Biology subjects

Hong, S.-J.

Publications and source records attributed to Hong, S.-J..

34 records · Page 2Linked to original sources

bioRxiv2021

A self-amplifying mRNA COVID-19 vaccine drives potent and broad immune responses at low doses that protects non-human primates against SARS-CoV-2

The coronavirus disease 2019 (COVID-19) pandemic continues to spread globally, highlighting the urgent need for safe and effective vaccines that could be rapidly mobilized to immunize large populations. We report the preclinical development of a self-amplifying mRNA (SAM) vaccine encoding a prefusion stabilized severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein and demonstrate potent cellular and humoral immune responses at low doses in mice and rhesus macaques. The homologous prime-boost vaccination regimen of SAM at 3, 10 and 30 g induced potent neutralizing antibody titers in rhesus macaques following two SAM vaccinations at all dose levels, with the 10 g dose generating geometric mean titers (GMT) 48-fold greater than the GMT of a panel of SARS-CoV-2 convalescent human sera. Spike-specific T cell responses were observed at all dose levels. SAM vaccination provided protective efficacy against SARS-CoV-2 challenge as both a homologous prime-boost and as a single boost following ChAd prime, demonstrating reduction of viral replication in both the upper and lower airways. Protection was most effective with a SAM prime-boost vaccination regimen at 10 and 30 g and with a ChAd/SAM heterologous prime-boost regimen. The SAM vaccine is currently being evaluated in clinical trials as both a homologous prime-boost regimen at low doses and as a boost following heterologous prime.

immunology↗

bioRxiv2021

Long-range connections mirror and link microarchitectural and cognitive hierarchies in the human brain

AO_SCPLOWBSTRACTC_SCPLOWCore features of higher-order cognition are hypothesized to be implemented via distributed cortical networks that are linked via long-range connections. However, these connections are biologically expensive, and it is unknown how the computational advantages long-range connections provide overcome the associated wiring costs. Our study investigated this question by exploring the relationship between long-range functional connections and local cortical microarchitecture. Specifically, our work (i) profiled distant cortical connectivity using resting-state fMRI and cortico-cortical geodesic distance mapping, (ii) assessed how long-range connections reflect local brain microarchitecture, and (iii) studied the microarchitectural similarity of regions connected through long-range connections. Analysis of two independent datasets indicated that sensory and motor areas had more clustered short-range connectivity patterns, while transmodal association cortices, including regions of the default mode network, were characterized by distributed, long-range connections. Confirmatory meta-analysis suggested that this topographical difference mirrored a shift in cognitive function, from perception/action towards emotional and social cognitive processing. Analysis of myelin-sensitive in vivo MRI in the same participants as well as post mortem histology and gene expression established that gradients in functional connectivity distance are paralleled by those present in cortical microarchitecture. Moreover, long-range connections were found to link together spatially remote regions of association cortex with an unexpectedly similar microarchitecture. These findings provide novel insights into how the organization of distributed functional networks in transmodal association cortex contribute to cognition, because they suggest that long-range connections link together distant islands of association cortex with similar microstructural features.

neuroscience↗

bioRxiv2021

The Cost of Untracked Diversity in Brain-Imaging Prediction

Brain-imaging research enjoys increasing adoption of supervised machine learning for singlesubject disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population stratification. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in two separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n=297) and the Healthy Brain Network (HBN, n=551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially to the default mode network. Our collective findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.

neuroscience↗

bioRxiv2021

Discovery of μ,δ-Opioid receptor dual biased agonists that overcome the limitation of prior biased agonists

Morphine is widely used to manage pain in patients, although the risk of side effects is significant. The use of biased agonists to the G protein of -opioid receptors has been suggested as a potential solution, although Oliceridine and PZM21 have previously failed to demonstrate benefits in clinical studies. An amplification-induced confusion in the process of comparing G protein and beta-arrestin pathways may account for previous biased agonist mis-identification. Here, we have devised a strategy to discover biased agonists with intrinsic efficacy. We computationally simulated 430,000 molecular dockings to the -opioid receptor to construct a compound library. Hits were then verified by experiment. Using the verified compounds, we performed simulations to build a second library with a common scaffold, and selected compounds which show biased features to and {delta}-opioid receptors through a cell-based assay. Three compounds (ID110460001, ID110460002, and ID110460003) with a dual biased agonistic effect for and {delta}-opioid receptors were identified. These candidates are full agonists for the -opioid receptor, and they show specific binding modes. Based on our findings, we expect our novel compound to act as a biased agonist than conventional drugs such as Oliceridine.

pharmacology and toxicology↗

bioRxiv2021

A convergent structure-function substrate of cognitive imbalances in autism

Autism is a common neurodevelopmental condition characterized by substantial phenotypic heterogeneity, which hinders diagnosis, research, and intervention. A leading example can be found in marked imbalances in language and perceptual skills, where deficits in one domain often co-exist with normal or even superior performance in the other domain. The current work capitalized on multiple data analytics including data-driven subtyping and dimensional approaches to quantify cognitive imbalances in multi-site datasets of individuals diagnosed with autism spectrum disorder (ASD) and neurotypical controls, and assessed structural and functional brain network substrates. Studying cognitive dimensions as well as multimodal neuroimaging signatures in 155 ASD and 151 neurotypical individuals, we observed robust evidence for a structure-function substrate of cognitive imbalances in ASD. Specifically, ASD presented with marked imbalances in cognitive profiles relative to neurotypical controls, characterized by verbal to non-verbal intelligence discrepancy. Different analytical approaches including subtyping and dimensional regression methods converged in showing that these imbalances were reflected in atypical cortical thickening and functional integration of language networks, alongside with sensory and higher cognitive networks. Phenotypic findings could be replicated in an independent sample of 325 ASD and 569 neurotypical controls. Although verbal and non-verbal intelligence are currently considered as specifiers unrelated to the categorical diagnosis of autism, our results show that intelligence disparities are accentuated in ASD and relate to a consistent structure-function substrate affecting multiple brain networks. Our findings motivate the incorporation of cognitive imbalances in future autism research, which may help to parse the phenotypic heterogeneity of autism and potentially inform intervention-oriented subtyping.

neuroscience↗

bioRxiv2020

Functional idiosyncrasy has a shared topography with group-level connectivity alterations in autism

Autism spectrum disorder (ASD) is commonly understood as a network disorder, yet case-control analyses against typically-developing controls (TD) have yielded somewhat inconsistent patterns of results. The current work was centered on a novel approach to profile functional network idiosyncrasy, the inter-individual variability in the association between functional network organization and brain anatomy, and we tested the hypothesis that idiosyncrasy contributes to connectivity alterations in ASD. Studying functional network idiosyncrasy in a multi-centric dataset with 157 ASD and 172 TD, our approach revealed higher idiosyncrasy in ASD in the default mode, somatomotor and attention networks together with reduced idiosyncrasy in the lateral temporal lobe. Idiosyncrasy was found to increase with age in both ASD and TD, and was significantly correlated with symptom severity in the former group. Association analysis with structural and molecular brain features indicated that patterns of functional network idiosyncrasy were not correlated with ASD-related cortical thickness alterations, but closely with the spatial expression patterns of intracortical ASD risk genes. In line with our main hypothesis, we could demonstrate that idiosyncrasy indeed plays a strong role in the manifestation of connectivity alterations that are measurable with conventional case-control designs and may, thus, be a principal driver of inconsistency in the autism connectomics literature. These findings support important interactions between the heterogeneity of individuals with an autism diagnosis and group-level functional signatures, and help to consolidate prior research findings on the highly variable nature of the functional connectome in ASD. Our study promotes idiosyncrasy as a potential individualized diagnostic marker of atypical brain network development.

neuroscience↗

bioRxiv2020

A low-dimensional connectome manifold governs the organization and plasticity of social brain functions in humans.

The human brain scaffolds social cognitive functions, including Theory of Mind, empathy and compassion, through its functional and microstructural organization. However, it remains unclear how the learning and refinement of these skills may, in turn, shape brain function and structure. Here we studied if different types of social mental training can lead to plastic changes in brain function and microstructure. We studied a group of 332 healthy adults (197 women, 20-55 years) with repeated multimodal neuroimaging and behavioral testing. Our neuroimaging approach capitalized on the quantification of cortical functional gradients and myelin-sensitive T1 relaxometry, two emerging measures of cortical functional organization and microstructure. Longitudinal analysis indicated marked changes in intrinsic cortical function and microstructure, which varied as a function of social training content. In particular, we observed consistent differential change in function and microstructure between attention-mindfulness and socio-cognitive training in regions functionally associated with attention and interoception, including insular and parietal cortices. Conversely, socio-affective and socio-cognitive training resulted in differential microstructural changes in regions classically implicated in interoceptive and emotional processing, including insular and orbitofrontal areas, but did not result in functional reorganization. Notably, longitudinal changes in cortical function and microstructure were predictive of behavioral change in attention, compassion and perspective-taking, suggesting behavioral relevance. In sum, our work provides evidence for functional and microstructural plasticity after the training of social-interoceptive functions, and provides a causal perspective on the neural basis of behavioral adaptation in human adults.

neuroscience↗

bioRxiv2020

Connectome and microcircuit models implicate atypical subcortico-cortical interactions in autism pathophysiology

AO_SCPLOWBSTRACTC_SCPLOWBoth macroscale connectome miswiring and microcircuit anomalies have been suggested to play a role in the pathophysiology of autism. However, an overarching framework that consolidates these macro and microscale perspectives of the condition is lacking. Here, we combined connectome-wide manifold learning and biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. Our analysis established that autism showed significant differences in structural connectome organization relative to neurotypical controls, with strong effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models revealed that the degree of macroscale anomalies was related to atypical increases of subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic decoding and developmental gene enrichment analyses provided biological context and pointed to genes expressed in cortical and thalamic areas during childhood and adolescence. Supervised machine learning showed the macroscale perturbations predicted socio-cognitive symptoms and repetitive behaviors. Our analyses provide convergent support that atypical subcortico-cortical interactions may contribute to both microcircuit and macroscale connectome anomalies in autism.

neuroscience↗

bioRxiv2020

Decomposing complex links between the childhood environment and brain structure in school-aged youth

Childhood experiences play a profound role in conferring risk and resilience for brain and behavioral development. However, how different facets of the environment shape neurodevelopment remains largely unknown. Here we sought to decompose heterogeneous relationships between environmental factors and brain structure in 989 school-aged children from the Adolescent Brain Cognitive Development Study. We applied a cross-modal integration and clustering approach called Similarity Network Fusion, which combined two brain morphometrics (i.e., cortical thickness and myelin-surrogate markers), and key environmental factors (i.e., trauma exposure, neighborhood safety, school environment, and family environment) to identify homogeneous subtypes. Depending on the subtyping resolution, results identified two or five subgroups, each characterized by distinct brain structure-environment profiles. Notably, more supportive caregiving and school environments were associated with increased myelination, whereas less supportive caregiving, higher family conflict and psychopathology, and higher perceived neighborhood safety were observed with increased cortical thickness. These subtypes were highly reproducible and predicted externalizing symptoms and overall mental health problems. Our findings support the theory that distinct environmental exposures differentially influence neurodevelopment. Delineating more precise associations between risk factors, protective factors, and brain development may inform approaches to enhance risk identification and optimize interventions targeting specific experiences.

neuroscience↗

bioRxiv2020

TOWARD A CONNECTIVITY GRADIENT-BASED FRAMEWORK FOR REPRODUCIBLE BIOMARKER DISCOVERY

AO_SCPLOWBSTRACTC_SCPLOWDespite myriad demonstrations of feasibility, the high dimensionality of fMRI data remains a critical barrier to its utility for reproducible biomarker discovery. Recent studies applying dimensionality reduction techniques to resting-state fMRI (R-fMRI) have unveiled neurocognitively meaningful connectivity gradients that are present in both human and primate brains, and appear to differ meaningfully among individuals and clinical populations. Here, we provide a critical assessment of the suitability of connectivity gradients for biomarker discovery. Using the Human Connectome Project (discovery subsample=209; two replication subsamples= 209x2) and the Midnight scan club (n=9), we tested the following key biomarker traits - reliability, reproducibility and predictive validity - of functional gradients. In doing so, we systematically assessed the effects of three analytical settings, including i) dimensionality reduction algorithms (i.e., linear vs. non-linear methods), ii) input data types (i.e., raw time series, [un-]thresholded functional connectivity), and iii) amount of the data (R-fMRI time-series lengths). We found that the reproducibility of functional gradients across algorithms and subsamples is generally higher for those explaining more variances of whole-brain connectivity data, as well as those having higher reliability. Notably, among different analytical settings, a linear dimensionality reduction (principal component analysis in our study), more conservatively thresholded functional connectivity (e.g., 95-97%) and longer time-series data (at least [≥]20mins) was found to be preferential conditions to obtain higher reliability. Those gradients with higher reliability were able to predict unseen phenotypic scores with a higher accuracy, highlighting reliability as a critical prerequisite for validity. Importantly, prediction accuracy with connectivity gradients exceeded that observed with more traditional edge-based connectivity measures, suggesting the added value of a low-dimensional gradient approach. Finally, the present work highlights the importance and benefits of systematically exploring the parameter space for new imaging methods before widespread deployment. HO_SCPLOWIGHLIGHTSC_SCPLOW- There is a growing need to identify benchmark parameters in advancing functional connectivity gradients into a reliable biomarker. - Here, we explored multidimensional parameter space in calculating functional gradients to improve their reproducibility, reliability and predictive validity. - We demonstrated that more reproducible and reliable gradient markers tend to have higher predictive power for unseen phenotypic scores across various cognitive domains. - We showed that the low-dimensional connectivity gradient approach could outperform raw edge-based analyses in terms of predicting phenotypic scores. - We highlight the necessity of optimizing parameters for new imaging methods before their widespread deployment.

neuroscience↗

bioRxiv2020

Shared and distinct patterns of atypical cortical morphometry in children with autism and anxiety

Autism spectrum disorder (ASD) and anxiety disorders (ANX) are common neurodevelopmental conditions with several overlapping symptoms. Notably, many children and adolescents with ASD also have an ANX diagnosis, suggesting shared pathological mechanisms. Here, we leveraged structural imaging and phenotypic data from 82 closely matched children (28 ASD, 28 ANX, 26 typically developing controls). Our neuroimaging paradigm assessed cortical thickness and studied inter-regional structural covariance networks. Both ASD and ANX presented with atypical structural network organization relative to controls. Specifically, ASD presented with increased thickness in temporal and parietal midline cortices, while ANX was associated with increased cortical thickness in the left inferior frontal and precentral gyri. Despite the overall difference in the spatial distributions these clusters, unconstrained spin permutation testing showed that statistical maps from the ANX-vs-controls and ASD-vs-controls analyses were significantly correlated. The two diagnostic groups also presented with common decreases in structural covariance patterns, collectively pointing to decreased structural coupling between lateral frontal, lateral temporal, and temporo-parietal regions. Dimensional analysis of trait anxiety and social responsiveness partially recapitulated diagnosis-based findings. Collectively, our findings provide evidence for both shared as well as distinct effects of ASD and ANX on on regional and inter-regional structural network organization.

neuroscience↗

bioRxiv2020

Altered Sensorimotor-to-Transmodal Hierarchical Organization in Schizophrenia

For decades, schizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. We applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state functional magnetic resonance imaging (rsfMRI) to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients vs. 122 controls). Using these techniques, we demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal-parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and frontoparietal and ventral attention regions along the classical sensation-to-cognition continuum established in prior neuroanatomical work. The compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascaded impairments stemming from the disrupted somatosensory-motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.

neuroscience↗

bioRxiv2019

BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

Understanding how higher order cognitive function emerges from the underlying brain structure depends on quantifying how the behaviour of discrete regions are integrated within the broader cortical landscape. Recent work has established that this macroscale brain organization and function can be quantified in a compact manner through the use of multivariate machine learning approaches that identify manifolds often described as cortical gradients. By quantifying topographic principles of macroscale organization, cortical gradients lend an analytical framework to study structural and functional brain organization across species, throughout development and aging, and its perturbations in disease. More generally, its macroscale perspective on brain organization offers novel possibilities to investigate the complex relationships between brain structure, function, and cognition in a quantified manner. Here, we present a compact workflow and open-access toolbox that allows for (i) the identification of gradients (from structural or functional imaging data), (ii) their alignment (across subjects or modalities), and (iii) their visualization (in embedding or cortical space). Our toolbox also allows for controlled association studies between gradients with other brain-level features, adjusted with respect to several null models that account for spatial autocorrelation. The toolbox is implemented in both Python and Matlab, programming languages widely used by the neuroimaging and network neuroscience communities. Several use-case examples and validation experiments demonstrate the usage and consistency of our tools for the analysis of functional and microstructural gradients across different spatial scales.

neuroscience↗

bioRxiv2019

Cross-species Functional Alignment Reveals Evolutionary Hierarchy Within the Connectome

Evolution provides an important window into how cortical organization shapes function and vice versa. The complex mosaic of changes in brain morphology and functional organization that have shaped the mammalian cortex during evolution, complicates attempts to chart cortical differences across species. It limits our ability to fully appreciate how evolution has shaped our brain, especially in systems associated with unique human cognitive capabilities that lack anatomical homologues in other species. Here, we demonstrate a function-based method for cross-species cortical alignment that leverages recent advances in understanding cortical organization and that enables the quantification of homologous regions across species, even when their location is decoupled from anatomical landmarks. Critically, our method establishes that cross-species similarity in cortical organization decreases with geodesic distance from unimodal systems, and culminates in the most pronounced changes in posterior regions of the default network (angular gyrus, posterior cingulate and middle temporal cortices). Our findings suggest that the establishment of the default network, as the apex of a cognitive hierarchy, as is seen in humans, is a relatively recent evolutionary adaptation. They also highlight functional changes in regions such as the posterior cingulate cortex and angular gyrus as key influences on uniquely human features of cognition.

neuroscience↗

bioRxiv2018

Dissociations between microstructural and functional hierarchies within regions of transmodal cortex

While the role of cortical microstructure in organising neural function is well established, it remains unclear how structural constraints can give rise to more flexible elements of cognition. While non-human primate research has demonstrated a close structure-function correspondence, the relationship between microstructure and function remains poorly understood in humans, in part because of the reliance on post mortem analyses which cannot be directly related to functional data. To overcome this barrier, we developed a novel approach to model the similarity of microstructural profiles sampled in the direction of cortical columns. Our approach was initially formulated based on an ultra-high-resolution 3D histological reconstruction of an entire human brain and then translated to myelin-sensitive MRI data in a large cohort of healthy adults. This novel method identified a system-level gradient of microstructural differentiation traversing from primary sensory to limbic regions that followed shifts in laminar differentiation and cytoarchitectural complexity. Importantly, while microstructural and functional gradients described a similar hierarchy, they became increasingly dissociated in transmodal default mode and fronto-parietal networks. Meta analytic decoding of these topographic dissociations highlighted involvement in higher-level aspects of cognition such as cognitive control and social cognition. Our findings demonstrate a relative decoupling of macroscale functional from microstructural gradients in transmodal regions, which likely contributes to the flexible role these regions play in human cognition.

neuroscience↗

bioRxiv2017

Label-free non-invasive quantitative measurement of lipid contents in individual microalgal cells using refractive index tomography

Microalgae are promising candidates for biofuel production due to their high lipid content. To facilitate utilization of the microalgae for biofuel, rapid quantification of the lipid contents in microalgae is necessary. However, conventional methods based on the chemical extraction of lipids require a time-consuming destructive extraction process. Here, we demonstrate label-free, non-invasive, rapid quantification of the lipid contents in individual micro-algal cells measuring the three-dimensional refractive index tomograms. We measure three-dimensional refractive index distributions within Nannochloropsis oculata cells and find that lipid droplets are identifiable in tomograms by their high refractive index. In addition, we alter N. oculata under nitrogen deficiency by measuring the volume, lipid weight, and dry cell weight of individual cells. Characterization of individual cells allows correlative analysis between the lipid content and size of individual cells.

bioengineering↗