Abstract
The neural circuits that support human cognition are a topic of enduring interest. Yet, the lack of tools available to map human brain circuits has precluded our ability to trace the human and non-human primate connectome. We harnessed high-resolution connectomic, anatomic, and transcriptomic data 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 cortico-cortical 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 connectomic data across temporal dimensions is a robust approach to trace the evolution of brain connectomics in primates.
Significance Statement We lack appropriate tools to visualize the human brain connectome. We develop new approaches to study connections in the human and non-human primate brains. The integration of transcription with structure offers an unprecedented opportunity to study circuitry evolution. 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 connections in primates, which opens new avenues to study connections 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 (1-3). The expansion of the PFC white matter points to possible modifications in neural circuits across species. However, the lack of available tools has precluded our ability to visualize connections in the human brain (4). 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 (5-9). At the macroscale, diffusion MR tractography offers an exquisite three-dimensional perspective of pathways across the brain (10-15), which makes diffusion MR tractography an exciting tool to explore the connectome. The human brain scans we used in this study are of unprecedentedly high resolution for the study of primate brain connectomics (14, 15), yet the termination sites of tracts can be imprecise. Consequently, results from tractography necessitate validation (16, 17). We harnessed transcriptomic and connectomic data across temporal scales to trace the evolution of connections in the primate brain (18-20). We show that the integration of RNA sequencing with diffusion MR tractography is a robust approach to study connections.
Neuronal populations across the depth of the cortex can be used to predict connectivity patterns (21-22). For example, the number of excitatory layer III FC neurons, which preferentially form long-range cross-cortical projections, is increased in humans relative to mice (5, 23-24). These observations suggest a major expansion in long-range cortico-cortical pathways in humans relative to mice. Comparative analyses of diffusion MR tractography have likewise revealed an expansion of cortico-cortical fibers in primates relative to rodents (23). 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 (18, 24). 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 cortico-cortical pathways given major reported differences between humans and mice (5, 23-24).
Developmental timelines vary tremendously across species, with human development extended compared with many other model systems (4, 25, 26). Because 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 (25, 26, 27). These data reveal that the transcriptional and structural features of FC circuits, presumed to be unique to humans, are in fact shared with macaques.
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; Table S1). These data included time points from a glmnet model applied to normalized gene expression in reads per kilobase per million (RPKM) from human and macaque PFC of different ages (n=38 humans, n=31 macaques; Fig. 1 and SI Appendix, Fig. S1; (28)). We selected genes with minimum expression averaged across samples (i.e., log10(RPKM)>1; n=8,014). We did not predict ages beyond 10.6 years in macaques because samples at late stages are sparse (SI Appendix, Fig. S1). 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; lambda=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+ 2.74x-0.31x2; R2=0.95, n=96) to find corresponding ages across fetal and postnatal time points. Early in development, ages are roughly 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 and 2) is unusually extended in humans.
Transcriptional-defined Neural Circuits to Detect Variation in Neural Circuit Maturation
We used the expression of SE genes (n=16) and LRP markers to test for cross-species variation in FC circuit development (Figs. 1 and 2, SI Appendix, Figs. S2–S4; Table S2). We mapped age in macaques onto humans, extrapolated normalized gene expression at corresponding time points in both species, and then correlated SE gene expression in humans and macaques (Fig. 1). We found that these correlation coefficients are not significantly different across 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 across species.
We identified LRP markers by testing for associations between white matter maturation and gene expression (Fig. 2; (8, 29) across cortical areas. Genes were filtered to have minimum expression averaged across regions and ages (log10(RPKM)>1). We extrapolated myelin water fraction (MWF) values from previous work (29) (Table S3). We fit a smooth spline through log-based 10(RPKM) values versus age (i.e., log-10 days 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 with a linear model to normalized gene expression. We selected significant and positive associations (slope>0) after correcting for multiple testing (Benjamini & Yekutieli: BY; p<05). We used single-cell RNA sequencing (30) from the motor cortex to exclude genes expressed by non-neuronal cells but include those expressed by layer II-III neurons (SI Appendix, Table S4). These filtering steps resulted in 250 LRP markers. These genes are expressed by large pyramidal neurons, which increase in expression postnatally, and a subset of these are SE genes (e.g., NEFH, VAMP1)(SI Appendix, Table S5 Figs. S2–S4). These observations support the validity of 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 to extrapolate gene expression at equivalent ages in macaques and humans (n=10). We correlated the expression of LRP markers (n=234) over the course of development and across cortical areas (n=11). An ANOVA on the correlation coefficients pointed to significant differences across cortical areas (ANOVA: F=3.12, p<0.01; n=2,574), but post hoc Tukey 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; statistics in SI Appendix, Table S6). These data showcase the 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 (Fig. 1 and SI Appendix, Fig. S6). We fit non-linear 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 (Fig. 1; see SI Appendix, Table S7; Figs. S5 and S6). We captured the ages at which the FC and PFC white matter reach percentages of adult volumes (e.g., 80%, 90%, 100%; SI Appendix, Fig. S5). We found that these time points overlap with other time points (Fig. 1). 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) and at ∼1 year of age in macaques (R2=0.66, n=32, p<0.001). An ANOVA showed that the addition of time points from these growth trajectories did not account for a significant percentage (p=0.24) of the variance (F=1,355, p<0.05; n=102). We subsampled these data and showed that the age of growth cessation was largely invariant with respect to sample size, demonstrating that these results were not driven by outliers (SI Appendix, Fig. S6). We found no evidence for protracted FC white matter maturation in humans.
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. 3 and 4; SI Appendix, Figs. S7–S14, Tables S8-S10). We developed a new approach to quantify pathways. In developing this approach, 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 (Fig. 4 and SI Appendix, Figs. S7– S14). In mice, the direction and location of tracts within the white matter were concordant across methods but tracking accuracy appeared limited at the grey to white matter boundary. We therefore classified pathways based on the orientation and direction of pathways within the white matter (Fig. 4). We placed voxels in spaced sections through the FC white matter and we classified pathways according to their orientation (Figs. 3 and 4). The percentage of cortico-cortical pathways was significantly greater in the primate FC relative to mice but 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. 3, statistics in SI Appendix, Table S8). 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 (SI Appendix, Figs. S12–S14). 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.
Cross-species Variation in Transcription of Adult FC Circuits
Given that FC cortico-cortical pathways are expanded in primates, we focused on layer II-III neurons, many of which form cortico-cortical projections. First, we considered the cytoarchitecture of the layer to test for modifications in FC long-range projecting fibers across mice, macaques, and humans (Fig. 3). We observed that layer II-III in the FC, as defined transcriptionally (i.e., CALB1 expression) and cyto-architecturally, 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. 3I). Post-hoc Tukey HSD tests showed the relative thickness of FC layer II-III is significantly different between primates and mice but not between humans and macaques (Fig. 3; statistics in SI Appendix, Table S12 and 13). Moreover, the expansion of layer II-III in macaques is concomitant with an expansion of SE gene expression in the layer (SI Appendix, Figs. S15-–S18). Our results showed that there are major differences in layer II-III between primates and mice but strong similarity across humans and macaques.
We next considered transcriptional variation of SE and LRP markers to investigate cross-species variation in adult FC circuits (Fig. 3 and SI Appendix, S15–S18). In a principal component analysis (PCA) applied to log-transformed expressed orthologous genes, genes clustered according to species (Fig. 3L; first 3 principal components (PCs): 71.77% of the variance; n=10,682). In PCAs on the log-transformed expressed SE genes (Fig. 3M; first 3 PCs: 73.9% of the variance; n=17) and LRP markers (Fig. 3N; first 3 PCs: 77.07%; n=235), primates clustered together, demonstrating the strong similarity in expression of SE and LRP genes between humans and macaques. We also evaluated CRYM expression (i.e., an SE gene) to confirm similarities in SE gene expression across humans and macaques (Fig. 3O–R and SI Appendix, Fig. S17, Table S12). We quantified the relative expression of CRYM across supra- and infragranular layers in humans, macaques, and mice and found that expression patterns varied across species whether we compared the mouse FC with the superior frontal gyrus (ANOVA: F=8.94; n=11; p<0.01), the anterior cingulate (F=4.86; n=11; p<0.01), or the precentral gyrus (F=22.04; n=8; p<0.01) of macaques and humans (Fig. 3R). Tukey HSD tests showed no significant differences in the expression profile of CRYM between humans and macaques but differences between primates and mice (SI Appendix, Table S13). These data showcase the strong similarities in FC neural circuitry between humans and macaques.
Discussion
The integration of transcription with neuroimaging is an effective approach to identify conservation and variation in biological programs linked to modifications of circuits in human evolution. This integrative approach identified corresponding ages, and tested for FC neural circuit variation across species, which 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 (www.translatingtime.org), which relied on abrupt changes that unfold during development to find corresponding ages across model organisms and humans (8, 25-27). 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 (8, 23). Each metric may have uncertainties, but the use of multiple metrics ensures the robust determination of corresponding ages across species.
Limitations and Opportunities for Diffusion MR Imaging
The integration of transcription with diffusion MR tractography permits tracing the evolution of FC neural circuits. Diffusion MR tractography reveals a three-dimensional perspective of pathways (12-14), However, 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 (Fig. 4 I-J, K-L). Considering these caveats, we compared FC pathway types coursing through the white matter. We selected this approach because of our comparisons of tractography with tract-tracers, immunohistochemistry, and myelin stains. The orientation and location of fibers aligned with observations from histology and tract-tracers (Fig. 4). Our analyses, which were designed to overcome limitations of diffusion MR tractography, withstood variations in sampling, direction, and resolution (Figs. S13 and S14). Nevertheless, we still lack methods to ensure the accuracy of diffusion MR tractography (10, 31). The lack of alternative methods to map connections motivated us to integrate transcriptomic and structural information to rigorously trace connections 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 (32). We, therefore, identified LRP markers by aligning structural and transcriptomic variation during human development. This novel approach was instrumental in systematically identifying genes expressed by LRP neurons. The concordance of findings from transcription and neuroimaging enabled tracing modifications to circuits in primates.
We drew from multiple lines of evidence to test for modifications in FC circuitry across species. We compared trajectories in transcriptional profiles of LRP neurons, white matter maturation, pathway types, and transcription across layers. Testing for differences across these scales revealed no evidence that human FC circuits are unusual relative to macaques. Rather, these results systematically highlighted conservation in FC circuits across humans and other primates (8, 33). Past work considered white matter volumes or transcriptional information in isolation to assess whether FC circuits are unusual in humans but these studies did not reach a consensus. We show that tracing human FC circuits from connectomic, transcriptomic, and temporal dimensions moves us forward in mapping circuits in the human and non-human primate brain.
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. All ages were expressed in days after conception.
Transcriptional and Structural Variation to Infer Corresponding Ages Across Species
We used 104 time points to find corresponding ages between humans and macaques (Fig. 1 and SI Appendix, Table S1, Fig. S1; 28). These included time points extracted from an RNA sequencing dataset from the PFC of humans and macaques. We selected genes with log-based 10 expressions in RPKM>1 averaged across samples per species. 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). Due to possible variation in extrapolating ages from machine learning tools, the time points from the glmnet model were included with other time points to find corresponding ages across species.
Transcriptional Definition of Cortical Long-Range Projecting Neurons
We used gene expression data to test for modifications to FC circuits. 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 (34). It is, however, not clear whether other genes are better suited to study LRP neurons. We therefore developed novel approaches to systematically identify LRP markers by aligning temporal variation in gene expression and structure.
We identified LRP markers by testing for associations between transcription and maturation over the course of human development. We considered MWF as an index of white matter maturation across lobes (29) (SI Appendix, Table S3) and used multiple RNA sequencing datasets (8, 28) (Fig. S1). 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 (degrees of freedom=4) through log10(RPKM) values versus age to extrapolate data at matching ages (n=10; from 405 DAC to 6 years of age). Only expression profiles that 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; 18-68 years, n=76,535 nuclei) (30) to filter these genes by cell types (Table S4). An expressed gene was defined by a count>0. We also used in situ hybridization from the Allen Brain Atlas dataset to evaluate the spatial expression of LRP markers and SE genes in order to test whether FC neural circuitry maturation is unusual in humans relative to macaques. We considered RNA sequencing data from macaques (n=26) and humans (n=36) and across cortical areas (n=11) (8). We translated age in macaques to that of humans and fit smooth splines (degrees of freedom=4) to compare normalized gene expression across these two species.
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 (19, 26). Structural MR scans of macaque brains were obtained from the UNC-Wisconsin Rhesus Macaque Neurodevelopment database (n=32; SI Appendix, Table S11). 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. 1 and SI Appendix, S5). For this, we used data generated for this study other studies (26, 33, 35). We measured the PFC white matter area across sections (in at least every other section) using an approach similar to that used previously (26). 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. 2 and SI Appendix, Figs. S5 and S6) 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 individuals: humans n=5), mice (n=4), macaques (n=4), and Sykes monkey(n=1). Some of these datasets were previously collected (e.g., Japanese Monkey Brain Center; 36-38). We used diffusion MR datasets of human brains (n=4) scanned on a 3T Siemens Tim Trio scanner with a 32-channel head coil 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 hours). 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 = 4,080 s/mm2; 12 b0s; TR∼28.87ms, TE∼24.44ms). We collected diffusion MR scans of mouse brains (n=8) at postnatal day (P) 21 and P 60 using a 9.4T Bruker scanner at the University of Delaware (Fig. 4 and SI Appendix, Figs. S9 and S14). A three-dimensional diffusion-weighted spin-echo echo planar imaging (SE-EPI) sequence (TR of ∼500 ms, TE∼40 ms; resolution: 100 um isotropic) was used to image the mouse brains. Sixty diffusion-weighted measurements (b=4,000 s/mm2) and non-diffusion-weighted measurements (5 b=0s) were acquired.
Tractography Quantification and Comparison with Tract-tracers
The accuracy of diffusion MR tractography has remained elusive due to a lack of alternative tools to map the connectome in humans. We compared diffusion MR tractography with EGFP injections to assess which metrics should be extracted from tractography in mice (Fig. 4 and SI Appendix, Fig. S13). Given that the accuracy of tractography appeared compromised at the grey-white matter boundary (Fig. 4 and SI Appendix, Fig. S11), 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 cortico-cortical, or subcortical-cortical pathways (SI Appendix, Fig. S14 and Table S10). 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 cortico-cortical. We focused on cortico-cortical 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 (SI Appendix, Figs. S12–14). We observed that high angular resolution diffusion imaging (HARDI) and diffusion tensor imaging (DTI) reconstruction yielded comparable results in the mouse FC at P60 (y=0.82x+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 (19); therefore, P60 should represent adult proportions in pathway types.
Comparative Analyses of Supragranular-Enriched Gene expression in the FC
We leveraged RNA sequencing datasets from bulk samples (n=24) (39-42), single cells, and in situ hybridization (ISH) to detect cross-species variation in SE and LRP gene expression across the human, macaque, and mouse FC (SI Appendix, Table S2). We performed PCAs on these data, and measured gene expression intensity across layers as we had done previously (23, 26) (SI Appendix).
Data Availability
Data and scripts will be available on dryad.
Funding
This work was supported by an INBRE grant from the NIGMS (P20GM103446) to [C.J.C], a COBRE (5P20GM103653), an R21 from NINDS (R21NS109627) to [B.L.E.], and a James S. McDonnell Foundation grant to [B.L.E.]. Opinions are not necessarily those of the NIH.
SI Appendix
We discuss how we reconstructed pathways for diffusion MR tractography, the tract-tracers we selected to study in mice, and how we varied imaging parameters to ensure our results were not driven by outliers.
Diffusion MR Tractography
Diffusion MRI data were processed with Diffusion Toolkit (www.trackvis.org; threshold angle: 45 angles for 13/14 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 (1). We used fiber assessment by continuous tracking (FACT) with HARDI. No fractional anisotropy threshold was applied in reconstructing tracts, which is consistent with previous work (1-4). We used TrackVis (http://trackvis.org) to visualize and quantify pathways. Table S9 provides details on the individuals scanned, including their age, and the spatial resolution used.
Tract-Tracers
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. 4). We identified injection sites and set regions of interest (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 grey matter in the same location as the tract-tracers. Axons make an abrupt turn at the junction of the grey and the white matter (Fig. 4). These sharp turns challenge the accuracy of tractography, as evident from the qualitative observations of tract-tracers and tractography (Fig. 4 and Fig. S11).
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.
Varying Sampling and Imaging Procedures of Diffusion MR Scans
Variation in imaging resolution was inevitable considering the wide range of brain sizes used in the present study. 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 sub-sampled the number of sites and extracted the average proportion of pathway types in the FC of each individual (Fig. S13). Variation in the percentage of pathway types was minor regardless of the number of randomly selected voxels, imaging procedures, and resolution (Fig. S12–S14). Our analyses suggest that the differences in the pathway types or a lack thereof are robust to sampling size, resolution, and imaging protocols.
Transcriptional Variation Across Layers
We measured the intensity of gene expression across layers in a manner similar to that done previously (5-7). We downloaded ISH images of the FC 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 (Table S4). We used Image J 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 1,000 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.
Supplementary tables
Table S1. Corresponding time points across humans and macaques.
Table S2. List of RNA sequencing datasets and age of samples used in the present study.
Table S3. Equations used to capture temporal trajectories in myelin water fraction (MWF)
Table S4. Cell clusters from the human primary motor cortex used to define layer II-III neurons and non-neuronal cells.
Table S5. List of LRP markers identified from associations between normalized gene expression and MWF.
Table S6. Post hoc Tukey tests between correlation coefficients of LRP markers across different regions and cortico-cortical pathway types across humans, macaques, and mice.
Table S7. Non-linear regressions used to compare growth of the PFC and FC white matter.
Table S8. Statistics on pathway types through the PFC and remaining FC in humans and macaques.
Table S9. Scanning parameters used for diffusion MR tractography of humans, macaques, and mice.
Table S10. Proportion of pathway types across the FC of humans, macaques, and mice. We only
Table S11. PFC measurements collected at various time points in macaques.
Table S12. Relative gene expression and thickness measurements across FC layers in humans, macaques, and mice.
Table S13. Statistics results from Tukey HSD tests to compare the relative thickness of FC layer II-III in humans, macaques, and mice.
Table S14. List of tract-tracer experiments used to compare diffusion MR tractography with tract-tracers.
Acknowledgments
We thank Rohina Niamat and Drs. Harrington, Whitaker, and Halley for their help. Images were taken from the Allen Institute website and the Brainspan Atlas of the developing human brain. Data are available at http://www.brainspan.org and http://developingmouse.brain-map.org, which is supported by the NIH Contract HHSN-271-2008-00 047-C.