Biology subjects

Van Essen, D. C.

Publications and source records attributed to Van Essen, D. C..

At least 19 recordsLinked to original sources

bioRxiv2026

DRUMS - A Flexible New Deep Learning Tool for Precise Cortical Surface Alignment across Individuals

Accurate inter-individual alignment of human cerebral cortex is challenging because of the high variability of human cortical folding patterns and the regionally non-uniform and inconsistent spatial relationships across individuals of cortical folds versus the functional networks and cortical areas that we wish to study. To achieve precise alignment across individuals, an algorithm must use multi-modal neuroimaging features related to cortical areas and functional networks, ideally as inputs to surface-based registration, but at least during training if only folding patterns will be available during inference, so that it can learn where folding patterns are trustworthy and learn a spatially non-uniform regularization function that reflects the true gamut of human inter-individual variability in cortical organization. Additionally, an algorithm ideally will be capable of denoising its own input registration features to avoid overfitting to noise, enabling reproducible registration in test-retest data, and will not require precise hand tuning of input regularization parameters. To address these challenges, we developed Deep-learning Registration Using U-Net with Multimodal Supervision (DRUMS), a novel framework for multi-modal cortical surface registration. DRUMS applies its deep-learning approach to cortical surface registration using multi-resolution spheres, a well-validated approach used in other registration algorithms such as Multi-modal Surface Matching (MSM) (Robinson, et al., 2014; Robinson, et al., 2018). It also includes the same physically inspired strain energy regularization that we pioneered for MSM and the same precise barycentric interpolation on spherical surface meshes. DRUMS has a three-stage architecture: (1) multiscale feature extraction, (2) multiscale feature integration, and (3) deformation field generation. The framework's multimodal design supports flexible integration of diverse imaging modalities, enabling registration using either folding features or multi-modal features as inputs with independent control over supervision during training (e.g., training DRUMS with folding inputs and multi-modal supervision to learn which folds best correlate with multi-modal features). DRUMS outperforms Multi-modal Surface Matching (MSM), the current state-of-the-art method used in the Human Connectome Project (HCP) pipelines, over a wide range of input regularization settings in both registration accuracy and test-retest reproducibility of registration results for both supervised folding registrations and multi-modal registrations across all tested modalities, including held out modalities. DRUMS further learns a biologically plausible spatially non-uniform regularization function, with registration induced distortion correctly matched to known human inter-individual cortical variability. These results suggest that DRUMS should replace MSM in the HCP Pipelines and position DRUMS as a versatile and reliable tool for cortical surface registration in neuroimaging research.

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bioRxiv2026

Iterative co-creation of harmonized human and non-human primate cellular and structural ontologies and 3D common coordinate frameworks for the basal ganglia

A major goal of the BRAIN Initiative Cell Atlas Network (BICAN) is to create a suite of foundational reference cell atlases and associated standards for human and non-human primate brains. Central to this goal is the creation of cross-species harmonized cellular taxonomies and structural parcellations with formal ontologies that can be mapped into 3D reference frameworks bridging neuroimaging and cellular and histological resolutions. We describe here an iterative approach, focused initially on the basal ganglia, to co-create structural and cellular ontologies in human, macaque and marmoset brains, including a Harmonized Ontology of Mammalian Brain Anatomy (HOMBA), and to map and refine structural parcellations into neuroimaging-based common coordinate frameworks. These references provide the framework for documenting and mapping all experimental sampling in BICAN, allowing analyses of cellular and molecular variation as a function of topographic position, and enabling comparisons of cellular, molecular and neuroimaging-based functional variation within and between primate species. HighlightsO_LIA hierarchical Harmonized Ontology of Mammalian Brain Anatomy (HOMBA) covering 2348 structures C_LIO_LIHOMBA-annotated 3D common coordinate frameworks (CCFs) of the basal ganglia across species C_LIO_LIHistologically informed 3D parcellation/atlas of 280 human subcortical structures indexed by HOMBA C_LIO_LIMapping and integration of structural, cellular and functional data with HOMBA and CCFs C_LI

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bioRxiv2025

A cross-species spatial transcriptomic atlas of the human and non-human primate basal ganglia

The basal ganglia are interconnected subcortical nuclei with complex topographical organization that orchestrate goal-directed behaviors and are implicated in neurodegenerative movement disorders. We generated a cellular-resolution, spatial transcriptomic atlas of the basal ganglia in human, rhesus macaque, and common marmoset, sampling over one million cells in each species. By integrating spatial data with a cross-species, consensus snRNA-seq cell type taxonomy, this atlas reveals conserved principles of molecular organization within and across structures. The cellular architecture is complex but highly stereotyped, with gene expression gradients superimposed onto discrete compartments. Extensive spatial sampling illuminates 3D gradients of molecular organization in the striatum and reveals cell type-specific core and shell compartments in the primate internal globus pallidus, which is conserved with mouse. This unified, cross-species spatial transcriptomic atlas will be a foundational resource for characterizing the molecular and functional organization of the basal ganglia and their roles in health and disease.

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bioRxiv2025

Compensating Cortical Thickness for Cortical Folding-Related Variation

Cortical thickness is a widely used biomarker of brain morphology and health, yet it is dependent on local cortical folding. Because gyral crowns are consistently thicker than sulcal fundi and cortical folds vary widely across individuals, these fluctuations introduce unmodeled nuisance variance that can obscure meaningful biological effects of interest. Previous global methods of folding compensation incompletely compensate for folding effects on cortical thickness. Spatial smoothing is commonly used to reduce these effects in the literature, but this markedly degrades spatial localization precision. To address these limitations, we developed a novel method for folding-compensated cortical thickness estimation that uses nonlinear local multiple regression with five folding measures to model and more completely remove folding-related variance from cortical thickness. This approach estimates what cortical thickness would have been in the absence of folding, yielding a more biologically interpretable measure of cortical architecture. We applied this new approach to data from the Young Adult Human Connectome Project (HCP-YA) and Aging Human Connectome Project (HCA), demonstrating substantial reductions in intra-areal and inter-individual variability, substantially increasing standardized effect sizes of age on cortical thickness (41% increase) while preserving neurobiologically expected patterns, and avoiding the loss of spatial precision that occurs with the spatial smoothing that has traditionally been used in the literature. The method has been integrated into the HCP pipelines, facilitating its widespread use. By attenuating folding-induced variability, this technique enhances cortical thickness as a structural phenotype and may support more accurate cortical parcellation, longitudinal tracking, and biomarker discovery in brain health and disease.

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bioRxiv2025

Automatic Individual Cortical Parcellation for the Human Connectome Project

The original Human Connectome Project multimodal cortical parcellation (HCP_MMP1.0) used MRI-derived local features and long-distance functional connectivity measures to define a multimodal parcellation at the group level, accompanied by an automated areal classifier, to create subject-specific mappings of the human cerebral cortex. These mappings, referred to as individual (cortex) parcellations, aim to capture individual variability in areal organization by learning from both structural and functional data. However, a strict supervised learning approach using the group parcellation as labels would have no incentive to learn individual differences that registration is unable to reconcile (e.g., atypical 55b topologies). Furthermore, there are many types of resting state network (RSN) feature maps, and it is unclear which type would most accurately or effectively classify areas, or even what should be the primary criteria for evaluating classification performance. Here, we introduce an Areal Recognition Ensemble with Nested Approach (ARENA) classifier that learns from uncertain labels, using a novel application of weakly supervised learning to this type of problem. Additionally, in comparing multiple candidate RSN decompositions, temporal ICA and PROFUMO maps outperformed the original spatial ICA-based approach based on objective criteria. With these refinements, the ensemble classifier achieved a reliable individual variability score of 8380, an average areal detection rate of 97.8%, and test-retest reproducibility of 73.3%, outperforming a retrained version of the original Multi-layer Perceptron (MLP) model (whose reliable individual variability score was 3128, average areal detection rate was 97.2%, and test-retest reproducibility was 71.6% on the same dataset). Furthermore, the ARENA classifier demonstrated stronger generalization for all three measures when applied to task fMRI data that were not part of the training dataset. Using the refined classifier and leveraging all 1071 HCP-Young Adult subjects, we identified new types of atypical organization of language-related area 55b. Here we provide the fully data-driven HCP_MMP1.0_1071_MPM (Maximum Probability Map) group parcellation and a summary of area 55b organization in both hemispheres. Our automated individual parcellation pipeline powered by the novel ARENA classifier is now integrated into the HCP pipelines, offering a user-friendly tool for the neuroimaging community.

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bioRxiv2025

Development of a 24-Channel 3T Phased-Array Coil for fMRI in Awake Monkeys - Mitigating Spatiotemporal Artifacts in Ferumoxytol-Weighted Functional Connectivity Estimation

Functional magnetic resonance imaging (fMRI) of awake macaque monkeys holds promise for advancing our understanding of primate brain organization, including humans. However, estimating functional connectivity in awake animals is challenging due to the limited duration of imaging sessions and the relatively low sensitivity to neural activity. To overcome these challenges, we developed a 24-channel 3T receive radiofrequency (RF) coil optimized for parallel imaging of awake macaques. This enabled the acquisition of multiband and GRAPPA-accelerated ferumoxytol-weighted resting-state fMRI. The Human Connectome Project-style data processing pipelines were adapted to address the unique preprocessing demands of cerebral blood volume-weighted (CBVw) imaging, including motion correction, functional-to-structural image co-registration, and training a multi-run independent component analysis-based X-noiseifier (ICA-FIX) classifier for removal of structured artifacts. Our CBVw fMRI approach resulted in an elevated contrast-to-noise ratio compared to blood oxygenation level dependent (BOLD) imaging in anesthetized macaques. However, structured imaging artifacts still contributed more variance to the functional timeseries than neural activity. By applying the ICA-FIX classifier, we achieved highly reproducible parcellated functional connectivity at the single-subject level. At the group-level, we identified dense functional networks with spatial features homologous to those observed in humans. The developed RF receive coil, image acquisition protocols, and data analysis pipelines are publicly available, providing the broader scientific community with tools to leverage these advances for further research.

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bioRxiv2024

Charting Cortical-Layer Specific Area Boundaries Using Gibbs Ringing Attenuated T1w/T2w-FLAIR Myelin MRI

Cortical areas have traditionally been defined by their distinctive layer cyto- and/or myelo- architecture using postmortem histology. Recent studies have delineated many areas by measuring overall cortical myelin content and its spatial gradients using the T1w/T2w ratio MRI in living primates, including humans. While T1w/T2w studies of areal transitions might benefit from using the layer profile of this myelin-related contrast, a significant confound is Gibbs ringing artefact, which produces signal fluctuations resembling cortical layers. Here, we address these issues with a novel approach using cortical layer thickness-adjusted T1w/T2w-FLAIR imaging, which effectively cancels out Gibbs ringing artefacts while enhancing intra-cortical myelin contrast. Whole-brain MRI measures were mapped onto twelve equivolumetric layers, and layer-specific sharp myeloarchitectonic transitions were identified using spatial gradients resulting in a putative 182 area/subarea partition of the macaque cerebral cortex. The myelin maps exhibit notably high homology with those in humans, suggesting cortical myelin shares a similar developmental program across species. Comparison with histological Gallyas myelin stains explains over 80% of the variance in the laminar T1w/T2w-FLAIR profiles, substantiating the validity of the method. Altogether, our approach provides a novel, noninvasive means for precision mapping layer myeloarchitecture in the primate cerebral cortex, advancing the pioneering work of classical neuroanatomists.

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bioRxiv2024

Mapping vascular network architecture in primate brain using ferumoxytol-weighted laminar MRI

Mapping the vascular organization of the brain is of great importance across various domains of basic neuroimaging research, diagnostic radiology, and neurology. However, the intricate task of precisely mapping vasculature across brain regions and cortical layers presents formidable challenges, resulting in a limited understanding of neurometabolic factors influencing the brains microvasculature. Addressing this gap, our study investigates whole-brain vascular volume using ferumoxytol-weighted laminar-resolution multi-echo gradient-echo imaging in macaque monkeys. We validate the results with published data for vascular densities and compare them with cytoarchitecture, neuron and synaptic densities. The ferumoxytol-induced change in transverse relaxation rate ({Delta}R2*), an indirect proxy measure of cerebral blood volume (CBV), was mapped onto twelve equivolumetric laminar cortical surfaces. Our findings reveal that CBV varies 3-fold across the brain, with the highest vascular volume observed in the inferior colliculus and lowest in the corpus callosum. In the cerebral cortex, CBV is notably high in early primary sensory areas and low in association areas responsible for higher cognitive functions. Classification of CBV into distinct groups unveils extensive replication of translaminar vascular network motifs, suggesting distinct computational energy supply requirements in areas with varying cytoarchitecture types. Regionally, baseline R2* and CBV exhibit positive correlations with neuron density and negative correlations with receptor densities. Adjusting image resolution based on the critical sampling frequency of penetrating cortical vessels allows us to delineate approximately 30% of the arterial-venous vessels. Collectively, these results mark significant methodological and conceptual advancements, contributing to the refinement of cerebrovascular MRI. Furthermore, our study establishes a linkage between neurometabolic factors and the vascular network architecture in the primate brain. HighlightsCortical layer vascular mapping using ferumoxytol-weighted R2* MRI Vascular volume is high in primary sensory areas and low in association areas Correlation between R2* and vascular volume with neuron and receptor densities Vascularization co-varies with densities of specific interneuron types

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bioRxiv2024

Automating the Human Connectome Project's Temporal ICA Pipeline

Functional magnetic resonance imaging (fMRI) data are dominated by noise and artifacts, with only a small fraction of the variance relating to neural activity. Temporal independent component analysis (tICA) is a recently developed method that enables selective denoising of fMRI artifacts related to physiology such as respiration. However, an automated and easy to use pipeline for tICA has not previously been available; instead, two manual steps have been necessary: 1) setting the group spatial ICA dimensionality after MELODICs Incremental Group-PCA (MIGP) and 2) labeling tICA components as artifacts versus signals. Moreover, guidance has been lacking as to how many subjects and timepoints are needed to adequately re-estimate the temporal ICA decomposition and what alternatives are available for smaller groups or even individual subjects. Here, we introduce a nine-step fully automated tICA pipeline which removes global artifacts from fMRI dense timeseries after sICA+FIX cleaning and MSMAll alignment driven by functionally relevant areal features. Additionally, we have developed an automated "reclean" Pipeline for improved spatial ICA (sICA) artifact removal. Two major automated components of the pipeline are 1) an automatic group spatial ICA (sICA) dimensionality selection for MIGP data enabled by fitting multiple Wishart distributions; 2) a hierarchical classifier to distinguish group tICA signal components from artifactual components, equipped with a combination of handcrafted features from domain expert knowledge and latent features obtained via self-supervised learning on spatial maps. We demonstrate that the dimensionality estimated for the MIGP data from HCP Young Adult 3T and 7T datasets is comparable to previous manual tICA estimates, and that the group sICA decomposition is highly reproducible. We also show that the tICA classifier achieved over 0.98 Precision-Recall Area Under Curve (PR-AUC) and that the correctly classified components account for over 95% of the tICA-represented variance on multiple held-out evaluation datasets including the HCP-Young Adult, HCP-Aging and HCP-Development datasets under various settings. Our automated tICA pipeline is now available as part of the HCP pipelines, providing a powerful and user-friendly tool for the neuroimaging community.

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bioRxiv2023

Evaluating functional brain organization in individuals and identifying contributions to network overlap

Individual differences in the spatial organization of resting state networks have received increased attention in recent years. Measures of individual-specific spatial organization of brain networks and overlapping network organization have been linked to important behavioral and clinical traits and are therefore potential biomarker targets for personalized psychiatry approaches. To better understand individual-specific spatial brain organization, this paper addressed three key goals. First, we determined whether it is possible to reliably estimate weighted (non-binarized) resting state network maps using data from only a single individual, while also maintaining maximum spatial correspondence across individuals. Second, we determined the degree of spatial overlap between distinct networks, using test-retest and twin data. Third, we systematically tested multiple hypotheses (spatial mixing, temporal switching, and coupling) as candidate explanations for why networks overlap spatially. To estimate weighted network organization, we adopt the Probabilistic Functional Modes (PROFUMO) algorithm, which implements a Bayesian framework with hemodynamic and connectivity priors to supplement optimization for spatial sparsity/independence. Our findings showed that replicable individual-specific estimates of weighted resting state networks can be derived using high quality fMRI data within individual subjects. Network organization estimates using only data from each individual subject closely resembled group-informed network estimates (which was not explicitly modeled in our individual-specific analyses), suggesting that cross-subject correspondence was largely maintained. Furthermore, our results confirmed the presence of spatial overlap in network organization, which was replicable across sessions within individuals and in monozygotic twin pairs. Intriguingly, our findings provide evidence that network overlap is indicative of linear additive coupling. These results suggest that regions of network overlap concurrently process information from all contributing networks, potentially pointing to the role of overlapping network organization in the integration of information across multiple brain systems.

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bioRxiv2023

Geneneralising the HCP multimodal cortical parcellation to UK Biobank

The Human Connectome Project Multimodal Parcellation (HCP_MMP1.0) provides a robust in vivo map of the cerebral cortex, which demonstrates variability in structure and function that cannot be captured through diffeomorphic image registration alone. The HCP successfully employed a fully-connected neural network architecture to capture this variation, however it is unclear whether this approach generalises to other datasets with less rich imaging protocols. In this paper we propose and validate a novel geometric deep learning framework for generating individualised HCP_MMP1.0 parcellations in UK Biobank (UKB), an extremely rich resource that has led to numerous breakthroughs in neuroscience. To address substantial differences in image acquisition (for example, 6 minutes of resting-state fMRI per subject for UKB vs. 60 minutes per subject for HCP), we introduce a multi-step learning procedure including pretraining with a convolutional autoencoder. Compared to a fully-connected baseline, our proposed framework improved average detection rate across all areas by 10.4% (99.1% vs 88.7%), and detection of the worst performing area by 51.0% (80.9% vs. 29.9%). Importantly, this was not a result of the framework predicting one consistent parcellation across subjects, as visual inspection indicated that our method was sensitive to atypical cortical topographies. Code and trained models will be made available at https://www.github.com.

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bioRxiv2022

Anatomical variability, multi-modal coordinate systems, and precision targeting in the marmoset brain

Localising accurate brain regions needs careful evaluation in each experimental species due to their individual variability. However, the function and connectivity of brain areas is commonly studied using a single-subject cranial landmark-based stereotactic atlas in animal neuroscience. Here, we address this issue in a small primate, the common marmoset, which is increasingly widely used in systems neuroscience. We developed a non-invasive multi-modal neuroimaging-based targeting pipeline, which accounts for intersubject anatomical variability in cranial and cortical landmarks in marmosets. This methodology allowed creation of multi-modal templates (MarmosetRIKEN20) including head CT and brain MR images, embedded in coordinate systems of anterior and posterior commissures (AC-PC) and CIFTI grayordinates. We found that the horizontal plane of the stereotactic coordinate was significantly rotated in pitch relative to the AC-PC coordinate system (10 degrees, frontal downwards), and had a significant bias and uncertainty due to positioning procedures. We also found that many common cranial and brain landmarks (e.g., bregma, intraparietal sulcus) vary in location across subjects and are substantial relative to average marmoset cortical area dimensions. Combining the neuroimaging-based targeting pipeline with robot-guided surgery enabled proof-of-concept targeting of deep brain structures with an accuracy of 0.2 mm. Altogether, our findings demonstrate substantial intersubject variability in marmoset brain and cranial landmarks, implying that subject-specific neuroimaging-based localization is needed for precision targeting in marmosets. The population-based templates and atlases in grayordinates, created for the first time in marmoset monkeys, should help bridging between macroscale and microscale analyses. HighlightsO_LIAchieved sub-millimeter localization accuracy of subject-wise brain region C_LIO_LIPropose a dedicated non-invasive multi-modal subject-specific registration pipeline C_LIO_LIConstruct brain coordinate system in AC-PC and grayordinate spaces C_LIO_LIEstablish multi-modal MRI and CT brain and cortical templates, MarmosetRIKEN20 C_LIO_LIQuantify intersubject variabilities in marmoset brain C_LIO_LISignificant bias and uncertainty exist in marmoset stereotactic positioning C_LI

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bioRxiv2021

Graded variation in cortical T1w/T2w myelination during adolescence

Myelination influences brain connectivity during sensitive periods of development by enhancing neural signaling speed and regulating synapse formation to reduce plasticity. However, in vivo studies characterizing the maturational timing of cortical myelination during human development remain scant. Here, we take advantage of recent advances in high-resolution cortical T1w/T2w myelin mapping methods, including principled correction of B1+ transmit field effects, using data from the Human Connectome Project in Development (N=628, ages 8-21) to characterize the maturational timing of myelination from childhood through early adulthood throughout the cerebral neocortex. We apply Bayesian spline models and functional latent clustering analysis to demonstrate graded variation in the rate of cortical T1w/T2w myelin growth in neocortical areas that is strongly correlated with the sensorimotor-association (S-A) axis of cortical organization reported by others. In sensorimotor areas T1w/T2w myelin starts at high levels at early ages, increases at a fast pace, and decelerates at later ages (18-21). In intermediate multimodal areas along the S-A axis, T1w/T2w myelin tends to start at intermediate levels and increase linearly at an intermediate pace. In transmodal/paralimbic association areas high along the S-A axis, T1w/T2w myelin tends to start at low levels and increase linearly at the slowest pace. These data provide evidence for graded variation along the S-A axis in the rate of cortical myelination during adolescence, which could reflect ongoing plasticity underlying the development of complex information processing and psychological functioning. Significance StatementMyelin is a lipid membrane that is essential to healthy brain function. Myelin wraps axons to increase neural signaling speed, enabling complex neuronal functioning underlying learning and cognition. Here we characterize the developmental timing of myelination across the cerebral cortex during adolescence using recent advances in non-invasive myelin mapping. Our results provide new evidence demonstrating graded variation across the cortex in the timing of myelination during adolescence, with rapid myelination in lower-order sensory areas and gradual myelination in higher-order association areas. This spatial pattern of microstructural brain development closely parallels the sensorimotor-to-association axis of cortical organization and plasticity during ontogeny.

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bioRxiv2021

Transmit Field Bias Correction of T1w/T2w Myelin Maps

T1-weighted divided by T2-weighted (T1w/T2w) myelin maps were initially developed for neuroanatomical analyses such as identifying cortical areas, but they are increasingly used in statistical comparisons across individuals and groups with other variables of interest. Existing T1w/T2w myelin maps contain radiofrequency transmit field (B1+) biases, which may be correlated with these variables of interest, leading to potentially spurious results. Here we propose two empirical methods for correcting these transmit field biases using either explicit measures of the transmit field or alternatively a pseudo-transmit approach that is highly correlated with the transmit field at 3T. We find that the resulting corrected T1w/T2w myelin maps are both better neuroanatomical measures (e.g., for use in cross-species comparisons), and more appropriate for statistical comparisons of relative T1w/T2w differences across individuals and groups (e.g., sex, age, or body-mass-index) within a consistently acquired study at 3T. We recommend that investigators who use the T1w/T2w approach for mapping cortical myelin use these B1+ transmit field corrected myelin maps going forward.

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bioRxiv2020

Modelling white matter in gyral blades as a continuous vector field

1Many brain imaging studies aim to measure structural connectivity with diffusion tractography. However, biases in tractography data, particularly near the boundary between white matter and cortical grey matter can limit the accuracy of such studies. When seeding from the white matter, streamlines tend to travel parallel to the convoluted cortical surface, largely avoiding sulcal fundi and terminating preferentially on gyral crowns. When seeding from the cortical grey matter, streamlines generally run near the cortical surface until reaching deep white matter. These so-called "gyral biases" limit the accuracy and effective resolution of cortical structural connectivity profiles estimated by tractography algorithms, and they do not reflect the expected distributions of axonal densities seen in invasive tracer studies or stains of myelinated fibres. We propose an algorithm that concurrently models fibre density and orientation using a divergence-free vector field within gyral blades to encourage an anatomically-justified streamline density distribution along the cortical white/grey-matter boundary while maintaining alignment with the diffusion MRI estimated fibre orientations. Using in vivo data from the Human Connectome Project, we show that this algorithm reduces tractography biases. We compare the structural connectomes to functional connectomes from resting-state fMRI, showing that our model improves cross-modal agreement. Finally, we find that after parcellation the changes in the structural connectome are very minor with slightly improved interhemispheric connections (i.e, more homotopic connectivity) and slightly worse intrahemispheric connections when compared to tracers.

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bioRxiv2020

Brain/MINDS Beyond Human Brain MRI Study: Multi-Site Harmonization for Brain Disorders Throughout the Lifespan

Psychiatric and neurological disorders are afflictions of the brain that can affect individuals throughout their lifespan. Many brain magnetic resonance imaging (MRI) studies have been conducted; however, imaging-based biomarkers are not yet well established for diagnostic and therapeutic use. This article describes an outline of the planned study, the Brain/MINDS Beyond human brain MRI project (FY2018 [~] FY2023), which aims to establish clinically-relevant imaging biomarkers with multi-site harmonization by collecting data from healthy traveling subjects (TS) at 13 research sites. Collection of data in psychiatric and neurological disorders across the lifespan is also scheduled at 13 sites, whereas designing measurement procedures, developing and analyzing neuroimaging protocols, and databasing are done at three research sites. The Harmonization protocol (HARP) was established for five high-quality 3T scanners to obtain multimodal brain images including T1 and T2-weighted, resting state and task functional and diffusion-weighted MRI. Data are preprocessed and analyzed using approaches developed by the Human Connectome Project. Preliminary results in 30 TS demonstrated cortical thickness, myelin, functional connectivity measures are comparable across 5 scanners, providing high reproducibility and sensitivity to subject-specific connectome. A total of 75 TS, as well as patients with various psychiatric and neurological disorders, are scheduled to participate in the project, allowing a mixed model statistical harmonization. The HARP protocols are publicly available online, and all the imaging, demographic and clinical information, harmonizing database will also be made available by 2024. To the best of our knowledge, this is the first project to implement a rigorous, prospective harmonization protocol with multi-site TS data. It explores intractable brain disorders across the lifespan and may help to identify the disease-specific pathophysiology and imaging biomarkers for clinical practice.

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bioRxiv2019

Towards HCP-Style Macaque Connectomes: 24-Channel 3T Multi-Array Coil, MRI Sequences and Preprocessing

Macaque monkeys are an important model species for understanding cortical organization of primates, yet tools and methods for noninvasive image acquisition (e.g. MRI RF coils and pulse sequence protocols) and image data preprocessing have lagged behind those developed for humans. To resolve the structural and functional characteristics of the relatively thin macaque cortex, high spatial, temporal, and angular resolutions are required while maintaining high signal-to-noise ratio to ensure good image quality. To address these challenges, we developed a macaque 24-channel receive coil for 3-T MRI with parallel imaging capabilities. This coil enabled adaptation of the Human Connectome Project (HCP) image acquisition protocols to the macaque brain. We also adapted HCP preprocessing methods optimized for the macaque brain, including spatial minimal preprocessing of structural, functional MRI (fMRI), and diffusion MRI (dMRI). The coil provided high signal-to-noise ratio and high efficiency in data acquisition, allowing four- and five-fold acceleration for dMRI and fMRI, respectively. Automated parcellation of cortex, reconstruction of cortical surface, removal of artefacts and nuisance signals in fMRI, and distortion correction of dMRI performed well, and the overall quality of basic neurobiological measures was comparable with those for the HCP. The resulting HCP-style in vivo macaque MRI data show considerable promise for analyzing cortical architecture and functional and structural connectivity using advanced methods that have previously only been available for humans.\n\nHighlights[tpltrtarr] 24-channel 3T MR receive coil designed for the smaller macaque brain.\n[tpltrtarr]In vivo macaque imaging protocols adapted according to guidelines from the HCP.\n[tpltrtarr]Parallel imaging yields five- and four-fold acceleration in fMRI and dMRI sampling.\n[tpltrtarr]HCPs minimal preprocessing and denoising pipelines adapted for macaques.\n[tpltrtarr]The multi-modal MRI data show considerable promise for HCP-style analyses.

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bioRxiv2019

A Domain-general Cognitive Core defined in Multimodally Parcellated Human Cortex

Numerous brain imaging studies identified a domain-general or "multiple-demand" (MD) activation pattern accompanying many tasks and may play a core role in cognitive control. Though this finding is well established, the limited spatial localization provided by traditional imaging methods precluded a consensus regarding the precise anatomy, functional differentiation and connectivity of the MD system. To address these limitations, we used data from 449 subjects from the Human Connectome Project, with cortex of each individual parcellated using neurobiologically grounded multi-modal MRI features. The conjunction of three cognitive contrasts reveals a core of 10 widely distributed MD parcels per hemisphere that are most strongly activated and functionally interconnected, surrounded by a penumbra of 17 additional areas. Outside cerebral cortex, MD activation is most prominent in the caudate and cerebellum. Comparison with canonical resting state networks shows MD regions concentrated in the fronto-parietal network but also engaging three other networks. MD activations show modest relative task preferences accompanying strong co-recruitment. With distributed anatomical organization, mosaic functional preferences, and strong interconnectivity, we suggest MD regions are well positioned to integrate and assemble the diverse components of cognitive operations. Our precise delineation of MD regions provides a basis for refined analyses of their functions.

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