Infrared neural stimulation with 7T fMRI: A rapid in vivo method for mapping cortical connections of primate amygdala

We have previously shown that INS-fMRI is a rapid method for mapping mesoscale brain networks in the macaque monkey brain. Focal stimulation of single cortical sites led to the activation of connected cortical locations, resulting in a global connectivity map. Here, we have extended this method for mapping brainwide networks following stimulation of single subcortical sites. As a testbed, we focused on the basal nucleus of the amygdala in the macaque monkey. We describe methods to target basal nucleus locations with submillimeter precision, pulse train stimulation methods, and statistical tests for assessing non-random nature of activations. Using these methods, we report that stimulation of precisely targeted loci in the basal nucleus produced sparse and specific activations in the brain. Activations were observed in the insular and sensory association cortices as well as activations in the cingulate cortex, consistent with known anatomical connections. What is new here is that the activations were focal and, in some cases, exhibited shifting topography with millimeter shifts in stimulation site. The precision of the method enables networks mapped from different nearby sites in the basal nucleus to be distinguished. While further investigation is needed to improve the sensitivity of this method, our analyses do support the reproducibility and non-random nature of some of the activations. We suggest that INS-fMRI is a promising method for mapping large-scale cortical and subcortical networks at high spatial resolution. © 2021 The Author(s)Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Intergenic and Repeat Transcription in Human, Chimpanzee and Macaque Brains Measured by RNA-Seq

Transcription is the first step connecting genetic information with an organism's phenotype. While expression of annotated genes in the human brain has been characterized extensively, our knowledge about the scope and the conservation of transcripts located outside of the known genes' boundaries is limited. Here, we use high-throughput transcriptome sequencing (RNA-Seq) to characterize the total non-ribosomal transcriptome of human, chimpanzee, and rhesus macaque brain. In all species, only 20–28% of non-ribosomal transcripts correspond to annotated exons and 20–23% to introns. By contrast, transcripts originating within intronic and intergenic repetitive sequences constitute 40–48% of the total brain transcriptome. Notably, some repeat families show elevated transcription. In non-repetitive intergenic regions, we identify and characterize 1,093 distinct regions highly expressed in the human brain. These regions are conserved at the RNA expression level across primates studied and at the DNA sequence level across mammals. A large proportion of these transcripts (20%) represents 3′UTR extensions of known genes and may play roles in alternative microRNA-directed regulation. Finally, we show that while transcriptome divergence between species increases with evolutionary time, intergenic transcripts show more expression differences among species and exons show less. Our results show that many yet uncharacterized evolutionary conserved transcripts exist in the human brain. Some of these transcripts may play roles in transcriptional regulation and contribute to evolution of human-specific phenotypic traits

Mouse-specific responses to human-chimpanzee dietary differences.

<p>(A) Venn diagram showing the numbers of human-mouse orthologous genes differentially expressed between mice fed human and chimpanzee diets in liver (left), and genes differentially expressed between human and chimpanzee livers (right). Top panel: genes showing differential expression at a stringent cutoff, FDR <10% in each of the two primate datasets and the primate diet dataset; lower panel: genes showing diet/species effects at a loose cutoff, |effect size|>0.8. Numbers outside the circles indicate orthologous genes showing no species or diet effects. Only genes detected in both primate and mice datasets are represented. Note that upon relaxing the differential expression cutoff, the number of genes showing species effects increases by ∼5 times, while those showing diet effects increases by ∼2. This is caused by differences in the distribution of effect size and statistical power between the two datasets (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043915#pone.0043915.s005" target="_blank">Figure S5</a>). In the mouse dataset, small effects are more easily detected as statistically significant, likely due to lower within-group variance. (B) Median transcriptional liver-specificity among different groups of genes. Liver-specificity is calculated as the difference between liver expression and mean gene expression level across various tissues, in units of standard deviation (i.e. a z-score). Shown are four groups of genes that were differentially expressed only between mice fed human and chimpanzee diets, only between human and chimpanzee, in both primates and mice, or in neither. Black diamonds show median liver-specificity in mouse; white diamonds show liver-specificity in human (using data specific to each species). The range of whiskers is M±1.58×IQR/n^0.5, where M, IQR and n are the median, interquantile range, and number of observations. Asterisks indicate significance based on two-sided Wilcoxon test. ***: <i>p</i><0.001. n.s.: <i>p</i>>0.1. (C) The difference between mouse- and human liver-specificity distributions, across the same gene sets as in panel B. The mouse and human distributions were each converted into Gaussian kernel densities (estimated using the “density” function in R); the y-axis shows the difference between these densities. The x-axis shows liver-specificity as in panel B. For example, positive x- and y-axis values indicate that the mouse shows an excess of genes showing high liver-specificity, compared to human. Black solid line: Genes differentially expressed only in mouse; double-dashed gray line: only between human and chimpanzee; gray dotted line: in both mouse and primates; gray solid line: in neither. While genes differentially expressed in neither dataset have higher mouse liver-specificities relative to human, this is significantly more pronounced among mouse-specific differentially expressed genes (one-sided Wilcoxon test, <i>p</i> = 0.0077; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043915#s4" target="_blank">Methods</a>), and is not seen for the primate-specific differentially expressed genes.</p

Mechanisms of Dietary Response in Mice and Primates: A Role for EGR1 in Regulating the Reaction to Human-Specific Nutritional Content

<div><h3>Background</h3><p>Humans have a widely different diet from other primate species, and are dependent on its high nutritional content. The molecular mechanisms responsible for adaptation to the human diet are currently unknown. Here, we addressed this question by investigating whether the gene expression response observed in mice fed human and chimpanzee diets involves the same regulatory mechanisms as expression differences between humans and chimpanzees.</p> <h3>Results</h3><p>Using mouse and primate transcriptomic data, we identified the transcription factor EGR1 (early growth response 1) as a putative regulator of diet-related differential gene expression between human and chimpanzee livers. Specifically, we predict that EGR1 regulates the response to the high caloric content of human diets. However, we also show that close to 90% of the dietary response to the primate diet found in mice, is not observed in primates. This might be explained by changes in tissue-specific gene expression between taxa.</p> <h3>Conclusion</h3><p>Our results suggest that the gene expression response to the nutritionally rich human diet is partially mediated by the transcription factor EGR1. While this EGR1-driven response is conserved between mice and primates, the bulk of the mouse response to human and chimpanzee dietary differences is not observed in primates. This result highlights the rapid evolution of diet-related expression regulation and underscores potential limitations of mouse models in dietary studies.</p> </div

Liver gene expression variance among primate species and mice fed human and chimpanzee diets.

<p>The first two principal components of liver gene expression (A) in four primate species (the combined primate dataset, including the RNA-sequencing and microarrays datasets) and (B) in mice fed human ‘cafeteria’, human ‘fast-food’, or chimpanzee diets. The analysis was performed by singular value decomposition, using the “prcomp” function in the R “stats” package <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043915#pone.0043915-Team1" target="_blank">[60]</a>); each gene's expression level was scaled to unit variance before analysis, to yield a z-score. The proportion of variance explained by each principal component is shown in parentheses.</p

Development and application of a modified dynamic time warping algorithm (DTW-S) to analyses of primate brain expression time series

<p>Abstract</p> <p>Background</p> <p>Comparing biological time series data across different conditions, or different specimens, is a common but still challenging task. Algorithms aligning two time series represent a valuable tool for such comparisons. While many powerful computation tools for time series alignment have been developed, they do not provide significance estimates for time shift measurements.</p> <p>Results</p> <p>Here, we present an extended version of the original DTW algorithm that allows us to determine the significance of time shift estimates in time series alignments, the DTW-Significance (DTW-S) algorithm. The DTW-S combines important properties of the original algorithm and other published time series alignment tools: DTW-S calculates the optimal alignment for each time point of each gene, it uses interpolated time points for time shift estimation, and it does not require alignment of the time-series end points. As a new feature, we implement a simulation procedure based on parameters estimated from real time series data, on a series-by-series basis, allowing us to determine the false positive rate (FPR) and the significance of the estimated time shift values. We assess the performance of our method using simulation data and real expression time series from two published primate brain expression datasets. Our results show that this method can provide accurate and robust time shift estimates for each time point on a gene-by-gene basis. Using these estimates, we are able to uncover novel features of the biological processes underlying human brain development and maturation.</p> <p>Conclusions</p> <p>The DTW-S provides a convenient tool for calculating accurate and robust time shift estimates at each time point for each gene, based on time series data. The estimates can be used to uncover novel biological features of the system being studied. The DTW-S is freely available as an R package <it>TimeShift </it>at <url>http://www.picb.ac.cn/Comparative/data.html</url>.</p

Accelerating the Evolution of Nonhuman Primate Neuroimaging

© 2019 Elsevier Inc. Nonhuman primate neuroimaging is on the cusp of a transformation, much in the same way its human counterpart was in 2010, when the Human Connectome Project was launched to accelerate progress. Inspired by an open data-sharing initiative, the global community recently met and, in this article, breaks through obstacles to define its ambitions