bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/178640

The functional and genetic associations of neuroimaging data: a toolbox

Liu, Z.·Rolls, E. T.·Zhang, J.·Yang, M.·Du, J.·Gong, W.·Cheng, W.·Wang, H.·Ugurbil, K.·Feng, J.
View original source ↗Export metadata ↓

Abstract

Advances in neuroimaging and sequencing techniques provide an unprecedented opportunity to map the function of brain regions and to identify the roots of psychiatric diseases. However, the results generated by most neuroimaging studies, i.e., activated clusters/regions or functional connectivities between brain regions, frequently cannot be conveniently and systematically interpreted, rendering the biological meaning unclear. We describe a Brain Annotation Toolbox (BAT), a toolbox that helps to generate functional and genetic annotations for neuroimaging results. The toolbox can take data from brain regions identified with an atlas, or from brain regions identified as activated in tasks, or from functional connectivity links or networks of links. Then, the voxel-level functional description from the Neurosynth database and the gene expression profile from the Allen Brain Atlas are used to generate functional and genetic knowledge for such region-level data. Parametric (Fishers exact test) or non-parametric (permutation test) statistical tests are adopted to identify significantly related functional descriptors and genes for the neuroimaging results. The validity of the approach is demonstrated by showing that the functional and genetic annotations for specific brain regions are consistent with each other; and further the region by region functional similarity network and gene co-expression networks are highly correlated for many major brain atlases. One application of BAT is to help provide functional and genetic annotations for the newly discovered regions with unknown functions, e.g., the 97 new regions identified in the Human Connectome Project. Importantly too, this toolbox can help understand differences between patients with psychiatric disorders and controls, and this is demonstrated using data for schizophrenia and autism, for which the functional and genetic annotations for the neuroimaging data differences between patients and controls are consistent with each other and help with the interpretation of the differences.

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Liu, Z., Rolls, E. T., Zhang, J., Yang, M., Du, J., Gong, W., Cheng, W., Wang, H., Ugurbil, K., Feng, J.. 2017-08-20. The functional and genetic associations of neuroimaging data: a toolbox. https://doi.org/10.1101/178640

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Recurrent Spiking Neural Networks for Continuous Motor Control: A Biologically Plausible Model of Motor Cortex

Motor cortex acquires skilled movements through recurrent spiking dynamics and experience-dependent synaptic plasticity, yet how such circuits learn continuous motor control using biologically local signals remains unclear. Here, we develop a recurrent spiking neural network model of motor cortex that addresses both the computational and biological requirements of motor learning. We first analytically derived the recurrent firing-rate state directly from synaptic input and trained the network by implicit differentiation, thereby avoiding backpropagation through long spike trajectories. We then introduced State-Carry Initialization, which enables closed-loop motor control by continuously carrying the ongoing recurrent state across successive actions. Building on this formulation, we devised a learning rule termed Feedback-Spike Nudging (FSN), in which teaching signals are delivered as spikes through a feedback pathway. Synaptic changes depend only on presynaptic activity and the feedback-evoked change in postsynaptic firing, consistent with biological mechanisms of synaptic plasticity in motor cortex. The resulting networks learned continuous motor control through both supervised and reinforcement learning, which could be mapped respectively onto interactions of motor cortex with the cerebellum and basal ganglia. Moreover, the FSN learning rule was consistent with experimental observations of structural spine plasticity in mouse motor cortex. Thus, our study lays a foundation for recurrent spiking models of motor cortex that are biologically grounded at both the functional and anatomical levels.

neuroscience↗
bioRxiv2026

Automated multiscale high-content imaging connects plaque population heterogeneity to localized synapse loss

Synapse loss is a major correlate of cognitive decline in neurodegenerative disease, yet its relationship to localized pathology remains difficult to study at scale. Conventional confocal imaging approaches are limited by low throughput, selection bias, and restricted sampling of pathological features. Here, we describe an automated high-content imaging and analysis workflow that enables large-scale quantification of amyloid beta plaque-associated synapse loss within mouse brain tissue. Using Preciscan-based multiscale acquisition on the Opera Phenix platform, this workflow combines tissue-wide pathology mapping with targeted high-magnification imaging and subcellular analysis. We apply this framework to acquire and analyze hundreds of plaque images in the CRND8 mouse model of A{beta} proteinopathy, demonstrating plaque-associated synapse loss within developing plaque populations over disease progression. We find that small diffuse plaques accumulate rapidly at late stages in CRND8, but that the primary synapse loss comes from large cored plaques. More broadly, this scalable framework enables systematic investigation of subcellular phenomena across pathological microenvironments in disease models.

neuroscience↗
bioRxiv2026

The Canadian Pediatric Imaging Platform (C-PIP) Reference Sample: Neuroimaging Protocol and Cross-site Validation for Harmonized Multimodal Pediatric Imaging

Understanding how the human brain matures from infancy through adolescence is essential for identifying factors that influence health outcomes. Magnetic resonance imaging (MRI) provides powerful tools for characterizing structural, microstructural, functional, and metabolic aspects of brain development; however, many pediatric imaging studies are constrained by small sample sizes, limited geographic diversity, lack of longitudinal measurement, restricted age ranges, and heterogeneous acquisition protocols. While recent large-scale neuroimaging consortia have begun to address these challenges, such resources remain limited in the Canadian context, creating a critical gap. The Canadian Pediatric Imaging Platform (C-PIP) has developed a multisite neuroimaging framework across three of Canada's largest pediatric hospitals: Alberta Children's Hospital (Calgary), Centre Hospitalier Universitaire Sainte-Justine (Montreal), and the Hospital for Sick Children (Toronto). These three sites were united through a national initiative, leveraging high-performance MRI systems to accelerate Canadian neurodevelopmental research. In addition, C-PIP has implemented a centralized data sharing infrastructure to facilitate open science and collaborative research across institutions. Here, we describe the development and implementation of a multimodal MRI acquisition protocol designed to support multimodal longitudinal studies of typical and atypical brain development spanning infancy through adolescence. Within-site stability and cross-site stability of C-PIP core MRI sequences were evaluated using a travelling-participant study. The C-PIP MRI protocol establishes a harmonized, multisite imaging framework that supports large-scale investigations of pediatric brain development and disorders.

neuroscience↗