Biology subjects

Lau, J. C.

Publications and source records attributed to Lau, J. C..

18 recordsLinked to original sources

bioRxiv2026

AFIDs-Validator: An Open-Access AI-Guided Platform for Learning Anatomical Landmark Placement

Accurate localization of anatomical landmarks is a foundational skill in anatomy and imaging that is often taught informally through expert mentorship, requiring access to data and desktop software. There is no openly accessible, interactive resource that teaches neuroanatomy with quantitative feedback. We present the AFIDs-Validator (validator.afids.io), an open-access, browser-based platform that pairs guided instruction with quantitative assessment. The platform combines (1) a learning mode in which a language-model neuroanatomy tutor operates inside an MRI viewer, giving anatomy-first instruction that responds to the learner's current image slice, orientation, and cursor position; and (2) a validation engine that accepts a learner's landmark file and returns per-landmark Euclidean error against expert-annotated references spanning 21 brain templates. To make the feedback interpretable, we analyzed 15,000 landmark annotations across 132 human subjects and found that landmark difficulty varies fourfold (median error ranged from 0.37 mm at the anterior commissure to 1.50 mm at the temporal horns) with heavy-tailed distributions at every landmark. These distributions are compiled into per-landmark reliability priors, so learners are scored against the empirical spread of trained raters rather than an arbitrary threshold, and difficult landmarks are not mistaken for poor performance. The AFIDs-Validator requires no installation, licensed software, or local data, and all code, reference data, and tutor design are openly released.

scientific communication and education↗

bioRxiv2026

A surface-intrinsic framework for topology-preserving hippocampal alignment and precision morphometry

Accurate alignment of hippocampal anatomy across individuals remains challenging due to complex and highly variable folding patterns that are not well captured by conventional volumetric approaches. HippUnfold introduced a surface-based representation of the hippocampus, but key components--including coordinate estimation and inter-subject correspondence--were defined in the volumetric domain, making them susceptible to topological errors and interpolation artifacts. Here, we introduce a surface-intrinsic formulation of hippocampal unfolding in which geometry, intrinsic coordinates, and correspondence are defined directly on subject-specific surface manifolds. Intrinsic anterior-posterior and proximal-distal coordinates are computed by solving Laplace equations on the surface, and correspondence is established through surface-based resampling in unfolded space, replacing inverse volumetric warping. Relative to the original HippUnfold approach, this formulation improves test-retest consistency, subject identifiability, and mesh quality, while better preserving subject-specific gyral and sulcal morphology. Surface representations show reduced distortion between folded and unfolded spaces and eliminate misplaced or outlier vertices associated with volumetric warping. These improvements translate to enhanced sensitivity in a clinical application, improving lateralization of temporal lobe epilepsy. These results demonstrate that a surface-intrinsic formulation provides a principled and robust foundation for hippocampal unfolding, enabling topology-preserving alignment and more accurate characterization of inter-individual variability in health and disease.

neuroscience↗

bioRxiv2026

An anatomically distinct dopaminergic cell population of the zona incerta as evidence for the human A13 nucleus

The zona incerta (ZI) is a functionally and molecularly diverse deep brain region increasingly recognized as an integrative hub. The rodent ZI contains the dopaminergic A13 nucleus, with studies supporting a central role in modulating sensorimotor integration, nociception, and confers resilience in models of neurodegeneration; however, to date, no clear human homologue has been identified. Here, we identified a tyrosine hydroxylase-positive subregion in the human ZI consistent with the A13, and refined its borders using ex vivo MRI and histology. We translate these findings to in vivo MRI, demonstrating the A13 is distinguishable from surrounding regions by elevated T1 and T2* values. We further revealed a principal rostral-caudal axis of molecular and structural connectivity variation in the ZI, and identified the A13 as a localized molecular specialization. Altogether, we report a distinct ZI subregion corresponding to the A13 nucleus, providing an anatomical foundation for further mechanistic and translational investigation.

neuroscience↗

bioRxiv2025

Compositional neural dynamics during reaching

The complex mechanics of the arm make the neural control of reaching inherently posture dependent. Because previous reaching studies confound reach direction with final posture, it remains unknown how neural population dynamics in the motor cortex account for arm posture. Here we address this gap with high-density neural recordings and a reaching task in which the same targets serve as start points on some trials and end points on others. We show that neural population dynamics in monkey primary motor cortex and dorsal premotor cortex exhibit a compositional structure with three components that enable posture-dependent control: first, a posture subspace containing fixed points visited whenever the arm is in a specific posture; second, rotational dynamics that transition between these fixed points, systematically organized so that similar rotations produce similar movements while continuously updating the posture representation; third, a condition-independent shift dimension that tracks trial progression across all movements. This compositional structure advances the population-level account of how motor cortical dynamics support skilled reaching.

neuroscience↗

bioRxiv2024

Mapping the topographic organization of the human zona incerta using diffusion MRI

The zona incerta (ZI) is a deep brain region originally described by Auguste Forel as an "immensely confusing area about which nothing can be said." Despite the elusive nature of this structure, mounting evidence supports the role of the ZI and surrounding regions across a diverse range of brain functions and as a candidate target for neuromodulatory therapies. Using in vivo diffusion MRI and data-driven connectivity, we identify a topographic organization between the ZI and neocortex. Specifically, our methods identify a rostral-caudal gradient predominantly connecting the frontopolar and ventral prefrontal cortices with the rostral ZI, and the primary sensorimotor cortices with the caudal ZI. Moreover, we demonstrate how clustering and gradient approaches build complementary evidence including facilitating the mapping of a central region of the ZI, connected with the dorsal prefrontal cortex. These results were shown to be replicable across multiple datasets and at the individual subject level, building evidence for the important role of the ZI in mediating frontal lobe-associated tasks, ranging from motor to cognitive to emotional control. Finally, we consider the impact of this topographic organization on the refinement of neuromodulatory targets. These results pave the way for an increasingly detailed understanding of ZI substructures, and considerations for in vivo targeting of the ZI for neuromodulation.

neuroscience↗

bioRxiv2024

Neural Ensembles in the Lateral Prefrontal Cortex Temporally Multiplex Task Features During Virtual Navigation

Neuronal populations can expand their information encoding capacity using mixed selective neurons. This is particularly prominent in association areas such as the Lateral Prefrontal Cortex (LPFC) that integrate information from multiple sensory systems. During naturalistic conditions where subjects have agency, it is unclear how LPFC neuronal ensembles process space and time varying information. Here we show that during a virtual navigation task that requires associative memory and decision making, individual neurons and neuronal ensembles in the LPFC time-multiplex their selectivity for different task features to fluidly encode the temporal contingencies of the task and the animals choice. Neurons in ventral regions showed more selectivity for non-spatial features, while dorsal regions showed selectivity for space and eye movements. These results demonstrate that during naturalistic tasks with fluid scenery and spatiotemporal dynamics, LPFC neurons and neuronal ensembles time-multiplex the components of their selectivity, expanding the spatiotemporal capacity of their neural codes.

neuroscience↗

bioRxiv2023

A population-averaged structural connectomic brain atlas of 422 HCP-Aging subjects

Population-averaged brain atlases, that are represented in a standard space with anatomical labels, are instrumental tools in neurosurgical planning and the study of neurodegenerative conditions. Traditional brain atlases are primarily derived from anatomical scans and contain limited information regarding the axonal organization of the white matter. With the advance of diffusion MRI that allows the modelling of fiber orientation distribution (FOD) in the brain tissue, there is an increasing interest for a population-averaged FOD template, especially based on a large healthy aging cohort, to offer structural connectivity information for connectomic surgery and analysis of neurodegeneration. The dataset described in this article contains a set of multi-contrast structural connectomic MRI atlases, including T1w, T2w, and FOD templates, along with the associated whole brain tractograms. The templates were made using multi-contrast group-wise registration based on 3T MRIs of 422 Human Connectome Project in Aging (HCP-A) subjects. To enhance the usability, probabilistic tissue maps and segmentation of 22 subcortical structures are provided. Finally, the subthalamic nucleus shown in the atlas is parcellated into sensorimotor, limbic, and associative sub-regions based on their structural connectivity to facilitate the analysis and planning of deep brain stimulation procedures. The dataset is available on the OSF Repository: https://osf.io/p7syt. Specifications table O_TBL View this table: org.highwire.dtl.DTLVardef@59fb82org.highwire.dtl.DTLVardef@18f1f44org.highwire.dtl.DTLVardef@14de18dorg.highwire.dtl.DTLVardef@1318a8org.highwire.dtl.DTLVardef@1b7bea8_HPS_FORMAT_FIGEXP M_TBL C_TBL Value of the dataO_LIThese publicly available templates were created using 422 HCP-Aging subjects, representing averaged anatomical and structural connectivity features of the healthy aging brain. C_LIO_LIMatching T1w, T2w, and fiber orientation distribution (FOD) templates are provided, together with the associated tractograms that contain 20K and 2 million streamlines. C_LIO_LISegmentation of 22 subcortical structures and probabilistic tissue maps are provided to enhance the usability of the templates. C_LIO_LIParcellation of the subthalamic nucleus, based on structural connectivity of the included cohort at 0.3 x 0.3 x 0.3 mm3 resolution, is provided. C_LI

bioengineering↗

bioRxiv2022

Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration

Tools available for reproducible, quantitative assessment of brain correspondence have been limited. We previously validated the anatomical fiducial (AFID) placement protocol for point-based assessment of image registration with millimetric (mm) accuracy. In this data descriptor, we release curated AFID placements for some of the most commonly used structural magnetic resonance imaging templates and datasets. The release of our accurate placements allows for rapid quality control of image registration, teaching neuroanatomy, and clinical applications such as disease diagnosis and surgical targeting. We release placements on individual subjects from four datasets (n = 132 subjects for a total of 15,232 fiducials) and more than 10 brain templates (4,288 fiducials), compiling over 300 human rater hours of annotation. We also validate human rater accuracy of released placements to be within 1-2 mm (using a total of 50,336 Euclidean distances), consistent with prior studies. Our data is compliant with the Brain Imaging Data Structure (BIDS) allowing for facile incorporation into modern neuroimaging analysis pipelines. Data is accessible on GitHub (https://github.com/afids/afids-data).

neuroscience↗

bioRxiv2022

A method for chronic and semi-chronic microelectrode array implantation in deep brain structures using image guided neuronavigation

Precise targeting of deep brain structures in humans and large animal models has been a challenge for neuroscientists. Conventional protocols used in animal models typically require large access chambers which are prone to infection and involve assembly and implantation of complex microdrives for semi-chronic applications. Here we present a methodology for improving targeting of subcortical structures in large animals such as macaque monkeys, using image guided neuronavigation. Design of custom cranial caps allowed for incorporation of stable fiducial markers, required for increased targeting accuracy in neuronavigation procedures, resulting in an average targeting error of 1.6 mm over three implantations. Incorporation of anchor bolt chambers, commonly used in human neurosurgery, provided a minimally invasive entrance to the brain parenchyma, allowing for chronic recordings. By leveraging existing 3D printing technology, we fabricated an anchor bolt-mounted microdrive for semi-chronic applications. Our protocol leverages commercially available tools for implantation, decreases the risk of infection and complications of open craniotomies, and improves the accuracy and precision of chronic electrode implantations targeting deep brain structures in large animal models.

neuroscience↗

bioRxiv2022

Detection of local growth patterns in longitudinally imaged low-grade gliomas

BackgroundDiffuse low-grade gliomas (LGGs) are primary brain tumors with infiltrative, anisotropic growth related to surrounding white and grey matter structures. In this study, we illustrate the use of deformation-based morphometry (DBM) as a simple and objective method to study the local change in growth patterns of LGGs. MethodsAn imaging pipeline was developed involving the creation of patient-specific average templates and nonlinear registration of pre-treatment follow-up MRIs to the average template. Jacobian maps were derived and analyzed to identify areas of tissue expansion and contraction over time. ResultsOur analysis demonstrates that tissue expansion occurs primarily around the edges of the tumor, while the lesion core and areas adjacent to obstacles, such as the skull, show no significant growth. Tumors also appeared to grow faster and predominantly in areas of white matter. Regions of the brain surrounding the lesion showed slight contraction over time, likely representing compression due to mass effect of the tumor. ConclusionsWe demonstrate that DBM is a useful clinical tool to understand the long-term clinical course of an individuals tumor and identify areas of rapid growth, which can explain the clinical signs and symptoms, predict future symptoms, and guide targeted diagnostics and therapy. HighlightsO_LILow-grade glioma expansion occurs primarily around the edges of the tumor. C_LIO_LITumor cores and tissue next to obstacles show no significant growth over time. C_LIO_LIDBM provides a clinically valuable assessment of local tumor growth and activity. C_LI

cancer biology↗

bioRxiv2022

Mapping the subcortical connectome using in vivo diffusion MRI: feasability and reliability

Tractography combined with regions of interest (ROIs) has been used to non-invasively study the structural connectivity of the cortex as well as to assess the reliability of these connections. However, the subcortical connectome (subcortex to subcortex) has not been comprehensively examined, in part due to the difficulty of performing tractography in this complex and compact region. In this study, we performed an in vivo investigation using tractography to assess the feasibility and reliability of mapping known connections between structures of the subcortex using the test-retest dataset from the Human Connectome Project (HCP). We further validated our observations using a separate unrelated subjects dataset from the HCP. Quantitative assessment was performed by computing tract densities and spatial overlap of identified connections between subcortical ROIs. Further, known connections between structures of the basal ganglia and thalamus were identified and visually inspected, comparing tractography reconstructed trajectories with descriptions from tract-tracing studies. Our observations demonstrate both the feasibility and reliability of using a data-driven tractography-based approach to map the subcortical connectome in vivo.

neuroscience↗

bioRxiv2020

Application of the Anatomical Fiducials Framework to a Clinical Dataset of Patients with Parkinsons Disease

Establishing spatial correspondence between subject and template images is necessary in neuroimaging research and clinical applications such as brain mapping and stereotactic neurosurgery. Our anatomical fiducials (AFIDs) framework has recently been validated to serve as a quantitative measure of image registration based on salient anatomical features. In this study, we sought to apply the AFIDs protocol to the clinic, focusing on structural magnetic resonance images obtained from patients with Parkinsons Disease (PD). We confirmed AFIDs could be placed to millimetric accuracy in the PD dataset with results comparable to those in normal control subjects. We evaluated subject-to-template registration using this framework by aligning the clinical scans to standard template space using a robust open preprocessing workflow. We found that registration errors measured using AFIDs were higher than previously reported, suggesting the need for optimization of image processing pipelines for clinical grade datasets. Finally, we examined the utility of using point-to-point distances between AFIDs as a morphometric biomarker of PD, finding evidence of reduced distances between AFIDs that circumscribe regions known to be affected in PD including the substantia nigra. Overall, we provide evidence that AFIDs can be successfully applied in a clinical setting and utilized to provide localized and quantitative measures of registration error. AFIDs provide clinicians and researchers with a common, open framework for quality control and validation of spatial correspondence and the location of anatomical structures, facilitating aggregation of imaging datasets and comparisons between various neurological conditions.

neuroscience↗

bioRxiv2020

Direct visualization and characterization of the human zona incerta and surrounding structures

The zona incerta (ZI) is a small gray matter region of the deep brain first identified in the 19th century, yet direct in vivo visualization and characterization has remained elusive. Noninvasive detection of the ZI and surrounding region could be critical to further our understanding of this widely connected but poorly understood deep brain region and could contribute to the development and optimization of neuromodulatory therapies. We demonstrate that high resolution (submillimetric) longitudinal (T1) relaxometry measurements at high magnetic field strength (7 Tesla) can be used to delineate the ZI from surrounding white matter structures, specifically the fasciculus cerebellothalamicus, fields of Forel (fasciculus lenticularis, fasciculus thalamicus, field H), and medial lemniscus. Using this approach, we successfully derived in vivo estimates of the size, shape, location, and tissue characteristics of substructures in the ZI region, confirming observations only previously possible through histological evaluation that this region is not just a space between structures but contains distinct morphological entities that should be considered separately. Our findings pave the way for increasingly detailed in vivo study and provide a structural foundation for precise functional and neuromodulatory investigation.

neuroscience↗

bioRxiv2020

Effects of MP2RAGE B1+ sensitivity on inter-site T1 reproducibility and morphometry at 7T

Most neuroanatomical studies are based on MR images, whose intensity profiles are not solely determined by the tissues longitudinal relaxation times (T1) but also affected by varying non-T1 contributions, hampering data reproducibility. In contrast, quantitative imaging using the MP2RAGE sequence, for example, allows direct characterization of the brain based on the tissue property of interest. Combined with 7 Tesla (7T) MRI, this offers unique opportunities to obtain robust high-resolution brain data characterized by a high reproducibility, sensitivity and specificity. However, specific MP2RAGE parameters choices - e.g., to emphasize intracortical myelin-dependent contrast variations - can substantially impact image quality and cortical analyses through remnants of B1+-related intensity variations, as illustrated in our previous work. To follow up on this: we (1) validate this protocol effect using a dataset acquired with a particularly B1+ insensitive set of MP2RAGE parameters combined with parallel transmission excitation; and (2) extend our analyses to evaluate the effects on hippocampal and subcortical morphometry. The latter remained unexplored initially but will provide important insights related to generalizability and reproducibility of neurodegenerative research using 7T MRI. We confirm that B1+ inhomogeneities have a considerably variable effect on cortical T1 and thickness estimates, as well as on hippocampal and subcortical morphometry depending on MP2RAGE setup. While T1 differed substantially across datasets initially, we show inter-site T1 comparability improves after correcting for the spatially varying B1+ field using a separately acquired Sa2RAGE B1+ map. Finally, as for cortical thickness, removal of B1+ residuals affects hippocampal and subcortical volumetry and boundary definitions, particularly near structures characterized by strong intensity changes (e.g. cerebral spinal fluid and arteries). Taken together, we show that the choice of MP2RAGE parameters can impact T1 comparability across sites and present evidence that hippocampal and subcortical segmentation results are modulated by B1+ inhomogeneities. This calls for careful (1) consideration of sequence parameters when setting acquisition protocols; as well as (2) interpretation of results focused on neuroanatomical changes due to disease. HighlightsO_LIPreviously observed effects of B1+ inhomogeneities on cortical T1 and thickness depend strongly on MP2RAGE parameters C_LIO_LIInter-site comparability of cortical T1 and thickness greatly improves after removal of B1+ residuals C_LIO_LIPost-hoc MP2RAGE B1+ correction affects hippocampal (and subcortical) size and shape analyses C_LIO_LINeuroradiological research would benefit from careful examination of imaging protocols and their impact on results, especially when B1+ maps are not acquired C_LI

neuroscience↗

bioRxiv2019

Hippocampal morphology and cytoarchitecture in the 3D BigBrain

The internal structure of the human hippocampus is challenging to map using histology or neuroimaging due to its complex archicortical folding. Here, we aimed to overcome this challenge using a unique combination of three methods. First, we leveraged a histological dataset with unprecedented 3D coverage, 3D BigBrain. Second, we imposed a computational unfolding framework that respects the topological continuity of hippocampal subfields, which are traditionally defined by laminar composition. Third, we adapted neocortical parcellation techniques to map the hippocampus with respect to not only laminar but also morphological features. Unsupervised clustering of these features revealed subdivisions that closely resemble ground-truth manual subfield segmentations. Critically, we also show that morphological features alone are sufficient to derive most hippocampal subfield boundaries. Moreover, some features showed differences within subfields along the hippocampal longitudinal axis. Our findings highlight new characteristics of internal hippocampal structure, and offer new avenues for its characterization with in-vivo neuroimaging.

neuroscience↗

bioRxiv2019

Bridging micro and macro: accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases

Brain atlases that encompass detailed anatomical or physiological features are instrumental in the research and surgical planning of various neurological conditions. Magnetic resonance imaging (MRI) has played important roles in neuro-image analysis while histological data remain crucial as a gold standard to guide and validate such analyses. With cellular-scale resolution, the BigBrain atlas offers 3D histology of a complete human brain, and is highly valuable to the research and clinical community. To bridge the insights at macro- and micro-levels, accurate mapping of BigBrain and established MRI brain atlases is necessary, but the existing registration is unsatisfactory. The described dataset includes co-registration of the BigBrain atlas to the MNI PD25 atlas and the ICBM152 2009b atlases (symmetric and asymmetric versions) in addition to manual segmentation of the basal ganglia, red nucleus, and hippocampus for all mentioned atlases. The dataset intends to provide the bridge between insights from histological data and MRI studies in research and neurosurgical planning. The registered atlases, anatomical segmentations, and deformation matrices are available at: nist.mni.mcgill.ca/?p=1209.

bioengineering↗

bioRxiv2018

A framework for evaluating correspondence between brain images using anatomical fiducials

Accurate spatial correspondence between template and subject images is a crucial step in neuroimaging studies and clinical applications like stereotactic neurosurgery. In the absence of a robust quantitative approach, we sought to propose and validate a set of point landmarks, anatomical fiducials (AFIDs), that could be quickly, accurately, and reliably placed on magnetic resonance images of the human brain. Using several publicly available brain templates and individual participant datasets, novice users could be trained to place a set of 32 AFIDs with millimetric accuracy. Furthermore, the utility of the AFIDs protocol is demonstrated for evaluating subject-to-template and template-to-template registration. Specifically, we found that commonly used voxel overlap metrics were relatively insensitive to focal misregistrations compared to AFID point-based measures. Our entire protocol and study framework leverages open resources and tools, and has been developed with full transparency in mind so that others may freely use, adopt, and modify. This protocol holds value for a broad number of applications including alignment of brain images and teaching neuroanatomy.

neuroscience↗

bioRxiv2017

Unfolding the hippocampus: An intrinsic coordinate system for subfield segmentations and quantitative mapping

The hippocampus, like the neocortex, has a morphological structure that is complex and variable in its folding pattern, especially in the hippocampal head. The current study presents a computational method to unfold hippocampal grey matter, with a particular focus on the hippocampal head where complexity is highest due to medial curving of the structure and the variable presence of digitations. This unfolding was performed on segmentations from high-resolution, T2-weighted 7T MRI data from 12 healthy participants and one surgical patient with epilepsy whose resected hippocampal tissue was used for histological validation. We traced a critical hippocampal component, the hippocampal sulcus and stratum radiatum, lacunosum moleculaire, (SRLM) in these images, then employed user-guided semi-automated techniques to detect and subsequently unfold the surrounding hippocampal grey matter. This unfolding was performed by solving Laplaces equation in three dimensions of interest (long-axis, proximal-distal, and laminar). The resulting unfolded coordinate space provides an intuitive way of mapping the hippocampal subfields in 2D space (long-axis and proximal-distal), such that similar borders can be applied in the head, body, and tail of the hippocampus independently of variability in folding. This unfolded coordinate space was employed to map intracortical myelin and thickness in relation to subfield borders, which revealed intracortical myelin differences that closely follow the subfield borders used here. Examination of a histological sample from a patient with epilepsy reveals that our unfolded coordinate system shows biological validity, and that subfield segmentations applied in this space are able to capture features not seen in manual tracing protocols.\n\nResearch highlightsO_LISRLM in hippocampal head consistently detected with 7T, T2 isotropic MRI\nC_LIO_LIHippocampal grey matter unfolded using Laplaces equation in 3D\nC_LIO_LIIntracortical myelin and thickness mapped in unfolded coordinate space\nC_LIO_LIUnfolded subfields capture critical structural regularities and agree with histology\nC_LI

neuroscience↗