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Epilepsy is a neurological disorder characterized by recurrent, unprovoked seizures. Altered connectivity within the default mode network (DMN) has been associated with epilepsy, highlighting its role in seizure propagation. In this study, we investigate the temporal patterns of DMN connectivity in epilepsy patients compared to healthy controls using data-driven models of dynamic functional connectivity (dFC). Specifically, we employ one Hidden Markov Model (HMM) and two Hidden Semi-Markov Models (HSMMs) with Gamma and Poisson sojourn distributions to capture latent brain state transitions, as well as hidden connectivity states and their temporal properties. Dynamic metrics (i.e., fractional occupancy, switching rate, and mean lifetime) were derived for each subject, revealing prolonged dwell times in low-connectivity states and reduced flexibility in state transitions, particularly in low-connectivity DMN states. HSMMs, especially the Gamma variant, demonstrated superior sensitivity in capturing these alterations compared to the standard HMM, highlighting the importance of flexible sojourn modeling in dynamic functional connectivity analysis. Additionally, group-specific transition patterns suggested disrupted temporal progression of DMN state transitions. Our findings highlight the potential of HSMMs in capturing alterations in functional brain states and provide new insights into the dynamic reorganization of the DMN in epilepsy.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10382-3.

Alzheimer's disease (AD) and frontotemporal dementia (FTD) have insidious, similar and ambiguous clinical symptoms, which make their diagnosis difficult. Currently, in the field of EEG signal analysis, there are relatively few studies on the interpretability analysis of feature selection using intelligent optimization algorithms. To analyze the EEG signals of AD and FTD patients more comprehensively, first, 16 features in three dimensions of entropy, time-frequency domain and SODP were extracted in this paper. Secondly, Pearson correlation analysis, importance ranking and SHAP interpretability analysis methods were adopted to select SE, SW, ZCR, STA, CTM2 and CTM5 as the best discriminative features, and the Relief algorithm was used for fusion and dimension reduction based on weights. Thirdly, GWOCS was used for channel screening to determine the optimal channel combination of Fz, F7, Fp1, Fp2, F3, T3, P4 and C3, achieving the three-classification identification of the two patient groups and the normal control group, with the classification accuracy reaching 89.35[Formula: see text] and 81.12[Formula: see text] in cross-validation and LOSO validation, respectively. Finally, the SHAP method was used to prove that for the diagnosis of dementia, the prefrontal and temporal lobe brain regions play a decisive role, verifying the effectiveness of this framework in rapid channel selection and improving the efficiency of disease detection.

Given the complex relationship between the gut microbiome and chronic kidney disease (CKD), exploring the potential role and scope of microbiota-targeted therapies in pediatric CKD is highly relevant. We aim to provide an overview of gut-targeted therapeutic strategies, including nutritional interventions (fiber, phytochemicals, fermented foods, and traditional Chinese medicines), probiotics, synbiotics, oral absorbents, and fecal microbial transplantation. Enhancing physical activity and preventing constipation are additional strategies that may promote gut microbiome health. In a uremic environment, gut microbiota-targeted therapies could potentially rebalance the gut microbiota, improve gut barrier function, decrease uremic toxin concentrations, enhance the production of short-chain fatty acids (SCFA), and reduce inflammation. While research in adult CKD patients has provided insights into these approaches, there are limited data in children with CKD. This review aims to summarize potential targeted therapies for restoring a balanced gut microbiota, emphasizing the need for studies that evaluate their effects on clinical outcomes in pediatric CKD.

Background: Serum creatinine (Scr) centile values were recently described in a cohort of 1136 (near)-term neonates that underwent therapeutic hypothermia (TH) because of moderate to severe hypoxic-ischemic encephalopathy. Recent methodological progress enables conversion of these Scr centiles to estimated glomerular filtration rate (eGFR) values.

Methods: Scr centiles in the TH dataset during the first 10 days of life were converted to eGFR values, using the Schwartz formula, with the Smeets k-value (0.31) and fixed body length (50 cm) to generate postnatal reference eGFR values, centiles, and an equation for median eGFRs. These findings were compared to published eGFR data in term controls.

Results: A polynomial function was estimated: $\mathrm{eGFR}\left(\frac{\mathrm{mL}}{\min },\bullet ,1.73,m^2\right)=9.1667+7.1173-0.3439x^2,(x=\mathrm{days})$ for eGFR in TH neonates. The median eGFR increases 2- to threefold over the first week (day 1: 16.1; day 2: 19.4; day 7: 41.2 mL/min∙1.73 m²), while the polynomial function does not fully reflect the interindividual variability in eGFR values (intra-day variability is also 2- to threefold). Patterns in acute kidney injury (AKI) TH cases differ significantly from non-AKI TH cases. Based on pooling of published eGFR data, this was compared to a function in healthy term neonates: $\mathrm{eGFR}\left(\frac{\mathrm{mL}}{\min },\bullet ,1.73,m^2\right)=14.2167+6.7644-0.3901x^2(x=\mathrm{days})$ (day 1: 20; day 2: 26; day 7: 42 mL/min/1.73 m²).

Conclusions: Based on a pooled dataset in TH cases, we converted Scr centiles to eGFR centiles. Based on median values, this resulted in a polynomial function in TH cases, compared to healthy term neonates. This eGFR function enables precision pharmacotherapy for GFR-cleared drugs in this vulnerable population.

The reversed cortico‑medullary differentiation in fetal kidneys on ultrasound has been described in the literature, but there have been no descriptions of such a finding on fetal magnetic resonance imaging (MRI) so far. The authors present three unrelated fetuses with hyperechoic kidneys on ultrasound (US) and reversed signal intensity of their cortex and pyramids on SSFSE/T2WI and FIESTA images on magnetic resonance imaging (MRI). All of them shared the same deletion of the long arm of chromosome 17 in the 17q12 region, responsible for the expression of clinical features of renal cysts and diabetes (RCAD) syndrome. All of them had multiple tiny kidney cysts on US after birth. This specific finding on fetal MRI may point at this specific genetic condition.

Background: Aquaporins (AQPs) are a class of proteins that transport water molecules across membranes, which can promote water transport in cells. We aimed to explore the correlation between different polymorphisms of AQP1 and peritoneal function in children on peritoneal dialysis (PD).

Methods: Children who underwent PD at the Children's Hospital of Fudan University from January 1, 2014, to December 31, 2023, were included. The AQP1 genotypes of the four polymorphisms were rs2075574 (TT, CT, CC), rs1049305 (GG, CG, CC), rs10253374 (TT, CT, CC) and rs17159702 (TT, CT, CC).

Results: A total of 187 children on chronic PD were included in the study. We found that the TT group with rs2075574 exhibited a lower baseline peritoneal equilibration test (PET) ultrafiltration level than the CC group (302 ± 129 vs. 408 ± 168 ml/m², P = 0.015). For rs1049305, the CC group had a higher pKT/V than both the GG (2.71 ± 1.25 vs. 2.27 ± 0.79, P = 0.04) and CG groups (2.71 ± 1.25 vs. 2.24 ± 0.88, P = 0.03). Additionally, at 12-month follow-up, the CC (410 ± 160 ml/m², P = 0.04) and CG (393 ± 174 ml/m², P = 0.04) groups of rs1049305 showed higher PET ultrafiltration than the GG group (239 ± 288 ml/m²). No significant correlation was observed between the four genotypes and adverse events.

Conclusions: AQP1 rs2075574 and rs1049305 polymorphisms might be associated with ultrafiltration and urea transport in children with PD.

Detecting Psychogenic Nonepileptic Seizures (PNES) is vital because PNES mimics epileptic seizures but has psychological-not electrical-origins, leading to frequent misdiagnosis and ineffective treatment. Electroencephalography (EEG) provides a non-invasive view of brain activity for distinguishing PNES from true epilepsy. Current PNES detection methods remain limited. This study introduces a curated PNES EEG dataset and a novel explainable feature-engineering (XFE) model. Expert neurologists annotated three classes: Normal, PNES with Verbal Suggestion Provocation (VSP+), and PNES without VSP (VSP -). The introduced explainable feature engineering (XFE) framework includes four components: (i) Distance Counter Pattern (DCPat) for channel-pair feature extraction (190 features for 20 channels), (ii) Cumulative Weight-based Neighborhood Component Analysis (CWNCA) for feature selection (threshold = 0.99), (iii) t-algorithm k-Nearest Neighbors (tkNN) ensemble classifier with Iterative Majority Voting (IMV) and greedy optimization, and (iv) Directed Lobish (DLob) for symbolic interpretation and cortical connectome mapping. For this research, we curated an EEG dataset and four cases are created using the curated dataset. These four cases are: Case 1 (Normal vs. PNES VSP+), Case 2 (Normal vs. PNES VSP-), Case 3 (PNES VSP + vs. PNES VSP-), and Case 4 (all three classes).). The introduced DCPat XFE framework reached accuracy above 96.5% in all four cases; Case 2 attained the best overall value (99.11%). DLob strings and connectome diagrams provided clear symbolic explanations of PNES-related patterns. The DCPat-based XFE framework yields high accuracy and interpretable outputs for PNES detection on EEG. These results support its use as a reliable, explainable tool for clinical decision support.

EEG microstates sequence analysis gained a lot of attention in recent years and different sequence analysis methods have been applied to study microstates sequence randomness, complexity, speed, periodicity, and long-range memory. Although several studies have reported on the reliability of temporal parameters, the stability of sequence-based metrics within subjects has not yet been systematically examined. In this study, we analysed EEG recordings from 60 healthy young adults and assessed short-term (90 min) and long-term (30 days) test-retest reliability and agreement of sequence measures: long-range memory (Hurst exponent), complexity (two Lempel-Ziv algorithms), and randomness (entropy and entropy rate). Across metrics, short-term reliability was consistently good to excellent (ICC = 0.831-0.902), and long-term reliability was moderate to good (ICC = 0.651-0.793). Entropy and entropy rate emerged as the most stable measures across both intervals, confirmed by minimal bias and strong agreement. These findings demonstrate that EEG microstate sequence dynamics represent a stable trait of neural activity, providing a solid methodological foundation for future studies that aim to embed these metrics into computational models and explore their translational value as neurophysiological biomarkers.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10391-2.

Abnormal τ and β-amyloid (Aβ) deposition in the brains of patients with Alzheimer's disease (AD) is significantly associated with cognitive decline. This abnormal deposition has been reported to be linked to increased excitatory and inhibitory time constants in neural circuits. In this paper, we focus on three typical electroencephalography (EEG) slowdowns clinically reported in association with AD, including decreased dominant frequency, decreased α rhythmic activity, and increased δ + θ rhythmic activity. Firstly, we demonstrate that changes in excitatory time constant, inhibitory time constants, and synaptic connection strength can induce the phenomenon of EEG slowdowns in early AD. Then, we are interested in the regulation of AD by traditional deep brain stimulation (DBS) and emerging optogenetic stimulation. High-frequency, high-pulse width, and high-amplitude DBS are more effective in reversing brain rhythm in AD, supporting the experiment that cortical high-frequency DBS may be an effective therapeutic way for dementia-related diseases. In particular, as a modification of traditional DBS, we find that oscillatory bursty stimulation can compensate for the shortcomings of DBS at low amplitude. However, it is physiologically difficult to target inhibitory interneurons with conventional electrical stimulation. Optogenetics is able to precisely stimulate pyramidal neurons and inhibitory interneurons observed in animal experiments. Our numerical results indicate that medium and low-frequency stimulation can better eliminate AD pathology. It should be noted that stimulation of inhibitory interneurons requires greater light intensity than stimulation of pyramidal neurons. Finally, we propose two optimization intermittent optogenetic stimulation protocols. These modeling results can reproduce some experimental phenomena and are expected to reveal the underlying pathological mechanisms and control strategies associated with cognitive dysfunction such as AD.