The effects of the NMDAR co-agonist D-serine on the structure and function of optic tectal neurons in the developing visual system.

The N-methyl-D-aspartate type glutamate receptor (NMDAR) is a molecular coincidence detector which converts correlated patterns of neuronal activity into cues for the structural and functional refinement of developing circuits in the brain. D-serine is an endogenous co-agonist of the NMDAR. We investigated the effects of potent enhancement of NMDAR-mediated currents by chronic administration of saturating levels of D-serine on the developing Xenopus retinotectal circuit. Chronic exposure to the NMDAR co-agonist D-serine resulted in structural and functional changes in the optic tectum. In immature tectal neurons, D-serine administration led to more compact and less dynamic tectal dendritic arbors, and increased synapse density. Calcium imaging to examine retinotopy of tectal neurons revealed that animals raised in D-serine had more compact visual receptive fields. These findings provide insight into how the availability of endogenous NMDAR co-agonists like D-serine at glutamatergic synapses can regulate the refinement of circuits in the developing brain.

Estimating brain age from structural MRI and MEG data: Insights from dimensionality reduction techniques.

Brain age prediction studies aim at reliably estimating the difference between the chronological age of an individual and their predicted age based on neuroimaging data, which has been proposed as an informative measure of disease and cognitive decline. As most previous studies relied exclusively on magnetic resonance imaging (MRI) data, we hereby investigate whether combining structural MRI with functional magnetoencephalography (MEG) information improves age prediction using a large cohort of healthy subjects (N = 613, age 18-88 years) from the Cam-CAN repository. To this end, we examined the performance of dimensionality reduction and multivariate associative techniques, namely Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA), to tackle the high dimensionality of neuroimaging data. Using MEG features (mean absolute error (MAE) of 9.60 years) yielded worse performance when compared to using MRI features (MAE of 5.33 years), but a stacking model combining both feature sets improved age prediction performance (MAE of 4.88 years). Furthermore, we found that PCA resulted in inferior performance, whereas CCA in conjunction with Gaussian process regression models yielded the best prediction performance. Notably, CCA allowed us to visualize the features that significantly contributed to brain age prediction. We found that MRI features from subcortical structures were more reliable age predictors than cortical features, and that spectral MEG measures were more reliable than connectivity metrics. Our results provide an insight into the underlying processes that are reflective of brain aging, yielding promise for the identification of reliable biomarkers of neurodegenerative diseases that emerge later during the lifespan.

Unfolding the Effects of Acute Cardiovascular Exercise on Neural Correlates of Motor Learning Using Convolutional Neural Networks.

Cardiovascular exercise is known to promote the consolidation of newly acquired motor skills. Previous studies seeking to understand the neural correlates underlying motor memory consolidation that is modulated by exercise, have relied so far on using traditional statistical approaches for a priori selected features from neuroimaging data, including EEG. With recent advances in machine learning, data-driven techniques such as deep learning have shown great potential for EEG data decoding for brain-computer interfaces, but have not been explored in the context of exercise. Here, we present a novel Convolutional Neural Network (CNN)-based pipeline for analysis of EEG data to study the brain areas and spectral EEG measures modulated by exercise. To the best of our knowledge, this work is the first one to demonstrate the ability of CNNs to be trained in a limited sample size setting. Our approach revealed discriminative spectral features within a refined frequency band (27-29 Hz) as compared to the wider beta bandwidth (15-30 Hz), which is commonly used in data analyses, as well as corresponding brain regions that were modulated by exercise. These results indicate the presence of finer EEG spectral features that could have been overlooked using conventional hypothesis-driven statistical approaches. Our study thus demonstrates the feasibility of using deep network architectures for neuroimaging analysis, even in small-scale studies, to identify robust brain biomarkers and investigate neuroscience-based questions.