Biology subjects

Chai, X. J.

Publications and source records attributed to Chai, X. J..

7 recordsLinked to original sources

bioRxiv2025

Characterising the association between posterior parietal metabolite levels and cortical macrostructure in a cohort spanning childhood to adulthood.

Postnatal brain development is characterised by dynamic macrostructural changes, including cortical thinning and cortical flattening during childhood and adolescence. These macro-structural changes are parallel with developmental changes in brain neurochemistry, probed in the human brain using Magnetic Resonance Spectroscopy (MRS). This includes neurotransmitters such glutamate and gamma-aminobutyric acid (GABA), as well as building blocks of neuronal and associated tissue such as N-acetyl aspartate (NAA), and those involved in metabolism such as creatine (Cr). While previous research has linked MRS-measured neuro-metabolite levels to bulk tissue composition (e.g., gray matter, white matter, and cerebrospinal fluid), the relationship between neurochemistry and more granular macrostructural metrics, such as cortical thickness, area, volume, and local gyrification, remains unexplored. This study investigates the association between MRS-measured neuro-metabolite levels in the posterior parietal cortex (PPC) and PPC-voxel cortical macrostructural metrics in a developmental cohort of 86 individuals aged 5-35 years. We also examine whether PPC metabolite concentrations associate with whole-brain structural metrics to determine whether associations are region-specific or more broadly generalisable. Our findings reveal significant positive associations between PPC cortical thickness, volume, local gyrification index (LGI) and Glx (glutamate + glutamine) levels, likely because differences in cortical microstructure, including dendritic arbour complexity, contributes to variation in both cortical macrostructure and Glx activity across development. Additionally, PPC Glx:GABA+ ratio negatively associated with subcortical gray matter volume, while PPC total NAA positively associated with cerebral white matter volume, suggesting a link between regional neurochemistry and broader brain structure. These results highlight the importance of accounting for macrostructural and broader brain structural characteristics when interpreting the neuroanatomical correlates of MRS-measured metabolites, beyond controlling for bulk tissue composition. This approach is particularly crucial when comparing neuro-metabolite levels across groups with known structural differences, such as developmental cohorts or individuals with neurodevelopmental conditions. Key pointsO_LIPosterior parietal cortex (PPC) Glx levels are positively associated with PPC cortical thickness and local gyrification index, likely because differences in cortical microstructure, including dendritic arbour complexity, contributes to both cortical macrostructure and neuro-metabolic traits across development. C_LIO_LIThe PPC Glx:GABA+ ratio is negatively associated with subcortical gray matter volume, while PPC total NAA is positively associated with cerebral white matter volume, suggesting a link between regional neurochemistry and broader brain structure. C_LIO_LIThese findings emphasise the importance of considering more detailed macrostructural characteristics, as well as bulk tissue composition (white matter, gray matter, cerebral spinal fluid), when interpreting MRS-measured metabolite differences. C_LI

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bioRxiv2024

Developmental changes in hippocampal neurite colocalize with the expression of genes involved in modulating low-theta oscillations

The hippocampus is a critical brain structure supporting memory encoding and retrieval, yet the development of its microstructure in humans remains unknown. Understanding this development may provide insight into the mechanisms underlying memory and their disruption in disease. To address this, we non-invasively estimated the density and branching complexity of neurite (dendrites, axons, glial processes) using diffusion-weighted MRI in 364 participants aged 8-21. With development, we observed large increases in neurite density and branching complexity that persisted until approximately 15 years of age before stabilizing at adult-like values. Increases in neurite density were relatively homogenous across hippocampal axes, whereas increases in branching complexity were heterogeneous; increasing primarily in CA1, SRLM, subiculum, and anterior hippocampus. To assess whether this development may be attributable to specific cell-types, we tested for spatial overlap between age-related change in neurite and the cell-type composition of hippocampal tissue via cross-reference with an out-of-sample gene-expression atlas. We found age-related changes in neurite density spatially overlapped with a granule cell component; whereas age-related changes in neurite branching complexity overlapped with a pyramidal neuron component. These results provide the first glimpse at the nonlinear maturation of hippocampal microstructure and the cell-type composition of hippocampal tissue underlying these changes.

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bioRxiv2024

Contracted Functional Connectivity Profiles in Autism

ObjectiveAutism spectrum disorder (ASD) is a pervasive neurodevelopmental condition that is associated with atypical brain network organization, with prior work suggesting differential connectivity alterations with respect to functional connection length. Here, we tested whether functional connectopathy in ASD specifically relates to disruptions in long-relative to short-range functional connectivity profiles. Our approach combined functional connectomics with geodesic distance mapping, and we studied associations to macroscale networks, microarchitectural patterns, as well as socio-demographic and clinical phenotypes. MethodsWe studied 211 males from three sites of the ABIDE-I dataset comprising 103 participants with an ASD diagnosis (mean{+/-}SD age=20.8{+/-}8.1 years) and 108 neurotypical controls (NT, 19.2{+/-}7.2 years). For each participant, we computed cortex-wide connectivity distance (CD) measures by combining geodesic distance mapping with resting-state functional connectivity profiling. We compared CD between ASD and NT participants using surface-based linear models, and studied associations with age, symptom severity, and intelligence scores. We contextualized CD alterations relative to canonical networks and explored spatial associations with functional and microstructural cortical gradients as well as cytoarchitectonic cortical types. ResultsCompared to NT, ASD participants presented with widespread reductions in CD, generally indicating shorter average connection length and thus suggesting reduced long-range connectivity but increased short-range connections. Peak reductions were localized in transmodal systems (i.e., heteromodal and paralimbic regions in the prefrontal, temporal, and parietal and temporo-parieto-occipital cortex), and effect sizes correlated with the sensory-transmodal gradient of brain function. ASD-related CD reductions appeared consistent across inter-individual differences in age and symptom severity, and we observed a positive correlation of CD to IQ scores. ConclusionsOur study showed reductions in CD as a relatively stable imaging phenotype of ASD that preferentially impacted paralimbic and heteromodal association systems. CD reductions in ASD corroborate previous reports of ASD-related imbalance between short-range overconnectivity and long-range underconnectivity.

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bioRxiv2023

Enhanced efficiency in the bilingual brain through the inter-hemispheric cortico-cerebellar pathway in early second language acquisition

The bilingual experience has a profound impact on the functional and structural organization of the brain, but it is not yet well known how this experience influences whole-brain functional network connectivity. We examined a well-characterized large sample (151 participants) of monolinguals and bilinguals with varied age of second language acquisition, who underwent resting-state functional magnetic brain imaging. We constructed comprehensive functional brain networks for each participant, encompassing cortical, subcortical, and cerebellar regions of interest. Whole-brain analyses revealed that bilingual individuals exhibit higher global efficiency than monolinguals, indicating enhanced functional integration in the brain. Moreover, the age at which the second language was acquired correlated with this increased efficiency, suggesting that earlier exposure to a second language has lasting positive effects on brain functional organization. Further investigation through the network-based statistics (NBS) approach indicates that this effect is primarily driven by heightened functional connectivity between association networks and the cerebellum. This work shows that early learning enhances global whole-brain efficiency and that the timing of learning of two languages has an impact on functional brain organization. Significance statementLong-term learning impacts brain organization at different spatial scales, and this may be particularly enhanced during early stages of life. Bilingualism offers a unique opportunity to test long-term learning effects in the human brain, given that exposure to a second language can occur from birth or later in life, and can be maintained over long periods of time. We found that second language acquisition in early childhood (before five years of age) enhances brain network efficiency, and that this effect goes beyond the language and cognitive control regions, in fact, the interhemispheric cortico-cerebellar circuit plays a key role. This work shows that the timing of bilingual learning experience alters the brain functional organization at the global and local levels.

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bioRxiv2023

The developmental trajectory of 1H-MRS brain metabolites from childhood to adulthood.

Human brain development is ongoing throughout childhood, with for example myelination of nerve fibres and refinement of synaptic connections continuing until early adulthood. 1H-Magnetic Resonance Spectroscopy (1H-MRS) can be used to quantify the concentrations of endogenous metabolites (e.g., glutamate and {gamma}-aminobutyric acid (GABA)) in the human brain in vivo and so can provide valuable, tractable insight into the biochemical processes that support postnatal neurodevelopment. This can feasibly provide new insight into and aid management of neurodevelopmental disorders by providing chemical markers of atypical development. This study aims to characterize the normative developmental trajectory of various brain metabolites, as measured by 1H-MRS from a midline posterior parietal voxel. We find significant non-linear trajectories for GABA+, Glx, tNAA and tCr concentrations. Glx and GABA+ concentrations steeply decrease across childhood. tNAA concentrations are relatively stable in childhood but gradually decrease from early adulthood, while tCr concentrations increase from childhood to early adulthood. tCho was the only metabolite to have a strictly linear association with age. Trajectories likely reflect fundamental neurodevelopmental processes (including local circuit refinement) which occur from childhood to early adulthood and can be associated with cognitive development; we find GABA+ concentrations significantly positively correlate with recognition memory scores across post-natal development.

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bioRxiv2023

Transcriptomic signatures of Abeta- and tau-induced neuronal dysfunction reveal inflammatory processes at the core of Alzheimer's disease pathophysiology

Molecular mechanisms enabling pathology-induced neuronal dysfunction in Alzheimers disease (AD) remain elusive. Here, we use mechanistic computational models to infer the combined influence of PET-measured A{beta} and tau burdens on fMRI-derived neuronal activity and to subsequently identify the transcriptomic spatial correlates of AD pathophysiology. Our results reveal overrepresented genes and biological processes that participate in synaptic degeneration and interact with A{beta} and tau deposits. Furthermore, we confirmed the central role of the immune system and neuroinflammatory pathways within AD pathogenesis; microglia were significantly enriched in the gene set associated with A{beta} and tau synergistic influences on neuronal activity. Lastly, our computational approach unveiled drug candidates with the potential to halt or reduce the observed pathological effects on neuronal activity, including existing medication for cancer, immune disorders, and cardiovascular diseases, many currently under clinical evaluation in AD. Overall, these findings support the notion that the AD brain experiences functional changes intricately associated with a diverse spectrum of molecular processes.

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bioRxiv2023

Predicting depression risk in early adolescence via multimodal brain imaging

Depression is an incapacitating psychiatric disorder with high prevalence in adolescent populations that is influenced by many risk factors, including family history of depression. The ability to predict who may develop depression before adolescence, when rates of depression increase markedly, is important for early intervention and prevention. Using a large longitudinal sample from the Adolescent Brain Cognitive Development (ABCD) Study (2658 participants after imaging quality control, between 9-10 years at baseline), we applied machine learning methods on a set of comprehensive multimodal neuroimaging features to predict depression risk at the two-year follow-up from the baseline visit. Features include derivatives from structural MRI, diffusion tensor imaging, and task and rest functional MRI. A rigorous cross-validation method of leave-one-site-out was used. Additionally, we tested the prediction models in a high-risk group of participants with parental history of depression (N=625). The results showed all brain features had prediction scores significantly better than expected by chance. When predicting depression onset in the high-risk group, brain features from resting-state functional connectomes showed the best classification performance, outperforming other brain features based on structural MRI and task-based fMRI. Results demonstrate that the functional connectivity of the brain can predict the risk of depression in early adolescence better than other univariate neuroimaging derivatives, highlighting the key role of the interacting elements of the connectome capturing more individual variability in psychopathology compared to measures of single brain regions.

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