Abstract
The neural circuits that support human cognition are a topic of enduring interest. Yet, there are limited tools available to map brain circuits in the human and nonhuman primate brain. We harnessed high-resolution diffusion MR tractography, anatomic, and transcriptomic data from individuals of either sex to investigate the evolution and development of frontal cortex circuitry. We applied machine learning to RNA sequencing data to find corresponding ages between humans and macaques and to compare the development of circuits across species. We transcriptionally defined neural circuits by testing for associations between gene expression and white matter maturation. We then considered transcriptional and structural growth to test whether frontal cortex circuit maturation is unusually extended in humans relative to other species. We also considered gene expression and high-resolution diffusion MR tractography of adult brains to test for cross-species variation in frontal cortex circuits. We found that frontal cortex circuitry development is extended in primates, and concomitant with an expansion in corticocortical pathways compared with mice in adulthood. Importantly, we found that these parameters varied relatively little across humans and studied primates. These data identify a surprising collection of conserved features in frontal cortex circuits across humans and Old World monkeys. Our work demonstrates that integrating transcriptional and structural data across temporal dimensions is a robust approach to trace the evolution of brain pathways in primates.
SIGNIFICANCE STATEMENT Diffusion MR tractography is an exciting method to explore pathways, but there are uncertainties in the accuracy of reconstructed tracts. We broaden the repertoire of toolkits to enhance our ability to trace human brain pathways from diffusion MR tractography. Our integrative approach finds corresponding ages across species and transcriptionally defines neural circuits. We used this information to test for variation in circuit maturation across species and found a surprising constellation of similar features in frontal cortex neural circuits across humans and primates. Integrating across scales of biological organization expands the repertoire of tools available to study pathways in primates, which opens new avenues to study pathways in health and diseases of the human brain.
Introduction
The human frontal cortex (FC) is larger than that of many other species. The claim that the FC, and particularly the prefrontal cortex (PFC) white matter, is unusually large in humans compared with other primates is not without controversy (Semendeferi et al., 2002; Carlén, 2017; Donahue et al., 2018). The expansion of the PFC white matter points to possible modifications in neural circuits across species. However, there are limitations in the tools available to study human brain pathways (Charvet, 2020). At the microscale, RNA sequencing from bulk samples and single cells offers an unprecedented perspective on the classification of cell types and transcriptional profiles. These data, however, lack key information about the structure of circuits (Luo et al., 2017; Nguyen et al., 2017; Saunders et al., 2018; Zhu et al., 2018; Krienen et al., 2020). At the macroscale, diffusion MR tractography offers an exquisite three-dimensional perspective of pathways across the brain (McNab et al., 2009; Takahashi et al., 2012; Wedeen et al., 2012; Edlow et al., 2019; Charvet et al., 2020; Nolan et al., 2021), which makes diffusion MR tractography an exciting tool to explore pathways. The human brain scans we used in this study are of unprecedentedly high resolution for the study of primate brain pathways (Edlow et al., 2019; Nolan et al., 2021), yet the termination sites of tracts can be imprecise. Consequently, results from tractography necessitate validation (Jones et al., 2013; Maier-Hein et al., 2017). We harnessed transcriptomic and structural data across temporal scales to trace the evolution of pathways in the primate brain (Zeng et al., 2012; Krienen et al., 2016; Hendy et al., 2020).
Neuronal populations across the depth of the cortex can be used to predict connectivity patterns (Barbas, 1986; Barbas and Rempel-Clower, 1997). For example, the number of excitatory layer III FC neurons, which preferentially form long-range cross-cortical projections, and the relative number of corticocortical pathways are increased in humans relative to mice (Charvet et al., 2017, 2019; Luo et al., 2017). Several genes, such as some supragranular-enriched (SE) genes, identify long-range projecting (LRP) neurons (e.g., NEFH, VAMP1, SCN4B) in layers II–III and V–VI. However, a major hurdle in linking transcription with tractography is the lack of adequate markers to transcriptionally define neurons that project over long distances (Krienen et al., 2016; Charvet et al., 2019). To overcome this hurdle, we aligned transcriptional and structural variation over the course of human development to transcriptionally define neurons that project through the white matter. We focused our attention on the evolution of corticocortical pathways given major reported differences between humans and mice (Charvet et al., 2017; Luo et al., 2017; Charvet et al., 2019).
Developmental timelines vary tremendously across species, with human development extended compared with many other model systems (Clancy et al., 2001; Charvet, 2020, 2021). We lack appropriate norming procedures to compare brain development across species. We applied machine learning to RNA sequencing data to align ages in humans and macaques. We then tested whether FC maturation is unusually extended in humans relative to macaques after accounting for variation in developmental schedules (Finlay and Darlington, 1995; Clancy et al., 2001; Charvet, 2021). These data reveal that many transcriptional and structural features of FC circuits, previously presumed to be unique to humans, are in fact shared with macaques.
Materials and Methods
We discuss how we found corresponding ages across species and how we tested for cross-species modifications in FC circuitry. All statistics were performed with the programming language R. Ages were expressed in days after conception.
Transcriptional and structural variation to infer corresponding ages.
We used 104 time points to find corresponding ages between humans and macaques (Fig. 1; Liu et al., 2016). These included time points extracted from an RNA sequencing dataset from the PFC of humans and macaques. Details of RNA sequencing data used for these analyses are shown in Figure 2. We selected genes with log-based 10 expressions in reads per kilobase per million (RPKM) > 1 averaged across samples per species, but we did not filter RNA sequencing datasets by hemisphere or sex. We fit a glmnet model (cross-validation: n = 10; repeat = 5; tune length = 5) to log10(RPKM + 1) in humans. We then used this model to predict ages from normalized gene expression in macaques. We considered this model accurate because ages predicted from these and other time points accounted for 95% of the variance when nonlinear regressions were fit to these data (Fig. 1). There may be variation in extrapolating ages across methods so that time points from the glmnet model were included with other time points to find corresponding ages across humans and macaques.
Transcriptional definition of cortical long-range projecting neurons.
We used gene expression data to test for modifications to FC circuits (Figs. 2, 3). We considered SE genes because of differences in expression patterns between humans and mice, and because they are expressed in layers III–V where neuronal somas that project over long distances are located (Gilbert and Kelly, 1975). It is, however, not clear whether other genes are better suited to study LRP neurons. For example, supragranular-enriched genes were identified from a list of only ∼1000 genes so that many genes are here excluded from consideration (Zeng et al., 2012). Moreover, evolutionary modifications in the expression of supragranular-enriched genes may reflect modifications to large layer III neurons as well as other cell populations. We aligned temporal variation in gene expression and structure to identify genes that are expressed by LRP neurons.
We identified LRP markers by testing for associations between transcription and maturation over the course of human development. We considered myelin water fraction (MWF) as an index of white matter maturation across lobes (Deoni et al., 2012), and we used multiple RNA sequencing datasets (Liu et al., 2016; Zhu et al., 2018; Fig. 2). LRP markers were defined as genes (1) that were expressed by layer II–III neurons, but not non-neuronal cells; and (2) that have expression patterns significantly associated with MWF. We added a value of 1 before logging the expression of each sample to consider genes that may not be expressed at a specific age. We fit smooth splines (df = 4) through log10(RPKM) values versus age to extrapolate data at matching ages [n = 10; from 405 d after conception (DAC) to 6 years of age]. Only expression profiles that were significantly associated with MWF across all tested areas were considered LRP markers. We used single-nucleus transcriptomes from the human primary motor cortex (n = 2; age, 18–68 years; n = 76,535 nuclei; Bakken et al., 2020) to filter these genes by cell types. An expressed gene was defined by a count > 0. We also used an in situ hybridization (ISH) Allen Brain Atlas dataset to evaluate the spatial expression of LRP markers and SE genes, but we did not filter these data by hemisphere or sex. We considered RNA sequencing data from macaques (n = 26) and humans (n = 36) and across cortical areas (n = 11; Zhu et al., 2018). We translated age in macaques to that of humans and fit smooth splines (df = 4) to compare normalized gene expression across these two species (Fig. 1).
Structural MR scans to test for variation in FC white matter maturation.
The white matter houses long-range projections. We compared white matter growth to test for variation in the timeline of FC circuits across species. Our definitions of FC and PFC follow those used previously (Hendy et al., 2020; Charvet, 2021). Structural MR scans of macaque brains were obtained from the UNC-Wisconsin Rhesus Macaque Neurodevelopment database (n = 32). We used Fiji to measure the PFC white matter, which was defined as white matter anterior to the corpus callosum, consistent with previous definitions (Fig. 4). For this, we used data generated by this and other studies (Sakai et al., 2011; Young et al., 2017; Charvet, 2021). We measured the PFC white matter area across sections (in at least every other section) using an approach similar to that used previously (Charvet, 2021). We reconstructed volumes by multiplying the area, section thickness, and section spacing. Nonlinear regressions (library easynls; model = 3) were used to detect the age of growth cessation (Fig. 4) with the caveat that some growth may persist beyond identified time points.
Diffusion MR imaging protocols and tractography.
We used diffusion MRI datasets of 14 individual subjects (humans, n = 5; mice, n = 4; macaques, n = 4; and Sykes monkey, n = 1) and combined some of these with tract-tracers (Figs. 5–10). Some of these datasets were previously collected (e.g., Japanese Monkey Brain Center; Calabrese et al., 2015; Ding et al., 2016; Sakai et al., 2018). We used diffusion MR datasets of human brains (n = 4) scanned on a 3 T scanner with a 32-channel head coil (Tim Trio, Siemens) at the Massachusetts General Hospital Athinoula A. Martinos Center for Biomedical Imaging. The resolution of the human MR scans was 0.75 mm isotropic (diffusion MRI data acquisition duration, ∼31 h). Diffusion-weighted data were acquired with a 3D steady-state free precession sequence. Diffusion weighting was applied along 90 directions distributed over the unit sphere (effective b value = 4080 s/mm2; 12 b0 values; TR, ∼28.87 ms; TE, ∼24.44 ms). We also used one diffusion MR scan of a 34-year-old human brain made available by the Allen Brain Institute (Ding et al., 2016). Diffusion-weighted data were acquired with a 3D steady-state free precession sequence (TR = 29.9 ms; α = 60°; TE = 24.96 ms; 900 μm and 1.2 mm; two averages). Diffusion weighting was applied along 44 directions distributed over the unit sphere (effective b value = 3686 s/mm2; 8 b = 0 images; Ding et al., 2016). An additional lower-resolution scan was also acquired in the same individual. We also collected diffusion MR scans of mouse brains (n = 8) at postnatal day 21 (P21) and P60 using a 9.4 T Bruker scanner at the University of Delaware (Figs. 7–9). A 3D diffusion-weighted spin-echo echoplanar imaging sequence (TR, ∼500 ms; TE, ∼40 ms; resolution, 100 μm isotropic) was used to image the mouse brains. Sixty diffusion-weighted measurements (b = 4000 s/mm2) and non-diffusion-weighted measurements (5 b = 0 s) were acquired.
Diffusion MRI data were processed with Diffusion Toolkit (www.trackvis.org; threshold angle, 45° angles for 13 of 14 brains). In some panels, fibers were skipped for visualization but not for the analyses. With the exception of the human brain scans from the Allen Brain Atlas, we used high-angular resolution diffusion MR imaging (HARDI) tractography to generate whole-brain tractography. The orientation distribution functions (ODFs) were normalized according to the maximum ODF length within each voxel. Fractional anisotropy (FA) was calculated from orientation vectors by fitting the data to the tensor model (Takahashi et al., 2012). We used fiber assessment by continuous tracking with HARDI. No fractional anisotropy threshold was applied in reconstructing tracts, which is consistent with previous work (Takahashi et al., 2012). We used TrackVis (http://trackvis.org) to visualize and quantify pathways. Details on the individuals scanned, including their age, and the spatial resolution are in the legend of Figure 5.
We quantified the relative number of pathway types coursing through the frontal cortex white matter. We randomly selected a region of interest (ROI) consisting of a voxel in the frontal cortex white matter. Voxels ranged from 0.1 × 0.1 × 0.1 mm to 1.2 × 1.2 × 1.2 mm in humans. ROIs were placed at equidistant sites across the anterior-to-posterior axis of the frontal cortex. We used coronal planes to randomly select sites along the dorsal-to-ventral and medial-to-lateral axis of the frontal cortex white matter. We then classified pathway types coursing through these ROIs to test for species differences in pathway types.
Varying sampling and imaging procedures of diffusion MR scans.
We tested how sampling, resolution, and imaging protocols impact the percentage of pathway types, as it is unclear how these parameters impact results from tractography. We randomly subsampled the number of sites and extracted the average proportion of pathway types in the FC for each individual (Fig. 8). Variation in the percentage of pathway types was minor regardless of the number of randomly selected voxels, imaging procedures, and resolution (Fig. 8). Our analyses suggest that the differences in the pathway types or a lack thereof are robust to sampling size, resolution, and imaging protocols.
Tractography quantification and comparison with tract-tracers.
The accuracy of diffusion MR tractography has remained elusive because of a lack of alternative tools to map the pathways in the human brain. We compared diffusion MR tractography with EGFP injections to assess which metrics should be extracted from tractography in mice (Fig. 7). Given that the accuracy of tractography appeared compromised at the gray matter–white matter boundary (Figs. 7a–h, 9j–s), we randomly selected voxels across spaced planes along the anterior-to-posterior axis of the FC white matter, with planes varying across individuals. We then classified pathways based on orientation and direction within the white matter. Fibers were classified as those belonging to the corpus callosum, cingulate bundle, other corticocortical, or subcortical–cortical pathways (Fig. 10). If a pathway was observed coursing through the dorsal midline, it was considered callosal regardless of its terminations. Pathways connecting the cortical and lateral limbic structures were considered cortico–subcortical pathways. U fibers or long-range pathways connecting cortical areas (e.g., arcuate fasciculus) were considered corticocortical. We focused on corticocortical pathways because of the expansion of layer II–III in primates relative to rodents. We tested how sampling, resolution, and imaging protocols impacted pathway types (Fig. 8). We observed that HARDI and diffusion tensor imaging (DTI) reconstruction yielded comparable results in the mouse FC at P60 (y = 0.82× + 4.52; R2 = 0.89; p < 0.01). We also tested for temporal variation in the relative percentage of pathway types of mouse brains at P21 versus P60. An ANOVA with age and pathway types as factors showed trends but no signific effect for age (p < 0.05) on the pathway types (F = 13.16, p < 0.01; n = 32). Frontal cortex white matter growth in mice ceases before P60 (Hendy et al., 2020); therefore, P60 should represent adult proportions in pathway types.
We compared diffusion MR tractography with tract-tracers in mice to assess which metrics should be extracted from tractography. We compared diffusion MR tractography in P60 mouse brains with viral tracer experiments, which involved EGFP injections into selected regions of the mouse brain. These data were made available by the Allen Mouse Brain Connectivity Atlas (https://connectivity.brain-map.org/; Fig. 7). We identified injection sites and set ROIs to compare projections identified from the tract-tracers with those from tractography. We found strong concordance in the location and the orientation of pathways coursing through the white matter. We observed that tracts did not necessarily penetrate the gray matter in the same location as the tract-tracers. Axons make an abrupt turn at the junction of the gray matter and the white matter (Figs. 7, 9). These sharp turns challenge the accuracy of tractography, as evident from the qualitative observations of tract-tracers and tractography (Figs. 7, 9).
We compared tract-tracers with diffusion MR tractography to guide the development of quantitative approaches to study FC pathways. As there is no evidence of a relationship between fiber numbers and circuits (e.g., axons), we did not quantify fibers. Instead, we selected voxels through the FC white matter, classified pathway types, and quantified the proportion of pathway types across the FC and the PFC. In relatively rare cases, we refrained from classifying the pathway types if the tractography was not clearly classifiable.
Comparative analyses of supragranular-enriched gene expression in the FC.
We leveraged RNA sequencing datasets from bulk samples (n = 24; Hawrylycz et al., 2012; Bozek et al., 2014; Miller et al., 2014; Li et al., 2018), single cells, and ISH to detect cross-species variation in SE and LRP gene expression across the mouse, macaque, and right FC of humans (Figs. 2, 11). We performed principal component analyses (PCAs) on these data, and measured gene expression intensity across layers as we had done previously (Charvet et al., 2017; Charvet, 2021).
We measured the intensity of gene expression across layers in a manner similar to that done previously (Charvet et al., 2019; Hendy et al., 2020; Charvet, 2021). We downloaded ISH images of the FCs from humans, macaques, and mice from the Allen Brain Atlas. We analyzed the expression of select SE genes within layers II–IV and V–VI. The boundary across layers IV and V was based on the cytoarchitecture from Nissl staining and RORB mRNA expression. Layer IV was defined as a cell-dense zone characterized by preferential expression of RORB. Layer VI was bound by the white matter. We measured the expression intensity from ISH images of CRYM in humans, macaques, and mice (Fig. 10m). We used ImageJ software to randomly select areas within the FC, then we placed a rectangular grid to capture gene expression intensity across layers II–IV and V–VI. The grid was perpendicular to the cortical surface. The height varied with cortical thickness. Frame widths were 1000 µm in mice, macaques, and humans. We binarized the images and measured the intensity of expression in layers II–IV and V–VI. We computed the ratio of these values to compare the relative expression of genes of interest in the upper and the lower layers. A value >1 indicates that the gene is preferentially expressed in layers II–IV.
Data availability.
Data and scripts are available on DRYAD (https://datadryad.org/stash/share/gcCyHjb2tbV2Irb5D2H5U3gIt5Apq1v1dKCF83C-nA8) and as Extended Data 1.
Results
We used structural and transcriptional variation to find corresponding ages across species. We then integrated these data to test for cross-species variation in FC neural circuits.
Corresponding time points during postnatal development
We used time points to find corresponding ages between humans and macaques (n = 96). These data included time points from a glmnet model applied to normalized gene expression in RPKM from human and macaque PFCs of different ages (n = 38 humans, n = 31 macaques; Figs. 1, 2; Liu et al., 2016). We selected genes with minimum expression averaged across samples [i.e., log10(RPKM) > 1; n = 8014]. We did not predict ages beyond 10.6 years in macaques because samples at late stages are sparse (Fig. 2i,j). We trained a glmnet model to predict ages from normalized gene expression in humans (cross-validation = 10; n = 38). This model has high predictive accuracy because sampling 70% of the data to train the model (R2 = 0.99) resulted in strong correlations between log-10 transformed predicted and observed ages (R2 = 0.98). The same approach applied to normalized gene expression in macaques predicted age of macaques (R2 = 0.99). We then used a glmnet model (R2 = 0.97; λ = 0.053) trained from human samples (n = 38) to predict ages from normalized gene expression in macaques. This approach yielded 23 corresponding time points in humans and macaques. We fit a quadratic model on the log-transformed time points in humans and macaques (y = −1.96 + 2.74× – 0.31 × 2; R2 = 0.95, n = 96) to find corresponding ages across these two species. Early in development, ages are approximately similar in the two species, but time points occur much later in humans than in macaques by adulthood (Fig. 1). We next tested whether FC development at the transcriptional and the structural level (Figs. 1, 2, 4) is unusually extended in humans.
Table 1-1
Corresponding time points across humans and macaques. Download Table 1-1, XLSX file.
Table 1-2
List of RNA sequencing datasets and ages of samples used in the present study. Download Table 1-2, XLSX file.
Table 2-1
Post hoc Tukey's tests between correlation coefficients of LRP markers across different regions and corticocortical pathway types across humans, macaques, and mice. Download Table 2-1, XLSX file.
Transcriptionally defining neural circuits
We considered the expression of SE genes (n = 16) and other sets of markers to test for cross-species variation in FC circuit development (Figs. 1–3, 10–15). We mapped age in macaques onto humans, and we extrapolated normalized gene expression at corresponding time points in both species. We then correlated SE gene expression over the course of development in humans and macaques. We extracted correlation coefficients to test whether the pattern of gene expression over the course of development is unusual in the frontal cortex of humans versus macaques (Figs. 2, 11). We found that these correlation coefficients are not significantly different between frontal and other cortical areas (ANOVA: F = 0.145; p = 0.99; n = 176). Although these findings suggest conservation in FC circuit maturation, it is not clear whether genes other than SE genes are best suited to track FC circuit maturation. We therefore systematically tested for genes that could be used as markers of neurons that project through the white matter.
We identified LRP markers by testing for associations between gene expression and myelination used as an index for long-range projection maturation (Fig. 11; Deoni et al., 2012; Zhu et al., 2018) across cortical areas. Genes were filtered to have minimum expression averaged across regions and ages [log10(RPKM) > 1]. We extrapolated MWF values from previous work (Fig. 11d; Deoni et al., 2012). We fit a smooth spline through log-based 10(RPKM) values versus age (i.e., log-10 d after conception) to extrapolate gene expression and MWF at matching ages (n = 10) and across cortical areas (n = 10). We iteratively tested for associations between gene expression and MWF across cortical areas. We selected significant and positive associations (slope > 0) after correcting for multiple testing [Benjamini–Yekutieli (BY) test, p < 05]. We then used single-cell RNA sequencing (Bakken et al., 2020) from the motor cortex to exclude genes expressed by non-neuronal cells but include those expressed by layer II–III neurons. These filtering steps resulted in 250 LRP markers. These genes are expressed by large pyramidal neurons in the cortical gray matter and increase in expression postnatally, and a subset of these are SE genes (e.g., NEFH, VAMP1; Figs. 2, 3, 10–12, 15). These observations confirm the validity in using these genes as LRP markers.
We tested whether temporal profiles in the expression of these LRP markers are different between humans and macaques. We fit a smooth spline through the log2(RPKM) values versus log-10 ages expressed in days after conception, and we extrapolate gene expression at equivalent ages in macaques and humans (n = 10). We correlated the expression of LRP markers (n = 234) across cortical areas in humans and macaques (n = 11) over the course of development. An ANOVA on the correlation coefficients pointed to significant differences across cortical areas (ANOVA: F = 3.12, p < 0.01; n = 2574), but post hoc Tukey's HSD tests showed that correlation coefficients from the FC areas were not significantly different (significance threshold set to p < 0.01) from those of other cortical areas (Fig. 2). These data showcase strong similarity in FC development between humans and macaques.
Structural variation in FC circuit development
We compared white matter growth across species to test whether LRP neuron maturation is unusually extended in humans relative to macaques (Fig. 4). Over the course of development, the white matter grows and ceases postnatally in both humans and macaques. We fit nonlinear regressions with age in days after conception as the predictor variable, and the FC and log10-based PFC white matter volume as the dependent variable to compare the time course of white matter growth across species (Fig. 4). We captured the ages at which the FC and PFC white matter reach percentages of adult volumes (e.g., 80%, 90%, 100%; Fig. 3d–h). We found that these time points overlap with other time points (Fig. 4c). For example, the PFC white matter ceases to grow at 1.74 years of age in humans (R2 = 0.76, n = 28, p < 0.001; Fig. 4f) and at ∼1 year of age in macaques (R2 = 0.66, n = 32, p < 0.001; Fig. 4g). The addition of time points from these growth trajectories did not account for a significant percentage (p = 0.24) of the variance (ANOVA: F = 1355, p < 0.05; n = 102). The age of growth cessation was largely invariant of sample size, demonstrating that these results were not driven by outliers (Fig. 4i–n). These data show no evidence for protracted FC white matter maturation in humans once cross-species variation in developmental schedules are accounted for.
Table 4-1
Nonlinear regressions used to compare growth of the PFC and FC white matter. Download Table 4-1, XLSX file.
Table 4-2
PFC measurements collected at various time points in macaques. Download Table 4-2, XLSX file.
Cross-species variation in tractography of adult FC circuits
We used diffusion MR tractography to test for modifications to FC connectivity across humans, macaques, and mice (Figs. 5–10). We compared the orientation and location of fibers from tractography, tract-tracers, immunohistochemical markers, and myelin stains to assess sites of potential inaccuracies in the tractography. These observations were used to develop a quantitative procedure to compare FC pathway types across species (Figs. 7, 9). In mice, the direction and location of tracts within the white matter were concordant across methods, but tracking accuracy appeared to be limited at the gray matter–white matter boundary. We therefore classified pathways based on the orientation and direction of pathways within the white matter (Fig. 9a–i). We placed voxels in spaced sections through the FC white matter, and we classified pathways according to their orientation (Figs. 7–10). The percentage of corticocortical pathways was significantly greater in the primate FC relative to mice. No significant differences were observed between humans and macaques (ANOVA: F = 14.82, p < 0.01; n = 14). Overall, we found no significant differences in the relative proportion of FC and PFC pathway types across primates (t tests; Fig. 10g–i). Subcortical pathways were significantly expanded in the PFC of humans versus macaques (t = 5.1, p < 0.008). Relative FC pathway types were relatively invariant with respect to sampling, tested directions, pathway reconstruction, and resolution (Figs. 8–10). Although the cross-species variation in pathway types or lack thereof were robust to variations in imaging parameters, the limitations inherent to tractography led us to generate evidence from multiple scales to ensure the accuracy of these findings.
Table 5-1
Scanning parameters used for diffusion MR tractography of humans, macaques, and mice. Download Table 5-1, XLSX file.
Table 7-1
List of tract-tracer experiments used to compare diffusion MR tractography with tract-tracers. Download Table 7-1, XLSX file.
Table 10-1
Statistics on pathway types through the PFC and remaining FC in humans and macaques. Download Table 10-1, XLSX file.
Table 10-2
Proportion of pathway types across the FC of humans, macaques, and mice. Download Table 10-2, XLSX file.
Table 10-3
Relative gene expression and thickness measurements across FC layers in humans, macaques, and mice. Download Table 10-3, XLSX file.
Table 10-4
Statistics results from Tukey's HSD tests to compare the relative thickness of FC layer II–III in humans, macaques, and mice. Download Table 10-4, XLSX file.
Cross-species variation in transcription of adult FC circuits
Given that FC corticocortical pathways are expanded in primates, we focused on layer II–III neurons, many of which form corticocortical projections. First, we considered the cytoarchitecture to test for modifications in FC long-range projecting fibers across mice, macaques, and humans (Fig. 10). We observed that layer II–III in the FC, as defined transcriptionally (i.e., CALB1 expression) and cytoarchitecturally, is relatively expanded in humans and macaques compared with mice. This was true whether we considered the anterior cingulate (CALB1: ANOVA: F = 22.72, p < 0.01; n = 8; Nissl: F = 20.17, p < 0.01) or the superior frontal gyrus (CALB1: ANOVA: F = 15.62, p < 0.01; n = 8; Nissl: F = 15.16; p < 0.01; Fig. 10j). Post hoc Tukey's HSD tests showed that the relative thickness of FC layer II–III is significantly different between primates and mice but not between humans and macaques (Fig. 10k). Moreover, the expansion of layer II–III in macaques is concomitant with an expansion of SE gene expression in the layer (Figs. 10–15). There are major differences in layer II–III between primates and mice, but strong similarity between humans and macaques.
We next considered transcriptional variation of SE and LRP markers to investigate cross-species variation in adult FC circuits (Figs. 10–15). In a PCA applied to log-transformed expressed orthologous genes, genes clustered according to the phylogenetic relationship of species [Fig. 10m; first three principal components (PCs): 71.77% of the variance; n = 10 682]. In PCAs on the log-transformed expressed SE genes (Fig. 10n; first three PCs: 73.9% of the variance; n = 17) and LRP markers (Fig. 10o; first three PCs: 77.07%; n = 235), primates clustered together, demonstrating the strong similarity in expression of SE and LRP genes between humans and macaques. We further evaluated the spatial pattern of expression of an SE gene named CRYM to confirm similarities in SE gene expression in the FC of humans and macaques (Figs. 10m–p, 14). We quantified the relative expression of CRYM across supragranular and infragranular layers in different cortical areas of humans, macaques, and mice. We found that expression patterns varied across species whether we compared the mouse FC with the superior frontal gyrus (ANOVA: F = 8.94, p < 0.01; n = 11), the anterior cingulate (F = 4.86, p < 0.01; n = 11), or the precentral gyrus (F = 22.04, p < 0.01; n = 8) of macaques and humans (Fig. 10m). Tukey's HSD tests showed no significant differences in the expression profile of CRYM between humans and macaques. We found significant differences between primates and mice. Together, these data showcase the strong similarities in FC neural circuitry between humans and macaques.
Table 11-1
Cell clusters from the human primary motor cortex used to define layer II–III neurons and non-neuronal cells. Download Table 11-1, XLSX file.
Table 11-2
Equations used to capture temporal trajectories in MWF. Download Table 11-2, XLSX file.
Table 11-3
List of LRP markers identified from associations between normalized gene expression and MWF. Download Table 11-3, XLSX file.
Discussion
The integration of transcription with neuroimaging is an effective approach to identify conservation and variation in biological programs linked to modifications of circuits. This integrative approach creates novel opportunities to study circuits in human health and disease, and across species.
Corresponding ages from transcriptional and structural variation
We identified corresponding postnatal ages in humans and macaques. This work builds on a previous line of work called the Translating Time Project, which relied on abrupt changes that unfold during development to find corresponding ages across model organisms and humans (Finlay and Darlington, 1995; Clancy et al., 2001; Zhu et al., 2018; Charvet, 2021). We collected 354 transformations across 19 mammalian model organisms to find corresponding time points during prenatal development. Extracting time points from gradual changes in transcription and structure, in addition to abrupt transformations, reveals corresponding postnatal ages across species (Charvet et al., 2017; Zhu et al., 2018). Each metric has uncertainties, but the use of multiple metrics ensures the robust determination of age alignment across species. The findings serve to extrapolate information from model systems to humans and to identify which developmental programs occur for an unusually long or short time in humans.
Limitations and opportunities for diffusion MR imaging
Diffusion MR tractography reveals a three-dimensional perspective of pathways (Wedeen et al., 2012; Edlow et al., 2019; Charvet et al., 2020). The sole use of tractography to trace neural circuits is problematic because of its limited ability to resolve crossing fibers or locate tract termination sites (Figs. 7, 9). Considering these caveats, we developed a novel approach to quantify FC pathway types. This approach was guided by a host of techniques to label axons, including tract-tracers, immunohistochemistry, and myelin stains. The orientation and location of fibers from diffusion MR tractography aligned with observations from histology and tract-tracers in the white matter (Fig. 7). Our analyses, which were designed to overcome limitations of diffusion MR tractography, withstood variations in sampling, direction, and resolution (Fig. 8). Nevertheless, we still lack methods to ensure the accuracy of diffusion MR tractography (McNab et al., 2009; Thomas et al., 2014). Given the lack of alternative methods to map connections, we integrated our findings with transcriptomic and structural information to more accurately trace pathways across species.
Enhanced methods to study FC circuits in primates reveal conservation
Although RNA sequencing from bulk and single cells offers an unprecedented perspective to track developmental programs, these metrics lack key information about the structural composition of circuits. There is often a lack of one-to-one correspondence between projection patterns and gene expression (Molyneaux et al., 2009). We, therefore, identified LRP markers by aligning structural and transcriptomic variation during human development. This approach was instrumental in identifying genes that can be used to track modifications to long-range circuits in different species without specifically tracking the location of pathway end points. Modification in the absolute or temporal profiles in the expression of these genes may be used to detect modifications in connectivity profiles of long-range projection patterns.
Past work considered white matter volumes or transcriptional information to assess whether FC circuits are unusual in humans. However, these studies have traditionally used these methods in isolation, and did not reach a consensus on whether the size or the developmental timeline of the frontal cortex or prefrontal cortex is unusually expanded in humans versus other primates (Semendeferi et al., 2002; Carlén, 2017; Donahue et al., 2018). The lack of tools to compare brain development (e.g., age alignments) and the small samples available at a single scale of study have precluded our ability to rigorously test the notion that the human frontal cortex timeline is extended relative to other species. We have worked to expand the repertoire of integrative tools to study pathways across the mammalian brain. We integrated diffusion MR tractography with histologic information (Charvet et al., 2017) and, subsequently, with one or a handful of genes (Charvet et al., 2019; Charvet, 2021). The present study stands out relative to past studies in harnessing a multiomics approach to trace the evolution of frontal cortex pathways.
We drew from multiple lines of evidence to test for modifications in FC circuitry across species over the course of development and in adulthood. We compared trajectories in transcriptional profiles of LRP neurons, white matter maturation, pathway types, and transcription across layers. Testing for differences across scales highlighted many conserved features in FC circuitry across humans and macaques (Sakai et al., 2011; Zhu et al., 2018), though there were some intriguing differences across species. Specifically, we found that the human PFC white matter possesses increased cortico–subcortical pathways relative to macaques. More work is needed to transcriptionally define pathway types across the primate frontal cortex to explore these species differences. More generally, the study shows that tracing human FC circuits from tractography, transcriptomic, and temporal dimensions is an exciting approach, one that will provide a more complete understanding of the evolution of circuits in the human and nonhuman primate brain.
Footnotes
This work was supported by Eunice Kennedy Shriver National Institute of Child Health and Human Development Grants 1R21-HD-101964-01A1 and 7R21-HD101964-02 (to C.J.C.); Institutional Development Award (IDeA) Networks of Biomedical Research Excellence (INBRE) Pilot Grant P20-GM-103446 from the National Institute of General Medical Sciences (NIGMS; to C.J.C.); NIGMS Core Center Access Award P20-GM-103446 (to C.J.C.); startup funds from Auburn University (to C.J.C.); Centers of Biomedical Research Excellence Grant 5P20-GM-103653 for research at Delaware State University; National Institute of Neurological Disorders and Stroke Grant R21-NS-109627 (to B.L.E.); and a James S. McDonnell Foundation grant (to B.L.E.). Opinions are not necessarily those of the National Institutes of Health. We thank Drs. Harrington, Whitaker, and Halley for help, and the Japan Monkey Center for access to brain scans.
The authors declare no competing financial interests.
- Correspondence should be addressed to Christine J. Charvet at charvetcj{at}gmail.com