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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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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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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Non-alcoholic fatty liver disease is associated with structural covariance network reconfiguration in cognitively unimpaired adults with type 2 diabetes

Neuroscience, Journal Year: 2025, Volume and Issue: 568, P. 58 - 67

Published: Jan. 15, 2025

Language: Английский

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Hippocampal Functional Radiomic Features for Identification of the Cognitively Impaired Patients from Low-Back-Related Pain: A Prospective Machine Learning Study

Journal of Pain Research, Journal Year: 2025, Volume and Issue: Volume 18, P. 271 - 282

Published: Jan. 1, 2025

To investigate whether functional radiomic features in bilateral hippocampi can identify the cognitively impaired patients from low-back-related leg pain (LBLP). For this retrospective study, a total of 95 clinically definite LBLP (40 and 45 preserved patients) were included, all underwent MRI clinical assessments. After calculating amplitude low-frequency fluctuations (ALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) degree centrality (DC) imaging, (n = 819) extracted these images, respectively. feature selection, machine learning models trained. Finally, we further analyzed relationship between hippocampal measures, to explore significance features. The combined model logistic regression algorithm superior performance distinguishing (AUC 0.970, accuracy 92.3%, sensitivity specificity 92.3%) compared other models. Additionally, wavelet correlated with Montreal Cognitive Assessment (MoCA) Hamilton Anxiety Scale, present intensity scores (P < 0.05, Bonferroni correction). Hippocampal are valuable for diagnosing LBLP.

Language: Английский

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Key regions aberrantly connected within cerebello-thalamo-cortical circuit and their genetic mechanism in schizophrenia: an fMRI meta-analysis and transcriptome study

Schizophrenia, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 21, 2025

Recent studies have showed aberrant connectivity of cerebello-thalamo-cortical circuit (CTCC) in schizophrenia (SCZ), which might be a heritable trait. However, these individual vary greatly their methods and findings, important areas within CTCC related genetic mechanism are unclear. We searched for consistent regions dysfunction using functional magnetic resonance imaging (fMRI) meta-analysis, followed by meta-regression annotation analysis. Gene analysis was performed to identify genes over-expressed the Allen Human Brain Atlas, set gene feature analyses. 19 (1333 patients 1174 healthy controls) were included this meta-analysis. SCZ characterized hyperconnectivity auditory network, visual system, sensorimotor areas, hypoconnectivity frontal gyrus, cerebellum, thalamus, caudate nucleus, significantly linked age, sex, duration illness, severity symptoms functionally enriched domains involving self, sensory, action, social. 2922 regions, molecular functions, biological processes, cellular components neurons/cells brain as well other mental diseases. These specially expressed tissue, neurons subcortex cortex during nearly all developmental stages, constructed protein-protein interaction network supported 85 hub with significance. findings suggest key aberrantly connected SCZ, may indicate neural substrate "cognitive dysmetria" consequence complex interactions from wide range diverse features.

Language: Английский

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Increased Functional Connectivity Between Brainstem Substructures and Cortex in Treatment Resistant Depression

Psychiatry Research Neuroimaging, Journal Year: 2025, Volume and Issue: unknown, P. 111957 - 111957

Published: Jan. 1, 2025

Language: Английский

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Altered individual-based morphological brain network in type 2 diabetes mellitus

Brain Research Bulletin, Journal Year: 2025, Volume and Issue: unknown, P. 111228 - 111228

Published: Jan. 1, 2025

Type 2 diabetes mellitus (T2DM) is recognized as a risk factor for cognitive decline, potentially linked to disrupted network connectivity. However, few previous studies have examined individual-based morphological brain networks in T2DM and their association with clinical characteristics. In our study, we enrolled 123 patients 91 healthy controls (HC). We constructed the using symmetric KL divergence-based similarity (KLS) calculated various global nodal metrics based on graph theory describe topological properties of networks. Firstly, exhibited increased degree left para-hippocampus, amygdala, precuneus, bilateral putamen, right inferior temporal gyrus, concentrations glycosylated hemoglobin (HbA1c) were positively correlated precuneus. Secondly, identified hypo-connected hyper-connected subnetworks, primarily involved reward circuits attention network, respectively. Lastly, altered connectivity (MC) was performance, aforementioned subnetworks may serve predictors performance. conclusion, this study provided neuroimaging evidence understanding changes by analyzing connections iSCN patients.

Language: Английский

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Individualized resting-state functional connectivity abnormalities unveil two major depressive disorder subtypes with contrasting abnormal patterns of abnormality

Translational Psychiatry, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 6, 2025

Modern neuroimaging research has recognized that major depressive disorder (MDD) is a connectome disorder, characterized by altered functional connectivity across large-scale brain networks. However, the clinical heterogeneity, likely stemming from diverse neurobiological disturbances, complicates findings standard group comparison methods. This variability driven search for MDD subtypes using objective markers. In this study, we sought to identify potential subject-level abnormalities in connectivity, leveraging large multi-site dataset of resting-state MRI 1276 patients and 1104 matched healthy controls. Subject-level extreme connections, determined comparing against normative ranges derived controls tolerance intervals, were used biological MDD. We identified set connections predominantly between visual network frontoparietal network, default mode ventral attention with key regions anterior cingulate cortex, bilateral orbitofrontal supramarginal gyrus. patients, these linked age onset reward-related processes. Using features, two distinct patterns compared (p < 0.05, Bonferroni correction). When considering all together, no significant differences found. These significantly enhanced case-control discriminability showed strong internal subtypes. Furthermore, reproducible varying parameters, study sites, untreated patients. Our provide new insights into taxonomy have implications both diagnosis treatment

Language: Английский

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