Construction of the human forebrain

White matter microstructural integrity continues to develop from adolescence to young adulthood in mice and humans: Same phenotype, different mechanism

As direct evaluation of a mouse model of human neurodevelopment, adolescent and young adult mice and humans underwent MR diffusion tensor imaging to quantify age-related differences in microstructural integrity of brain white matter fibers. Fractional anisotropy (FA) was greater in older than younger mice and humans. Despite the cross-species commonality, the underlying developmental mechanism differed: whereas evidence for greater axonal extension contributed to higher FA in older mice, evidence for continuing myelination contributed to higher FA in human adolescent development. These differences occurred in the context of species distinctions in overall brain growth: whereas the continued growth of the brain and skull in the murine model can accommodate volume expansion into adulthood, human white matter volume and myelination continue growth into adulthood within a fixed intracranial volume. Appreciation of the similarities and differences in developmental mechanism can enhance the utility of animal models of brain white matter structure, function, and response to exogenous manipulation.

High activity and high functional connectivity are mutually exclusive in resting state zebrafish and human brains

Background

The structural connectivity of neurons in the brain allows active neurons to impact the physiology of target neuron types with which they are functionally connected. While the structural connectome is at the basis of functional connectome, it is the functional connectivity measured through correlations between time series of individual neurophysiological events that underlies behavioral and mental states. However, in light of the diverse neuronal cell types populating the brain and their unique connectivity properties, both neuronal activity and functional connectivity are heterogeneous across the brain, and the nature of their relationship is not clear. Here, we employ brain-wide calcium imaging at cellular resolution in larval zebrafish to understand the principles of resting state functional connectivity.

Results

We recorded the spontaneous activity of >12,000 neurons in the awake resting state forebrain. By classifying their activity (i.e., variances of ΔF/F across time) and functional connectivity into three levels (high, medium, low), we find that highly active neurons have low functional connections and highly connected neurons are of low activity. This finding holds true when neuronal activity and functional connectivity data are classified into five instead of three levels, and in whole brain spontaneous activity datasets. Moreover, such activity-connectivity relationship is not observed in randomly shuffled, noise-added, or simulated datasets, suggesting that it reflects an intrinsic brain network property. Intriguingly, deploying the same analytical tools on functional magnetic resonance imaging (fMRI) data from the resting state human brain, we uncover a similar relationship between activity (signal variance over time) and functional connectivity, that is, regions of high activity are non-overlapping with those of high connectivity.

Conclusions

We found a mutually exclusive relationship between high activity (signal variance over time) and high functional connectivity of neurons in zebrafish and human brains. These findings reveal a previously unknown and evolutionarily conserved brain organizational principle, which has implications for understanding disease states and designing artificial neuronal networks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01286-3.

Human Oligodendrocytes and Myelin In Vitro to Evaluate Developmental Neurotoxicity

Neurodevelopment is uniquely sensitive to toxic insults and there are concerns that environmental chemicals are contributing to widespread subclinical developmental neurotoxicity (DNT). Increased DNT evaluation is needed due to the lack of such information for most chemicals in common use, but in vivo studies recommended in regulatory guidelines are not practical for the large-scale screening of potential DNT chemicals. It is widely acknowledged that developmental neurotoxicity is a consequence of disruptions to basic processes in neurodevelopment and that testing strategies using human cell-based in vitro systems that mimic these processes could aid in prioritizing chemicals with DNT potential. Myelination is a fundamental process in neurodevelopment that should be included in a DNT testing strategy, but there are very few in vitro models of myelination. Thus, there is a need to establish an in vitro myelination assay for DNT. Here, we summarize the routes of myelin toxicity and the known models to study this particular endpoint.

Variability in Cerebrospinal Fluid MicroRNAs Through Life

The development of the human brain starts in the first weeks of embryo differentiation. However, there are many relevant neurodevelopmental processes that take place after birth and during lifespan. Such a fine and changing scenario requires the coordinated expression of thousands of genes to achieve the proper specialization and inter-connectivity. In this context, microRNAs (miRNAs), which can modulate mRNA stability and translation, are gaining recognition for their involvement in both brain development and neurodevelopmental disorders. Therefore, cerebrospinal fluid (CSF) miRNAs should be perfectly differentiated in relevant age periods. In this study, we aimed to highlight the biological variability of miRNA expression in the CSF throughout life, which is also crucial for biomarker discovery in CNS pathologies, especially in children, where they are desperately needed. We analyzed the CSF microRNAome of 14 healthy children (aged 0-7.4 years) by smallRNA-Seq and compared it with previously published data in adults (N = 7) and elders (N = 11). miR-423-5p and miR-22-3p were overexpressed in the < 1 and > 3 years groups, respectively. Additionally, we detected 18 miRNAs that reached their highest peak of expression at different time-points during the lifespan and sets of miRNAs that were exclusively expressed in a specific age group. On the contrary, miR-191-5p showed stable expression in CSF from the first year of life. Our results remark the complex differential miRNA expression profile that can be observed through life, which underlines the need for including appropriate age-matched controls when the expression of CSF miRNAs is analyzed in different pathological contexts. Graphical abstract.

Multivariate pattern classification of brain white matter connectivity predicts classic trigeminal neuralgia

Trigeminal neuralgia (TN) is a severe form of chronic facial neuropathic pain. Increasing interest in the neuroimaging of pain has highlighted changes in the root entry zone in TN, but also group-level central nervous system gray and white matter (WM) abnormalities. Group differences in neuroimaging data are frequently evaluated with univariate statistics; however, this approach is limited because it is based on single, or clusters of, voxels. By contrast, multivariate pattern analyses consider all the model's neuroanatomical features to capture a specific distributed spatial pattern. This approach has potential use as a prediction tool at the individual level. We hypothesized that a multivariate pattern classification method can distinguish specific patterns of abnormal WM connectivity of classic TN from healthy controls (HCs). Diffusion-weighted scans in 23 right-sided TN and matched controls were processed to extract whole-brain interregional streamlines. We used a linear support vector machine algorithm to differentiate interregional normalized streamline count between TN and HC. This algorithm successfully differentiated between TN and HC with an accuracy of 88%. The structural pattern emphasized WM connectivity of regions that subserve sensory, affective, and cognitive dimensions of pain, including the insula, precuneus, inferior and superior parietal lobules, and inferior and medial orbital frontal gyri. Normalized streamline counts were associated with longer pain duration and WM metric abnormality between the connections. This study demonstrates that machine-learning algorithms can detect characteristic patterns of structural alterations in TN and highlights the role of structural brain imaging for identification of neuroanatomical features associated with neuropathic pain disorders.