Biology subjects

Tamayo, Z.

Publications and source records attributed to Tamayo, Z..

5 recordsLinked to original sources

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

Ketamine Alters Tuning of Neural and Behavioral Spatial Working Memory Precision

Deficits in working memory (WM) are a hallmark of neuropsy-chiatric disorders such as schizophrenia, yet their neurobiological basis remains poorly understood. Glutamate N-methyl-D-aspartate receptors (NMDARs) are critical for spatial WM (sWM), with NMDAR antagonist ketamine known to attenuate task-evoked activation and reduce sWM accuracy. Cortical microcircuit models hypothesize that NMDAR antagonism impairs sWM by broadening neural spatial tuning, but this mechanism has not been directly tested in humans. Using a pharmacological fMRI approach, we showed how ketamine broadened neural spatial tuning, attenuated activation across visual, parietal, and frontal areas, and worsened sWM performance in healthy humans. Ketamine-induced changes in tuning were more consistent across individuals and brain regions than changes in overall activation and correlated with individual differences in sWM performance. These findings provide empirical evidence linking NMDAR antagonism to disruptions in cortical microcircuit dynamics, the resulting neural tuning alterations, and sWM impairments, advancing frameworks for therapeutic development.

neuroscience↗

bioRxiv2022

Ketamine induces multiple individually distinct whole-brain functional connectivity signatures

BackgroundKetamine has emerged as one of the most promising therapies for treatment-resistant depression. However, inter-individual variability in response to ketamine is still not well understood and it is unclear how ketamines molecular mechanisms connect to its neural and behavioral effects. MethodsWe conducted a double-blind placebo-controlled study in which 40 healthy participants received acute ketamine (initial bolus 0.23 mg/kg, continuous infusion 0.58 mg/kg/hour). We quantified resting-state functional connectivity via data-driven global brain connectivity, related it to individual ketamine-induced symptom variation, and compared it to cortical gene expression targets. ResultsWe found that: i) both the neural and behavioral effects of acute ketamine are multi-dimensional, reflecting robust inter-individual variability; ii) ketamines data-driven principal neural gradient effect matched somatostatin (SST) and parvalbumin (PVALB) cortical gene expression patterns in humans, implicating the role of SST and PVALB interneurons in ketamines acute effects; and iii) behavioral data-driven individual symptom variation mapped onto distinct neural gradients of ketamine, which were resolvable at the single-subject level. ConclusionsCollectively, these findings support the possibility for developing individually precise pharmacological biomarkers for treatment selection in psychiatry. FundingThis study was supported by NIH grants DP5OD012109-01 (A.A.), 1U01MH121766 (A.A.), R01MH112746 (J.D.M.), 5R01MH112189 (A.A.), 5R01MH108590 (A.A.), NIAAA grant 2P50AA012870-11 (A.A.); NSF NeuroNex grant 2015276 (J.D.M.); Brain and Behavior Research Foundation Young Investigator Award (A.A.); SFARI Pilot Award (J.D.M., A.A.); Heffter Research Institute (Grant No. 1-190420); Swiss Neuromatrix Foundation (Grant No. 2016-0111m Grant No. 2015 - 010); Swiss National Science Foundation under the frame-work of Neuron Cofund (Grant No. 01EW1908), Usona Institute (2015 - 2056).

neuroscience↗

bioRxiv2022

QuNex -- An Integrative Platform forReproducible Neuroimaging Analytics

Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration (1-3), particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability. To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a "turnkey" command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features. The platform enables inter-operable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia (4), including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform. Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease.

neuroscience↗

bioRxiv2020

Mapping Brain-Behavior Space Relationships Along the Psychosis Spectrum

Difficulties in advancing effective patient-specific therapies for psychiatric disorders highlight a need to develop a stable neurobiologically-grounded mapping between neural and symptom variation. This gap is particularly acute for psychosis-spectrum disorders (PSD). Here, in a sample of 436 cross-diagnostic PSD patients, we derived and replicated a dimensionality-reduced symptom space across hallmark psychopathology symptoms and cognitive deficits. In turn, these symptom axes mapped onto distinct, reproducible brain maps. Critically, we found that multivariate brain-behavior mapping techniques (e.g. canonical correlation analysis) do not produce stable results. Instead, we show that a univariate brain-behavioral space (BBS) can resolve stable individualized prediction. Finally, we show a proof-of-principle framework for relating personalized BBS metrics with molecular targets via serotonin and glutamate receptor manipulations and gene expression maps. Collectively, these results highlight a stable and data-driven BBS mapping across PSD, which offers an actionable path that can be iteratively optimized for personalized clinical biomarker endpoints.

neuroscience↗