MRI morphometry of the anterior and posterior cerebellar vermis and its relationship to sensorimotor and cognitive functions in children.

INTRODUCTION: The human cerebellum emerges as a posterior brain structure integrating neural networks for sensorimotor, cognitive, and emotional processing across the lifespan. Developmental studies of the cerebellar anatomy and function are scant. We examine age-dependent MRI morphometry of the anterior cerebellar vermis, lobules I-V and posterior neocortical lobules VI-VII and their relationship to sensorimotor and cognitive functions. METHODS: Typically developing children (TDC; n=38; age 9-15) and healthy adults (HAC; n=31; 18-40) participated in high-resolution MRI. Rigorous anatomically informed morphometry of the vermis lobules I-V and VI-VII and total brain volume (TBV) employed manual segmentation computer-assisted FreeSurfer Image Analysis Program [http://surfer.nmr.mgh.harvard.edu]. The neuropsychological scores (WASI-II) were normalized and related to volumes of anterior, posterior vermis, and TBV. RESULTS: TBVs were age independent. Volumes of I-V and VI-VII were significantly reduced in TDC. The ratio of VI-VII to I-V (∼60%) was stable across age-groups; I-V correlated with visual-spatial-motor skills; VI-VII with verbal, visual-abstract and FSIQ. CONCLUSIONS: In TDC neither anterior I-V nor posterior VI-VII vermis attained adult volumes. The "inverted U" developmental trajectory of gray matter peaking in adolescence does not explain this finding. The hypothesis of protracted development of oligodendrocyte/myelination is suggested as a contributor to TDC's lower cerebellar vermis volumes.

Anterior pituitary gland volume mediates associations between pubertal hormones and changes in transdiagnostic symptoms in youth.

The pituitary gland (PG) plays a central role in the production and secretion of pubertal hormones, with documented links to the emergence and increase in mental health symptoms known to occur during adolescence. Although much of the literature has focused on examining whole PG volume, recent findings suggest that there are associations among pubertal hormone levels, including dehydroepiandrosterone (DHEA), subregions of the PG, and elevated mental health symptoms (e.g., internalizing symptoms) during adolescence. Surprisingly, studies have not yet examined associations among these factors and increasing transdiagnostic symptomology, despite DHEA being a primary output of the anterior PG. Therefore, the current study sought to fill this gap by examining whether anterior PG volume specifically mediates associations between DHEA levels and changes in dysregulation symptoms in an adolescent sample ( N = 114, 9 - 17 years, M age = 12.87, SD = 1.88). Following manual tracing of the anterior and posterior PG, structural equation modeling revealed that greater anterior, not posterior, PG volume mediated the association between greater DHEA levels and increasing dysregulation symptoms across time, controlling for baseline dysregulation symptom levels. These results suggest specificity in the role of the anterior PG in adrenarcheal processes that may confer risk for psychopathology during adolescence. This work not only highlights the importance of separately tracing the anterior and posterior PG, but also suggests that transdiagnostic factors like dysregulation are useful in parsing hormone-related increases in mental health symptoms in youth.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds.

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevel-opmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients.

Aims: The population pharmacokinetic (PPK) model-based machine learning (ML) approach offers a novel perspective on individual concentration prediction. This study aimed to establish a PPK-based ML model for predicting tacrolimus (TAC) concentrations in Chinese renal transplant recipients. Methods: Conventional TAC monitoring data from 127 Chinese renal transplant patients were divided into training (80%) and testing (20%) datasets. A PPK model was developed using the training group data. ML models were then established based on individual pharmacokinetic data derived from the PPK basic model. The prediction performances of the PPK-based ML model and Bayesian forecasting approach were compared using data from the test group. Results: The final PPK model, incorporating hematocrit and CYP3A5 genotypes as covariates, was successfully established. Individual predictions of TAC using the PPK basic model, postoperative date, CYP3A5 genotype, and hematocrit showed improved rankings in ML model construction. XGBoost, based on the TAC PPK, exhibited the best prediction performance. Conclusion: The PPK-based machine learning approach emerges as a superior option for predicting TAC concentrations in Chinese renal transplant recipients.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds.

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

Developmentally sensitive multispectral cortical connectivity profiles serving visual selective attention.

Throughout childhood and adolescence, the brain undergoes significant structural and functional changes that contribute to the maturation of multiple cognitive domains, including selective attention. Selective attention is crucial for healthy executive functioning and while key brain regions serving selective attention have been identified, their age-related changes in neural oscillatory dynamics and connectivity remain largely unknown. We examined the developmental sensitivity of selective attention circuitry in 91 typically developing youth aged 6 - 13 years old. Participants completed a number-based Simon task while undergoing magnetoencephalography (MEG) and the resulting data were preprocessed and transformed into the time-frequency domain. Significant oscillatory brain responses were imaged using a beamforming approach, and task-related peak voxels in the occipital, parietal, and cerebellar cortices were used as seeds for subsequent whole-brain connectivity analyses in the alpha and gamma range. Our key findings revealed developmentally sensitive connectivity profiles in multiple regions crucial for selective attention, including the temporoparietal junction (alpha) and prefrontal cortex (gamma). Overall, these findings suggest that brain regions serving selective attention are highly sensitive to developmental changes during the pubertal transition period.

PCKRF: Point Cloud Completion and Keypoint Refinement With Fusion Data for 6D Pose Estimation.

Some robust point cloud registration approaches with controllable pose refinement magnitude, such as ICP and its variants, are commonly used to improve 6D pose estimation accuracy. However, the effectiveness of these methods gradually diminishes with the advancement of deep learning techniques and the enhancement of initial pose accuracy, primarily due to their lack of specific design for pose refinement. In this paper, we propose Point Cloud Completion and Keypoint Refinement with Fusion Data (PCKRF), a new pose refinement pipeline for 6D pose estimation. The pipeline consists of two steps. First, it completes the input point clouds via a novel pose-sensitive point completion network. The network uses both local and global features with pose information during point completion. Then, it registers the completed object point cloud with the corresponding target point cloud by our proposed Color supported Iterative KeyPoint (CIKP) method. The CIKP method introduces color information into registration and registers a point cloud around each keypoint to increase stability. The PCKRF pipeline can be integrated with existing popular 6D pose estimation methods, such as the full flow bidirectional fusion network, to further improve their pose estimation accuracy. Experiments demonstrate that our method exhibits superior stability compared to existing approaches when optimizing initial poses with relatively high precision. Notably, the results indicate that our method effectively complements most existing pose estimation techniques, leading to improved performance in most cases. Furthermore, our method achieves promising results even in challenging scenarios involving textureless and symmetrical objects. Our source code is available at https://github.com/zhanhz/KRF.

Somatomotor-visual resting state functional connectivity increases after 2 years in the UK Biobank longitudinal cohort.

Purpose: Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, in which high connectivity among all brain regions changes to a more modular structure with maturation. We examine FC changes in older adults after 2 years of aging in the UK Biobank (UKB) longitudinal cohort. Approach: We process fMRI connectivity data using the Power264 atlas and then test whether the average internetwork FC changes in the 2722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t-test. We also compare the ability of Power264 and UKB-provided, independent component analysis (ICA)-based FC to determine which of a longitudinal scan pair is older. Finally, we investigate cross-sectional FC changes as well as differences due to differing scanner tasks in the UKB, Philadelphia Neurodevelopmental Cohort, and Alzheimer's Disease Neuroimaging Initiative datasets. Results: We find a 6.8% average increase in somatomotor network (SMT)-visual network (VIS) connectivity from younger to older scans (corrected p<10-15) that occurs in male, female, older subject (>65 years old), and younger subject (<55 years old) groups. Among all internetwork connections, the average SMT-VIS connectivity is the best predictor of relative scan age. Using the full FC and a training set of 2000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions: We conclude that SMT-VIS connectivity increases with age in the UKB longitudinal cohort and that resting state FC increases with age in the UKB cross-sectional cohort.

Multiview hyperedge-aware hypergraph embedding learning for multisite, multiatlas fMRI based functional connectivity network analysis.

Recently, functional magnetic resonance imaging (fMRI) based functional connectivity network (FCN) analysis via graph convolutional networks (GCNs) has shown promise for automated diagnosis of brain diseases by regarding the FCNs as irregular graph-structured data. However, multiview information and site influences of the FCNs in a multisite, multiatlas fMRI scenario have been understudied. In this paper, we propose a Class-consistency and Site-independence Multiview Hyperedge-Aware HyperGraph Embedding Learning (CcSi-MHAHGEL) framework to integrate FCNs constructed on multiple brain atlases in a multisite fMRI study. Specifically, for each subject, we first model brain network as a hypergraph for every brain atlas to characterize high-order relations among multiple vertexes, and then introduce a multiview hyperedge-aware hypergraph convolutional network (HGCN) to extract a multiatlas-based FCN embedding where hyperedge weights are adaptively learned rather than employing the fixed weights precalculated in traditional HGCNs. In addition, we formulate two modules to jointly learn the multiatlas-based FCN embeddings by considering the between-subject associations across classes and sites, respectively, i.e., a class-consistency module to encourage both compactness within every class and separation between classes for promoting discrimination in the embedding space, and a site-independence module to minimize the site dependence of the embeddings for mitigating undesired site influences due to differences in scanning platforms and/or protocols at multiple sites. Finally, the multiatlas-based FCN embeddings are fed into a few fully connected layers followed by the soft-max classifier for diagnosis decision. Extensive experiments on the ABIDE demonstrate the effectiveness of our method for autism spectrum disorder (ASD) identification. Furthermore, our method is interpretable by revealing ASD-relevant brain regions that are biologically significant.

Vertical migration behavior simulation and prediction of Pb and Cd in co-contaminated soil around Pb-Zn smelting slag site.

Heavy metal migration in soil poses a serious threat to the soil and groundwater. Understanding the migration pattern of heavy metals (HMs) under different factors could provide a more reasonable position for pollution evaluation and targetoriented treatment of soil heavy metal. In this study, the migration behavior of Pb and Cd in co-contaminated soil under different pH and ionic strength (NaCl concentration) was simulated using convective dispersion equation (CDE). We predicted the migration trends of Pb and Cd in soils after 5, 10, and 20 years via PHREEQC. The results showed that the migration time of Cd in the soil column experiment was about 60 days faster than that of Pb, and the migration trend was much steeper. The CDE was proved to describe the migration behavior of Pb and Cd (R2 > 0.75) in soil. The predicted results showed that Cd migrated to 15-20 cm of soil within 7 years and Pb stayed mainly in the top 0-6 cm of soil within 5 years as the duration of irrigation increased. Overall, our study is expected to provide new insight into the migration of heavy metal in soil ecosystems and guidance for reducing risk of heavy metal in the environment.

sh- Ambra1 inhibits IRS-1/PI3K/Akt signalling pathway to reduce autophagy in gestational diabetes.

INTRODUCTION: Gestational diabetes mellitus (GDM) is the most common metabolic disease in pregnancy. However, studies of activating molecule of Beclin1-regulated autophagy (Ambra1) affecting the insulin substrate receptor 1/phosphatidylinositol 3 kinase/protein kinase B (IRS-1/PI3K/Akt) signalling pathway in GDM have not been reported. The aim of the study was to detect the difference of Ambra1 expression in the placenta of normal pregnant women and GDM patients. MATERIAL AND METHODS: An in vitro model of gestational diabetes mellitus was established by inducing HTR8/Svneo cells from human chorionic trophoblast layer with high glucose. The changes of cell morphology were observed by inverted microscope, and the expression levels of Ambra1 gene and protein in model cells were detected. After this, Ambra1 gene was silenced by shRNA transfection, and PI3K inhibitor was added to detect changes in Ambra1, autophagy, and insulin (INS) signalling pathways. RESULTS: The protein expression levels of Ambra1, Bcl-2 interacting protein (Beclin-1), and microtubule-associated proteins 1A/1B light chain 3B (LC3-II) in the placentas of GDM pregnant women were higher than those of normal pregnant women. High glucose induces morphological changes in HTR8/Svneo cells and increases Ambra1 transcription and translation levels. sh-Ambra1 increased survival of HTR8/SvNEO-HG cells and inhibited Ambra1, Beclin1, and LC3-II transcription and translation levels. Also, sh-Ambra1 increased IRS-1/PI3K/Akt protein phosphorylation levels and inhibited the IRS-1/PI3K/Akt signalling pathway and its resulting autophagy. CONCLUSIONS: sh-Ambra1 increased IRS-1/PI3K/Akt protein phosphorylation levels to reduce autophagy in gestational diabetes.

Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies.

Functional connectivity (FC) as derived from fMRI has emerged as a pivotal tool in elucidating the intricacies of various psychiatric disorders and delineating the neural pathways that underpin cognitive and behavioral dynamics inherent to the human brain. While Graph Neural Networks (GNNs) offer a structured approach to represent neuroimaging data, they are limited by their need for a predefined graph structure to depict associations between brain regions, a detail not solely provided by FCs. To bridge this gap, we introduce the Gated Graph Transformer (GGT) framework, designed to predict cognitive metrics based on FCs. Empirical validation on the Philadelphia Neurodevelopmental Cohort (PNC) underscores the superior predictive prowess of our model, further accentuating its potential in identifying pivotal neural connectivities that correlate with human cognitive processes.

Better with age: Developmental changes in oscillatory activity during verbal working memory encoding and maintenance.

Numerous investigations have characterized the oscillatory dynamics serving working memory in adults, but few have probed its relationship with chronological age in developing youth. We recorded magnetoencephalography during a modified Sternberg verbal working memory task in 82 youth participants aged 6-14 years old. Significant oscillatory responses were identified and imaged using a beamforming approach and the resulting whole-brain maps were probed for developmental effects during the encoding and maintenance phases. Our results indicated robust oscillatory responses in the theta (4-7 Hz) and alpha (8-14 Hz) range, with older participants exhibiting stronger alpha oscillations in left-hemispheric language regions. Older participants also had greater occipital theta power during encoding. Interestingly, there were sex-by-age interaction effects in cerebellar cortices during encoding and in the right superior temporal region during maintenance. These results extend the existing literature on working memory development by showing strong associations between age and oscillatory dynamics across a distributed network. To our knowledge, these findings are the first to link chronological age to alpha and theta oscillatory responses serving working memory encoding and maintenance, both across and between male and female youth; they reveal robust developmental effects in crucial brain regions serving higher order functions.

Learning Spatiotemporal Brain Dynamics in Adolescents via Multimodal MEG and fMRI Data Fusion Using Joint Tensor/Matrix Decomposition.

OBJECTIVE: Brain function is understood to be regulated by complex spatiotemporal dynamics, and can be characterized by a combination of observed brain response patterns in time and space. Magnetoencephalography (MEG), with its high temporal resolution, and functional magnetic resonance imaging (fMRI), with its high spatial resolution, are complementary imaging techniques with great potential to reveal information about spatiotemporal brain dynamics. Hence, the complementary nature of these imaging techniques holds much promise to study brain function in time and space, especially when the two data types are allowed to fully interact. METHODS: We employed coupled tensor/matrix factorization (CMTF) to extract joint latent components in the form of unique spatiotemporal brain patterns that can be used to study brain development and function on a millisecond scale. RESULTS: Using the CMTF model, we extracted distinct brain patterns that revealed fine-grained spatiotemporal brain dynamics and typical sensory processing pathways informative of high-level cognitive functions in healthy adolescents. The components extracted from multimodal tensor fusion possessed better discriminative ability between high- and low-performance subjects than single-modality data-driven models. CONCLUSION: Multimodal tensor fusion successfully identified spatiotemporal brain dynamics of brain function and produced unique components with high discriminatory power. SIGNIFICANCE: The CMTF model is a promising tool for high-order, multimodal data fusion that exploits the functional resolution of MEG and fMRI, and provides a comprehensive picture of the developing brain in time and space.

Theta oscillatory dynamics serving cognitive control index psychosocial distress in youth.

Background: Psychosocial distress among youth is a major public health issue characterized by disruptions in cognitive control processing. Using the National Institute of Mental Health's Research Domain Criteria (RDoC) framework, we quantified multidimensional neural oscillatory markers of psychosocial distress serving cognitive control in youth. Methods: The sample consisted of 39 peri-adolescent participants who completed the NIH Toolbox Emotion Battery (NIHTB-EB) and the Eriksen flanker task during magnetoencephalography (MEG). A psychosocial distress index was computed with exploratory factor analysis using assessments from the NIHTB-EB. MEG data were analyzed in the time-frequency domain and peak voxels from oscillatory maps depicting the neural cognitive interference effect were extracted for voxel time series analyses to identify spontaneous and oscillatory aberrations in dynamics serving cognitive control as a function of psychosocial distress. Further, we quantified the relationship between psychosocial distress and dynamic functional connectivity between regions supporting cognitive control. Results: The continuous psychosocial distress index was strongly associated with validated measures of pediatric psychopathology. Theta-band neural cognitive interference was identified in the left dorsolateral prefrontal cortex (dlPFC) and middle cingulate cortex (MCC). Time series analyses of these regions indicated that greater psychosocial distress was associated with elevated spontaneous activity in both the dlPFC and MCC and blunted theta oscillations in the MCC. Finally, we found that stronger phase coherence between the dlPFC and MCC was associated with greater psychosocial distress. Conclusions: Greater psychosocial distress was marked by alterations in spontaneous and oscillatory theta activity serving cognitive control, along with hyperconnectivity between the dlPFC and MCC.

Autosurv: interpretable deep learning framework for cancer survival analysis incorporating clinical and multi-omics data.

Accurate prognosis for cancer patients can provide critical information for optimizing treatment plans and improving life quality. Combining omics data and demographic/clinical information can offer a more comprehensive view of cancer prognosis than using omics or clinical data alone and can also reveal the underlying disease mechanisms at the molecular level. In this study, we developed and validated a deep learning framework to extract information from high-dimensional gene expression and miRNA expression data and conduct prognosis prediction for breast cancer and ovarian-cancer patients using multiple independent multi-omics datasets. Our model achieved significantly better prognosis prediction than the current machine learning and deep learning approaches in various settings. Moreover, an interpretation method was applied to tackle the "black-box" nature of deep neural networks and we identified features (i.e., genes, miRNA, demographic/clinical variables) that were important to distinguish predicted high- and low-risk patients. The significance of the identified features was partially supported by previous studies.

Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks.

Graph convolutional deep learning has emerged as a promising method to explore the functional organization of the human brain in neuroscience research. This paper presents a novel framework that utilizes the gated graph transformer (GGT) model to predict individuals' cognitive ability based on functional connectivity (FC) derived from fMRI. Our framework incorporates prior spatial knowledge and uses a random-walk diffusion strategy that captures the intricate structural and functional relationships between different brain regions. Specifically, our approach employs learnable structural and positional encodings (LSPE) in conjunction with a gating mechanism to efficiently disentangle the learning of positional encoding (PE) and graph embeddings. Additionally, we utilize the attention mechanism to derive multi-view node feature embeddings and dynamically distribute propagation weights between each node and its neighbors, which facilitates the identification of significant biomarkers from functional brain networks and thus enhances the interpretability of the findings. To evaluate our proposed model in cognitive ability prediction, we conduct experiments on two large-scale brain imaging datasets: the Philadelphia Neurodevelopmental Cohort (PNC) and the Human Connectome Project (HCP). The results show that our approach not only outperforms existing methods in prediction accuracy but also provides superior explainability, which can be used to identify important FCs underlying cognitive behaviors.

Re-examination of the risk of dementia after dengue virus infection: A population-based cohort study.

Dengue infection can affect the central nervous system and cause various neurological complications. Previous studies also suggest dengue was associated with a significantly increased long-term risk of dementia. A population-based cohort study was conducted using national health databases in Taiwan and included 37,928 laboratory-confirmed dengue patients aged ≥ 45 years between 2002 and 2015, along with 151,712 matched nondengue individuals. Subdistribution hazard regression models showed a slightly increased risk of Alzheimer's disease, and unspecified dementia, non-vascular dementia, and overall dementia in dengue patients than the nondengue group, adjusted for age, sex, area of residence, urbanization level, income, comorbidities, and all-cause clinical visits within one year before the index date. After considering multiple comparisons using Bonferroni correction, only overall dementia and non-vascular dementia remained statistically significant (adjusted SHR 1.13, 95% CI 1.05-1.21, p = 0.0009; E-value 1.51, 95% CI 1.28-NA). Sensitivity analyses in which dementia cases occurring in the first three or five years after the index dates were excluded revealed no association between dengue and dementia. In conclusion, this study found dengue patients had a slightly increased risk of non-vascular dementia and total dementia than those without dengue. However, the small corresponding E-values and sensitivity analyses suggest the association between dengue and dementia may not be causal.