Biology subjects

Shu, N.

Publications and source records attributed to Shu, N..

9 recordsLinked to original sources

bioRxiv2026

Human brain white matter charts in international populations

Brain charts provide normative references for identifying neurodevelopmental milestones and clinically meaningful deviations. Existing white matter (WM) charts are derived predominantly from European and North American populations, limiting generalizability. Here, we established the first set of population-specific WM charts of 198 morphological and microstructural phenotypes for both Chinese (N=19,215) and Western (N=15,584) populations across the human lifespan. Developmental trajectories and spatial organization of WM maturation and degeneration were broadly conserved across the populations, whereas maturational timing varied across pathways and metrics. The Chinese population showed earlier maturation in several projection and sensorimotor pathways, but more protracted maturation in higher-order association and limbic pathways. Chinese population-matched charts more accurately predicted WM phenotypes and detected more extreme disease-related deviations in brain health in the Chinese population than Western-derived models. Individual deviation patterns further discriminated patients from healthy controls and predicted cognitive performance and plasma biomarkers. Our findings reveal population-specific WM maturation and show that reference-population mismatch can reduce the accuracy and clinical sensitivity of normative brain charts.

neuroscience↗

bioRxiv2025

Shared genomic architecture of the brain white matter structural connectome and intelligence

White matter (WM) connections, which facilitate communication between brain regions, substantially impact intellectual performance. However, the shared genetic underpinnings of WM connections and intelligence remain unclear. In the present study, we first conducted genome-wide association studies on the global and regional topological properties of WM connectomes constructed from diffusion-weighted and T1-weighted magnetic resonance images of 26,655 participants from the UK Biobank. Forty-one independent significant single-nucleotide polymorphisms (SNPs) associated with global WM connectome efficiency and 45 SNPs linked to nodal efficiency for 246 brain regions were identified. Then, regional heterogeneity was demonstrated for the genetic correlations between WM connectome efficiency and intelligence. The nodal efficiency of 128 regions with nominally significant genetic correlations with intelligence was determined. Among these, 44 regions and 3 regions shared SNPs with intelligence within the chromosome 6q21 and 3p21.1 locus respectively, and significant causal effects on intelligence were found for 63 regions, mainly in the orbital gyrus and superior frontal gyrus. Finally, we found that the integration of polygenetic score and WM connectome efficiency data was more accurate for predicting individual intelligence than the use of a single data type. These findings improve our understanding of how genes shape the individual WM connectome and intelligence and the relationship between WM connectomes and intelligence.

genomics↗

bioRxiv2023

Spatial and temporal pattern of structure-function coupling of human brain connectome with development

Brain structural circuitry shapes a richly patterned functional synchronization, supporting for complex cognitive and behavioural abilities. However, how coupling of structural connectome (SC) and functional connectome (FC) develops and its relationships with cognitive functions and transcriptomic architecture remain unclear. We used multimodal magnetic resonance imaging data from 439 participants aged 5.7 to 21.9 years to predict functional connectivity by incorporating intracortical and extracortical structural connectivity, characterizing SC-FC coupling. Our findings revealed that SC-FC coupling was strongest in the visual and somatomotor networks, consistent with evolutionary expansion, myelin content, and functional principal gradient. As development progressed, SC-FC coupling exhibited heterogeneous alterations dominated by an increase in cortical regions, broadly distributed across the somatomotor, frontoparietal, dorsal attention, and default mode networks. Moreover, we discovered that SC-FC coupling significantly predicted individual variability in general intelligence, mainly influencing frontoparietal and default mode networks. Finally, our results demonstrated that the heterogeneous development of SC-FC coupling is positively associated with genes in oligodendrocyte-related pathways and negatively associated with astrocyte-related genes. This study offers insight into the maturational principles of SC-FC coupling in typical development.

neuroscience↗

bioRxiv2023

Individual Variability in Structural Connectivity Architecture of the Human Brain across the Middle-aged and Old Adult

The human brain shows higher variability in functional connectivity in the heteromodal association cortex but lower variability in the unimodal cortex. As the anatomical substrate of functional connectivity, the temporal-spatial pattern of individual variability in structural connectivity remains largely unknown. In the present study, we depicted the temporal-spatial pattern of individual variability in structural connectivity, which is highest in the limbic regions and lowest in the unimodal sensorimotor regions. With increasing age, the variability in structural connectivity increased. Our results demonstrated that this specific spatial distribution relates to the plasticity of synapses and white matter. We proposed a modified ridge regression model to predict cognition individually and generate idiographic brain mapping. Individual variability in structural connectivity is significantly correlated with idiographic brain mapping. Overall, our study has potential implications for understanding biological and genetic mechanisms of variability in structural connectivity, guiding interventions to promote successful cognitive ageing and interpreting statistical maps in the human connectome.

neuroscience↗

bioRxiv2020

The evolutionary history of topological variations in the CPA/AT superfamily

CPA/AT transporters consist of two structurally and evolutionarily related inverted repeat units, each of them with one core and one scaffold subdomain. During evolution, these families have undergone substantial changes in structure, topology and function. Central to the function of the transporters is the existence of two non-canonical helices that are involved in the transport process. In different families, two different types of these helices have been identified, reentrant and broken. Here, we use an integrated topology annotation method to identify novel topologies in the families. It combines topology prediction, similarity to families with known structure, and the difference in positively charged residues present in inside and outside loops in alternative topological models. We identified families with diverse topologies containing broken or reentrant helix. We classified all families based on 3 distinct evolutionary groups that each share a structurally similar C-terminal repeat unit newly termed as "Fold-types". Using the evolutionary relationship between families we propose topological transitions including, a transition between broken and reentrant helices, complete change of orientation, changes in the number of scaffold helices and even in some rare cases, losses of core helices. The evolutionary history of the repeat units shows gene duplication and repeat shuffling events to result in these extensive topology variations. The novel structure-based classification, together with supporting structural models and other information, is presented in a searchable database, CPAfold (cpafold.bioinfo.se). Our comprehensive study of topology variations within the CPA superfamily provides better insight about their structure and evolution.

bioinformatics↗

bioRxiv2020

DCP: a pipeline toolbox for diffusion connectome

The brain structure network constructed by diffusion tensor imaging (DTI) reflects the anatomical connections between brain regions, so the brain structure network can quantitatively describes the anatomical connectivity pattern of the entire brain. The structure network based diffusion tensor imaging is widely used in scientific research. While a number of post processing packages have been developed, fully automated processing of DTI datasets on Windows Operating System remains challenging. Here, we developed a MATLAB toolbox named "Diffusion Connectome Pipeline" (DCP) for fully automated constructing brain structure network. The processing modules of a few developed packages, including Diffusion Toolkit, DiffusionKit, SPM and MRIcron, were employed in DCP. Using any number of raw DTI datasets from different subjects, in either DICOM or NIfTI format, DCP can automatically perform a series of steps to construct network. In addition, DCP has a friendly graphical user interface (GUI) running on the Windows Operating System, allowing the user to be interactive and to adjust the input/output settings, as well as the processing parameters. As an open-source package, DCP is freely available at https://www.nitrc.org/projects/dcp. This novel toolbox is expected to substantially simplify the image processing of DTI datasets and facilitate human brain structural connectome studies.

neuroscience↗

bioRxiv2020

The white matter network in cognitive normal elderly predict the rate of cognitive decline

White matter degradation has been proposed as one possible explanation for age-related cognitive decline. The human brain is, however, a network and it may be more appropriate to relate cognitive functions to properties of the network rather than specific brain regions. Cognitive domains were measured annually (mean follow-up = 1.25 {+/-} 0.61 years), including processing speed, memory, language, visuospatial, and executive functions. Diffusion tensor imaging was performed at baseline in 90 clinically normal older adults (aged 54-86). We report on graph theory-based analyses of diffusion tensor imaging tract-derived connectivity. The machine learning approach was used to predict the rate of cognitive decline from white matter connectivity data. The reduced efficacy of white matter networks could predict the performance of these cognitive domains except memory. The predicted scores were significantly correlated with the real scores. For the local regions for predicting the cognitive changes, the right precuneus, left inferior parietal lobe and cuneus are the most important regions for predicting monthly change of executive function; some left partial and occipital regions are the most important for the changed of attention; the right frontal and temporal regions are the most important for the changed of language. Our findings suggested that the global white matter connectivity characteristics are the valuable predictive index for the longitudinal cognitive decline. For the first time, topological efficiency of white matter connectivity maps which related to special domains of cognitive decline in the elderly are identified.

neuroscience↗

bioRxiv2018

Improved protein model quality assessments by changing the target function

Protein modeling quality is an important part of protein structure prediction. We have for more than a decade developed a set of methods for this problem. We have used various types of description of the protein and different machine learning methodologies. However, common to all these methods has been the target function used for training. The target function in ProQ describes the local quality of a residue in a protein model. In all versions of ProQ the target function has been the S-score. However, other quality estimation functions also exist, which can be divided into superposition- and contact-based methods. The superposition-based methods, such as S-score, are based on a rigid body superposition of a protein model and the native structure, while the contact-based methods compare the local environment of each residue. Here, we examine the effects of retraining our latest predictor, ProQ3D, using identical inputs but different target functions. We find that the c ntact-based methods are easier to predict and that predictors trained on these measures provide some advantages when it comes to identifying the best model. One possible reason for this is that contact based methods are better at estimating the quality of multi-domain targets. However, training on the S-score gives the best correlation with the GDT_TS score, which is commonly used in CASP to score the global model quality. To take the advantage of both of these features we provide an updated version of ProQ3D that predicts local and global model quality estimates based on different quality estimates.

bioinformatics↗

bioRxiv2017

Structural network maturation of the preterm human brain

During the 3rd trimester, large-scale of neural circuits are formed in the human brain, resulting in the adult-like brain networks at birth. However, how the brain circuits develop into a highly efficient and segregated connectome during this period is unknown. We hypothesized that faster increases of connectivity efficiency and strength at the brain hubs and rich-club are critical for emergence of an efficient and segregated brain connectome. Here, using high resolution diffusion MRI of 77 preterm-born and term-born neonates scanned at 31-42 postmenstrual weeks (PMW), we constructed the structural connectivity matrices and performed graph-theory-based analyses. We found faster increases of nodal efficiency mainly at the brain hubs, distributed in primary sensorimotor regions, superior-middle frontal and posterior cingulate gyrus during 31-42PMW. The rich-club and within-module connections were characterized by higher rates of edge strength increases. Edge strength of short-range connections increased faster than that of long-range connections. The nodal efficiencies of the hubs predicted individual postmenstrual ages more accurately than those of non-hubs. Collectively, these findings revealed regionally differentiated maturation in the baby brain structural connectome and more rapid increases of the hub and rich-club connections, which underlie network segregation and differentiated brain function emergence.

neuroscience↗