Metaplasticity: Dark exposure boosts local excitability and visual plasticity in adult human cortex.

An interlude of dark exposure for about 1 week is known to shift excitatory/inhibitory (E/I) balance of the mammalian visual cortex, promoting plasticity and accelerating visual recovery in animals that have experienced cortical lesions during development. However, the translational impact of our understanding of dark exposure from animal studies to humans remains elusive. Here, we used magnetic resonance spectroscopy as a probe for E/I balance in the primary visual cortex (V1) to determine the effect of 60 min of dark exposure, and measured binocular combination as a behavioural assay to assess visual plasticity in 14 normally sighted human adults. To induce neuroplastic changes in the observers, we introduced 60 min of monocular deprivation, which is known to temporarily shift sensory eye balance in favour of the previously deprived eye. We report that prior dark exposure for 60 min strengthens local excitability in V1 and boosts visual plasticity in normal adults. However, we show that it does not promote plasticity in amblyopic adults. Nevertheless, our findings are surprising, given the fact that the interlude is very brief. Interestingly, we find that the increased concentration of the excitatory neurotransmitter is not strongly correlated with the enhanced functional plasticity. Instead, the absolute degree of change in its concentration is related to the boost, suggesting that the dichotomy of cortical excitation and inhibition might not explain the physiological basis of plasticity in humans. We present the first evidence that an environmental manipulation that shifts cortical E/I balance can also act as a metaplastic facilitator for visual plasticity in humans. KEY POINTS: A brief interlude (60 min) of dark exposure increased the local concentration of glutamine/glutamate but not that of GABA in the visual cortex of adult humans. After dark exposure, the degree of the shift in sensory eye dominance in favour of the previously deprived eye from short-term monocular deprivation was larger than that from only monocular deprivation. The neurochemical and behavioural measures were associated: the magnitude of the shift in the concentration of glutamine/glutamate was correlated with the boost in perceptual plasticity after dark exposure. Surprisingly, the increase in the concentration of glutamine/glutamate was not correlated with the perceptual boost after dark exposure, suggesting that the physiological mechanism of how E/I balance regulates plasticity is not deterministic. In other words, an increased excitation did not unilaterally promote plasticity.

Self-supervised pretraining improves the performance of classification of task functional magnetic resonance imaging.

Introduction: Decoding brain activities is one of the most popular topics in neuroscience in recent years. And deep learning has shown high performance in fMRI data classification and regression, but its requirement for large amounts of data conflicts with the high cost of acquiring fMRI data. Methods: In this study, we propose an end-to-end temporal contrastive self-supervised learning algorithm, which learns internal spatiotemporal patterns within fMRI and allows the model to transfer learning to datasets of small size. For a given fMRI signal, we segmented it into three sections: the beginning, middle, and end. We then utilized contrastive learning by taking the end-middle (i.e., neighboring) pair as the positive pair, and the beginning-end (i.e., distant) pair as the negative pair. Results: We pretrained the model on 5 out of 7 tasks from the Human Connectome Project (HCP) and applied it in a downstream classification of the remaining two tasks. The pretrained model converged on data from 12 subjects, while a randomly initialized model required 100 subjects. We then transferred the pretrained model to a dataset containing unpreprocessed whole-brain fMRI from 30 participants, achieving an accuracy of 80.2 ± 4.7%, while the randomly initialized model failed to converge. We further validated the model's performance on the Multiple Domain Task Dataset (MDTB), which contains fMRI data of 26 tasks from 24 participants. Thirteen tasks of fMRI were selected as inputs, and the results showed that the pre-trained model succeeded in classifying 11 of the 13 tasks. When using the 7 brain networks as input, variations of the performance were observed, with the visual network performed as well as whole brain inputs, while the limbic network almost failed in all 13 tasks. Discussion: Our results demonstrated the potential of self-supervised learning for fMRI analysis with small datasets and unpreprocessed data, and for analysis of the correlation between regional fMRI activity and cognitive tasks.

Attention module improves both performance and interpretability of four-dimensional functional magnetic resonance imaging decoding neural network.

Decoding brain cognitive states from neuroimaging signals is an important topic in neuroscience. In recent years, deep neural networks (DNNs) have been recruited for multiple brain state decoding and achieved good performance. However, the open question of how to interpret the DNN black box remains unanswered. Capitalizing on advances in machine learning, we integrated attention modules into brain decoders to facilitate an in-depth interpretation of DNN channels. A four-dimensional (4D) convolution operation was also included to extract temporo-spatial interaction within the fMRI signal. The experiments showed that the proposed model obtains a very high accuracy (97.4%) and outperforms previous researches on the seven different task benchmarks from the Human Connectome Project (HCP) dataset. The visualization analysis further illustrated the hierarchical emergence of task-specific masks with depth. Finally, the model was retrained to regress individual traits within the HCP and to classify viewing images from the BOLD5000 dataset, respectively. Transfer learning also achieves good performance. Further visualization analysis shows that, after transfer learning, low-level attention masks remained similar to the source domain, whereas high-level attention masks changed adaptively. In conclusion, the proposed 4D model with attention module performed well and facilitated interpretation of DNNs, which is helpful for subsequent research.

Hydrogeochemical Characteristics and Water Quality Evaluation of Carboniferous Taiyuan Formation Limestone Water in Sulin Mining Area in Northern Anhui, China.

The Taiyuan formation limestone water in the Huaibei coalfield is not only the water source for coal mining, but also the water source for industry and agriculture in mining areas. Its hydrogeochemical characteristics and water quality are generally concerning. In this paper, conventional ion tests were carried out on the Taiyuan formation limestone water of 16 coal mines in the Sunan and Linhuan mining areas of the Huaibei coalfield. Piper trigram, Gibbs diagram and an ion scale coefficient map were used to analyze the hydrogeochemical characteristics of the Taiyuan formation limestone water. The water quality was evaluated in a fuzzy comprehensive manner. The results show that the main cation and anion contents in the Taiyuan formation limestone water were Na+ > Mg2+ > Ca2+ > K+, SO42- > HCO3- > Cl-. There were differences in the hydrogeochemical types of the Taiyuan formation limestone water in the two mining areas; HCO3-Na type water was dominant in the Sunan mining area and SO4·Cl-Na type water was dominant in the Linhuan mining area. The chemical composition of the Taiyuan formation limestone water is mainly affected by the weathering of the rock and is related to the dissolution of the evaporated salt and the weathering of the silicate. The fuzzy comprehensive evaluation results show that the V-type water accounts for a large proportion of the Taiyuan formation limestone water in the study area and the water quality is poor. This study provides a basis for the development and utilization of the Taiyuan formation limestone water and water environmental protection in the future.