Brain network localization of gray matter atrophy, neurocognitive and social cognitive dysfunction in schizophrenia

Biological Psychiatry, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 1, 2024

Language: Английский

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Multi-omics analyses identify gut microbiota-fecal metabolites-brain-cognition pathways in the Alzheimer’s disease continuum

Alzheimer s Research & Therapy, Journal Year: 2025, Volume and Issue: 17(1)

Published: Feb. 1, 2025

Gut microbiota dysbiosis is linked to Alzheimer's disease (AD), but our understanding of the molecular and neuropathological bases underlying such association remains fragmentary. Using 16S rDNA amplicon sequencing, untargeted metabolomics, multi-modal magnetic resonance imaging, we examined group differences in gut microbiome, fecal metabolome, neuroimaging measures, cognitive variables across 30 patients with AD, 75 individuals mild impairment (MCI), 61 healthy controls (HC). Furthermore, assessed associations between these multi-omics changes using correlation mediation analyses. There were significant microbial composition, which driven by 8 taxa (e.g., Staphylococcus Bacillus) exhibiting a progressive increase relative abundance from HC MCI 2 Anaerostipes) showing gradual decrease. 26 metabolites Arachidonic, Adrenic, Lithocholic acids) exhibited AD. We also observed gray matter atrophy broadly distributed regions micro-structural integrity damage widespread white tracts along AD continuum. Integration revealed microbiota, metabolites, neuroimaging, cognition. More importantly, identified two potential pathways: (1) → cognition, (2) Aside elucidating mechanism whereby findings may contribute groundwork for future interventions targeting microbiota-metabolites-brain-cognition pathways as therapeutic strategy

Language: Английский

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Cerebellum abnormalities in vascular mild cognitive impairment with depression symptom patients: A multimodal magnetic resonance imaging study

Brain Research Bulletin, Journal Year: 2025, Volume and Issue: 221, P. 111213 - 111213

Published: Jan. 15, 2025

Language: Английский

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Heterogeneous brain abnormalities in subjective cognitive decline converge on a common network and their transcriptional signature

Alzheimer s & Dementia, Journal Year: 2025, Volume and Issue: 21(3)

Published: March 1, 2025

Abstract INTRODUCTION Subjective cognitive decline (SCD) is increasingly recognized as closely related to future Alzheimer's disease (AD). Numerous neuroimaging findings in SCD are inconsistent. We tested whether the various localize a common brain network. METHODS Using novel coordinate network mapping approach, we delineated damage networks that were functionally connected reported findings. then decoded these using microscale transcriptomic and chemo‐architectures psychological processes. RESULTS enrolled 45 studies comprising 2453 patients 3017 healthy controls. The identified largely localized somatosensory (SMN) default mode (DMN). Both robust perturbations of analyzed parameters an independent validation dataset. Neurobiology correlation analyses some key biological pathways neurotransmitters linked networks. DISCUSSION Our reconcile heterogeneous abnormalities provide richer neurobiological underpinning, which has implications for understanding with SCD. Highlights on reconciled framework. SCD‐related functional involves changes DMN, while structural mainly primary sensory areas. genes predominantly enriched processes synaptic structure, calcium ion binding, cellular metabolism. An ALE meta‐analysis was conducted comparison.

Language: Английский

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Molecular mechanisms underlying the neural correlates of working memory

BMC Biology, Journal Year: 2024, Volume and Issue: 22(1)

Published: Oct. 21, 2024

Language: Английский

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Understanding structural-functional connectivity coupling in patients with major depressive disorder: A white matter perspective

Journal of Affective Disorders, Journal Year: 2025, Volume and Issue: 373, P. 219 - 226

Published: Jan. 5, 2025

Language: Английский

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Delineating specific structural and functional patterns for neurophysiological biotypes of childhood maltreatment

Cerebral Cortex, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 6, 2025

Abstract Childhood maltreatment (CM) is a major risk factor for numerous mental disorders. The long-term consequences of CM on brain structural and functional plasticity have been well documented. However, the neurophysiological biotypes remain unclear although childhood trauma questionnaire uses different dimensions to assess types. Here, we investigated using spectral clustering further evaluated neurobiological heterogeneities with multimodal magnetic resonance imaging. Moreover, established molecular basis each biotype brain-wide transcriptome-neuroimaging spatial association analyses. Three distinct characteristics were found. specific pattern subtype was found be associated genes primarily mediating synapse development neuron projection. Taken together, our findings provide initial evidence that has biotypes, which may facilitate early precise prevention better links this high psychiatric

Language: Английский

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Transitioning from perceived stress to mental health: The mediating role of self-control in a longitudinal investigation with MRI scans

International Journal of Clinical and Health Psychology, Journal Year: 2025, Volume and Issue: 25(1), P. 100539 - 100539

Published: Jan. 1, 2025

The neural mechanisms and long-term effects of perceived stress (PS) self-control (SC) on mental health (MH) are not fully understood. This study seeks to investigate the influence PS SC MH identify their correlates using fMRI. A total 817 college students participated in behavioral assessments, including Perceived Stress Scale (PSS), Self-Control (SCS), Mental Health Continuum Short Form (MHC-SF). Among them, 371 underwent fMRI scans calculate zfALFF whole-brain functional connectivity. Additionally, measures were reassessed two years later. Longitudinal data revealed significant fixed health. significantly predicted decreased at Time 2, acted as a mediator such relationship. results brain model analyses found that right temporal region negatively self-control. Functional connectivity between precentral gyrus was also predict highlights mediating role relationship It identifies specific regions associated with self-control, providing new neurobiological evidence for interventions.

Language: Английский

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Dissociable ventral and dorsal sensorimotor functional circuits linking the hypomanic personality traits to aggression via behavioral inhibition system

International Journal of Clinical and Health Psychology, Journal Year: 2025, Volume and Issue: 25(1), P. 100537 - 100537

Published: Jan. 1, 2025

Hypomanic personality traits (HPT) are susceptibility markers for psychiatric disorders, particularly bipolar disorder, and strongly associated with aggressive behaviors. However, the neuropsychological mechanisms underlying this association remain unclear. This study utilized psychometric network analysis Inter-Subject Representation Similarity Analysis (IS-RSA) to explore circuits that link HPT aggression in a large non-clinical population. Psychometric (n = 716) identified two key nodes: Behavioral Inhibition System (BIS) mood volatility, core dimension of HPT. We observed positive correlation between volatility aggression, BIS serving as mediating factor. Task-based functional imaging 53) further revealed double dissociation dorsal (dSMC) ventral (vSMC) sensorimotor cortices HPT, specifically during processing reward magnitude delay delayed paradigm. Functional patterns within these regions mediated relationship individual differences acting mediator through parallel pathways. Resting-state 505) replicated segregation distinct integrative patterns: dSMC was functionally connected frontoparietal (FPN) vSMC (SMN). These collectively associations among BIS. findings highlight critical role understanding pathways linking HPT-related aggression.

Language: Английский

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Prognostic value of multi-PLD ASL radiomics in acute ischemic stroke

Frontiers in Neurology, Journal Year: 2025, Volume and Issue: 15

Published: Jan. 13, 2025

Introduction Early prognosis prediction of acute ischemic stroke (AIS) can support clinicians in choosing personalized treatment plans. The aim this study is to develop a machine learning (ML) model that uses multiple post-labeling delay times (multi-PLD) arterial spin labeling (ASL) radiomics features achieve early and precise AIS prognosis. Methods This enrolled 102 patients admitted between December 2020 September 2024. Clinical data, such as age baseline National Institutes Health Stroke Scale (NIHSS) score, were collected. Radiomics extracted from cerebral blood flow (CBF) images acquired through multi-PLD ASL. Features selected using least absolute shrinkage selection operator regression, three models developed: clinical model, CBF combined employing eight ML algorithms. Model performance was assessed receiver operating characteristic curves decision curve analysis (DCA). Shapley Additive exPlanations applied interpret feature contributions. Results extreme gradient boosting demonstrated superior predictive performance, achieving an area under the (AUC) 0.876. Statistical DeLong test revealed its significant outperformance compared both (AUC = 0.658, p < 0.001) 0.755, 0.002). robustness all confirmed permutation testing. Furthermore, DCA underscored utility model. prognostic notably influenced by NIHSS age, well texture shape CBF. Conclusion integration data ASL offers secure dependable approach for predicting AIS, particularly beneficial with contraindications contrast agents. aids devising individualized plans, ultimately enhancing patient

Language: Английский

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