Search bioRxiv⌕ Search

Biology subjects

Li, Z.

Publications and source records attributed to Li, Z..

At least 55 records · Page 3Linked to original sources

Gene dosage effect and reduced core fucosylation in B lymphocytes of FUT8 heterozygous mice

Core fucosylation (alpha1,6-fucosylation) of N-glycans, catalyzed solely by fucosyltransferase 8 (FUT8), plays important roles in development, immunity, and antibody effector functions. Here we report the generation of Fut8 floxed mice using a CRISPR/Cas9-based Extreme Genome Editing (EGE) system, in which loxP sites flank exon 8 of the Fut8 gene and a LoxP-STOP-LoxP-RFP cassette was knocked in downstream of the promoter, rendering the targeted allele functionally null before Cre-mediated recombination. Crossing floxed mice with CMV-Cre transgenic mice yielded complete knockout mice carrying the Cre-recombined allele. A PCR-based genotyping method was established that unambiguously distinguishes the wild-type, heterozygous, and homozygous genotypes (+/+, delta/+, and delta/delta). Intercrossing of heterozygous mice revealed that homozygous knockout mice die during the early postnatal period, consistent with previous reports, whereas heterozygous mice are viable. Flow cytometric analysis of AAL binding to splenic B cells and AAL lectin blotting both demonstrated markedly reduced core fucosylation in heterozygous mice (60.0 +/- 6.8% and approximately 48% of wild-type levels, respectively). These results show that disruption of a single Fut8 allele substantially reduces core fucosylation, and the mouse model described here provides a practical tool for functional studies of core fucosylation.

immunology↗

Structural streamlining of axonemal doublet microtubules in Plasmodium male gametes

Male gametogenesis in malaria parasites produces up to eight flagellated gametes within approximately 15 min, requiring de novo construction of motile axonemes on an unusually short timescale. Here we combine cellular cryo-electron tomography, high-resolution cryo-EM, proteomics, and parasite genetics to define the molecular architecture and assembly states of the Plasmodium male gamete axoneme. The doublet microtubules retain the principal surface-associated motility modules but contain a markedly reduced luminal microtubule inner protein network. Their inner junction lacks PACRG and is instead organized by CFAP20 and CFAP52, whose combined loss disrupts doublet integrity and exflagellation. We further identify a calmodulin protein at the outer dynein arm docking region and show that outer dynein arms accumulate progressively during gametogenesis. These findings establish distinct form of axonemal remodeling in Plasmodium and reveal the ordered assembly of structural and motor modules during rapid male gamete formation.

cell biology↗

KRAS inhibition drives adaptive surfaceome remodeling and reveals ALPP/ALPPL2 as a targetable vulnerability in pancreatic cancer

KRAS is mutated in over 90% of pancreatic ductal adenocarcinoma (PDAC) and is required for tumor initiation and maintenance. While clinical trials testing mutant-selective and pan-RAS inhibitors (KRASi) have demonstrated significant clinical benefit, genetic and non-genetic mechanisms of resistance limit the long-term efficacy of monotherapy strategies. Here, we used quantitative temporal analysis of the global, phosphorylation, and surface proteome to comprehensively identify non-genetic adaptive pathways and cell surface biomarkers in response to acute and long-term KRAS inhibition in PDAC. Through integrative machine learning analysis of the KRASi-proteome and publicly available datasets, we discovered top priority cell surface targets for combination therapy with KRASi. We identified Placental Alkaline Phosphatase (ALPP) as upregulated in response to KRASi through compensatory JAK2/STAT3 signaling. We developed a novel clinical grade ALPP/ALPPL2 (ALPG)-targeting antibody drug conjugate (ADC) with site-specific MMAE conjugation and demonstrated single agent efficacy in PDAC models, as well as enhanced internalization and anti-tumor efficacy in combination with KRASi. Together, these studies comprehensively define the KRASi-induced proteome and identify ALPP/ALPPL2 as a promising cell surface target for combination with KRASi to enhance response and forestall KRASi resistance.

cancer biology↗

DECIPHER integrates disentangled representation learning and prototype-based cell-type deconvolution across molecular modalities

The growing availability of single-cell resources creates new opportunities to extract cell-type-resolved information from the vast body of existing bulk omics data through cell-type deconvolution. Conventional deconvolution methods often rely on linear mixture models or specific probabilistic assumptions and can be sensitive to batch effects, whereas many deep-learning approaches are modality-specific or lack a unified end-to-end learning framework. Here, we developed DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that can be applied across multiple molecular modalities. DECIPHER learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation. By integrating nonlinear representation learning with differentiable non-negative least-squares optimization, DECIPHER estimates cell-type proportions from Zc. Across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets, and multiple molecular modalities, DECIPHER showed robust and competitive cell-type deconvolution performance. Beyond cell-type proportion estimation, the learned Zc supported chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma, demonstrating that DECIPHER can transform high-dimensional bulk omics data into low-dimensional, biologically informative representations. DECIPHER thus broadens the utility of existing bulk omics resources for biological discovery and clinical research.

bioinformatics↗

Predictive Modeling of Cancer Cell Growth Kinetics with Machine Learning

Quantitative characterization of cell proliferation is central to preclinical drug discovery. Here, we evaluated Random Forest (RF) regression for predicting confluence-based cell growth trends using data from human cancer cell lines and benchmarked its performance against the widely used logistic and Gompertz models. The RF model achieved higher predictive accuracy within the evaluated dataset, particularly at higher initial seeding densities. We further extended the RF framework to predict growth curves across seeding densities. Collectively, these findings highlight the potential of data-driven modeling to complement conventional mathematical approaches and support more informed cell-culture design.

bioinformatics↗

Profiling of Aggregated Amyloid β42-induced Proteomic Alterations in KOLF2.1J Induced Pluripotent Stem Cell-derived Neurons

Amyloid[beta]; (A[beta];) plaques are a hallmark of Alzheimer[prime]s disease (AD). A human cellular neuronal model that recaptures A[beta];-induced pathology is critical for advancing AD research. However, comprehensive proteomic profiling of A[beta];-induced cellular model remains elusive. In this study, we investigated the proteomic changes in induced pluripotent stem cell (iPSC)-derived neurons (iNs) exposed to synthetic A[beta]; (1-42) peptides (A[beta];42A) to improve our understanding of the cellular responses of A[beta]; aggregates and to establish a human-related platform for AD research. A[beta];42A formed extracellular aggregates around neuronal soma and neurites, impaired neurite outgrowth, and induced the expression of multiple AD-associated genes, such as APOE, BACE1, ADAM10. To define the molecular landscape of A[beta];-induced neuronal dysfunction, we performed proteomic analyses of whole-cell lysates as well as soma- and neurite-enriched fractions. Proteomic profiling revealed extensive gene alterations in pathways associated with synaptic function, neuronal maintenance, and AD pathogenesis, such as the upregulation of APOE. Importantly, the A[beta];42A-iNs system recapitulated molecular signatures observed in AD brain tissue and cerebrospinal fluid. Representative protein changes, including APOE upregulation and its colocalization with A[beta]; aggregates, were further validated in postmortem human AD brain tissue. Together, these findings manifest that the A[beta];42A-iNs system reproduces multiple cellular and molecular features of AD and exhibits strongly consensus with clinical observations. It provides a valuable platform for investigating A[beta];-driven neurodegeneration and for the discovery of therapeutic targets for AD.

neuroscience↗

An accumbal cholinergic ISR-HCN2 axis sustains maladaptive cue motivation

Environmental cues associated with rewarding outcomes can exert persistent control over behavior long after the reward is absent, yet the cellular adaptations that stabilize such maladaptive motivation remain unclear. Here we identify nucleus accumbens cholinergic interneurons (CINs) as causal regulators of persistent cue-driven seeking. Self-administration of addictive drugs or high-fat reward, but not sucrose, selectively increased CIN pacemaker firing. Cocaine-paired cues evoked rapid acetylcholine release in the nucleus accumbens core, and bidirectional manipulation of CINs suppressed or enhanced cue-induced cocaine seeking. Mechanistically, cocaine experience engaged the integrated stress response (ISR) in CINs, increased HCN2 expression and Ih, and stabilized a hyperexcitable state. CIN-specific ISR suppression or HCN2 knockdown reduced cocaine seeking, whereas HCN2 overexpression enhanced it. Local HCN blockade and systemic treatment with the clinically approved HCN blocker ivabradine reduced cue-driven seeking across addictive-drug and high-fat-reward paradigms while sparing sucrose seeking. These findings identify an ISR-HCN2-dependent cholinergic mechanism that stabilizes maladaptive cue-driven motivation and nominate HCN-dependent excitability as a tractable target for therapeutic intervention.

neuroscience↗

Exploring roles for essential proteins in yeast filamentous growth identifies the WASP homolog Las17 as a regulator of the Cdc42-dependent fMAPK pathway

Cell differentiation into distinct cell types generates functional specialization in eukaryotic organisms. Many fungal species undergo filamentous growth, where cells differentiate into elongated and adhesive filaments capable of expansion and invasion into new environments. The budding yeast Saccharomyces cerevisiae also undergoes filamentous growth, and the regulatory pathways that control the response have been well characterized; however, the roles essential proteins play in this response have not been systematically explored. To address this gap in understanding, we constructed a collection of 332 temperature-sensitive (ts) alleles in 320 essential genes in a strain background that undergoes filamentous growth (called {Sigma}1278b or Sigma). The ts Sigma collection showed differences in temperature sensitivity compared to a ts laboratory strain collection, revealing unexpected phenotypic diversity in essential alleles across populations of individuals. Screening the ts Sigma collection for phenotypes related to filamentous growth uncovered new phenotypes for >35% of essential alleles. New regulators of the Mitogen-Activated Protein Kinase (MAPK) pathway that regulates filamentous growth (fMAPK) were identified, including Las17, a homolog of Wiskott-Aldrich Syndrome Protein (WASP) in humans. Las17 regulated the fMAPK pathway by promoting delivery of the sensor protein, Sho1, and Rho GTPase Cdc42 to the plasma membrane. Las17 also functioned as a hub coordinating separate parallel aspects of the filamentation response. The widespread roles for essential proteins in regulating a eukaryotic differentiation response suggest broader roles essential proteins than is currently appreciated.

genetics↗

Density-Yield Relationship of an Invasive Annual Grass inCalifornia Chaparral and Its Restoration Implications

The density-yield relationship of wild plant populations, which in agricultural systems is typically consistent with the constant final yield hypothesis, has remained largely unexplored in a restoration context despite its potential importance. In degraded California shrublands, non-native annual grasses are associated with reduced native shrub recruitment. By studying their density-yield relationship, we can potentially optimize current restoration practices by identifying the level of grass control needed to reduce the competitive pressure they create for woody plants. This in turn could enhance woody plant establishment. Here, we conducted field experiments at a degraded chaparral site in Santa Barbara County, California. We manipulated the density of Bromus diandrus, a dominant invasive annual grass, through 0-100% removal treatments to test its density-yield relationship. Simultaneously, we outplanted Ceanothus megacarpus seedlings as phytometers while also monitoring soil moisture and light availability throughout the growing season. Our results demonstrate that the Bromus diandrus density-yield relationship follows the constant final yield hypothesis, with strong intraspecific competition observed from moderate to high grass density contributing to a constant population biomass and a density dependence allometric exponent comparable to previous agricultural studies. A reduction in grass biomass (yield) was observed at 75% grass removal, yet an increase in resource availability (i.e., light and soil moisture) was only observed at 100% grass removal, as indicated by both direct measurements and enhanced phytometer performance. Applied to restoration in California Chaparral and other Mediterranean ecosystems, where the reduction of "weed" biomass is a common management goal, our findings suggest full removal of non-native annual grasses is likely needed to aid in the restoration of native woody seedlings.

ecology↗

ACAN regulates VSMC mitochondrial homeostasis through the YAP1/TAZ pathway and contributes to the onset and progression of aortic dissection

Objective: Aortic dissection (AD) is a serious, life-threatening cardiovascular crisis. While vascular proteoglycans normally buffer hemodynamic stress, their aberrant accumulation in the AD aorta predisposes to rupture. Aggrecan (ACAN) is among the most frequently elevated proteoglycans in this setting, yet its pathogenic relationship to AD remains undefined. This study aimed to investigate the role of ACAN in AD and identify potential therapeutic targets. Approach and Results: We employed human tissues, animal models, and cell cultures. Western blotting and immunohistochemistry (IHC) were performed on aortic samples from AD patients and controls to assess contractile markers, MMPs, ACAN, and SOX9. In vivo, AD was induced in mice using {beta}-aminoisobutyric acid (BAPN) and Ang II, with AAV-mediated ACAN knockdown in VSMCs. Histological staining confirmed model validity and enabled assessment of AD incidence, mortality, and protein expression changes. In vitro, human aortic vascular smooth muscle cells (HAVSMCs) were stimulated with Ang II, and SOX9 and ACAN were silenced via siRNA to evaluate the functional impact of ACAN downregulation. In human AD aortas, elevated ACAN and its upstream transcription factor SOX9 drove VSMC phenotypic switching and ECM degradation. In mice, ACAN knockdown corrected AD-induced medial structural disruption, elastin fragmentation, and collagen deposition, markedly reducing incidence and mortality while curbing excessive mitochondrial fission and rescuing functional integrity. ACAN silencing attenuated Ang II-induced mitochondrial damage in HAVSMCs via the RNA-seq-identified YAP1/TAZ pathway, and Verteporfin restored homeostasis. Conclusion: ACAN is aberrantly expressed in AD and murine aortas. ACAN downregulation delays disease progression by preserving mitochondrial homeostasis and function via the YAP1/TAZ pathway.

pharmacology and toxicology↗

ProtPipe2: Multi-Platform Downstream Proteomics Analysis Tool

Proteomics studies increasingly use different technologies, including mass spectrometry, the antibody-based Olink platform, and the aptamer-based SomaScan platform. However, downstream analysis often depends on platform-specific scripts or point-and-click tools, limits flexibility and makes analyses difficult to reuse across platforms. We developed ProtPipe2, a downstream proteomics analysis framework for mass spectrometry, Olink, and SomaScan data that is available as both an R package and an interactive web application. ProtPipe2 supports quality control, preprocessing, imputation, batch correction, statistical analysis, visualization, pathway analysis, and result export. ProtPipe2 expands the original Protpipe workflow to additional proteomics platforms and provides reusable R functions for imputation, batch correction, and other downstream analyses. Its redesigned web application provides user-controlled methods and parameters through the same R-based workflow. Case studies across mass spectrometry, Olink, and SomaScan datasets demonstrated a consistent downstream analysis workflow across platforms. By supporting three proteomics platforms through both graphical and scripted workflows, ProtPipe2 makes downstream analysis easier to perform, repeat, and review. ProtPipe2 is freely available at https://nih-card.github.io/ProtPipe2/.

bioinformatics↗

Field-Based Characterization of Temporal Interference Stimulation: Beyond the Target-Centric Perspective

Temporal interference (TI) stimulation is a promising non invasive technique for deep brain neuromodulation, yet accurately characterizing its complex and spatially distributed electric fields remains a fundamental challenge. This study proposes a novel computational framework that integrates topological analysis with mesh-aware metrics to overcome the limitations of conventional global field descriptors. The method introduces three interconnected components: (1) connected component , which extracts spatially continuous field regions above thresholds; (2) discrete ellipsoid modeling, which quantifies the geometric centroid, directional spread, and anisotropy of each region; and (3) Local Moran's I to identify robust spatial clusters while suppressing numerical noise. Applied to TI simulations using individualized tetrahedral head models, the framework demonstrates that the few largest connected components above an elevated intensity threshold collectively provide a more accurate representation of the stimulation targets. The approach provides highly interpretable visual outputs that directly map field topology to brain anatomy, including layered component maps, discrete ellipsoids, and spatially weighted clustering landscapes. By offering a topologically coherent, noise robust, and visually intuitive analytical toolbox, this work advances the precision and interpretability of TI field assessment, supporting more reliable target localization, focality quantification, and parameter exploration in translational neuromodulation research.

neuroscience↗

Phenolic Chemical Defense Contributes to Resistance against Multiple Soybean Cyst Nematode Populations in Wild Soybean

Soybean cyst nematode (Heterodera glycines, SCN) is one of the most damaging pathogens of soybean worldwide. Virulence diversity among SCN populations and reliance on a limited number of resistance sources present major challenges for durable SCN management. Here, we investigated a wild soybean (Glycine soja) genotype (WsR), that is resistant to two SCN populations, race 2 (HG type 1.2.5.7) and race 5 (HG type 2.5.7), and compared its transcriptional and metabolic responses with those of the susceptible genotype (WsS). WsR exhibited a substantially stronger defense-associated transcriptional response to both SCN populations, including preferential induction of genes associated with Ca2+/calmodulin and salicylic acid signaling. Integration of transcriptomic and time-resolved metabolomic analyses revealed convergence on phenolic metabolism, with enhanced accumulation of phenolic acids, flavonoids, and isoflavonoids in resistant WsR. Genes involved in phenolic biosynthesis and modification were also preferentially induced in WsR, linking transcriptional reprogramming with the observed metabolic response. Importantly, two resistance-associated phenolic compounds, 4-hydroxybenzaldehyde and 2,3-dihydroxybenzoic acid, directly increased mortality of SCN second-stage juveniles from both populations in a concentration-dependent manner. By connecting resistance-associated transcriptional responses and metabolic reprogramming with direct activity of specific phenolic compounds against SCN, our study provides functional evidence linking phenolic metabolism to chemical defense against SCN. Together, these findings identify enhanced phenolic chemical defense as a major component of resistance to multiple SCN populations in wild soybean and highlight wild soybean as a valuable source of molecular and biochemical diversity for SCN resistance.

plant biology↗

Seasonal influenza vaccine strain selection by quantifying viral fitness

The long-standing challenge of seasonal influenza vaccines providing poor protection is attributed to the rapid and continuous evolution of the virus. In theory, the recommendation of vaccine strain is to predict the dominant strain with best fitness in the upcoming season. Here, we develop FutureFlu, a biologically-grounded framework that quantifies the fitness score of influenza variants in upcoming seasons, by integrating metrics on three levels: molecular genetic divergence, individual immune escape, and population-scale transmission dynamics. The fitness scores of influenza variants present strong positive correlation with their actual observed frequencies in the next seasons. Validation across 24 seasons of three influenza subtypes shows FutureFlu recommends antigenically matched vaccine strains more frequently than annual recommendations, particularly for challenging subtypes: 83.3% versus 45.8% seasons for A/H3N2, and 75.0% versus 33.3% seasons for B/Victoria. Furthermore, FutureFlu significantly outperforms the currently used methods in vaccine strain selection whether or not there is available antigenic data from hemagglutination inhibition (HI) assay, which provides a valuable supplement for WHO vaccine recommendation. To support global public health implementation, an open online platform (futureflu.com.cn) has been established to offer real-time viral fitness predictions and vaccine recommendations.

evolutionary biology↗

A therapeutic vulnerability linking MNK1/2 inhibition and G1/S cyclin-dependent kinase blockade in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a clinically challenging disease with limited therapeutic options. MAP kinase-interacting kinases 1 and 2 (MNK1/2)-mediated phosphorylation of eukaryotic initiation factor 4E (eIF4E) promotes oncogenic translation and represents a potential therapeutic target. We previously showed that loss of eIF4E phosphorylation suppresses metastasis but not primary tumor growth. Here, an shRNA screen surveying druggable genes unveiled cyclin-dependent kinase 4 (CDK4) as a genetic vulnerability to MNK1/2 inhibition in MDA-MB-231 TNBC cells. Although CDK4/6 inhibitors are approved for hormone receptor (HR)-positive, HER2-negative breast cancer, they are not approved for TNBC. We demonstrate that MNK1/2 inhibition synergizes with CDK4/6 blockade suppresses TNBC cell growth, a synergy also observed in another independent TNBC cell line (SUM159) using a broader G1/S CDK (CDK2/4/6) inhibitor. Integrated RNA sequencing and ribosome profiling revealed combination-specific changes in translation associated with mitotic checkpoint control and DNA repair. Our findings document MNK1/2 inhibition as a strategy to sensitize TNBC to G1/S CDK inhibition and provide a new avenue for therapeutic combination.

cancer biology↗

Intranasal Fritillaria taipaiensis nanovesicles alleviate acute lung injury via the lung-brain axis

Acute lung injury (ALI) is a severe inflammatory syndrome frequently complicated by brain injury, which worsens outcomes. Here, we explored whether regulation of the lung-brain axis represents a therapeutic strategy for the management of ALI. We show that intranasal administration of Fritillaria taipaiensis-derived exosome-like nanovesicles (Ft-ELNs) ameliorates LPS-induced ALI and ALI-associated hypothalamic inflammation in mice. Ft-ELNs preferentially accumulate in the lungs, inhibiting TRPV1 in both the lungs and the hypothalamus to establish the lung-brain axis. In parallel, Ft-ELNs primarily regulate systemic humoral immunity by inhibiting the VEGFR1/PI3K/AKT/NF?B signaling pathway. In vitro experiments further confirmed that Ft-ELNs are efficiently internalized by alveolar epithelial MLE-12 cells, exerting protective effects by suppressing VEGFR1 expression. Mechanistically, we identified Ft-miR1 of Ft-ELNs as the key functional effector that directly targets the Flt1 3'UTR to inhibit VEGFR1 expression. These data suggest that Ft-ELNs attenuate lung-brain axis inflammation by targeting VEGFR1 and may hold potential as novel therapeutic agents for the treatment of severe ALI.

immunology↗

ForceFlowAb: physics-aware mixture-of-experts flow matching model for antibody CDRs design

Abstract Motivation: Antibodies are a major class of therapeutic molecules, and their recognition of target antigens is largely mediated by complementarity-determining regions (CDRs), making antigen-conditioned CDR design a central problem in antibody engineering. Recent generative methods have enabled antigen-conditioned co-design of CDR sequences and structures, but their limited capacity to capture local interface heterogeneity and lack explicit energy-based guidance during sampling, which may result in unfavorable antibody-antigen interaction energies. Overcoming these limitations requires methods that better represent diverse interface environments via adaptive routing and incorporate physical guidance to steer sampling toward energetically favorable conformations. Results: We present ForceFlowAb, a physics-aware mixture-of-experts flow-matching framework for antigen-conditioned CDR sequence-structure co-design. The framework models heterogeneous interface environments through specialized expert routing and applies differentiable force-field guidance during sampling to guide CDR generation toward energetically favorable conformations. For CDR-H3 design, ForceFlowAb achieved more favorable antibody-antigen interaction energies than FlowDesign and Diffab, with improvement rates (IMP) of 46.5% versus 35.0% and 35.5%, respectively. For simultaneous six-CDR design, ForceFlowAb also outperformed Diffab, with IMP values of 16% versus 9%. These results suggest complementary roles for interface-adaptive modeling and energy-based guidance, with the former capturing binding-mode diversity and the latter leveraging physical constraints to ensure biophysical feasibility. Availability and implementation: The web server is freely available at http://zhanglab-bioinf.com/ForceFlowAb. The source code and implementation are available at https://github.com/iobio-zjut/ForceFlowAb. Contact: zgj@zjut.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

bioinformatics↗

Brain deviation scores derived from normative modelling identify dimensions of psychopathology in children and adolescents

Psychiatric diagnoses lack neurobiological grounding, and brain based abnormalities correspond poorly to established diagnostic categories. While multivariate algorithms extract transdiagnostic dimensions from brain behavior covariation, most studies rely on conventional structural morphometric measures such as cortical thickness and surface area, which fail to capture atypical developmental trajectories. Normative modeling (NM) quantifies individual-level brain deviation scores by contrasting brain structural features with population derived percentiles, yielding continuous indices of atypical brain development. However, whether these scores can identify transdiagnostic dimensions remains unclear. We applied sparse canonical correlation analysis (sCCA) to investigate covariation between brain deviation scores and psychiatric symptoms in 1095 children and adolescents from the Healthy Brain Network. We identified an emotion-dysregulation dimension (r=0.33). When generalized to the independent cohort, this dimension showed nominal significance (r=0.18, PFDR = 0.05, uncorrectedP=0.01, n=198). By contrast, no significant dimension was detected using conventional structural brain features. Emotion dysregulation was primarily driven by deviations in cortical thickness and surface area, with distinct regional distributions; reductions in cortical surface area in the middle frontal gyrus, cingulate gyrus, superior temporal gyrus, and insula carried the highest weights. Latent brain scores for emotion dysregulation, computed using sCCA weights for brain deviation scores, showed specific correlations with out-of-model emotion dysregulation related phenotypes. In addition, females exhibited significantly higher latent brain scores than males. Collectively, NM based brain deviation scores were more sensitive than conventional structural metrics in identifying transdiagnostic psychiatric dimensions. Furthermore, latent brain scores derived from sCCA estimated weights may serve as a straightforward metric that captures the degree of dimension-specific atypical brain deviation.

neuroscience↗