Neurodevelopmental imprints of sociomarkers in adolescent brain connectomes.

Neural consequences of social disparities are not yet rigorously investigated. How socioeconomic conditions influence children's connectome development remains unknown. This paper endeavors to gauge how precisely the connectome structure of the brain can predict an individual's social environment, thereby inversely assessing how social influences are engraved in the neural development of the Adolescent brain.

A Learnable Counter-Condition Analysis Framework for Functional Connectivity-Based Neurological Disorder Diagnosis.

To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc analyses via explainable models to discover disease-related biomarkers. Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately. However, if the results at each stage lack reliability, it can cause misdiagnosis and incorrect analysis in afterward stages.

EAG-RS: A Novel Explainability-Guided ROI-Selection Framework for ASD Diagnosis via Inter-Regional Relation Learning.

Deep learning models based on resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used to diagnose brain diseases, particularly autism spectrum disorder (ASD). Existing studies have leveraged the functional connectivity (FC) of rs-fMRI, achieving notable classification performance. However, they have significant limitations, including the lack of adequate information while using linear low-order FC as inputs to the model, not considering individual characteristics (i.

Module of Axis-based Nexus Attention for weakly supervised object localization.

Weakly supervised object localization tasks remain challenging to identify and segment an entire object rather than only discriminative parts of the object. To tackle this problem, corruption-based approaches have been devised, which involve the training of non-discriminative regions by corrupting (e.g.

Site-Invariant Meta-Modulation Learning for Multisite Autism Spectrum Disorders Diagnosis.

Large amounts of fMRI data are essential to building generalized predictive models for brain disease diagnosis. In order to conduct extensive data analysis, it is often necessary to gather data from multiple organizations. However, the site variation inherent in multisite resting-state functional magnetic resonance imaging (rs-fMRI) leads to unfavorable heterogeneity in data distribution, negatively impacting the identification of biomarkers and the diagnostic decision.

Long-term renal outcomes of patients with non-proliferative lupus nephritis.

Background/aims: Although non-proliferative lupus nephritis (LN) (class I, II or V) has been considered as a less severe type of LN, data on long-term renal prognosis are limited. We investigated the long-term outcomes and prognostic factors in non-proliferative LN.

Methods: We retrospectively reviewed patients with systemic lupus erythematosus who were diagnosed with LN class I, II, V, or II + V by kidney biopsy from 1997 to 2021.

Altered functional connectivity in psychotic disorder not otherwise specified.

Background: Few studies have investigated functional connectivity (FC) in patients with psychotic disorder not otherwise specified (PNOS). We sought to identify distinct FC differentiating PNOS from schizophrenia (SZ).

Methods: In total, 49 patients with PNOS, 42 with SZ, and 55 healthy controls (HC) matched for age, sex, and education underwent functional magnetic resonance imaging (fMRI) brain scans and clinical evaluation.

A unified framework for personalized regions selection and functional relation modeling for early MCI identification.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely adopted to investigate functional abnormalities in brain diseases. Rs-fMRI data is unsupervised in nature because the psychological and neurological labels are coarse-grained, and no accurate region-wise label is provided along with the complex co-activities of multiple regions. To the best of our knowledge, most studies regarding univariate group analysis or multivariate pattern recognition for brain disease identification have focused on discovering functional characteristics shared across subjects; however, they have paid less attention to individual properties of neural activities that result from different symptoms or degrees of abnormality.

Modeling regional dynamics in low-frequency fluctuation and its application to Autism spectrum disorder diagnosis.

With the advent of neuroimaging techniques, many studies in the literature have validated the use of resting-state fMRI (rs-fMRI) for understanding functional mechanisms of the brain, as well as for identifying brain disorders or diseases. One of the main streams in recent studies of modeling and analyzing rs-fMRI data is to account for the dynamic characteristics of a brain. In this study, we propose a novel method that directly models the regional temporal BOLD fluctuations in a stochastic manner and estimates the dynamic characteristics in the form of likelihoods.