Biology subjects

Kong, R.

Publications and source records attributed to Kong, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Intestinal uptake regulates T cell responses to dietary antigens

Dietary proteins induce antigen-specific immune responses, leading to oral tolerance or food allergies. While mouse models of allergic sensitization rely on antigen context and adjuvant signaling, exposure to purified proteins is usually sufficient for tolerance development in studies with soluble model antigens. In previous work, we discovered that the maize protein zein induces robust antigen-specific intestinal Tregs following normal chow exposure, but unexpectedly does not elicit a T cell response when delivered as a purified protein. Here, we investigated the mechanisms underlying these differential T cell responses, considering both biochemical properties of zein and its food matrix, as well as intestinal antigen processing. We focused on three zein preparations that induced different frequencies of antigen-specific intestinal T cells. While in vitro studies showed comparable presentation by dendritic cells, in vivo studies revealed stark differences in intestinal uptake. We identified differences in protein solubility and active sampling mechanisms as cooperative drivers of intestinal zein uptake. Different zein preparations could be sampled by either goblet cells, a route previously described for the model antigen OVA, or M cells, a route hypothesized for dietary proteins and established for transport of pathogenic bacteria. These findings also extend to peanuts, where the allergen Ara h 1 was selectively transported by goblet or M cells depending on food preparation, establishing examples of naturally occurring dietary M cell ligands. Finally, solubilizing zein increased intestinal uptake, and correspondingly feeding a diet with solubilized zein led to more intestinal zein-specific T cells compared to feeding the insoluble form. Overall, these findings suggest that intestinal uptake of dietary antigens is a regulated and context-dependent determinant of antigen-specific T cell induction, with implications for development of tolerance-restoring immunotherapies.

immunology↗

bioRxiv2026

Microbiota- and diet-specific T cells become Tregs by default

CD4+ T cells recognize antigens from microbiota, diet, and pathogens via T cell receptors (TCRs) and orchestrate immunity by differentiating into tolerogenic regulatory (Treg) or pro-inflammatory effector (Teff) lineages (e.g. TH1 or TH17) (1). Dysregulation of these responses underlies numerous gastrointestinal inflammatory and infectious diseases (2-6). The prevailing paradigm suggests that individual microbes and dietary antigens drive distinct cell fates (e.g., segmented filamentous bacteria [SFB] induce TH17 cells (7) whereas Helicobacter hepaticus (8) and diet (9) induce Tregs). However, the generality of this model is uncertain: several key organisms are atypical, and foundational studies often omitted a complex microbiome or a diverse polyclonal TCR repertoire. Here we develop a high-throughput pipeline to screen hundreds of TCRs from mice colonized from birth with a 116-strain human microbiota (hCom2v), demonstrating that TCRs recognizing microbiota or dietary antigens are overwhelmingly enriched in the induced Treg (iTreg) lineage. Endogenous CD4+ T cells specific for these antigens adopt a uniform iTreg phenotype in vivo, both in hCom2v-colonized and conventional mice. This baseline tolerance is robust to acute inflammation but breaks down following a 'two-hit' combination of inflammation and genetic susceptibility, allowing Teff to emerge against otherwise Treg-restricted antigens. These data support a revised paradigm in which antigen-specific Treg induction is the default response to foreign antigens in the healthy gut, and effector responses are an exception reflecting a perceived threat. Reframing gastrointestinal immunity as a tolerance-first system provides a framework for understanding inflammatory disease pathogenesis and suggests that therapeutic strategies should aim to restore a Treg-predominant baseline.

immunology↗

bioRxiv2026

Lack of ancestral SARS-CoV-2 imprinting promotes BA.3.2.2 infection in children

The ongoing evolution of SARS-CoV-2 is heavily constrained by population-level immune imprinting. Recent genomic surveillance reveals an unexpected demographic shift: the highly mutated BA.3.2.2 sublineage is significantly enriched in paediatric populations globally, unlike concurrent variants XFG and NB.1.8.1. Here, the evidence demonstrates that this paediatric susceptibility arises from the absence of ancestral Wuhan-strain immune imprinting. Serological analyses reveal a profound failure to neutralise BA.3.2.2 in young children with no ancestral strain exposure. Concurrently, single-B-cell sequencing analyses demonstrate that BA.3.2.2 completely evades Omicron-specific Class 1/4 neutralising antibodies, encoded by IGHV2-5 and IGHV5-51, which dominate the repertoires of weakly imprinted individuals such as young children. Conversely, BA.3.2.2 remains uniquely susceptible to broadly cross-reactive Class 1 antibodies, encoded by IGHV3-53/66, typically enriched in adults with strong ancestral imprinting, such as mRNA vaccine recipients. Critically, sustained transmission of BA.3.2.2 in paediatric populations may catalyse the emergence of secondary variants that combine the paediatric-evading features of BA.3.2.2 with adult-evading Class 1 mutations. This could allow the lineage to breach the imprinted immunity of the adult population, potentially driving widespread global transmission. Together, these findings show that legacy immune imprinting paradoxically provides superior protection against a highly divergent saltation variant, directly explaining the age-biased transmission of BA.3.2.2, and informing future paediatric vaccination strategies.

immunology↗

bioRxiv2026

Impact of Censoring on the Quality of Cortical Parcellations and Personalized TMS Targets

Head motion systematically biases functional connectivity (FC) estimates in resting-state functional MRI (rs-fMRI). A common mitigation strategy is to censor high-motion volumes and discard high-motion runs. However, overly stringent censoring risks discarding signal alongside noise, potentially degrading FC estimates. Here, we test the efficacy of various censoring strategies on individual-specific cortical parcellations and personalized transcranial magnetic stimulation (TMS) target selection. Using precision-fMRI datasets comprising 50 individuals, we define individualized "ground-truth" references from [≥]1 hour of low-motion data per participant. We then simulate 10-min or 20-min rs-fMRI sessions with varying motion levels from the remaining data, yielding final samples of 22 and 19 participants, respectively. Higher motion produces parcellations and TMS targets that deviate further from the ground-truth references. However, at any given motion level, lenient censoring produces higher quality parcellations and personalized TMS targets than strict censoring. The improvement is comparable to doubling scan duration from 20 to 40 min under strict censoring. With personalized connectome-guided TMS, a common dilemma is whether to re-scan patients with only high-motion runs. A mixed-motion session with one low-motion run and one high-motion run may often be considered usable after discarding the high-motion run and strict censoring. We find that lenient censoring of high-motion-only sessions yields TMS targets comparable to - or even better than - those derived from strictly censored mixed-motion sessions. Therefore, within the motion range and parcellation/TMS targeting frameworks evaluated here, patients may not need to be re-scanned solely because all runs exceed strict censoring criteria.

neuroscience↗

bioRxiv2026

Focal brain stimulation sites that modify autonomic arousal map to a convergent brain circuit and potential therapeutic target

Causal modulation of autonomic outflow could yield new therapeutic targets for autonomic hyperactivation. We employed three natural experiments in which different brain regions were targeted using transcranial magnetic stimulation (TMS) (n=139 sites, n=14 individuals), deep brain stimulation (n=392 sites, n=58 individuals), or low-intensity focused ultrasound (n=46 sites, n=23 individuals) with subsequent autonomic measurements. Using a human connectome database (n=1000) as a wiring diagram, we identified a convergent brain circuit that, when focally modulated, transiently reduces autonomic arousal. This circuit significantly resembled previously reported causal circuits for posttraumatic stress disorder (PTSD) and anxiety. In independent datasets, TMS to the autonomic arousal circuit reduced laboratory startle in healthy volunteers (n=28), lesions to this circuit reduced exaggerated startle in PTSD (n=193), and TMS to this circuit reduced anxiety-related autonomic symptoms in patients with clinically significant anxiety (n=30). Thus, the convergent circuit may serve as a potential neuromodulation target for autonomic hyperactivation.

neuroscience↗

bioRxiv2025

Non-human primate LIBRA-Seq accelerates neutralizing antibody discovery in RM vaccinated against HIV-1

Broadly neutralizing antibodies (bNAbs) exhibit protective efficacy against HIV-1 infection making them an ideal archetype for HIV-1 vaccine design. Presently, no vaccine candidate has induced antibody responses capable of meaningful protection against the swathe of circulating, difficult to neutralize tier 2 HIV-1 viruses. However, the development of stabilized, native-like envelope (Env) trimers such as BG505.SOSIP.664.T332N (BG505 SOSIP) has marked a significant advancement in vaccine design, due to their ability to elicit NAbs that neutralize tier 2 viruses in rhesus macaques (RM). NAb development following envelope trimer immunization in RM remains poorly understood, with hypothesized contributions from genetic variation at the IG loci, naive B cell repertoire, and differential gene expression in B cell lineages. To address these knowledge gaps, we have developed a set of BG505 SOSIP probes capable of recovering paired clonotype identity, antigen specificity, and gene expression of B cells in a high throughput fashion. These probes were constructed by conjugating biotinylated BG505 SOSIP to streptavidin covalently linked to both sc-RNA-Seq compatible DNA oligonucleotides and flow cytometry compatible fluorophores. Using these reagents, we isolated and sequenced BG505 SOSIP specific memory B cells from the PBMCs of an RM that developed high titers of neutralizing antibodies. To benchmark the accuracy of our technology, we compared our recovered heavy and light chain sequences to those identified from the same animal using conventional methodology and recovered 100% of previously identified NAbs. We then applied this technology to recover BG505 SOSIP specific memory B cells from five additional vaccinated RMs, cloned 34 antibodies for functional characterization, and identified ten antibodies with autologous neutralizing activity. Author SummaryUnderstanding how effective antibodies arise after HIV vaccination is essential for developing a protective vaccine, yet studying these responses in non-human primates has been limited by low- throughput methods. In this study, we adapted a high-throughput single-cell sequencing approach to identify HIV envelope-specific antibodies from vaccinated rhesus macaques. This method allowed us to recover paired antibody sequences together with their antigen specificity from thousands of individual B cells. We successfully identified known neutralizing antibodies and discovered additional antibodies capable of neutralizing HIV across multiple animals. Our analysis revealed that vaccine-elicited antibody responses were dominated by a small number of expanded lineages, with shared genetic features among animals with stronger neutralization. These findings demonstrate that this approach can efficiently define the antibody repertoires generated by HIV vaccines and provide a powerful tool for evaluating and improving immunogens in preclinical vaccine studies.

immunology↗

bioRxiv2025

IGHV3-53 antibody abundance drives divergent SARS-CoV-2 immune imprinting

The mechanisms driving divergent SARS-CoV-2 immune imprinting in populations with distinct SARS-CoV-2 exposure histories remain unclear. However, conventional wild-type mouse models fail to recapitulate severe imprinting, even after potent ancestral-strain mRNA immunization, hindering imprinting-related mechanistic investigation and vaccine evaluation. Here, we surprisingly found that V(D)J-humanized mice could faithfully recapitulate human severe imprinting phenotypes. Comprehensive antibody repertoire analysis and deep mutational scanning-based epitope mapping of 583 monoclonal antibodies from these models revealed that the pre-existing IGHV3-53/66 antibody abundance determine imprinting severity through antibody-mediated masking of Omicron-specific epitopes. Both passive IGHV3-53/66 antibody transfer and IGHV3-53 knock-in were sufficient to induce severe imprinting in wild-type mice. Together, these findings demonstrate that the V(D)J germline repertoire--even a single germline-encoded antibody response--can profoundly shape humoral imprinting severity. Accordingly, we established an IGHV3-53 knock-in mouse model that accurately recapitulates the human antibody landscape, providing a valuable tool for guiding future COVID-19 vaccine updates.

immunology↗

bioRxiv2025

Convergent and divergent brain-cognition relationships during development revealed by cross-sectional and longitudinal analyses in the ABCD Study

How brain networks and cognition co-evolve during development remains poorly understood. Here, we use resting-state functional magnetic resonance imaging (rs-fMRI) and cognitive data at baseline and Year 2 of 2,949 individuals in the Adolescent Brain Cognitive Development (ABCD) Study to examine how stable and changing features of brain network organization predict cognitive development during early adolescence. We find that baseline resting-state functional connectivity (FC) more strongly predicts future cognitive ability than baseline cognitive ability. Models trained on baseline FC to predict baseline cognition generalize better to Year 2 FC and cognition, suggesting that brain-cognition relationships strengthen over time. Intriguingly, baseline FC outperforms longitudinal FC change in predicting future cognitive ability. One potential reason is the lower reliability of FC change compared to baseline FC: ICC = 0.24 vs. 0.56. However, reducing baseline FCs reliability by shortening scan duration only partially narrows the predictive gap, suggesting reliability alone cannot be the full explanation. Furthermore, neither baseline FC nor FC change meaningfully predicts longitudinal change in cognitive ability. We also identify converging and diverging predictive network features across cross-sectional and longitudinal models of brain-cognition relationships, revealing a multivariate twist on Simpsons paradox. Together, these findings suggest that during early adolescence, stable individual differences in brain functional network organization play a more critical role than dynamic changes in shaping future cognitive outcomes.

neuroscience↗

bioRxiv2025

Network-based Near-Scalp Personalized Brain Stimulation Targets

Functional connectivity (FC) is often used to identify personalized targets for transcranial magnetic stimulation (TMS). However, existing methods often overlook individual differences in whole-cortex network organization. Furthermore, in some personalized TMS protocols, lower stimulation intensity is used for targets closer to the scalp, which may improve patient tolerance. Here, we develop an algorithm to simultaneously optimize FC and scalp proximity for target localization. We first use the multi-session hierarchical Bayesian model (MS-HBM) to estimate high-quality individual-specific cortical networks. A tree-based algorithm is then used to select the optimal target. With essentially no parameter to tune, our framework may potentially improve generalizability across populations. We compare our approach to existing "cluster" and "cone" algorithms. In two test-retest datasets of healthy individuals from the United States and Singapore, tree-based MS-HBM reliably identifies personalized TMS targets for depression near the scalp. Tree-based MS-HBM targets compare favorably with cluster and cone targets in terms of reliability, scalp proximity, and FC to the subgenual anterior cingulate cortex (sACC) in new out-of-sample MRI sessions. To demonstrate versatility, the same algorithm identifies personalized anxiety targets without tuning any parameter. In patients with treatment-resistant depression, tree-based MS-HBM targets compare favorably with cluster and cone targets in terms of reliability, scalp proximity, and sACC FC, hypothetically reducing stimulation intensity by 15% and 5% respectively. MS-HBM also exhibits the best (most negative) electric-field hotspot sACC FC and highest reliability in induced electric fields. Overall, tree-based MS-HBM provides a robust, generalizable framework to estimate near-scalp personalized targets across populations.

neuroscience↗

bioRxiv2025

Novel HIV-1 fusion peptide immunogens using glycan-engineered alphavirus-like particles

Immunofocusing on conserved, subdominant epitopes is critical for vaccines against highly diverse viruses such as HIV-1, influenza, and SARS-CoV-2. The eight-residue N-terminus of the HIV-1 fusion peptide (FP) is one such example of a promising yet small target. We developed new FP immunogens using three alphavirus-like particles (VLPs) and introduced additional glycans to mask shared carrier-specific epitopes. In two independent guinea pig studies, sequential immunization with heterologous carriers enhanced FP-directed antibody titers, which were further improved with glycan engineering. Separately, using diverse FP variants sharing the same N-terminal six amino acids increased neutralizing antibody titers. When combined, these two strategies led to higher FP-directed titers and, after Env trimer boosting, induced FP-directed neutralizing antibodies against multi-clade wild-type HIV-1 in nearly all animals. These findings established the importance of minimizing recurrent off-target epitopes across immunizations and support the engineered VLPs as a promising platform for peptide immunization. HighlightsO_LINovel HIV-1 fusion peptide immunogens using glycan-engineered alphavirus-like particles C_LIO_LIImproved FP-directed response by minimizing recurrent carrier-specific epitopes across immunizations C_LIO_LIImproved neutralizing response by sequential immunization with diverse FP variants C_LIO_LIFP-directed antibodies neutralizing multi-clade wildtype viruses in nearly all animals C_LI

immunology↗

bioRxiv2025

Geometric influences on the regional organization of the mammalian brain

The mammalian brain comprises anatomically and functionally distinct regions, yet the principles governing their organization across scales remain unclear. Efforts to map regional boundaries use expert judgement or data-driven clustering of functional, connectional, and/or architectonic properties, but these approaches are often descriptive, have limited generalizability, and do not elucidate generative mechanisms. Here, we develop a geometrically constrained framework for the hierarchical, multiscale regional organization of neocortical and non-neocortical structures. The framework yields parcels at any resolution with high internal homogeneity across hundreds of anatomical, functional, cellular, and molecular properties in humans, macaques, marmosets, and mice, and it generalizes to understudied mammalian species. Finally, we demonstrate that the framework captures the essence of a reaction-diffusion mechanism in which brain geometry shapes the spatial expression of putative patterning molecules that establish a blueprint of regional identity during development. Our findings point to a conserved, universal influence of geometry on mammalian brain organization.

neuroscience↗

bioRxiv2024

Evaluation of Brain Age as a Specific Marker of Brain Health

Brain age is widely regarded as a powerful marker of general brain health. Brain age models are typically trained on large datasets to predict chronological age, which may offer advantages in predicting specific health outcomes, much like the success of finetuning large language models for specific applications. However, it is also well-accepted that machine learning models trained to directly predict specific outcomes (i.e., direct models) often outperform those trained on surrogate objectives. Therefore, despite their much larger training data, it is unclear whether brain age models outperform direct models in predicting specific brain health outcomes. Here, we compare large-scale brain age models (pretrained on 53,542 participants) and direct models for predicting specific health outcomes related to Alzheimers Disease (AD) dementia. Using anatomical T1 scans from three continents (N = 1,848), we find that summarizing brain age with a single scalar (i.e., brain age gap) led to poor prediction performance. Using higher-dimensional intermediate representations of brain age models led to better prediction, but was still worse than direct models without finetuning. Using intermediate representations of finetuned brain age models was necessary to achieve similar performance as direct models. Overall, our results do not discount brain age as a useful marker of general brain health, but suggest that using chronological age as a pretraining target might be suboptimal for predicting specific health outcomes.

neuroscience↗

bioRxiv2024

Discovery and characterization of dietary antigens in oral tolerance

Food antigens elicit immune tolerance through the action of intestinal regulatory T (Treg) cells. Unlike food allergens, the proteins that mediate tolerance are mostly undescribed. Here, we found that epitopes derived from seed storage proteins are targets of murine intestinal Treg cells, with the most frequent response targeting the C-terminus of the maize protein alpha-zein. An MHC tetramer loaded with this antigen revealed that zein-specific T cells are predominantly intestinal Treg cells, develop concurrently with weaning, and constitute up to 2% of the peripheral Treg cell pool. Zein-responsive Treg cells repressed naive T cell proliferation ex vivo and prior dietary exposure resulted in a constrained response upon multiple inflammatory challenges in vivo, supporting a specific role for gut-resident Treg cells in suppressing systemic immune responses. Together, our work reveals the development, immune suppressive characteristics, and function of naturally occurring Treg cells that recognize dietary seed-storage proteins, a previously undescribed class of antigens in oral tolerance. One-sentence summaryImmunodominant epitopes from seed storage proteins are a target of intestinal regulatory T cells that modulate immune challenge to food.

immunology↗

bioRxiv2024

MRI economics: Balancing sample size and scan duration in brain wide association studies

A pervasive dilemma in brain-wide association studies (BWAS) is whether to prioritize functional MRI (fMRI) scan time or sample size. We derive a theoretical model showing that individual-level phenotypic prediction accuracy increases with sample size and total scan duration (sample size x scan time per participant). The model explains empirical prediction accuracies extremely well across 76 phenotypes from nine resting-fMRI and task-fMRI datasets (R2 = 0.89), spanning a wide range of scanners, acquisitions, racial groups, disorders and ages. For scans [≤]20 mins, prediction accuracy increases linearly with the logarithm of total scan duration, suggesting interchangeability of sample size and scan time. However, sample size is ultimately more important than scan time in determining prediction accuracy. Nevertheless, when accounting for overhead costs associated with each participant (e.g., recruitment costs), to boost prediction accuracy, longer scans can yield substantial cost savings over larger sample size. To achieve high prediction performance, 10-min scans are highly cost inefficient. In most scenarios, the optimal scan time is [≥]20 mins. On average, 30-min scans are the most cost-effective, yielding 22% cost savings over 10-min scans. Overshooting is cheaper than undershooting the optimal scan time, so we recommend aiming for [≥]30 mins. Compared with resting-state whole-brain BWAS, the most cost-effective scan time is shorter for task-fMRI and longer for subcortical-cortical BWAS. Standard power calculations maximize sample size at the expense of scan time. Our study demonstrates that optimizing both sample size and scan time can boost prediction power while cutting costs. Our empirically informed reference is available for future study planning: WEB_APPLICATION_LINK

neuroscience↗

bioRxiv2024

Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching

Individualized phenotypic prediction based on structural MRI is an important goal in neuroscience. Prediction performance increases with larger samples, but small-scale datasets with fewer than 200 participants are often unavoidable. We have previously proposed a "meta-matching" framework to translate models trained from large datasets to improve the prediction of new unseen phenotypes in small collection efforts. Meta-matching exploits correlations between phenotypes, yielding large improvement over classical machine learning when applied to prediction models using resting-state functional connectivity as input features. Here, we adapt the two best performing meta-matching variants ("meta-matching finetune" and "meta-matching stacking") from our previous study to work with T1-weighted MRI data by changing the base neural network architecture to a 3D convolution neural network. We compare the two meta-matching variants with elastic net and classical transfer learning using the UK Biobank (N = 36,461), Human Connectome Project Young Adults (HCP-YA) dataset (N = 1,017) and HCP-Aging dataset (N = 656). We find that meta-matching outperforms elastic net and classical transfer learning by a large margin, both when translating models within the same dataset, as well as translating models across datasets with different MRI scanners, acquisition protocols and demographics. For example, when translating a UK Biobank model to 100 HCP-YA participants, meta-matching finetune yielded a 136% improvement in variance explained over transfer learning, with an average absolute gain of 2.6% (minimum = -0.9%, maximum = 17.6%) across 35 phenotypes. Overall, our results highlight the versatility of the meta-matching framework.

neuroscience↗

bioRxiv2023

Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data

Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a "meta-matching" approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated large improvement of meta-matching over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants ("meta-matching with dataset stacking" and "multilayer meta-matching") to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original "meta-matching with stacking" approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available at GITHUB_LINK.

neuroscience↗

bioRxiv2023

The brain's topographical organization shapes dynamic interaction patterns to support flexible behavior

Understanding how human cognition flexibly supports distinct forms of behavior is a key goal of neuroscience. Adaptive behavior relies on context-specific rules that vary across situations, as well as on stable knowledge gained from experience. However, the mechanisms that allow these influences to be appropriately balanced remain elusive. Here, we show that this cognitive flexibility is partly supported by the topographical organization of the cortex. The frontoparietal control network (FPCN) is located between regions implicated in top-down attention and memory-guided cognition. We hypothesized that the FPCN is topographically divided into discrete systems that support these distinct forms of behavior. These FPCN subsystems exhibit multiple anatomical and functional similarities to their neighboring systems (the dorsal attention network and default mode network respectively). This topographic architecture is also mirrored in the functional patterns that emerge in different situations: the FPCN subnetworks act as a unified system when long-term memory can support behavior, but they segregate into discrete units when working memory, rather than long term memory, is necessary for behavioral control. In this way, the topographic organization of brain function provides crucial insights into how the human cortex supports flexible behavior.

neuroscience↗

bioRxiv2023

In-vivo whole-cortex estimation of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth

A balanced excitation-inhibition ratio (E/I ratio) is critical for healthy brain function. Normative development of cortex-wide E/I ratio remains unknown. Here we non-invasively estimate a putative marker of whole-cortex E/I ratio by fitting a large-scale biophysically-plausible circuit model to resting-state functional MRI (fMRI) data. We first confirm that our model generates realistic brain dynamics in the Human Connectome Project. Next, we show that the estimated E/I ratio marker is sensitive to the GABA-agonist benzodiazepine alprazolam during fMRI. Alprazolam-induced E/I changes are spatially consistent with positron emission tomography measurement of benzodiazepine receptor density. We then investigate the relationship between the E/I ratio marker and neurodevelopment. We find that the E/I ratio marker declines heterogeneously across the cerebral cortex during youth, with the greatest reduction occurring in sensorimotor systems relative to association systems. Importantly, among children with the same chronological age, a lower E/I ratio marker (especially in association cortex) is linked to better cognitive performance. This result is replicated across North American (8.2 to 23.0 years old) and Asian (7.2 to 7.9 years old) cohorts, suggesting that a more mature E/I ratio indexes improved cognition during normative development. Overall, our findings open the door to studying how disrupted E/I trajectories may lead to cognitive dysfunction in psychopathology that emerges during youth. SignificanceHealthy brain function requires a delicate balance of neural excitation (E) and inhibition (I). In animals, this balance - the E/I ratio - is known to decrease with the maturation of inhibitory circuitry during healthy development. However, in humans, the normative development of cortex-wide E/I ratio remains unclear. Here, we use a biophysical model and non-invasive brain scans to estimate a marker of E/I ratio. Spatial changes in our E/I ratio marker are consistent with a drug that decreases E/I ratio. We also find that our cortex-wide E/I ratio marker decreases during development. Furthermore, North American and Asian children with lower E/I ratio, especially in higher-order cortex, have better cognitive performance. Overall, E/I ratio is a potential index of healthy neurocognitive development.

neuroscience↗