Thalamic connections of the caudal part of the posterior parietal cortex differ from the rostral part in galagos (Otolemur garnettii).

In this study, thalamic connections of the caudal part of the posterior parietal cortex (PPCc) are described and compared to connections of the rostral part of PPC (PPCr) in strepsirrhine galagos. PPC of galagos is divided into two parts, PPCr and PPCc, based on the responsiveness to electrical stimulation. Stimulation of PPC with long trains of electrical pulses evokes different types of ethologically relevant movements from different subregions ("domains") of PPCr, while it fails to evoke any movements from PPCc. Anatomical tracers were placed in both dorsal and ventral divisions of PPCc to reveal thalamic origins and targets of PPCc connections. We found major thalamic connections of PPCc with the lateral posterior and lateral pulvinar nuclei, distinct from those of PPCr that were mainly with the ventral lateral, anterior pulvinar, and posterior nuclei. The anterior, medial, and inferior pulvinar, ventral anterior, ventral lateral, and intralaminar nuclei had fewer connections with PPCc. Dominant connections of PPCc with lateral posterior and lateral pulvinar nuclei provide evidence that unlike the sensorimotor-orientated PPCr, PPCc is more involved in visual-related functions.

The basal ganglia are a target for sensorimotor domains in posterior parietal, premotor, and motor cortex in primates.

Our research focused on defining and characterizing parieto-frontal circuits for specific actions in primates. Part of the posterior parietal cortex is divided into eight or more domains where electrical stimulation evokes a meaningful complex movement. Domains in the posterior parietal cortex compete with each other over excitatory connections that activate inhibitory neurons, while selectively activating functionally matched domains in the premotor cortex and motor cortex. Thus, the selection process involves competition and cooperation between domains over three different regions of cortex. In addition, projections from functionally matched domains in motor regions converge in the matrix of the striatum, whereas projections from different functionally unmatched domains are separate. Thus, the projections of action-specific domains include the basal ganglia, where actions can be permitted or blocked.

The dorsal stream of visual processing and action-specific domains in parietal and frontal cortex in primates.

This review summarizes our findings obtained from over 15 years of research on parietal-frontal networks involved in the dorsal stream of cortical processing. We have presented considerable evidence for the existence of similar, partially independent, parietal-frontal networks involved in specific motor actions in a number of primates. These networks are formed by connections between action-specific domains representing the same complex movement evoked by electrical microstimulation. Functionally matched domains in the posterior parietal (PPC) and frontal (M1-PMC) motor regions are hierarchically related. M1 seems to be a critical link in these networks, since the outputs of M1 are essential to the evoked behavior, whereas PPC and PMC mediate complex movements mostly via their connections with M1. Thus, lesioning or deactivating M1 domains selectively blocks matching PMC and PPC domains, while having limited impact on other domains. When pairs of domains are stimulated together, domains within the same parietal-frontal network (matching domains) are cooperative in evoking movements, while they are mainly competitive with other domains (mismatched domains) within the same set of cortical areas. We propose that the interaction of different functional domains in each cortical region (as well as in striatum) occurs mainly via mutual suppression. Thus, the domains at each level are in competition with each other for mediating one of several possible behavioral outcomes.

Functional imaging and anatomical connections in squirrel monkeys reveal parietal-frontal circuits underlying eye movements.

The posterior parietal cortex (PPC) of squirrel monkeys contains subregions where long trains of intracortical microstimulation evoke complex, behaviorally meaningful movements. Recently, we showed that such stimulation of a part of the PPC in the caudal lateral sulcus (LS) elicits eye movements in these monkeys. Here, we studied the functional and anatomical connections of this oculomotor region we call parietal eye field (PEF) with frontal eye field (FEF) and other cortical regions in 2 squirrel monkeys. We demonstrated these connections with intrinsic optical imaging and injections of anatomical tracers. Optical imaging of frontal cortex during stimulation of the PEF evoked focal functional activation within FEF. Tracing studies confirmed the functional PEF-FEF connections. Moreover, tracer injections revealed PEF connections with other PPC regions on the dorsolateral and medial brain surface, cortex in the caudal LS, and visual and auditory cortical association areas. Subcortical projections of PEF were primarily with superior colliculus, and pontine nuclei as well as nuclei of the dorsal posterior thalamus and caudate. These findings suggest that PEF in squirrel monkey is homologous to lateral intraparietal (LIP) area of macaque, supporting the notion that these brain circuits are organized similarly to mediate ethologically relevant oculomotor behaviors.

Thalamocortical and corticothalamic connections of multiple functional domains in posterior parietal cortex of galagos.

In prosimian galagos, the posterior parietal cortex (PPC) is subdivided into a number of functional domains where long-train intracortical microstimulation evoked different types of complex movements. Here, we placed anatomical tracers in multiple locations of PPC to reveal the origins and targets of thalamic connections of four PPC domains for different types of hindlimb, forelimb, or face movements. Thalamic connections of all four domains included nuclei of the motor thalamus, ventral anterior and ventral lateral nuclei, as well as parts of the sensory thalamus, the anterior pulvinar, posterior and ventral posterior superior nuclei, consistent with the sensorimotor functions of PPC domains. PPC domains also projected to the thalamic reticular nucleus in a somatotopic pattern. Quantitative differences in the distributions of labeled neurons in thalamic nuclei suggested that connectional patterns of these domains differed from each other.

Escaping the nocturnal bottleneck, and the evolution of the dorsal and ventral streams of visual processing in primates.

Early mammals were small and nocturnal. Their visual systems had regressed and they had poor vision. After the extinction of the dinosaurs 66 mya, some but not all escaped the 'nocturnal bottleneck' by recovering high-acuity vision. By contrast, early primates escaped the bottleneck within the age of dinosaurs by having large forward-facing eyes and acute vision while remaining nocturnal. We propose that these primates differed from other mammals by changing the balance between two sources of visual information to cortex. Thus, cortical processing became less dependent on a relay of information from the superior colliculus (SC) to temporal cortex and more dependent on information distributed from primary visual cortex (V1). In addition, the two major classes of visual information from the retina became highly segregated into magnocellular (M cell) projections from V1 to the primate-specific temporal visual area (MT), and parvocellular-dominated projections to the dorsolateral visual area (DL or V4). The greatly expanded P cell inputs from V1 informed the ventral stream of cortical processing involving temporal and frontal cortex. The M cell pathways from V1 and the SC informed the dorsal stream of cortical processing involving MT, surrounding temporal cortex, and parietal-frontal sensorimotor domains. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.

Cortical connections of the functional domain for climbing or running in posterior parietal cortex of galagos.

Previous studies in prosimian galagos (Otolemur garnetti) have demonstrated that posterior parietal cortex (PPC) is subdivided into several functionally distinct domains, each of which mediates a specific type of complex movements (e.g., reaching, grasping, hand-to-mouth) and has a different pattern of cortical connections. Here we identified a medially located domain in PPC where combined forelimb and hindlimb movements, as if climbing or running, were evoked by long-train intracortical microstimulation. We injected anatomical tracers in this climbing/running domain of PPC to reveal its cortical connections. Our results showed the PPC climbing domain had dense intrinsic connections within rostral PPC and reciprocal connections with forelimb and hindlimb region in primary motor cortex (M1) of the ipsilateral hemisphere. Fewer connections were with dorsal premotor cortex (PMd), supplementary motor (SMA), and cingulate motor (CMA) areas, as well as somatosensory cortex including areas 3a, 3b, and 1-2, secondary somatosensory (S2), parietal ventral (PV), and retroinsular (Ri) areas. The rostral portion of the climbing domain had more connections with primary somatosensory cortex than the caudal portion. Cortical projections were found in functionally matched domains in M1 and premotor cortex (PMC). Similar patterns of connections with fewer labeled neurons and terminals were seen in the contralateral hemisphere. These connection patterns are consistent with the proposed role of the climbing/running domain as part of a parietal-frontal network for combined use of the limbs in locomotion as in climbing and running. The cortical connections identify this action-specific domain in PPC as a more somatosensory driven domain.

Interactions within and between parallel parietal-frontal networks involved in complex motor behaviors in prosimian galagos and a squirrel monkey.

Long-train intracortical microstimulation (ICMS) of motor (M1) and posterior parietal cortices (PPC) in primates reveals cortical domains for different ethologically relevant behaviors. How functional domains interact with each other in producing motor behaviors is not known. In this study, we tested our hypothesis that matching domains interact to produce a specific complex movement, whereas connections between nonmatching domains are involved in suppression of conflicting motor outputs to prevent competing movements. In anesthetized galagos, we used 500-ms trains of ICMS to evoke complex movements from a functional domain in M1 or PPC while simultaneously stimulating another mismatched or matched domain. We considered movements of different and similar directions evoked from chosen cortical sites distant or close to each other. Their trajectories and speeds were analyzed and compared with those evoked by simultaneous stimulation. Stimulation of two sites evoking same or complementary movements produced a similar but more pronounced movement or a combined movement, respectively. Stimulation of two sites representing movements of different directions resulted in partial or total suppression of one of these movements. Thus interactions between domains in M1 and PPC were additive when they were functionally matched across fields or antagonistic between functionally conflicting domains, especially in PPC, suggesting that mismatched domains are involved in mutual suppression. Simultaneous stimulation of unrelated domains (forelimb and face) produced both movements independently. Movements produced by the simultaneous stimulation of sites in domains of two cerebral hemispheres were largely independent, but some interactions were observed.NEW & NOTEWORTHY Long trains of electrical pulses applied simultaneously to two sites in motor cortical areas (M1, PPC) have shown that interactions of functionally matched domains (evoking similar movements) within these areas were additive to produce a specific complex movement. Interactions between functionally mismatched domains (evoking different movements) were mostly antagonistic, suggesting their involvement in mutual suppression of conflicting motor outputs to prevent competing movements. Simultaneous stimulation of unrelated domains (forelimb and face) produced both movements independently.

Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI.

PURPOSE: Diffusion-weighted magnetic resonance imaging (DW-MRI) is of critical importance for characterizing in-vivo white matter. Models relating microarchitecture to observed DW-MRI signals as a function of diffusion sensitization are the lens through which DW-MRI data are interpreted. Numerous modern approaches offer opportunities to assess more complex intra-voxel structures. Nevertheless, there remains a substantial gap between intra-voxel estimated structures and ground truth captured by 3-D histology. METHODS: Herein, we propose a novel data-driven approach to model the non-linear mapping between observed DW-MRI signals and ground truth structures using a sequential deep neural network regression using residual block deep neural network (ResDNN). Training was performed on two 3-D histology datasets of squirrel monkey brains and validated on a third. A second validation was performed using scan-rescan datasets of 12 subjects from Human Connectome Project. The ResDNN was compared with multiple micro-structure reconstruction methods and super resolved-constrained spherical deconvolution (sCSD) in particular as baseline for both the validations. RESULTS: Angular correlation coefficient (ACC) is a correlation/similarity measure and can be interpreted as accuracy when compared with a ground truth. The median ACC of ResDNN is 0.82 and median ACC's of different variants of CSD are 0.75, 0.77, 0.79. The mean, median and std. of ResDNN & sCSD ACC across 12 subjects from HCP are 0.74, 0.88, 0.31 and 0.61, 0.71, 0.31 respectively. CONCLUSION: This work highlights the ability of deep learning to capture linkages between ex-vivo ground truth data with feasible MRI sequences. The data-driven approach is applicable to human in-vivo data and results in intriguingly high reproducibility of orientation structure.

Histologically derived fiber response functions for diffusion MRI vary across white matter fibers-An ex vivo validation study in the squirrel monkey brain.

Understanding the relationship between the diffusion-weighted MRI signal and the arrangement of white matter fibers is fundamental for accurate voxel-wise reconstruction of the fiber orientation distribution (FOD) and subsequent fiber tractography. Spherical deconvolution reconstruction techniques model the diffusion signal as the convolution of the FOD with a response function that represents the signal profile of a single fiber orientation. Thus, given the signal and a fiber response function, the FOD can be estimated in every imaging voxel by deconvolution. However, the selection of the appropriate response function remains relatively under-studied, and requires further validation. In this work, using 3D histologically defined FODs and the corresponding diffusion signal from three ex vivo squirrel monkey brains, we derive the ground truth response functions. We find that the histologically derived response functions differ from those conventionally used. Next, we find that response functions statistically vary across brain regions, which suggests that the practice of using the same kernel throughout the brain is not optimal. We show that different kernels lead to different FOD reconstructions, which in turn can lead to different tractography results depending on algorithmic parameters, with large variations in the accuracy of resulting reconstructions. Together, these results suggest there is room for improvement in estimating and understanding the relationship between the diffusion signal and the underlying FOD.

Enabling Multi-Shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE.

Intra-voxel models of the diffusion signal are essential for interpreting organization of the tissue environment at micrometer level with data at millimeter resolution. Recent advances in data driven methods have enabled direct comparison and optimization of methods for in-vivo data with externally validated histological sections with both 2-D and 3-D histology. Yet, all existing methods make limiting assumptions of either (1) model-based linkages between b-values or (2) limited associations with single shell data. We generalize prior deep learning models that used single shell spherical harmonic transforms to integrate the recently developed simple harmonic oscillator reconstruction (SHORE) basis. To enable learning on the SHORE manifold, we present an alternative formulation of the fiber orientation distribution (FOD) object using the SHORE basis while representing the observed diffusion weighted data in the SHORE basis. To ensure consistency of hyper-parameter optimization for SHORE, we present our Deep SHORE approach to learn on a data-optimized manifold. Deep SHORE is evaluated with eight-fold cross-validation of a preclinical MRI-histology data with four b-values. Generalizability of in-vivo human data is evaluated on two separate 3T MRI scanners. Specificity in terms of angular correlation (ACC) with the preclinical data improved on single shell: 0.78 relative to 0.73 and 0.73, multi-shell: 0.80 relative to 0.74 (p < 0.001). In the in-vivo human data, Deep SHORE was more consistent across scanners with 0.63 relative to other multi-shell methods 0.39, 0.52 and 0.57 in terms of ACC. In conclusion, Deep SHORE is a promising method to enable data driven learning with DW-MRI under conditions with varying b-values, number of diffusion shells, and gradient directions per shell.

Inter-Scanner Harmonization of High Angular Resolution DW-MRI using Null Space Deep Learning.

Diffusion-weighted magnetic resonance imaging (DW-MRI) allows for non-invasive imaging of the local fiber architecture of the human brain at a millimetric scale. Multiple classical approaches have been proposed to detect both single (e.g., tensors) and multiple (e.g., constrained spherical deconvolution, CSD) fiber population orientations per voxel. However, existing techniques generally exhibit low reproducibility across MRI scanners. Herein, we propose a data-driven technique using a neural network design which exploits two categories of data. First, training data were acquired on three squirrel monkey brains using ex-vivo DW-MRI and histology of the brain. Second, repeated scans of human subjects were acquired on two different scanners to augment the learning of the network proposed. To use these data, we propose a new network architecture, the null space deep network (NSDN), to simultaneously learn on traditional observed/truth pairs (e.g., MRI-histology voxels) along with repeated observations without a known truth (e.g., scan-rescan MRI). The NSDN was tested on twenty percent of the histology voxels that were kept completely blind to the network. NSDN significantly improved absolute performance relative to histology by 3.87% over CSD and 1.42% over a recently proposed deep neural network approach. Moreover, it improved reproducibility on the paired data by 21.19% over CSD and 10.09% over a recently proposed deep approach. Finally, NSDN improved generalizability of the model to a third in vivo human scanner (which was not used in training) by 16.08% over CSD and 10.41% over a recently proposed deep learning approach. This work suggests that data-driven approaches for local fiber reconstruction are more reproducible, informative and precise and offers a novel, practical method for determining these models.

A Web-Based Atlas Combining MRI and Histology of the Squirrel Monkey Brain.

The squirrel monkey (Saimiri sciureus) is a commonly-used surrogate for humans in biomedical research. In the neuroimaging community, MRI and histological atlases serve as valuable resources for anatomical, physiological, and functional studies of the brain; however, no digital MRI/histology atlas is currently available for the squirrel monkey. This paper describes the construction of a web-based multi-modal atlas of the squirrel monkey brain. The MRI-derived information includes anatomical MRI contrast (i.e., T2-weighted and proton-density-weighted) and diffusion MRI metrics (i.e., fractional anisotropy and mean diffusivity) from data acquired both in vivo and ex vivo on a 9.4 Tesla scanner. The histological images include Nissl and myelin stains, co-registered to the corresponding MRI, allowing identification of cyto- and myelo-architecture. In addition, a bidirectional neuronal tracer, biotinylated dextran amine (BDA) was injected into the primary motor cortex, enabling highly specific identification of regions connected to the injection location. The atlas integrates the results of common image analysis methods including diffusion tensor imaging glyphs, labels of 57 white-matter tracts identified using DTI-tractography, and 18 cortical regions of interest identified from Nissl-revealed cyto-architecture. All data are presented in a common space, and all image types are accessible through a web-based atlas viewer, which allows visualization and interaction of user-selectable contrasts and varying resolutions. By providing an easy to use reference system of anatomical information, our web-accessible multi-contrast atlas forms a rich and convenient resource for comparisons of brain findings across subjects or modalities. The atlas is called the Combined Histology-MRI Integrated Atlas of the Squirrel Monkey (CHIASM). All images are accessible through our web-based viewer ( https://chiasm.vuse.vanderbilt.edu /), and data are available for download at ( https://www.nitrc.org/projects/smatlas/ ).

Harmonizing 1.5T/3T Diffusion Weighted MRI through Development of Deep Learning Stabilized Microarchitecture Estimators.

Diffusion weighted magnetic resonance imaging (DW-MRI) is interpreted as a quantitative method that is sensitive to tissue microarchitecture at a millimeter scale. However, the sensitization is dependent on acquisition sequences (e.g., diffusion time, gradient strength, etc.) and susceptible to imaging artifacts. Hence, comparison of quantitative DW-MRI biomarkers across field strengths (including different scanners, hardware performance, and sequence design considerations) is a challenging area of research. We propose a novel method to estimate microstructure using DW-MRI that is robust to scanner difference between 1.5T and 3T imaging. We propose to use a null space deep network (NSDN) architecture to model DW-MRI signal as fiber orientation distributions (FOD) to represent tissue microstructure. The NSDN approach is consistent with histologically observed microstructure (on previously acquired ex vivo squirrel monkey dataset) and scan-rescan data. The contribution of this work is that we incorporate identical dual networks (IDN) to minimize the influence of scanner effects via scan-rescan data. Briefly, our estimator is trained on two datasets. First, a histology dataset was acquired on three squirrel monkeys with corresponding DW-MRI and confocal histology (512 independent voxels). Second, 37 control subjects from the Baltimore Longitudinal Study of Aging (67-95 y/o) were identified who had been scanned at 1.5T and 3T scanners (b-value of 700 s/mm2, voxel resolution at 2.2mm, 30-32 gradient volumes) with an average interval of 4 years (standard deviation 1.3 years). After image registration, we used paired white matter (WM) voxels for 17 subjects and 440 histology voxels for training and 20 subjects and 72 histology voxels for testing. We compare the proposed estimator with super-resolved constrained spherical deconvolution (CSD) and a previously presented regression deep neural network (DNN). NSDN outperformed CSD and DNN in angular correlation coefficient (ACC) 0.81 versus 0.28 and 0.46, mean squared error (MSE) 0.001 versus 0.003 and 0.03, and general fractional anisotropy (GFA) 0.05 versus 0.05 and 0.09. Further validation and evaluation with contemporaneous imaging are necessary, but the NSDN is promising avenue for building understanding of microarchitecture in a consistent and device-independent manner.

Anatomical accuracy of standard-practice tractography algorithms in the motor system - A histological validation in the squirrel monkey brain.

For two decades diffusion fiber tractography has been used to probe both the spatial extent of white matter pathways and the region to region connectivity of the brain. In both cases, anatomical accuracy of tractography is critical for sound scientific conclusions. Here we assess and validate the algorithms and tractography implementations that have been most widely used - often because of ease of use, algorithm simplicity, or availability offered in open source software. Comparing forty tractography results to a ground truth defined by histological tracers in the primary motor cortex on the same squirrel monkey brains, we assess tract fidelity on the scale of voxels as well as over larger spatial domains or regional connectivity. No algorithms are successful in all metrics, and, in fact, some implementations fail to reconstruct large portions of pathways or identify major points of connectivity. The accuracy is most dependent on reconstruction method and tracking algorithm, as well as the seed region and how this region is utilized. We also note a tremendous variability in the results, even though the same MR images act as inputs to all algorithms. In addition, anatomical accuracy is significantly decreased at increased distances from the seed. An analysis of the spatial errors in tractography reveals that many techniques have trouble properly leaving the gray matter, and many only reveal connectivity to adjacent regions of interest. These results show that the most commonly implemented algorithms have several shortcomings and limitations, and choices in implementations lead to very different results. This study should provide guidance for algorithm choices based on study requirements for sensitivity, specificity, or the need to identify particular connections, and should serve as a heuristic for future developments in tractography.

Tests of clustering thalamic nuclei based on various dMRI models in the squirrel monkey brain.

BACKGROUND: Clustering thalamic nuclei is important for both research and clinical purposes. For example, ventral intermediate nuclei in thalami serve as targets in both deep brain stimulation neurosurgery and radiosurgery for treating patients suffering from movement disorders (e.g., Parkinson's disease and essential tremor). Diffusion magnetic resonance imaging (dMRI) is able to reflect tissue microstructure in the central nervous system via fitting different models, such as, the diffusion tensor (DT), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI), diffusion kurtosis imaging (DKI) and the spherical mean technique (SMT). PURPOSE: To test which of the above-mentioned dMRI models is better for thalamic parcellation, we proposed a framework of k-means clustering, implemented it on each model, and evaluated the agreement with histology. METHOD: An ex vivo monkey brain was scanned in a 9.4T MRI scanner at 0.3mm resolution with b values of 3000, 6000, 9000 and 12000 s/mm2. K-means clustering on each thalamus was implemented using maps of dMRI models fitted to the same data. Meanwhile, histological nuclei were identified by AChE and Nissl stains of the same brain. Overall agreement rate and agreement rate for each nucleus were calculated between clustering and histology. Sixteen thalamic nuclei on each hemisphere were included. RESULTS: Clustering with the DKI model has slightly higher overall agreement rate but clustering with other dMRI models result in higher agreement rate in some nuclei. CONCLUSION: dMRl models should be carefully selected to better parcellate the thalamus, depending on the specific purpose of the parcellation.

The evolution of parietal cortex in primates.

Many of the adaptive changes in the functional organization of parietal cortex of humans emerged in past in the early primates as they depended on visually guided forelimb use to grasp branches and food. Currently, human, apes and some monkeys have four well-defined subdivisions of anterior parietal cortex, areas 3a, 3b, 1 and 2 of Brodmann. In some of the smaller monkeys, and in stepsirrine primates (galagos, lemurs, and lorises), especially areas 1 and 2 are less developed, and the existence of an area 2 is questionable. In galagos, area 3b, the homologue of S1 in other mammals, has a more primitive somatotopy, is less devoted to representing the hand, and information from facial whiskers is more important. Humans and other primates also have more somatosensory areas in lateral parietal cortex than most mammals. While the regions of the second somatosensory area, S2 is divided into S2 and the parietal ventral area, PV in most mammals, primates have the additional caudal ad rostral ventral somatosensory areas, VSc and VSr. Posterior parietal cortex is another region of posterior cortex that has changed greatly from non-primate ancestors in having a more caudal half that is heavily devoted to further processing visual information for guiding different actions, such as running, reaching, looking, and grasping. All primates have at least 8 small subdivisions or domains in PPC, and have matching domains in premotor and motor cortex. In humans, domains for speech and tool use appear to have been added, and other parts of PPC have expanded. In addition, parts of PPC are differently specialized in the right and left cerebral hemispheres of humans more than in other primates.

Tests of cortical parcellation based on white matter connectivity using diffusion tensor imaging.

The cerebral cortex is conventionally divided into a number of domains based on cytoarchitectural features. Diffusion tensor imaging (DTI) enables noninvasive parcellation of the cortex based on white matter connectivity patterns. However, the correspondence between DTI-connectivity-based and cytoarchitectural parcellation has not been systematically established. In this study, we compared histological parcellation of New World monkey neocortex to DTI- connectivity-based classification and clustering in the same brains. First, we used supervised classification to parcellate parieto-frontal cortex based on DTI tractograms and the cytoarchitectural prior (obtained using Nissl staining). We performed both within and across sample classification, showing reasonable classification performance in both conditions. Second, we used unsupervised clustering to parcellate the cortex and compared the clusters to the cytoarchitectonic standard. We then explored the similarities and differences with several post-hoc analyses, highlighting underlying principles that drive the DTI-connectivity-based parcellation. The differences in parcellation between DTI-connectivity and Nissl histology probably represent both DTI's bias toward easily-tracked bundles and true differences between cytoarchitectural and connectivity defined domains. DTI tractograms appear to cluster more according to functional networks, rather than mapping directly onto cytoarchitectonic domains. Our results show that caution should be used when DTI-tractography classification, based on data from another brain, is used as a surrogate for cytoarchitectural parcellation.

Confirmation of a gyral bias in diffusion MRI fiber tractography.

Diffusion MRI fiber tractography has been increasingly used to map the structural connectivity of the human brain. However, this technique is not without limitations; for example, there is a growing concern over anatomically correlated bias in tractography findings. In this study, we demonstrate that there is a bias for fiber tracking algorithms to terminate preferentially on gyral crowns, rather than the banks of sulci. We investigate this issue by comparing diffusion MRI (dMRI) tractography with equivalent measures made on myelin-stained histological sections. We begin by investigating the orientation and trajectories of axons near the white matter/gray matter boundary, and the density of axons entering the cortex at different locations along gyral blades. These results are compared with dMRI orientations and tract densities at the same locations, where we find a significant gyral bias in many gyral blades across the brain. This effect is shown for a range of tracking algorithms, both deterministic and probabilistic, and multiple diffusion models, including the diffusion tensor and a high angular resolution diffusion imaging technique. Additionally, the gyral bias occurs for a range of diffusion weightings, and even for very high-resolution datasets. The bias could significantly affect connectivity results using the current generation of tracking algorithms.

Frontal eye field in prosimian galagos: Intracortical microstimulation and tracing studies.

The frontal eye field (FEF) in prosimian primates was identified as a small cortical region, above and anterior to the anterior frontal sulcus, from which saccadic eye movements were evoked with electrical stimulation. Tracer injections revealed FEF connections with cortical and subcortical structures participating in higher order visual processing. Ipsilateral cortical connections were the densest with adjoining parts of the dorsal premotor and prefrontal cortex (PFC). Label in a region corresponding to supplementary eye field (SEF) of other primates, suggests the existence of SEF in galagos. Other connections were with ventral premotor cortex (PMV), the caudal half of posterior parietal cortex, cingulate cortex, visual areas within the superior temporal sulcus, and inferotemporal cortex. Callosal connections were mostly with the region of the FEF of another hemisphere, SEF, PFC, and PMV. Most subcortical connections were ipsilateral, but some were bilateral. Dense bilateral connections were to caudate nuclei. Densest reciprocal ipsilateral connections were with the paralamellar portion of mediodorsal nucleus, intralaminar nuclei and magnocellular portion of ventral anterior nucleus. Other FEF connections were with the claustrum, reticular nucleus, zona incerta, lateral posterior and medial pulvinar nuclei, nucleus limitans, pretectal area, nucleus of Darkschewitsch, mesencephalic and pontine reticular formation and pontine nuclei. Surprisingly, the superior colliculus (SC) contained only sparse anterograde label. Although most FEF connections in galagos are similar to those in monkeys, the FEF-SC connections appear to be much less. This suggests that a major contribution of the FEF to visuomotor functions of SC emerged with the evolution of anthropoid primates.