Dense neuronal reconstruction through X-ray holographic nano-tomography, bioRxiv, 2019-05-30
AbstractElucidating the structure of neuronal networks provides a foundation for understanding how the nervous system processes information to generate behavior. Despite technological breakthroughs in visible light and electron microscopy, imaging dense nanometer-scale neuronal structures over millimeter-scale tissue volumes remains a challenge. Here, we demonstrate that X-ray holographic nano-tomography is capable of imaging large tissue volumes with sufficient resolution to disentangle dense neuronal circuitry in Drosophila melanogaster and mammalian central and peripheral nervous tissue. Furthermore, we show that automatic segmentation using convolutional neural networks enables rapid extraction of neuronal morphologies from these volumetric datasets. The technique we present allows rapid data collection and analysis of multiple specimens, and can be used correlatively with light microscopy and electron microscopy on the same samples. Thus, X-ray holographic nano-tomography provides a new avenue for discoveries in neuroscience and life sciences in general.
biorxiv neuroscience 100-200-users 2019Estimations of the weather effects on brain functions using functional MRI – a cautionary tale, bioRxiv, 2019-05-28
AbstractThe influences of environmental factors such as weather on human brain are still largely unknown. A few neuroimaging studies have demonstrated seasonal effects, but were limited by their cross-sectional design or sample sizes. Most importantly, the stability of MRI scanner hasn’t been taken into account, which may also be affected by environments. In the current study, we analyzed longitudinal resting-state functional MRI (fMRI) data from eight individuals, where the participants were scanned over months to years. We applied machine learning regression to use different resting-state parameters, including amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity matrix, to predict different weather and environmental parameters. For a careful control, the raw EPI and the anatomical images were also used in the prediction analysis. We first found that daylight length and temperatures could be reliability predicted using cross-validation using resting-state parameters. However, similar prediction accuracies could also achieved by using one frame of EPI image, and even higher accuracies could be achieved by using segmented or even the raw anatomical images. Finally, we verified that the signals outside of the brain in the anatomical images and signals in phantom scans could also achieve higher prediction accuracies, suggesting that the predictability may be due to the baseline signals of the MRI scanner. After all, we did not identify detectable influences of weather on brain functions other than the influences on the stability of MRI scanners. The results highlight the difficulty of studying long term effects on brain using MRI.
biorxiv neuroscience 100-200-users 2019An Algorithmic Barrier to Neural Circuit Understanding, bioRxiv, 2019-05-27
AbstractNeuroscience is witnessing extraordinary progress in experimental techniques, especially at the neural circuit level. These advances are largely aimed at enabling us to understand how neural circuit computations mechanistically cause behavior. Here, using techniques from Theoretical Computer Science, we examine how many experiments are needed to obtain such an empirical understanding. It is proved, mathematically, that establishing the most extensive notions of understanding need exponentially-many experiments in the number of neurons, in general, unless a widely-posited hypothesis about computation is false. Worse still, the feasible experimental regime is one where the number of experiments scales sub-linearly in the number of neurons, suggesting a fundamental impediment to such an understanding. Determining which notions of understanding are algorithmically tractable, thus, becomes an important new endeavor in Neuroscience.
biorxiv neuroscience 100-200-users 2019A shared genetic basis for personality traits and local cortical grey matter structure?, bioRxiv, 2019-05-25
AbstractPersonality traits are key indices of inter-individual variation. Personality is heritable and has been associated with brain structure and function. To date, it is unknown whether the relation between personality and brain macrostructure can be explained by genetic factors. In a large-scale twin sample (Human Connectome Project), we performed genetic correlation analyses to evaluate whether personality traits (NEO-FFI) and local brain structure have a shared genetic basis. We found a genetic overlap between personality traits and local brain structure in 11 of 22 observed phenotypic associations in predominantly frontal cortices. In these regions the proportion of phenotypic covariance accounted for by shared genetic effects was between 82 and 100%. Second, in the case of Agreeableness, Conscientiousness, and Openness, the phenotypic correlation between personality and local brain structure was observed to reflect genetic, more than environmental, factors. These observations indicate that genetic factors influence the relationship between personality traits and local brain structure. Importantly, observed associations between personality traits and cortical thickness did only partially replicate in two independent large-scale samples of unrelated individuals. Taken together, our findings demonstrate that genes impact the relationship between personality and local brain structure, but that phenotypic associations are, to a large extent, non-generalizable. These observations provide a novel perspective on the nature and nurture of the biological basis of personality.
biorxiv neuroscience 100-200-users 2019Cross-species cortical alignment identifies different types of neuroanatomical reorganization in the temporal lobe of higher primates, bioRxiv, 2019-05-23
AbstractEvolutionary modifications of the temporo-parietal cortex are considered to be a critical adaptation of the human brain. Cortical adaptations, however, can affect different aspects of brain architecture, including areal expansion or changes in connectivity profiles. We propose to distinguishing different types of brain reorganization using a computational neuroanatomy approach. We investigate the extent to which between-species alignment based on cortical myelin can predict changes in connectivity patterns across macaque, chimpanzee and human. We show that expansion and relocation of brain areas are sufficient to predict terminations of several white matter tracts in temporo-parietal cortex, including the middle and superior longitudinal fasciculus, but not of the arcuate fasciculus. This demonstrates that the arcuate fasciculus underwent additional evolutionary modifications affecting the connectivity pattern of the temporal lobe. The presented approach can flexibly be extended to include other features of cortical organization and other species, allowing direct tests of comparative hypotheses of brain organization.
biorxiv neuroscience 0-100-users 2019Npas1+-Nkx2.1+ Neurons Are an Integral Part of the Cortico-pallido-cortical Loop, bioRxiv, 2019-05-21
AbstractWithin the basal ganglia circuit, the external globus pallidus (GPe) is critically involved in motor control. Aside from Foxp2+ neurons and ChAT+ neurons that have been established as unique neuron types, there is little consensus on the classification of GPe neurons. Properties of the remaining neuron types are poorly-defined. In this study, we leverage new mouse lines, viral tools, and molecular markers to better define GPe neuron subtypes. We found that Sox6 represents a novel, defining marker for GPe neuron subtypes. Lhx6+ neurons that lack the expression of Sox6 were devoid of both parvalbumin and Npas1. This result confirms previous assertions of the existence of a unique Lhx6+ population. Neurons that arise from the Dbx1+ lineage were similarly abundant in the GPe and displayed a heterogeneous makeup. Importantly, tracing experiments revealed that Npas1+-Nkx2.1+ neurons represent the principal non-cholinergic, cortically-projecting neurons. In other words, they form the pallido-cortical arm of the cortico-pallido-cortical loop. Our data further described that pyramidal-tract neurons in the cortex collateralized within the GPe, forming a closed-loop system between the two brain structures. Overall, our findings reconcile some of the discrepancies that arose from differences in techniques or the reliance on pre-existing tools. While spatial distribution and electrophysiological properties of GPe neurons reaffirm the diversification of GPe subtypes, statistical analyses strongly support the notion that these neuron subtypes can be categorized under the two principal neuron classes—i.e., PV+ neurons and Npas1+ neurons.Significance statementThe poor understanding of the neuronal composition in the GPe undermines our ability to interrogate its precise behavioral and disease involvements. In this study, twelve different genetic crosses were used, hundreds of neurons were electrophysiologically-characterized, and over 100,000 neurons were histologically- andor anatomically-profiled. Our current study further establishes the segregation of GPe neuron classes and illustrates the complexity of GPe neurons in adult mice. Our results support the idea that Npas1+-Nkx2.1+ neurons are a distinct GPe neuron subclass. By providing a detailed analysis of the organization of the cortico-pallidal-cortical projection, our findings establish the cellular and circuit substrates that can be important for motor function and dysfunction.
biorxiv neuroscience 0-100-users 2019