Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features.

BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains challenging due to clinical subjectivity. To address this issue, we explore magnetic resonance imaging (MRI) as a tool to enhance SZ diagnosis and provide objective references and biomarkers. Using deep learning with graph convolution, we represent MRI data as graphs, aligning with brain structure, and improving feature extraction, and classification. Integration of multiple modalities is expected to enhance classification. STUDY DESIGN: Our study enrolled 683 SZ patients and 606 healthy controls from 7 hospitals, collecting structural MRI and functional MRI data. Both data types were represented as graphs, processed by 2 graph attention networks, and fused for classification. Grad-CAM with graph convolution ensured interpretability, and partial least squares analyzed gene expression in brain regions. STUDY RESULTS: Our method excelled in the classification task, achieving 83.32% accuracy, 83.41% sensitivity, and 83.20% specificity in 10-fold cross-validation, surpassing traditional methods. And our multimodal approach outperformed unimodal methods. Grad-CAM identified potential brain biomarkers consistent with gene analysis and prior research. CONCLUSIONS: Our study demonstrates the effectiveness of deep learning with graph attention networks, surpassing previous SZ diagnostic methods. Multimodal MRI's superiority over unimodal MRI confirms our initial hypothesis. Identifying potential brain biomarkers alongside gene biomarkers holds promise for advancing objective SZ diagnosis and research in SZ.

Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology.

The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.

Co-representation of Functional Brain Networks Is Shaped by Cortical Myeloarchitecture and Reveals Individual Behavioral Ability.

Large-scale functional networks are spatially distributed in the human brain. Despite recent progress in differentiating their functional roles, how the brain navigates the spatial coordination among them and the biological relevance of this coordination is still not fully understood. Capitalizing on canonical individualized networks derived from functional MRI data, we proposed a new concept, that is, co-representation of functional brain networks, to delineate the spatial coordination among them. To further quantify the co-representation pattern, we defined two indexes, that is, the co-representation specificity (CoRS) and intensity (CoRI), for separately measuring the extent of specific and average expression of functional networks at each brain location by using the data from both sexes. We found that the identified pattern of co-representation was anchored by cortical regions with three types of cytoarchitectural classes along a sensory-fugal axis, including, at the first end, primary (idiotypic) regions showing high CoRS, at the second end, heteromodal regions showing low CoRS and high CoRI, at the third end, paralimbic regions showing low CoRI. Importantly, we demonstrated the critical role of myeloarchitecture in sculpting the spatial distribution of co-representation by assessing the association with the myelin-related neuroanatomical and transcriptomic profiles. Furthermore, the significance of manifesting the co-representation was revealed in its prediction of individual behavioral ability. Our findings indicated that the spatial coordination among functional networks was built upon an anatomically configured blueprint to facilitate neural information processing, while advancing our understanding of the topographical organization of the brain by emphasizing the assembly of functional networks.

Common Sequential Organization of Face Processing in the Human Brain and Convolutional Neural Networks.

Face processing includes two crucial processing levels - face detection and face recognition. However, it remains unclear how human brains organize the two processing levels sequentially. While some studies found that faces are recognized as fast as they are detected, others have reported that faces are detected first, followed by recognition. We discriminated the two processing levels on a fine time scale by combining human intracranial EEG (two females, three males, and three subjects without reported sex information) and representation similarity analysis. Our results demonstrate that the human brain exhibits a "detection-first, recognition-later" pattern during face processing. In addition, we used convolutional neural networks to test the hypothesis that the sequential organization of the two face processing levels in the brain reflects computational optimization. Our findings showed that the networks trained on face recognition also exhibited the "detection-first, recognition-later" pattern. Moreover, this sequential organization mechanism developed gradually during the training of the networks and was observed only for correctly predicted images. These findings collectively support the computational account as to why the brain organizes them in this way.

Alterations of dynamic and static brain functional activities and integration in stroke patients.

Objective: The study aimed to investigate the comprehensive characteristics of brain functional activity and integration in patients with subcortical stroke using dynamic and static analysis methods and to examine whether alterations in brain functional activity and integration were associated with clinical symptoms of patients. Methods: Dynamic amplitude of low-frequency fluctuation (dALFF), static amplitude of low-frequency fluctuation (sALFF), dynamic degree centrality (dDC), and static degree centrality (sDC) were calculated for 19 patients with right subcortical stroke, 16 patients with left subcortical stroke, and 25 healthy controls (HC). Furthermore, correlation analysis was performed to investigate the relationships between changes in brain functional measurements of patients and clinical variables. Results: Group comparison results showed that significantly decreased dALFF in the left angular (ANG_L) and right inferior parietal gyrus (IPG_R), decreased sALFF in the left precuneus (PCUN_L), and decreased sDC in the left crus II of cerebellar hemisphere (CERCRU2_L) and IPG_R, while significantly increased sDC in the right lobule X of cerebellar hemisphere (CER10_R) were detected in patients with right subcortical stroke relative to HC. Patients with left subcortical stroke showed significantly decreased sALFF in the left precuneus (PCUN_L) but increased sDC in the right hippocampus (HIP_R) compared with HC. Additionally, the altered sDC values in the CER10_R of patients with right subcortical stroke and in the HIP_R of patients with left subcortical stroke were associated with the severity of stroke and lower extremities motor function. A correlation was also found between the altered sALFF values in the PCUN_L of patients with left subcortical stroke and lower extremities motor function. Conclusion: These findings suggest that time-varying brain activity analysis may supply complementary information for static brain activity analysis. Dynamic and static brain functional activity and integration analysis may contribute to a more comprehensive understanding of the underlying neuropathology of dysfunction in stroke patients.

Novel insights from spatial transcriptome analysis in solid tumors.

Since its first application in 2016, spatial transcriptomics has become a rapidly evolving technology in recent years. Spatial transcriptomics enables transcriptomic data to be acquired from intact tissue sections and provides spatial distribution information and remedies the disadvantage of single-cell RNA sequencing (scRNA-seq), whose data lack spatially resolved information. Presently, spatial transcriptomics has been widely applied to various tissue types, especially for the study of tumor heterogeneity. In this review, we provide a summary of the research progress in utilizing spatial transcriptomics to investigate tumor heterogeneity and the microenvironment with a focus on solid tumors. We summarize the research breakthroughs in various fields and perspectives due to the application of spatial transcriptomics, including cell clustering and interaction, cellular metabolism, gene expression, immune cell programs and combination with other techniques. As a combination of multiple transcriptomics, single-cell multiomics shows its superiority and validity in single-cell analysis. We also discuss the application prospect of single-cell multiomics, and we believe that with the progress of data integration from various transcriptomics, a multilayered subcellular landscape will be revealed.

A single-cell transcriptome atlas profiles early organogenesis in human embryos.

The early window of human embryogenesis is largely a black box for developmental biologists. Here we probed the cellular diversity of 4-6 week human embryos when essentially all organs are just laid out. On the basis of over 180,000 single-cell transcriptomes, we generated a comprehensive atlas of 313 clusters in 18 developmental systems, which were annotated with a collection of ontology and markers from 157 publications. Together with spatial transcriptome on embryonic sections, we characterized the molecule and spatial architecture of previously unappreciated cell types. Combined with data from other vertebrates, the rich information shed light on spatial patterning of axes, systemic temporal regulation of developmental progression and potential human-specific regulation. Our study provides a compendium of early progenitor cells of human organs, which can serve as the root of lineage analysis in organogenesis.

Brainnetome atlas of preadolescent children based on anatomical connectivity profiles.

During the preadolescent period, when the cerebral thickness, curvature, and myelin are constantly changing, the brain's regionalization patterns underwent persistent development, contributing to the continuous improvements of various higher cognitive functions. Using a brain atlas to study the development of these functions has attracted much attention. However, the brains of children do not always have the same topological patterns as those of adults. Therefore, age-specific brain mapping is particularly important, serving as a basic and indispensable tool to study the normal development of children. In this study, we took advantage of longitudinal data to create the brain atlas specifically for preadolescent children. The resulting human Child Brainnetome Atlas, with 188 cortical and 36 subcortical subregions, provides a precise period-specific and cross-validated version of the brain atlas that is more appropriate for adoption in the preadolescent period. In addition, we compared and illustrated for regions with different topological patterns in the child and adult atlases, providing a topologically consistent reference for subsequent research studying child and adolescent development.

Two subtypes of schizophrenia identified by an individual-level atypical pattern of tensor-based morphometric measurement.

Difficulties in parsing the multiaspect heterogeneity of schizophrenia (SCZ) based on current nosology highlight the need to subtype SCZ using objective biomarkers. Here, utilizing a large-scale multisite SCZ dataset, we identified and validated 2 neuroanatomical subtypes with individual-level abnormal patterns of the tensor-based morphometric measurement. Remarkably, compared with subtype 1, which showed moderate deficits of some subcortical nuclei and an enlarged striatum and cerebellum, subtype 2, which showed cerebellar atrophy and more severe subcortical nuclei atrophy, had a higher subscale score of negative symptoms, which is considered to be a core aspect of SCZ and is associated with functional outcome. Moreover, with the neuroimaging-clinic association analysis, we explored the detailed relationship between the heterogeneity of clinical symptoms and the heterogeneous abnormal neuroanatomical patterns with respect to the 2 subtypes. And the neuroimaging-transcription association analysis highlighted several potential heterogeneous biological factors that may underlie the subtypes. Our work provided an effective framework for investigating the heterogeneity of SCZ from multilevel aspects and may provide new insights for precision psychiatry.

Single-cell profiling of peripheral neuroblastic tumors identifies an aggressive transitional state that bridges an adrenergic-mesenchymal trajectory.

Peripheral neuroblastic tumors (PNTs) represent a spectrum of neural-crest-derived tumors, including neuroblastoma, ganglioneuroblastoma, and ganglioneuroma. Malignant cells in PNTs are theorized to interconvert between adrenergic/noradrenergic and mesenchymal/neural crest cell states. Here, single-cell RNA-sequencing analysis of 10 PNTs demonstrates extensive transcriptomic heterogeneity. Trajectory modeling suggests that malignant neuroblasts move between adrenergic and mesenchymal cell states via an intermediate state that we term "transitional." Transitional cells express programs linked to a sympathoadrenal development and aggressive tumor phenotypes such as rapid proliferation and tumor dissemination. Among primary bulk tumor patient cohorts, high expression of the transitional gene signature is predictive of poor prognosis compared with adrenergic and mesenchymal expression patterns. High transitional gene expression in neuroblastoma cell lines identifies a similar transitional H3K27-acetylation super-enhancer landscape. Collectively, our study supports the concept that PNTs have phenotypic plasticity and uncovers potential biomarkers and therapeutic targets.

BAI-Net: Individualized Anatomical Cerebral Cartography Using Graph Neural Network.

Brain atlas is an important tool in the diagnosis and treatment of neurological disorders. However, due to large variations in the organizational principles of individual brains, many challenges remain in clinical applications. Brain atlas individualization network (BAI-Net) is an algorithm that subdivides individual cerebral cortex into segregated areas using brain morphology and connectomes. The presented method integrates group priors derived from a population atlas, adjusts areal probabilities using the context of connectivity fingerprints derived from the fiber-tract embedding of tractography, and provides reliable and explainable individualized brain areas across multiple sessions and scanners. We demonstrate that BAI-Net outperforms the conventional iterative clustering approach by capturing significantly heritable topographic variations in individualized cartographies. The topographic variability of BAI-Net cartographies has shown strong associations with individual variability in brain morphology, connectivity as well as higher relationship on individual cognitive behaviors and genetics. This study provides an explainable framework for individualized brain cartography that may be useful in the precise localization of neuromodulation and treatments on individual brains.

ZnT8 loss-of-function accelerates functional maturation of hESC-derived ß cells and resists metabolic stress in diabetes.

Human embryonic stem cell-derived ß cells (SC-ß cells) hold great promise for treatment of diabetes, yet how to achieve functional maturation and protect them against metabolic stresses such as glucotoxicity and lipotoxicity remains elusive. Our single-cell RNA-seq analysis reveals that ZnT8 loss of function (LOF) accelerates the functional maturation of SC-ß cells. As a result, ZnT8 LOF improves glucose-stimulated insulin secretion (GSIS) by releasing the negative feedback of zinc inhibition on insulin secretion. Furthermore, we demonstrate that ZnT8 LOF mutations endow SC-ß cells with resistance to lipotoxicity/glucotoxicity-triggered cell death by alleviating endoplasmic reticulum (ER) stress through modulation of zinc levels. Importantly, transplantation of SC-ß cells with ZnT8 LOF into mice with preexisting diabetes significantly improves glycemia restoration and glucose tolerance. These findings highlight the beneficial effect of ZnT8 LOF on the functional maturation and survival of SC-ß cells that are useful as a potential source for cell replacement therapies.

Multimodal brain deficits shared in early-onset and adult-onset schizophrenia predict positive symptoms regardless of illness stage.

Incidence of schizophrenia (SZ) has two predominant peaks, in adolescent and young adult. Early-onset schizophrenia provides an opportunity to explore the neuropathology of SZ early in the disorder and without the confound of antipsychotic mediation. However, it remains unexplored what deficits are shared or differ between adolescent early-onset (EOS) and adult-onset schizophrenia (AOS) patients. Here, based on 529 participants recruited from three independent cohorts, we explored AOS and EOS common and unique co-varying patterns by jointly analyzing three MRI features: fractional amplitude of low-frequency fluctuations (fALFF), gray matter (GM), and functional network connectivity (FNC). Furthermore, a prediction model was built to evaluate whether the common deficits in drug-naive SZ could be replicated in chronic patients. Results demonstrated that (1) both EOS and AOS patients showed decreased fALFF and GM in default mode network, increased fALFF and GM in the sub-cortical network, and aberrant FNC primarily related to middle temporal gyrus; (2) the commonly identified regions in drug-naive SZ correlate with PANSS positive significantly, which can also predict PANSS positive in chronic SZ with longer duration of illness. Collectively, results suggest that multimodal imaging signatures shared by two types of drug-naive SZ are also associated with positive symptom severity in chronic SZ and may be vital for understanding the progressive schizophrenic brain structural and functional deficits.

The human mediodorsal thalamus: Organization, connectivity, and function.

The human mediodorsal thalamic nucleus (MD) is crucial for higher cognitive functions, while the fine anatomical organization of the MD and the function of each subregion remain elusive. In this study, using high-resolution data provided by the Human Connectome Project, an anatomical connectivity-based method was adopted to unveil the topographic organization of the MD. Four fine-grained subregions were identified in each hemisphere, including the medial (MDm), central (MDc), dorsal (MDd), and lateral (MDl), which recapitulated previous cytoarchitectonic boundaries from histological studies. The subsequent connectivity analysis of the subregions also demonstrated distinct anatomical and functional connectivity patterns, especially with the prefrontal cortex. To further evaluate the function of MD subregions, partial least squares analysis was performed to examine the relationship between different prefrontal-subregion connectivity and behavioral measures in 1012 subjects. The results showed subregion-specific involvement in a range of cognitive functions. Specifically, the MDm predominantly subserved emotional-cognition domains, while the MDl was involved in multiple cognitive functions especially cognitive flexibility and inhibition. The MDc and MDd were correlated with fluid intelligence, processing speed, and emotional cognition. In conclusion, our work provides new insights into the anatomical and functional organization of the MD and highlights the various roles of the prefrontal-thalamic circuitry in human cognition.

Anterior-Posterior Hippocampal Dynamics Support Working Memory Processing.

The hippocampus is a locus of working memory (WM) with anterior and posterior subregions that differ in their transcriptional and external connectivity patterns. However, the involvement and functional connections between these subregions in WM processing are poorly understood. To address these issues, we recorded intracranial EEG from the anterior and the posterior hippocampi in humans (seven females and seven males) who maintained a set of letters in their WM. We found that WM maintenance was accompanied by elevated low-frequency activity in both the anterior and posterior hippocampus and by increased theta/alpha band (3-12 Hz) phase synchronization between anterior and posterior subregions. Cross-frequency and Granger prediction analyses consistently showed that the correct WM trials were associated with theta/alpha band-coordinated unidirectional influence from the posterior to the anterior hippocampus. In contrast, WM errors were associated with bidirectional interactions between the anterior and posterior hippocampus. These findings imply that theta/alpha band synchrony within the hippocampus may support successful WM via a posterior to anterior influence. A combination of intracranial recording and a fine-grained atlas may be of value in understanding the neural mechanisms of WM processing.SIGNIFICANCE STATEMENT Working memory (WM) is crucial to everyday functioning. The hippocampus has been proposed to be a subcortical node involved in WM processes. Previous studies have suggested that the anterior and posterior hippocampi differ in their external connectivity patterns and gene expression. However, it remains unknown whether and how human hippocampal subregions are recruited and coordinated during WM tasks. Here, by recording intracranial electroencephalography simultaneously from both hippocampal subregions, we found enhanced power in both areas and increased phase synchronization between them. Furthermore, correct WM trials were associated with a unidirectional influence from the posterior to the anterior hippocampus, whereas error trials were correlated with bidirectional interactions. These findings indicate a long-axis specialization in the human hippocampus during WM processing.

Single-cell RNA Sequencing Reveals Sexually Dimorphic Transcriptome and Type 2 Diabetes Genes in Mouse Islet ß Cells.

Type 2 diabetes (T2D) is characterized by the malfunction of pancreatic ß cells. Susceptibility and pathogenesis of T2D can be affected by multiple factors, including sex differences. However, the mechanisms underlying sex differences in T2D susceptibility and pathogenesis remain unclear. Using single-cell RNA sequencing (scRNA-seq), we demonstrate the presence of sexually dimorphic transcriptomes in mouse ß cells. Using a high-fat diet-induced T2D mouse model, we identified sex-dependent T2D altered genes, suggesting sex-based differences in the pathological mechanisms of T2D. Furthermore, based on islet transplantation experiments, we found that compared to mice with sex-matched islet transplants, sex-mismatched islet transplants in healthy mice showed down-regulation of genes involved in the longevity regulating pathway of ß cells. Moreover, the diabetic mice with sex-mismatched islet transplants showed impaired glucose tolerance. These data suggest sexual dimorphism in T2D pathogenicity, indicating that sex should be considered when treating T2D. We hope that our findings could provide new insights for the development of precision medicine in T2D.

A single-cell analysis of the molecular lineage of chordate embryogenesis.

Progressive unfolding of gene expression cascades underlies diverse embryonic lineage development. Here, we report a single-cell RNA sequencing analysis of the complete and invariant embryonic cell lineage of the tunicate Ciona savignyi from fertilization to the onset of gastrulation. We reconstructed a developmental landscape of 47 cell types over eight cell cycles in the wild-type embryo and identified eight fate transformations upon fibroblast growth factor (FGF) inhibition. For most FGF-dependent asymmetric cell divisions, the bipotent mother cell displays the gene signature of the default daughter fate. In convergent differentiation of the two notochord lineages, we identified additional gene pathways parallel to the master regulator T/Brachyury Last, we showed that the defined Ciona cell types can be matched to E6.5-E8.5 stage mouse cell types and display conserved expression of limited number of transcription factors. This study provides a high-resolution single-cell dataset to understand chordate early embryogenesis and cell lineage differentiation.

Variations in melatonin levels in preterm and term human breast milk during the first month after delivery.

The objectives of the present study were to examine the dynamic changes in breast milk melatonin throughout the course of lactation and to explore factors associated with changes in melatonin concentrations and rhythms in both preterm and term breast milk. Breast milk was collected sequentially at 03:00, 09:00, 15:00, and 21:00 in one day. Melatonin was analyzed in 392 breast milk samples from 98 healthy nursing mothers at 0 to 30 days postpartum. In both preterm and term breast milk, the melatonin concentration presented a circadian rhythm with the acrophase at around 03:00. Subgroup analysis showed the peak melatonin concentrations differed significantly across lactation stages, with the highest concentration in the colostrum, followed by transitional and mature breast milk. At 03:00, preterm breast milk had a higher concentration of melatonin than term breast milk in the colostrum (28.67 pg/mL vs. 25.31 pg/mL, p < 0.022), transitional breast milk (24.70 pg/mL vs. 22.55 pg/mL), and mature breast milk (22.37 pg/mL vs. 20.12 pg /mL). Further studies are warranted for their roles and significance on melatonin in breast milk in nutrition and metabolism of neonates.

Sin3a-Tet1 interaction activates gene transcription and is required for embryonic stem cell pluripotency.

Sin3a is a core component of histone-deacetylation-activity-associated transcriptional repressor complex, playing important roles in early embryo development. Here, we reported that down-regulation of Sin3a led to the loss of embryonic stem cell (ESC) self-renewal and skewed differentiation into mesendoderm lineage. We found that Sin3a functioned as a transcriptional coactivator of the critical Nodal antagonist Lefty1 through interacting with Tet1 to de-methylate the Lefty1 promoter. Further studies showed that two amino acid residues (Phe147, Phe182) in the PAH1 domain of Sin3a are essential for Sin3a-Tet1 interaction and its activity in regulating pluripotency. Furthermore, genome-wide analyses of Sin3a, Tet1 and Pol II ChIP-seq and of 5mC MeDIP-seq revealed that Sin3a acted with Tet1 to facilitate the transcription of a set of their co-target genes. These results link Sin3a to epigenetic DNA modifications in transcriptional activation and have implications for understanding mechanisms underlying versatile functions of Sin3a in mouse ESCs.