Developmental Neurobiology最新文献

Establishing a proper balance between neuronal excitation (E) and inhibition (I) is essential for healthy brain development, with alterations in this dynamic linked to neurodevelopmental disorders. Animal models suggest that hippocampal activity rapidly increases in the early postnatal period, believed to support the development and stabilization of E/I neural circuitry. This process has not yet been examined in humans, however. Utilizing longitudinal data from the Developing Human Connectome Project, the present study evaluated the impact of early hippocampal activity and gestational age at birth on later outcomes in a cohort of preterm infants (N = 58). Hippocampal activity was assessed using the amplitude of low-frequency fluctuations (ALFF) derived from resting-state functional magnetic resonance imaging collected at two timepoints in the early postnatal period (prior to 20 weeks following birth). Increases in hippocampal activity during this early postnatal period predicted better cognitive and motor function at 18 months of age. Greater gestational age was associated with greater hippocampal activity increase between timepoints. Interestingly, no significant relationships were found between baseline hippocampal activity and 18-month outcomes, suggesting that dynamic changes rather than static measures may be especially sensitive to preterm birth and subsequent alterations in neurodevelopmental processes. These findings underscore the importance of changes in early hippocampal function and gestational age as key risk factors for future neurodevelopmental concerns.

Electromagnetic field (EMF) exposure, unavoidable in modern life, is linked to oxidative stress and ferroptosis, processes linked to neurodevelopmental disorders. This study investigated the effects of EMF exposure during different pregnancy trimesters on rat offspring trigeminal ganglia (TGs), focusing on transient receptor potential melastatin 2 (TRPM2) ion channels, and assessed the neuroprotective potential of ferrostatin-1 (Fer), a ferroptosis inhibitor, against EMF-induced damage. Pregnant rats were exposed to 900 MHz EMF for 2 h/day during early (1–7 days, EMF 1), mid (8–14 days, EMF 2), or late (15–21 days, EMF 3) gestation. Fer (2.5 µmol/kg, i.p.) was administered immediately after daily EMF exposure in Fer treatment groups. Offspring TG tissues were analyzed on postnatal Day 28 using histopathological, immunohistochemical, and biochemical approaches. EMF exposure significantly reduced antioxidant capacity and elevated lipid peroxidation, reactive oxygen species (ROS), pro-inflammatory cytokines, apoptotic markers, and TRPM2 activation, with the most pronounced alterations in mid-gestation exposure. Fer administration largely normalized these parameters and reduced structural damage in TG. In conclusion, these findings suggest that prenatal EMF triggers ferroptotic/apoptotic neurodegeneration via TRPM2, and that Fer holds promise as a neuroprotective agent.

Silent synapses in multiple sclerosis (MS) represent a key yet underexplored concept in the pathology of this disease, playing a crucial role in cognitive impairments and reduced neuroplasticity. These synapses, due to the inactivity of AMPA receptors under pathological conditions, are unable to efficiently transmit neural signals, leading to disrupted neural communication. This dysfunction is particularly influenced by chronic inflammation, alterations in neurotransmitter dynamics, and a reduction in neurotrophic factors in MS patients. One of the key aspects of understanding silent synapses is that they not only have the potential for reactivation, but they can also contribute to the restoration of neural networks by re-establishing neuroplasticity. Recent research has shown that targeted treatments, including activating NMDA receptors, increasing brain-derived neurotrophic factor (BDNF), and using drugs like ketamine, help restore patients’ cognitive function. Apart from pharmacological therapies, non-pharmacological strategies also include cognitive rehabilitation, physical activity, and noninvasive brain stimulation, which might promote synaptic plasticity and consequently quality of life. Therefore, reactivating latent synapses as a novel and interesting therapy strategy could not only improve cognitive performance in MS patients but also open the road for fresh methods to mend the nervous system and increase their quality of life. Though its specific form has not yet been thoroughly investigated, this approach offers great promise to become a viable MS treatment.

Hypoxia-induced neonatal seizures (HINSs) are a major cause of long-term cognitive deficits and heightened epilepsy risk in adulthood. Early inflammatory responses following HINS contribute to these pathological outcomes. This study examined the sustained neuroprotective benefits of exosomes derived from mesenchymal stem cells (MSC-exosomes) in a rat model of HINS, leveraging their anti-inflammatory and neuroregenerative properties. Forty-nine male and female Wistar rats were divided into four groups: (1) control + saline, (2) control + exosome, (3) hypoxia + saline, and (4) hypoxia + exosome. Neonatal rats (postnatal day 10) were subjected to hypoxia (5% O₂ for 15 min). Sixty minutes after the onset of hypoxia induction, pups received either MSC-exosomes (30 µg/100 µL) or saline for 12 consecutive days (lactation period). Behavioral tests, hippocampal tissue analysis (for RT-PCR and oxidative stress markers), and pentylenetetrazole (PTZ) kindling were performed at P60–P61.

The study revealed that treatment with exosomes improved memory performance and reduced anxiety-like behaviors in the hypoxia-exposed group, as evidenced by the novel object recognition and elevated plus maze tests. These benefits were linked to decreased oxidative stress (lower malondialdehyde/MDA levels), reduced pro-inflammatory markers (interleukin-6 [IL-6] and tumor necrosis factor-α [TNF-α]), and increased anti-inflammatory signaling (higher IL-10) in the hippocampus. Although exosome therapy delayed the onset of epileptogenesis, it did not lessen the intensity of seizures. The results indicate that administering MSC-derived exosomes after HINS can reduce susceptibility to PTZ-induced kindling, alleviate neuroinflammation, regulate oxidative stress, and protect against long-term cognitive impairments. Together, these findings highlight the potential of exosome-based interventions in mitigating the delayed neurological effects of HINS during adolescence.

Mammals are born into social groups: even species that become solitary begin life seeking social contact with family members. For solitary mammals, dispersal thus marks a major geographic and social transition from their natal group. This transition may be promoted by reduced social tolerance for and reduced interest in family members, and/or by unrelated factors such as increased exploration and activity. Dispersal may also coincide with other developmental events such as weaning or puberty. We investigated developmental changes in oxytocin receptor density in two solitary hamster species (Syrian hamsters: Mesocricetus auratus and Siberian hamsters: Phodopus sungorus) that disperse to individual burrows in the wild. We quantified oxytocin receptor density prior to and after separation from the natal group to determine whether and how neurobiological changes coincide with changes in social behavior. We also quantified transitions in social behavior across development in Syrian hamsters at 2.5, 4, and 8 weeks. Oxytocin receptor densities and distributions reorganized substantially from pre- to post-dispersal ages in both species. Binding decreased across brain regions, with declines in binding in the endopiriform nucleus of both species, and the greatest reduction in hippocampal CA2 of Syrian hamsters. All metrics of social interest and interaction declined across the 2.5–8 week interval—consistent with transition to a solitary lifestyle—except play behavior which peaked in the characteristic juvenile range. Developmental decline in oxytocin receptor density and oxytocin signaling may support transitions in social behavior in solitary mammals.

Autism spectrum disorder (ASD) presents unique challenges in diagnosis and treatment, necessitating innovative approaches to understanding its underlying neurophysiological mechanisms. Real-time classification of electroencephalography (EEG) data in children with ASD faces significant challenges due to variability in EEG signals caused by individual differences in brain activity, age, and behavioral states, complicating robust algorithm development. This study develops and validates a deep learning–based framework for real-time EEG classification in children with ASD, aiming to enhance diagnostic accuracy and enable timely interventions. The dataset includes EEG recordings from 60 children (30 with ASD and 30 typically developing), representing diverse age groups and behavioral profiles to improve generalizability. Pre-processing removes noise and artifacts through segmentation, short-time Fourier transform (STFT), and independent component analysis (ICA). Grid search optimization (GSO) enhances model performance by systematically searching hyperparameter combinations to find the optimal configuration. A hybrid convolutional neural network (CNN)–long short-term memory (LSTM) framework is proposed, combining convolutional layers for spatial feature extraction with LSTM layers for temporal sequence modeling. This hybrid model is the primary proposed solution for real-time EEG classification due to its ability to capture both spatial and temporal features critical for interpreting sequential EEG data in children with ASD. The model achieves an accuracy of 87.5%, a precision of 85.0%, a recall of 90.0%, and an F1 score of 87.5% implemented using MATLAB software. In comparison, ResNet, a baseline deep CNN model, achieves slightly higher accuracy (89.1%) but lacks temporal modeling capabilities essential for sequential EEG interpretation. Despite ResNet's marginally higher accuracy, the hybrid CNN–LSTM is favored as the final model for its superior temporal modeling, critical in EEG analysis. Future work may include real-time feedback mechanisms, mobile application development, and longitudinal data expansion.