) research, considerable attention has been given to principles and practices to improve reproducibility of neuroimaging findings, including promotion of openness, transparency, and data sharing. However, much less attention has been given to use of open access neuroimaging datasets to conduct replication studies. A major barrier to reproducing neuroimaging studies is their high cost, in money and labor, and utilizing such datasets is an obvious solution for breaking down this barrier. The Human Connectome Project (HCP) is an open access dataset consisting of extensive behavioral and neuroimaging data from over 1,100 individuals and there are numerous ongoing HCP-harmonized studies of lifespan and disease that will ultimately release data through HCP infrastructure. To bring attention to the HCP and related projects as an important resource for conducting replication studies, I used the HCP to conduct a replication of a highly cited neuroimaging study that showed correspondence between resting state and task brain networks.

neuroscience↗