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The human brain is organized as a complex, hierarchical network. However, the structural covariance patterns among brain regions and the underlying biological substrates of such covariance networks remain to be clarified. The present study proposed a novel individualized structural covariance network termed voxel-based texture similarity networks (vTSNs) based on 76 refined voxel-based textural features derived from structural magnetic resonance images. Validated in three independent longitudinal healthy cohorts (40, 23, and 60 healthy participants, respectively) with two common brain atlases, we found that the vTSN could robustly resolve inter-subject variability with high test-retest reliability. In contrast to the regional-based texture similarity networks (rTSNs) that calculate radiomic features based on region-of-interest information, vTSNs had higher inter- and intra-subject variability ratios and test-retest reliability in connectivity strength and network topological properties. Moreover, the Spearman correlation indicated a stronger association of the gene expression similarity network (GESN) with vTSNs than with rTSNs (vTSN: r = 0.600, rTSN: r = 0.433, z = 39.784, P < 0.001). Hierarchical clustering identified 3 vTSN subnets with differential association patterns with 13 coexpression modules, 16 neurotransmitters, 7 electrophysiology, 4 metabolism, and 2 large-scale structural and 4 functional organization maps. Moreover, these subnets had unique biological hierarchical organization from the subcortex-limbic system to the ventral neocortex and then to the dorsal neocortex. Based on 424 unrelated, qualified healthy subjects from the Human Connectome Project, we found that vTSNs could sensitively represent sex differences, especially for connections in the subcortex-limbic system and between the subcortex-limbic system and the ventral neocortex. Moreover, a multivariate variance component model revealed that vTSNs could explain a significant proportion of inter-subject behavioral variance in cognition (80.0 %) and motor functions (63.4 %). Finally, using 494 healthy adults (aged 19-80 years old) from the Southwest University Adult Lifespan Dataset, the Spearman correlation identified a significant association between aging and vTSN strength, especially within the subcortex-limbic system and between the subcortex-limbic system and the dorsal neocortex. In summary, our proposed vTSN is robust in uncovering individual variability and neurobiological brain processes, which can serve as biologically plausible measures for linking biological processes and human behavior.
Assuntos
Encéfalo , Imageamento por Ressonância Magnética , Humanos , Masculino , Feminino , Imageamento por Ressonância Magnética/métodos , Adulto , Encéfalo/diagnóstico por imagem , Encéfalo/anatomia & histologia , Encéfalo/fisiologia , Adulto Jovem , Ontologias Biológicas , Rede Nervosa/diagnóstico por imagem , Rede Nervosa/fisiologia , Rede Nervosa/anatomia & histologia , Pessoa de Meia-Idade , Conectoma/métodos , Reprodutibilidade dos Testes , IdosoRESUMO
Objective: Although extensive structural and functional abnormalities have been reported in schizophrenia, the gray matter volume (GMV) covariance of the amygdala remain unknown. The amygdala contains several subregions with different connection patterns and functions, but it is unclear whether the GMV covariance of these subregions are selectively affected in schizophrenia. Methods: To address this issue, we compared the GMV covariance of each amygdala subregion between 807 schizophrenia patients and 845 healthy controls from 11 centers. The amygdala was segmented into nine subregions using FreeSurfer (v7.1.1), including the lateral (La), basal (Ba), accessory-basal (AB), anterior-amygdaloid-area (AAA), central (Ce), medial (Me), cortical (Co), corticoamygdaloid-transition (CAT), and paralaminar (PL) nucleus. We developed an operational combat harmonization model for 11 centers, subsequently employing a voxel-wise general linear model to investigate the differences in GMV covariance between schizophrenia patients and healthy controls across these subregions and the entire brain, while adjusting for age, sex and TIV. Results: Our findings revealed that five amygdala subregions of schizophrenia patients, including bilateral AAA, CAT, and right Ba, demonstrated significantly increased GMV covariance with the hippocampus, striatum, orbitofrontal cortex, and so on (permutation test, P< 0.05, corrected). These findings could be replicated in most centers. Rigorous correlation analysis failed to identify relationships between the altered GMV covariance with positive and negative symptom scale, duration of illness, and antipsychotic medication measure. Conclusion: Our research is the first to discover selectively impaired GMV covariance patterns of amygdala subregion in a large multicenter sample size of patients with schizophrenia.
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Background: Alzheimer's disease (AD) is one of the most severe neurodegenerative diseases leading to dementia in the elderly. Cerebral atrophy and hypoperfusion are two important pathophysiological characteristics. However, it is still unknown about the area-specific causal pathways between regional gray matter atrophy, cerebral hypoperfusion, and cognitive impairment in AD patients. Method: Forty-two qualified AD patients and 49 healthy controls (HC) were recruited in this study. First, we explored voxel-wise inter-group differences in gray matter volume (GMV) and arterial spin labeling (ASL) -derived cerebral blood flow (CBF). Then we explored the voxel-wise associations between GMV and Mini-Mental State Examination (MMSE) score, GMV and CBF, and CBF and MMSE to identify brain targets contributing to cognitive impairment in AD patients. Finally, a mediation analysis was applied to test the causal pathways among atrophied GMV, hypoperfusion, and cognitive impairment in AD. Results: Voxel-wise permutation test identified that the left middle temporal gyrus (MTG) had both decreased GMV and CBF in the AD. Moreover, the GMV of this region was positively correlated with MMSE and its CBF, and CBF of this region was also positively correlated with MMSE in AD (p < 0.05, corrected). Finally, mediation analysis revealed that gray matter atrophy of left MTG drives cognitive impairment of AD via the mediation of CBF (proportion of mediation = 55.82%, ß = 0.242, 95% confidence interval by bias-corrected and accelerated bootstrap: 0.082 to 0.530). Conclusion: Our findings indicated suggested that left MTG is an important hub linking gray matter atrophy, hypoperfusion, and cognitive impairment for AD, and might be a potential treatment target for AD.
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Introduction: Schizophrenia is a severe psychiatric disorder with a high prevalence worldwide, however, its pathogenesis remains poorly understood. Methods and results: In this study, we used the non-competitive NMDA receptor antagonist MK-801 to induce schizophrenia-like behaviors and confirmed that mice exhibited stereotypic rotational behavior and hyperlocomotion, social interaction defects and cognitive dysfunction, similar to the clinical symptoms in patients. Here, the anterior cingulate cortex (ACC) and basolateral amygdala (BLA) were involved in the schizophrenia-like behaviors induced by MK-801. Furthermore, we confirmed BLA sent glutamatergic projection to the ACC. Chemogenetic and optogenetic regulation of BLA-ACC projecting neurons affected social and cognitive deficits but not stereotypic rotational behavior in MK-801-treated mice. Discussion: Overall, our study revealed that the BLA-ACC circuit plays a major role and may be a potential target for treating schizophrenia-related symptoms.
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A TiNb2O7 anode constructed with carbon-coated nanosheet arrays on carbon cloth is prepared by a facile solvothermal process and post carbon-coating for the first time. With nanosized diffusion-length and reduced polarization resistance, this anode exhibits superior high-rate capability based on relatively high mass-loading. Meanwhile, it demonstrates excellent cycling stability and mechanical flexibility as expected from flexible Li-ion batteries.
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The overall performance of electrochemical energy storage devices (EESDs) is intrinsically correlated with surfaces and interfaces. As a promising electrode architecture, 3D nanoarrays (3D-NAs) possess relatively ordered, continuous, and fully exposed active surfaces of individual nanostructures, facilitating mass and electron transport within the electrode and charge transfer across interfaces and providing an ideal platform for engineering. Herein, a critical overview of the surface and interface engineering of 3D-NAs, from electrode and interface designs to device integration, is presented. The general merits of 3D-NAs and surface/interface engineering principles of 3D-NA hybrid electrodes are highlighted. The focus is on the use of 3D-NAs as a superior platform to regulate the interface nature and unveiling new mechanism/materials without the interference of binders. The engineering and utilization of the surface of 3D-NAs to develop flexible/solid-state EESDs with 3D integrated electrode/electrolyte interfaces, or 3D triphase interfaces involving other active species, which are characteristic of (quasi-)solid-state electrolyte infiltration into the entire device, are also considered. Finally, the challenges and future directions of surface/interface engineering of 3D-NAs are outlined. In particular, potential strategies to obtain electrode charge balance, optimize the multiphase solid-state interface, and attain 3D solid electrolyte infiltration are proposed.