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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

ImageNomer: Description of a functional connectivity and omics analysis tool and case study identifying a race confound.

Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. FC in particular usually contains tens of thousands of features per subject, and can only be summarized and efficiently explored using visualizations. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and single nucleotide polymorphism (SNP) data of healthy adolescents. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer to visualize trends in achievement scores between races, we find a clear potential for confounding effects if race can be predicted using FC. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10-15% of WRAT score variation, this predictive ability disappears when controlling for race. We also use ImageNomer to investigate race-FC correlation in the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP) dataset. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.

Developmental changes in endogenous testosterone have sexually-dimorphic effects on spontaneous cortical dynamics.

The transition from childhood to adolescence is associated with an influx of sex hormones, which not only facilitates physical and behavioral changes, but also dramatic changes in neural circuitry. While previous work has shown that pubertal hormones modulate structural and functional brain development, few of these studies have focused on the impact that such hormones have on spontaneous cortical activity, and whether these effects are modulated by sex during this critical developmental window. Herein, we examined the effect of endogenous testosterone on spontaneous cortical activity in 71 typically-developing youth (ages 10-17 years; 32 male). Participants completed a resting-state magnetoencephalographic (MEG) recording, structural MRI, and provided a saliva sample for hormone analysis. MEG data were source-reconstructed and the power within five canonical frequency bands (delta, theta, alpha, beta, and gamma) was computed. The resulting power spectral density maps were analyzed via vertex-wise ANCOVAs to identify spatially specific effects of testosterone and sex by testosterone interactions, while covarying out age. We found robust sex differences in the modulatory effects of testosterone on spontaneous delta, beta, and gamma activity. These interactions were largely confined to frontal cortices and exhibited a stark switch in the directionality of the correlation from the low (delta) to high frequencies (beta/gamma). For example, in the delta band, greater testosterone related to lower relative power in prefrontal cortices in boys, while the reverse pattern was found for girls. These data suggest testosterone levels are uniquely related to the development of spontaneous cortical dynamics during adolescence, and such levels are associated with different developmental patterns in males and females within regions implicated in executive functioning.

Dehydroepiandrosterone mediates associations between trauma-related symptoms and anterior pituitary volume in children and adolescents.

INTRODUCTION: The anterior pituitary gland (PG) is a potential locus of hypothalamic-pituitary-adrenal (HPA) axis responsivity to early life stress, with documented associations between dehydroepiandrosterone (DHEA) levels and anterior PG volumes. In adults, elevated anxiety/depressive symptoms are related to diminished DHEA levels, and studies have shown a positive relationship between DHEA and anterior pituitary volumes. However, specific links between responses to stress, DHEA levels, and anterior pituitary volume have not been established in developmental samples. METHODS: High-resolution T1-weighted MRI scans were collected from 137 healthy youth (9-17 years; Mage = 12.99 (SD = 1.87); 49% female; 85% White, 4% Indigenous, 1% Asian, 4% Black, 4% multiracial, 2% not reported). The anterior and posterior PGs were manually traced by trained raters. We examined the mediating effects of salivary DHEA on trauma-related symptoms (i.e., anxiety, depression, and posttraumatic) and PG volumes as well as an alternative model examining mediating effects of PG volume on DHEA and trauma-related symptoms. RESULTS: DHEA mediated the association between anxiety symptoms and anterior PG volume. Specifically, higher anxiety symptoms related to lower DHEA levels, which in turn were related to smaller anterior PG. CONCLUSIONS: These results shed light on the neurobiological sequelae of elevated anxiety in youth and are consistent with adult findings showing suppressed levels of DHEA in those with greater comorbid anxiety and depression. Specifically, adolescents with greater subclinical anxiety may exhibit diminished levels of DHEA during the pubertal window, which may be associated with disruptions in anterior PG growth.