Evolutionary adaptations of temporo-parietal cortex are considered to be a critical specialization of the human brain. Cortical adaptations, however, can affect different aspects of brain architecture, including local expansion of the cortical sheet or changes in connectivity between cortical areas. We distinguish different types of changes in brain architecture 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 can predict terminations of several white matter tracts in temporo-parietal cortex, including the middle and superior longitudinal fasciculus, but not the arcuate fasciculus. This demonstrates that the arcuate fasciculus underwent additional evolutionary modifications affecting the temporal lobe connectivity pattern. This approach can flexibly be extended to include other features of cortical organization and other species, allowing direct tests of comparative hypotheses of brain organization.

The genetic basis and human-specific character of schizophrenia has led to the hypothesis that human brain evolution may have played a role in the development of the disorder. We examined schizophrenia-related changes in brain connectivity in the context of evolutionary changes in human brain wiring by comparing in vivo neuroimaging data from humans and chimpanzees, one of our closest living evolutionary relatives and a species with which we share a very recent common ancestor. We contrasted the connectome layout between the chimpanzee and human brain and compared differences with the pattern of schizophrenia-related changes in brain connectivity as observed in patients. We show evidence of evolutionary modifications of human brain connectivity to significantly overlap with the cortical pattern of schizophrenia-related dysconnectivity (P 5 0.001, permutation testing). We validated these effects in three additional, independent schizophrenia datasets. We further assessed the specificity of effects by examining brain dysconnectivity patterns in seven other psychiatric and neurological brain disorders (including, among others, major depressive disorder and obsessive-compulsive disorder, arguably characterized by behavioural symptoms that are less specific to humans), which showed no such associations with modifications of human brain connectivity. Comparisons of brain connectivity across humans, chimpanzee and macaques further suggest that features of connectivity that evolved in the human lineage showed the strongest association to the disorder, that is, brain circuits potentially related to human evolutionary specializations. Taken together, our findings suggest that human-specific features of connectome organization may be enriched for changes in brain connectivity related to schizophrenia. Modifications in human brain connectivity in service of higher order brain functions may have potentially also rendered the brain vulnerable to brain dysfunction.

Cognitive brain networks such as the default-mode network (DMN), frontoparietal network, and salience network, are key functional networks of the human brain. Here we show that the rapid evolutionary cortical expansion of cognitive networks in the human brain, and most pronounced the DMN, runs parallel with high expression of human-accelerated genes (HAR genes). Using comparative transcriptomics analysis, we present that HAR genes are differentially more expressed in higher-order cognitive networks in humans compared to chimpanzees and macaques and that genes with high expression in the DMN are involved in synapse and dendrite formation. Moreover, HAR and DMN genes show significant associations with individual variations in DMN functional activity, intelligence, sociability, and mental conditions such as schizophrenia and autism. Our results suggest that the expansion of higher-order functional networks subserving increasing cognitive properties has been an important locus of genetic changes in recent human brain evolution.

Interregional connections of the brain measured with diffusion tractography can be used to infer valuable information regarding both brain structure and function. However, different tractography algorithms can generate networks that exhibit different characteristics, resulting in poor reproducibility across studies. Therefore, it is important to benchmark different tractography algorithms to quantitatively assess their performance. Here we systematically evaluated a newly introduced tracking algorithm, global tractography, to derive anatomical brain networks in a fiber phantom, 2 post-mortem macaque brains, and 20 living humans, and compared the results with an established local tracking algorithm. Our results demonstrated that global tractography accurately characterized the phantom network in terms of graph-theoretic measures, and significantly outperformed the local tracking approach. Results in brain tissues (post-mortem macaques and in vivo humans), however, showed that although the performance of global tractography demonstrated a trend of improvement, the results were not vastly different than that of local tractography, possibly resulting from the increased fiber complexity of real tissues. When using macaque tracer-derived connections as the ground truth, we found that both global and local algorithms generated non-random patterns of false negative and false positive connections that were probably related to specific fiber systems and largely independent of the tractography algorithm or tissue type (post-mortem vs. in vivo) used in the current study. Moreover, a close examination of the transcallosal motor connections, reconstructed via either global or local tractography, demonstrated that the lateral transcallosal fibers in humans and macaques did not exhibit the denser homotopic connections found in primate tracer studies, indicating the need for more robust brain mapping techniques based on diffusion MRI data.

The thalamus is a relay center between various subcortical brain areas and the cerebral cortex with delineation of its constituent nuclei being of particular interest in many applications. While previous studies have demonstrated efficacy of connectivity-based thalamus segmentation, they used approaches that do not consider the dynamic nature of thalamo-cortical interactions. In this study, we explicitly exploited the dynamic variation of thalamo-cortical connections to identify different states of functional connectivity and performed state-specific thalamus parcellation. With normalized spectral clustering successively applied in temporal and spatial domains, nine thalamo-cortical connectivity states were identified and the dynamic thalamus parcellation revealed finer thalamic structures with improved atlas correspondence. The present results extend our understanding of thalamo-cortical connectivity and provide a more comprehensive view of the thalamo-cortical interaction. Hum Brain Mapp 37:954-967, 2016.

With the mapping of macroscale connectomes by means of in vivo diffusion-weighted MR Imaging (DWI) rapidly gaining in popularity, one of the necessary steps is the examination of metrics of connectivity strength derived from these reconstructions. In the field of human macroconnectomics the number of reconstructed fiber streamlines (NOS) is more and more used as a metric of cortico-cortical interareal connectivity strength, but the link between DWI NOS and in vivo animal tract-tracing measurements of anatomical connectivity strength remains poorly understood. In this technical report, we communicate on a comparison between DWI derived metrics and tract-tracing metrics of projection strength. Tract-tracing information on projection strength of interareal pathways was extracted from two commonly used macaque connectome datasets, including (1) the CoCoMac database of collated tract-tracing experiments of the macaque brain and (2) the high-resolution tract-tracing dataset of Markov and Kennedy and coworkers. NOS and density of reconstructed fiber pathways derived from DWI data acquired across 10 rhesus macaques was found to positively correlate to tract-tracing based measurements of connectivity strength across both the CoCoMac and Markov dataset (both P < 0.001), suggesting DWI NOS to form a valid method of assessment of the projection strength of white matter pathways. Our findings provide confidence of in vivo DWI connectome reconstructions to represent fairly realistic estimates of the wiring strength of white matter projections. Our cross-modal comparison supports the notion of in vivo DWI to be a valid methodology for robust description and interpretation of brain wiring. Hum Brain Mapp 36:3064–3075, 2015. © 2015 Wiley Periodicals, Inc.

Accurate assessment of connectional anatomy of primate brains can be an important avenue to better understand the structural and functional organization of brains. To this end, numerous connectome projects have been initiated to create a comprehensive map of the connectional anatomy over a large spatial expanse. Tractography based on diffusion MRI (dMRI) data has been used as a tool by many connectome projects in that it is widely used to visualize axonal pathways and reveal microstructural features on living brains. However, the measures obtained from dMRI are indirect inference of microstructures. This intrinsic limitation reduces the reliability of dMRI in constructing connectomes for brains. In this work, we proposed a framework to increase the accuracy of constructing a dMRI-based connectome on macaque brains by integrating meso-scale connective information from tract-tracing data and micro-scale axonal orientation information from myelin stain data. Our results suggest that this integrative framework could advance the mapping accuracy of dMRI based connections and axonal pathways, and demonstrate the prospect of the proposed framework in constructing a large-scale connectome on living primate brains.

Purpose: This work develops a compressive sensing approach for diffusion-weighted (DW) MRI.Theory and Methods: A phase-constrained low-rank (PCLR) approach was developed using the image coherence across the DW directions for efficient compressive DW MRI, while accounting for drastic phase changes across the DW directions, possibly as a result of eddy current, and rigid and nonrigid motions. In PCLR, a low-resolution phase estimation was used for removing phase inconsistency between DW directions. In our implementation, GRAPPA (generalized autocalibrating partial parallel acquisition) was incorporated for better phase estimation while allowing higher undersampling factor. An efficient and easy-to-implement image reconstruction algorithm, consisting mainly of partial Fourier update and singular value decomposition, was developed for solving PCLR.Results: The error measures based on diffusion-tensorderived metrics and tractography indicated that PCLR, with its joint reconstruction of all DW images using the image coherence, outperformed the frame-independent reconstruction through zero-padding FFT. Furthermore, using GRAPPA for phase estimation, PCLR readily achieved a four-fold undersampling.Conclusion: The PCLR is developed and demonstrated for compressive DW MRI. A four-fold reduction in k-space sampling could be readily achieved without substantial degradation of reconstructed images and diffusion tensor measures, making it possible to significantly reduce the data acquisition in DW MRI and/or improve spatial and angular resolutions.

Mapping anatomical brain networks with graph-theoretic analysis of diffusion tractography has recently gained popularity, because of its presumed value in understanding brain function. However, this approach has seldom been used to compare brain connectomes across species, which may provide insights into brain evolution. Here, we employed a data-driven approach to compare interregional brain connections across three primate species: 1) the intensively studied rhesus macaque, 2) our closest living primate relative, the chimpanzee, and 3) humans. Specifically, we first used random parcellations and surface-based probabilistic diffusion tractography to derive the brain networks of the three species under various network densities and resolutions. We then compared the characteristics of the networks using graph-theoretic measures. In rhesus macaques, our tractography-defined hubs showed reasonable overlap with hubs previously identified using anterograde and retrograde tracer data. Across all three species, hubs were largely symmetric in the two hemispheres and were consistently identified in medial parietal, insular, retrosplenial cingulate and ventrolateral prefrontal cortices, suggesting a conserved structural architecture within these regions. However, species differences were observed in the inferior parietal cortex, polar and medial prefrontal cortices. The potential significance of these interspecies differences is discussed.

Evolution of the brain has been an inherently interesting problem for centuries. Recent studies have indicated that neuroimaging is a powerful technique for studying brain evolution. In particular, a variety of reports have demonstrated that consistent white matter fiber connection patterns derived from diffusion tensor imaging (DTI) tractography reveal common brain architecture and are predictive of brain functions. In this paper, based on our recently discovered 358 dense individualized and common connectivity-based cortical landmarks (DICCCOL) defined by consistent fiber connection patterns in DTI datasets of human brains, we derived 65 DICCCOLs that are common in macaque monkey, chimpanzee and human brains and 175 DICCCOLs that exhibit significant discrepancies amongst these three primate species. Qualitative and quantitative evaluations not only demonstrated the consistencies of anatomical locations and structural fiber connection patterns of these 65 common DICCCOLs across three primates, suggesting an evolutionarily preserved common brain architecture but also revealed regional patterns of evolutionarily induced complexity and variability of those 175 discrepant DICCCOLs across the three species.