Biology subjects

Gutierrez, C. E.

Publications and source records attributed to Gutierrez, C. E..

4 recordsLinked to original sources

bioRxiv2025

Topological basal ganglia model with dopamine-modulated spike-timing-dependent plasticity reproduces reinforcement learning, discriminatory learning, and neuropsychiatric disorders

The basal ganglia (BG) are central to action selection and reinforcement learning, yet how the topological organization of the BG circuit with dopamine (DA) D1- and D2-receptors shape learning remains unclear. We present a topologically organized spiking model of macaque BG with cortico-striatal inputs organized into competing channels, D1/D2 medium spiny neurons (MSNs), three-factor DA-modulated STDP for cortical synapses, asymmetric intra-striatal collaterals, and partially overlapping direct/indirect pathways. We validate resting activity and action selection, then study conditioning and generalization-discrimination learning using DA bursts (CS+) and dips (CS). Two structural determinants emerged. First, pathway overlap ({lambda}) trades off selection efficiency and learning speed: higher overlap degrades GPi-based selection efficiency during conditioning, yet accelerates convergence during discrimination by strengthening D2 influence on GPi. Second, lateral inhibition from MSN-D2 to MSNs ({kappa}) helps constrain competing actions but is not sufficient alone; robust discrimination requires DA-dip-dependent up-modulation of D2 collateral efficacy (), which speeds and, at low overlap, enables convergence. Simulations under Parkinsonian and schizophrenia-like settings showed different deficits. A hypodopaminergic "Parkinsonian" STDP regime (D1 LTP loss, D2 LTD loss) impaired conditioning and failed to enhance discrimination. In contrast, attenuated D2 plasticity during DA dips (modeling methamphetamine-induced changes/schizophrenia-related dysregulation) selectively disrupted discrimination while sparing conditioning. Finally, we demonstrate efficient scaling on the Fugaku supercomputer to rodent and non-human primate-relevant sizes, supporting large-scale, biologically grounded BG simulations. Together, the results highlight how pathway overlap and D2 collateral dynamics jointly regulate the speed and reliability of discrimination learning, and how specific DA perturbations map to distinct learning impairments.

neuroscience↗

bioRxiv2024

Optical Neuroimage Studio (OptiNiSt): intuitive, scalable, extendable framework for optical neuroimage data analysis

Advancements in calcium indicators and optical techniques have made optical neural recording a common tool in neuroscience. As the volume of optical neural recording data grows, streamlining the data analysis pipelines for image preprocessing, signal extraction, and subsequent neural activity analyses becomes essential. There are a number of challenges in optical neural data analysis. 1) The quality of original and processed data needs to be carefully examined at each step. 2) As there are numerous image preprocessing, cell extraction, and activity analysis algorithms, each with pros and cons, experimenters need to implement or install them to compare and select optimal methods and parameters for each step of processing. 3) To ensure the reproducibility of the research, each analysis step needs to be recorded in a systematic way. 4) For data sharing and meta-analyses, adoption of standard data formats and processing protocols is required. To address these challenges, we developed Optical Neuroimage Studio (OptiNiSt) (https://github.com/oist/optinist), a framework for intuitively creating calcium data analysis pipelines that are scalable, extendable, and reproducible. OptiNiSt includes the following features. 1) Researchers can easily create analysis pipelines by selecting multiple processing modules, tuning their parameters, and visualizing the results at each step through a graphic user interface in a web browser. 2) In addition to common analytical tools that are pre-installed, new analysis algorithms in Python can be easily added. 3) Once a processing pipeline is designed, the entire workflow with its modules and parameters are stored in a YAML file, which makes the pipeline reproducible and deployable on high-performance computing clusters. 4) OptiNiSt can read image data in a variety of file formats and store the analysis results in NWB (Neurodata Without Borders), a standard data format for data sharing. We expect that this framework will be helpful in standardizing optical neural data analysis protocols.

neuroscience↗

bioRxiv2022

The Brain/MINDS Marmoset Connectivity Atlas: Exploring bidirectional tracing and tractography in the same stereotaxic space

We report on the implementation and features of the Brain/MINDS Marmoset Connectivity Atlas, BMCA, a new resource that provides access to anterograde neuronal tracer data in the prefrontal cortex of a marmoset brain. Neuronal tracers combined with fluorescence microscopy are a key technology for the systematic mapping of structural brain connectivity. We selected the prefrontal cortex for mapping due to its important role in higher brain functions. This work introduces the BMCA standard image preprocessing pipeline and tools for exploring and reviewing the data. We developed the BMCA-Explorer, which is an online image viewer designed for data exploration. Unlike other existing image explorers, it visualizes the data of different individuals in a common reference space at an unprecedented high resolution, facilitating comparative studies. To foster the integration with other marmoset brain image databases and cross-species comparisons, we added fiber tractography data from diffusion MRI, retrograde neural tracer data from the Marmoset Brain Connectivity Atlas project, and tools to map image data between marmoset and the human brain image space. This version of BMCA allows direct comparison between the results of 52 anterograde and 164 retrograde tracer injections in the cortex of the marmoset.

neuroscience↗

bioRxiv2021

Automated segmentation of insect anatomy from micro-CT images using deep learning

Three-dimensional (3D) imaging, such as micro-computed tomography (micro-CT), is increasingly being used by organismal biologists for precise and comprehensive anatomical characterization. However, the segmentation of anatomical structures remains a bottleneck in research, often requiring tedious manual work. Here, we propose a pipeline for the fully-automated segmentation of anatomical structures in micro-CT images utilizing state-of-the-art deep learning methods, selecting the ant brain as a test case. We implemented the U-Net architecture for 2D image segmentation for our convolutional neural network (CNN), combined with pixel-island detection. For training and validation of the network, we assembled a dataset of semi-manually segmented brain images of 94 ant species. The trained network predicted the brain area in ant images fast and accurately; its performance tested on validation sets showed good agreement between the prediction and the target, scoring 80% Intersection over Union (IoU) and 90% Dice Coefficient (F1) accuracy. While manual segmentation usually takes many hours for each brain, the trained network takes only a few minutes. Furthermore, our network is generalizable for segmenting the whole neural system in full-body scans, and works in tests on distantly related and morphologically divergent insects (e.g., fruit flies). The latter suggest that methods like the one presented here generally apply across diverse taxa. Our method makes the construction of segmented maps and the morphological quantification of different species more efficient and scalable to large datasets, a step toward a big data approach to organismal anatomy.

evolutionary biology↗