MGA-Net: A novel mask-guided attention neural network for precision neonatal brain imaging.

In this study, we introduce MGA-Net, a novel mask-guided attention neural network, which extends the U-net model for precision neonatal brain imaging. MGA-Net is designed to extract the brain from other structures and reconstruct high-quality brain images. The network employs a common encoder and two decoders: one for brain mask extraction and the other for brain region reconstruction. A key feature of MGA-Net is its high-level mask-guided attention module, which leverages features from the brain mask decoder to enhance image reconstruction. To enable the same encoder and decoder to process both MRI and ultrasound (US) images, MGA-Net integrates sinusoidal positional encoding. This encoding assigns distinct positional values to MRI and US images, allowing the model to effectively learn from both modalities. Consequently, features learned from a single modality can aid in learning a modality with less available data, such as US. We extensively validated the proposed MGA-Net on diverse and independent datasets from varied clinical settings and neonatal age groups. The metrics used for assessment included the DICE similarity coefficient, recall, and accuracy for image segmentation; structural similarity for image reconstruction; and root mean squared error for total brain volume estimation from 3D ultrasound images. Our results demonstrate that MGA-Net significantly outperforms traditional methods, offering superior performance in brain extraction and segmentation while achieving high precision in image reconstruction and volumetric analysis. Thus, MGA-Net represents a robust and effective preprocessing tool for MRI and 3D ultrasound images, marking a significant advance in neuroimaging that enhances both research and clinical diagnostics in the neonatal period and beyond.

Thalamic volume in very preterm infants: associations with severe brain injury and neurodevelopmental outcome at two years.

Introduction: Several studies demonstrate the relationship between preterm birth and a reduced thalamus volume at term-equivalent age. In contrast, this study aims to investigate the link between the thalamic growth trajectory during the early postnatal period and neurodevelopment at two years of age. Methods: Thalamic volume was extracted from 84 early MRI scans at postmenstrual age of 32.33 (± 2.63) weeks and 93 term-equivalent age MRI scans at postmenstrual age of 42.05 (± 3.33) weeks of 116 very preterm infants (56% male) with gestational age at birth of 29.32 (± 2.28) weeks and a birth weight of 1158.92 (± 348.59) grams. Cognitive, motor, and language outcomes at two years of age were assessed with Bayley Scales of Infant and Toddler Development Third Edition. Bivariate analysis was used to describe the clinical variables according to neurodevelopmental outcomes and multilevel linear regression models were used to examine the impact of these variables on thalamic volume and its relationship with neurodevelopmental outcomes. Results: The results suggest an association between severe brain injury and thalamic growth trajectory (ß coef = -0.611; p < 0.001). Moreover, thalamic growth trajectory during early postnatal life was associated with the three subscale scores of the neurodevelopmental assessment (cognitive: ß coef = 6.297; p = 0.004; motor: ß coef = 7.283; p = 0.001; language: ß coeficient = 9.053; p = 0.002). Discussion: These findings highlight (i) the impact of severe brain injury on thalamic growth trajectory during early extrauterine life after preterm birth and (ii) the relationship of thalamic growth trajectory with cognitive, motor, and language outcomes.

Non-invasive brain stimulation for patients and healthy subjects: Current challenges and future perspectives.

Non-invasive brain stimulation (NIBS) techniques have a rich historical background, yet their utilization has witnessed significant growth only recently. These techniques encompass transcranial electrical stimulation and transcranial magnetic stimulation, which were initially employed in neuroscience to explore the intricate relationship between the brain and behaviour. However, they are increasingly finding application in research contexts as a means to address various neurological, psychiatric, and neurodegenerative disorders. This article aims to fulfill two primary objectives. Firstly, it seeks to showcase the current state of the art in the clinical application of NIBS, highlighting how it can improve and complement existing treatments. Secondly, it provides a comprehensive overview of the utilization of NIBS in augmenting the brain function of healthy individuals, thereby enhancing their performance. Furthermore, the article delves into the points of convergence and divergence between these two techniques. It also addresses the existing challenges and future prospects associated with NIBS from ethical and research standpoints.

Cortico-Cerebellar Hyper-Connections and Reduced Purkinje Cells Behind Abnormal Eyeblink Conditioning in a Computational Model of Autism Spectrum Disorder.

Empirical evidence suggests that children with autism spectrum disorder (ASD) show abnormal behavior during delay eyeblink conditioning. They show a higher conditioned response learning rate and earlier peak latency of the conditioned response signal. The neuronal mechanisms underlying this autistic behavioral phenotype are still unclear. Here, we use a physiologically constrained spiking neuron model of the cerebellar-cortical system to investigate which features are critical to explaining atypical learning in ASD. Significantly, the computer simulations run with the model suggest that the higher conditioned responses learning rate mainly depends on the reduced number of Purkinje cells. In contrast, the earlier peak latency mainly depends on the hyper-connections of the cerebellum with sensory and motor cortex. Notably, the model has been validated by reproducing the behavioral data collected from studies with real children. Overall, this article is a starting point to understanding the link between the behavioral and neurobiological basis in ASD learning. At the end of the paper, we discuss how this knowledge could be critical for devising new treatments.