Biology subjects

Yu, J.-C.

Publications and source records attributed to Yu, J.-C..

6 recordsLinked to original sources

bioRxiv2025

IndivSTATIS: A multivariate approach to analyze brain network configurations with individualized parcellation

A critical step in the analysis of large-scale functional brain networks in neuroimaging is parcellation, which defines the nodes of a brain network. Group or atlas-based parcellation schemes use a shared common space, ensuring that each individual has the same number of brain parcels, which facilitates standard analytic approaches. However, studies reveal individual differences in the boundaries of brain areas. Extracting signals using atlas-based schemes can result in varying levels of blurring of signals across homogeneous areas within a specific individuals brain. Individualized parcellation schemes can be obtained when sufficient data are available; however, these approaches introduce a significant analytical challenge: the number of parcels and networks differ across individuals. Here, we introduce IndivSTATIS, a new multivariate method based on the STATIS framework, designed to integrate individualized parcellation schemes while maintaining comparability across participants in a shared component space. The resulting network/node component scores can be used to predict individual differences measures (e.g., age, behavior). By allowing individualized parcellations to be compared within a common component space, IndivSTATIS provides a solution for incorporating individual network variability into larger studies, with potential to improve the sensitivity and interpretability of functional brain markers across both basic neuroscience and clinical applications.

bioinformatics↗

bioRxiv2025

Transdiagnostic Profiles of BOLD Signal Variability in Autism and Schizophrenia Spectrum Disorders: Associations with Cognition and Functioning

BackgroundAutism spectrum disorder (autism) and schizophrenia spectrum disorders (schizophrenia) exhibit overlapping social and neurocognitive impairment and considerable neurobiological heterogeneity. Blood-oxygen-level-dependent (BOLD) signal variability captures the brains moment-to-moment fluctuations, offering a dynamic marker of neural flexibility that is sensitive to cognitive capacity. This study aimed to examine intra-regional BOLD signal variability during rest and task across schizophrenia, autism, and typically developing controls (TDC) to explore transdiagnostic patterns of brain signal variability and their relationship with cognitive and functional outcomes. MethodsIntra-regional BOLD variability, measured by mean squared successive difference (MSSD), was obtained from resting-state and Empathic Accuracy task fMRI in 176 SSD, 89 autism, and 149 TDC participants. ANCOVAs, controlling for age, sex, and motion, assessed group differences in regional and network-level BOLD variability and dimensional associations with social cognition, neurocognition, social functioning, and symptom severity. ResultsBoth autism and schizophrenia exhibited lower BOLD signal variability than TDC across rest and task, with reduced variability observed in somatomotor, visual, and auditory networks (pFDR < 0.01). Greater network variability was positively associated with better social cognitive, neurocognitive, and functional scores across the sample. Resting-state variability showed stronger group-based differences and cognitive associations than task-based variability. ConclusionsBOLD signal variability is positively associated with social cognition, neurocognition, and social functioning across groups, suggesting that variability impacts cognitive efficiency and behaviour. Reduced variability in autism and schizophrenia may indicate similar patterns of neural rigidity among these related conditions, positioning BOLD variability as a potential biomarker for neural flexibility and a valuable target for future transdiagnostic clinical interventions.

neuroscience↗

bioRxiv2024

Transdiagnostic Neurobiology of Social Cognition and Individual Variability as Measured by Fractional Amplitude of Low-Frequency Fluctuation in Schizophrenia and Autism Spectrum Disorders

Fractional amplitude of low-frequency fluctuation (fALFF) is a validated measure of resting-state spontaneous brain activity. Previous fALFF findings in autism and schizophrenia spectrum disorders (ASDs and SSDs) have been highly heterogeneous. We aimed to use fALFF in a large sample of typically developing control (TDC), ASD and SSD participants to explore group differences and relationships with inter-individual variability of fALFF maps and social cognition. fALFF from 495 participants (185 TDC, 68 ASD, and 242 SSD) was computed using functional magnetic resonance imaging as signal power within two frequency bands (i.e., slow-4 and slow-5), normalized by the power in the remaining frequency spectrum. Permutation analysis of linear models was employed to investigate the relationship of fALFF with diagnostic groups, higher-level social cognition, and lower-level social cognition. Each participants average distance of fALFF map to all others was defined as a variability score, with higher scores indicating less typical maps. Lower fALFF in the visual and higher fALFF in the frontal regions were found in both SSD and ASD participants compared with TDCs. Limited differences were observed between ASD and SSD participants in the cuneus regions only. Associations between slow-4 fALFF and higher-level social cognitive scores across the whole sample were observed in the lateral occipitotemporal and temporoparietal junction. Individual variability within the ASD and SSD groups was also significantly higher compared with TDC. Similar patterns of fALFF and individual variability in ASD and SSD suggest some common neurobiological deficits across these related heterogeneous conditions.

neuroscience↗

bioRxiv2023

Stable, efficient, and typical dynamic functional connectivity patterns relate to higher general intelligence

General intelligence, referred to as g, is hypothesized to emerge from the capacity to dynamically and adaptively reorganize macroscale brain connectivity. Temporal reconfiguration can be assessed using dynamic functional connectivity (dFC), which captures the propensity of brain connectivity to transition between a recurring repertoire of distinct states. Conventional dFC metrics commonly focus on categorical state switching frequencies which do not fully assess individual variation in continuous connectivity reconfiguration. Here, we supplement frequency measures by quantifying within-state connectivity consistency, dissimilarity between connectivity across states, and conformity of individual connectivity to group-average state connectivity. We utilized resting-state fMRI data from the large-scale Human Connectome Project and applied data-driven multivariate Partial Least Squares Correlation to explore emergent associations between dynamic network properties and cognitive ability. Our findings reveal a positive association between g and the stable maintenance of states characterized by distinct connectivity between higher-order networks, efficient reconfiguration (i.e., minimal connectivity changes during transitions between similar states, large connectivity changes between dissimilar states), and ability to sustain connectivity close to group-average state connectivity. This hints at fundamental properties of brain-behavior organization, suggesting that general cognitive processing capacity is supported by the ability to efficiently reconfigure between stable and population-typical connectivity patterns. Impact StatementNovel evidence for an association between the stability, efficiency, and typicality of macro-scale dynamic functional connectivity patterns of the brain and higher general intelligence.

neuroscience↗

bioRxiv2023

Comparing the stability and reproducibility of brain-behaviour relationships found using Canonical Correlation Analysis and Partial Least Squares within the ABCD Sample

IntroductionCanonical Correlation Analysis (CCA) and Partial Least Squares Correlation (PLS) detect associations between two data matrices based on computing a linear combination between the two matrices (called latent variables; LVs). These LVs maximize correlation (CCA) and covariance (PLS). These different maximization criteria may render one approach more stable and reproducible than the other when working with brain and behavioural data at the population-level. This study compared the LVs which emerged from CCA and PLS analyses of brain-behaviour relationships from the Adolescent Brain Cognitive Development (ABCD) dataset and examined their stability and reproducibility. MethodsStructural T1-weighted imaging and behavioural data were accessed from the baseline Adolescent Brain Cognitive Development dataset (N > 9000, ages = 9-11 years). The brain matrix consisted of cortical thickness estimates in different cortical regions. The behavioural matrix consisted of 11 subscale scores from the parent-reported Child Behavioral Checklist (CBCL) or 7 cognitive performance measures from the NIH Toolbox. CCA and PLS models were separately applied to the brain-CBCL analysis and brain-cognition analysis. A permutation test was used to assess whether identified LVs were statistically significant. A series of resampling statistical methods were used to assess stability and reproducibility of the LVs. ResultsWhen examining the relationship between cortical thickness and CBCL scores, the first LV was found to be significant across both CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV1 from the CCA model found that covariation of CBCL scores was linked to covariation of cortical thickness. LV1 from the PLS model identified decreased cortical thickness linked to lower CBCL scores. There was limited evidence of stability or reproducibility of LV1 for both CCA and PLS. When examining the relationship between cortical thickness and cognitive performance, there were 6 significant LVs for both CCA and PLS (p < .01). The first LV showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001). ConclusionCCA and PLS identify different brain-behaviour relationships with limited stability and reproducibility when examining the relationship between cortical thickness and parent-reported behavioural measures. However, both methods identified relatively similar brain-behaviour relationships that were stable and reproducible when examining the relationship between cortical thickness and cognitive performance. The results of the current study suggest that stability and reproducibility of brain-behaviour relationships identified by CCA and PLS are influenced by characteristics of the analyzed sample and the included behavioural measurements when applied to a large pediatric dataset.

neuroscience↗

bioRxiv2019

Effects of a novel three-dimensional grid intrauterine device on the uterus, steroid receptor and PAX2 of rhesus macaques

Intrauterine devices (IUDs) is the most effective methods of the reversible and long-acting contraception. 1) To develop a novel three-dimensional grid intrauterine device (3-DGIUD) with nickel-titanium (Ni-Ti) and silicone rubber. 2) To observe the effect of the 3-DGIUD on contraceptive efficacy and the change of uterus, endometrial sex steroid receptor, PAX2 in rhesus macaques (Macaca mulatta). The materials of the 3-DGIUD were the nitinol wire and the silicone rubber. The frame of the 3-DGIUD was three-dimensional and grid-like. Twenty adult female rhesus macaques were divided into the 3-DGIUD group (placing the 3-DGIUD, n=9), the sham operation group (no placing the 3-DGIUD, n=9) and the control group (n=2). On the 10th-day after surgery, the 3-DGIUD group and the sham operation group macaques were caged together with male macaques (female: male = 1:1). The uterus, 3-DGIUD and pregnancy of 18 female rhesus monkeys were examined by abdominal ultrasound every month. The endometrium pathological examination was carried out and the expression of PAX2 and hormone receptor (ER, PR) was detected by immunohistochemical staining. After 3-DGIUD was placed in case group for 3 and 12 months, only 1 of female macaque was pregnant in 9. The contraceptive effective rate was 88.9% (8/9). The 3-DGIUD in the uterus of macaques was observed by ultrasound. In the sham operation group, 9 macaques were pregnant (9/9). There was significant difference in uterine size of the 3-DGIUD group between pre-placement and after surgery for 3 and 12 months (P<0.05). The endometrial epithelium was intact, just a small number of glands vacuoles and a few neutrophils infiltration around the 3-DGIUD. The expression of endometrial ER, PR and PAX2 in 3-DGIUD group on 12 months after surgery was similar to those in control macaque. The 3-DGIUD has a good contraceptive effect on female macaques, and has no significant affection on the expression of endometrial steroid receptor and PAX2 in rhesus monkeys.

zoology↗