Initial Demonstration of in Vivo Tracing of Axonal Projections in the Macaque Brain and Comparison with the Human Brain Using Diffusion Tensor Imaging and Fast Marching Tractography

doi:10.1006/nimg.2001.0994

NeuroImage

Volume 15, Issue 4, April 2002, Pages 797-809

https://doi.org/10.1006/nimg.2001.0994 Get rights and content

Abstract

Diffusion tensor imaging (DTI), a magnetic resonance imaging technique, is used to infer major axonal projections in the macaque and human brain. This study investigates the feasibility of using known macaque anatomical connectivity as a “gold-standard” for the evaluation of DTI tractography methods. Connectivity information is determined from the DTI data using fast marching tractography (FMT), a novel tract-tracing (tractography) method. We show for the first time that it is possible to determine, in an entirely noninvasive manner, anatomical connection pathways and maps of an anatomical connectivity metric in the macaque brain using a standard clinical scanner and that these pathways are consistent with known anatomy. Analogous human anatomical connectivity is also presented for the first time using the FMT method, and the results are compared. The current limitations of the methodology and possibilities available for further studies are discussed.

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In white matter, this provides estimates of the local orientations of axon bundles. Tractography algorithms follow these orientation vectors from voxel to voxel and attempt to reconstruct the trajectories of white-matter pathways through the brain (Parker et al. 2002; Koch et al., 2002; Fillard et al., 2009; Kreher et al., 2008; Mori et al. 1999). Tractography is essential for characterizing brain networks (Hagmann et al., 2008; Bullmore and Sporns 2009) and, in combination with other microstructural properties derived from dMRI, has the potential to further our understanding of a plethora of neurological and psychiatric conditions.
Anatomic tracing is recognized as a critical source of knowledge on brain circuitry that can be used to assess the accuracy of diffusion MRI (dMRI) tractography. However, most prior studies that have performed such assessments have used dMRI and tracer data from different brains and/or have been limited in the scope of dMRI analysis methods allowed by the data. In this work, we perform a quantitative, voxel-wise comparison of dMRI tractography and anatomic tracing data in the same macaque brain. An ex vivo dMRI acquisition with high angular resolution and high maximum b-value allows us to compare a range of q-space sampling, orientation reconstruction, and tractography strategies. The availability of tracing in the same brain allows us to localize the sources of tractography errors and to identify axonal configurations that lead to such errors consistently, across dMRI acquisition and analysis strategies. We find that these common failure modes involve geometries such as branching or turning, which cannot be modeled well by crossing fibers. We also find that the default thresholds that are commonly used in tractography correspond to rather conservative, low-sensitivity operating points. While deterministic tractography tends to have higher sensitivity than probabilistic tractography in that very conservative threshold regime, the latter outperforms the former as the threshold is relaxed to avoid missing true anatomical connections. On the other hand, the q-space sampling scheme and maximum b-value have less of an impact on accuracy. Finally, using scans from a set of additional macaque brains, we show that there is enough inter-individual variability to warrant caution when dMRI and tracer data come from different animals, as is often the case in the tractography validation literature. Taken together, our results provide insights on the limitations of current tractography methods and on the critical role that anatomic tracing can play in identifying potential avenues for improvement.
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Diffusion MRI (dMRI) data acquisition protocols are well-established on modern high-field clinical scanners for human studies. However, these protocols are not suitable for the chimpanzee (or other large-brained mammals) because of its substantial difference in head geometry and brain volume compared with humans. Therefore, an optimal dMRI data acquisition protocol dedicated to chimpanzee neuroimaging is needed.
A multi-shot (4 segments) double spin-echo echo-planar imaging (MS-EPI) sequence and a single-shot double spin-echo EPI (SS-EPI) sequence were optimized separately for in vivo dMRI data acquisition of chimpanzees using a clinical 3T scanner. Correction for severe susceptibility-induced image distortion and signal drop-off of the chimpanzee brain was performed and evaluated using FSL software. DTI indices in different brain regions and probabilistic tractography were compared. A separate DTI data set from n=34 chimpanzees (13 to 56 years old) was collected using the optimal protocol. Age-related changes in diffusivity indices of optic nerve fibers were evaluated.
The SS-EPI sequence acquired dMRI data of the chimpanzee brain with approximately doubled the SNR as the MS-EPI sequence given the same scan time. The quality of white matter fiber tracking from the SS-EPI data was much higher than that from MS-EPI data. However, quantitative analysis of DTI indices showed no difference in most ROIs between the SS-EPI and MS-EPI sequences. The progressive evolution of diffusivity indices of optic nerves indicated mild changes in fiber bundles of chimpanzees aged 40 years and above.
The single-shot EPI-based acquisition protocol provided better image quality of dMRI for chimpanzee brains and is recommended for in vivo dMRI study or clinical diagnosis of chimpanzees (or other large animals) using a clinical scanner. Also, the tendency of FA decrease or diffusivity increase in the optic nerve of aged chimpanzees was seen but did not show significant age-related changes, suggesting aging may have less impact on optic nerve fiber integrity of chimpanzees, in contrast to previous results for both macaque monkeys and humans.
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HARDI improves the accuracy of tractography by using a large number of diffusion-encoding gradients with a reasonable scanning time. After the first validation study in the macaque brain (Parker et al., 2002), a number of validation studies have been performed, with rather positive or more critical conclusions. For example, the comparison of DSI in the light of extensive autoradiographic tract tracing data on long association pathways in the monkey cerebral hemispheres was found to replicate main features of these fiber tracts (Schmahmann et al., 2007).
The goal of this paper is to examine existing methods to study the “Human Brain Connectome” with a specific focus on the neurophysiological ones. In recent years, a new approach has been developed to evaluate the anatomical and functional organization of the human brain: the aim of this promising multimodality effort is to identify and classify neuronal networks with a number of neurobiologically meaningful and easily computable measures to create its connectome. By defining anatomical and functional connections of brain regions on the same map through an integrated approach, comprising both modern neurophysiological and neuroimaging (i.e. flow/metabolic) brain-mapping techniques, network analysis becomes a powerful tool for exploring structural–functional connectivity mechanisms and for revealing etiological relationships that link connectivity abnormalities to neuropsychiatric disorders. Following a recent IFCN-endorsed meeting, a panel of international experts was selected to produce this current state-of-art document, which covers the available knowledge on anatomical and functional connectivity, including the most commonly used structural and functional MRI, EEG, MEG and non-invasive brain stimulation techniques and measures of local and global brain connectivity.
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Comparative neuroanatomy studies improve understanding of brain structure and function and provide insight regarding brain development, evolution, and also what features of the brain are uniquely human. With modern methods such as diffusion MRI (dMRI) and quantitative MRI (qMRI), we are able to measure structural features of the brain with the same methods across human and non-human primates. In this review article, we discuss how recent dMRI measurements of vertical occipital connections in humans and macaques can be compared with previous findings from invasive anatomical studies that examined connectivity, including relatively forgotten classic strychnine neuronography studies. We then review recent progress in understanding the neuroanatomy of vertical connections within the occipitotemporal cortex by combining modern quantitative MRI and classical histological measurements in human and macaque. Finally, we a) discuss current limitations of dMRI and tractography and b) consider potential paths for future investigations using dMRI and tractography for comparative neuroanatomical studies of white matter tracts between species. While we focus on vertical association connections in visual cortex in the present paper, this same approach can be applied to other white matter tracts. Similar efforts are likely to continue to advance our understanding of the neuroanatomical features of the brain that are shared across species, as well as to distinguish the features that are uniquely human.
Fusing DTI and fMRI data: A survey of methods and applications
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The relationship between brain structure and function has been one of the centers of research in neuroimaging for decades. In recent years, diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) techniques have been widely available and popular in cognitive and clinical neurosciences for examining the brain's white matter (WM) micro-structures and gray matter (GM) functions, respectively. Given the intrinsic integration of WM/GM and the complementary information embedded in DTI/fMRI data, it is natural and well-justified to combine these two neuroimaging modalities together to investigate brain structure and function and their relationships simultaneously. In the past decade, there have been remarkable achievements of DTI/fMRI fusion methods and applications in neuroimaging and human brain mapping community. This survey paper aims to review recent advancements on methodologies and applications in incorporating multimodal DTI and fMRI data, and offer our perspectives on future research directions. We envision that effective fusion of DTI/fMRI techniques will play increasingly important roles in neuroimaging and brain sciences in the years to come.
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It is not a “strength of connection”, and says nothing at all about the strength of synapses, which can exhibit short-term plasticity that is not detectable with diffusion MRI. The streamline count shows a marked dependence on the length of the pathway, that might be alleviated by the use of other connectivity indices, such as comparison to the null distribution (Morris et al., 2008), or the “weakest link” approach (Parker et al., 2002; Campbell et al., 2011). Streamline count also depends on the size and shape of the reference region, and the shape of the tract itself.
Diffusion magnetic resonance imaging (MRI) is a tremendously promising tool for imaging tissue microstructure, and for inferring large scale structural connectivity in vivo. However, the sensitivity of the technique is highly dependent on methodological details. Acquisition parameters, pre-processing steps, reconstruction models, and statistical analysis all affect the final sensitivity, specificity, and accuracy of a study. In the case of fiber pathway reconstruction in the central nervous system, termed tractography, false positive and false negative results abound, and interpretation of results must take into account the potential shortcomings of the techniques used. This article will review the strengths and limitations of different types of diffusion MRI tractography analysis, and highlight what one can realistically hope to learn from such imaging studies of the human brain.

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¹: These authors contributed equally to this work.

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Regular ArticleInitial Demonstration of in Vivo Tracing of Axonal Projections in the Macaque Brain and Comparison with the Human Brain Using Diffusion Tensor Imaging and Fast Marching Tractography

Abstract

Biol. Psychiatry

J. Neurosci. Methods

J. Magn. Reson. B

J. Magn. Reson. B

Magn. Reson. Imaging

Schizophrenia Res.

NeuroImage

Schizophrenia Res.

Prog. Brain. Res.

NeuroImage

NeuroImage

A quantitative analysis of the corticospinal projections to the hand muscle motor nuclei from SMA and M1 in the macaque monkey

Cereb. Cortex

The Human Nervous System

In vivo fiber tractography using DT-MRI data

Magn. Reson. Med.

Human Neuroanatomy

Tracking neuronal fiber pathways in the living human brain

Proc. Natl. Acad. Sci. USA

Correlative Anatomy of the Nervous System

Direct interhemispheric visual inputs to human speech areas

Hum. Brain. Mapping

The origin of corticospinal projections from the premotor areas in the frontal lobe

J. Neurosci.

Distributed hierarchical processing in the primate cerebral cortex

Cereb. Cortex

Neuropathological abnormalities of the corpus callosum in schizophrenia: A diffusion tensor imaging study

J. Neurol. Neurosurg. Psychiatry

Anisotropy in high angular resolution diffusion-weighted MRI

Magn. Reson. Med.

Motor recovery following capsular stroke. Role of descending pathways from multiple motor areas

Brain

Indeterminate organization of the visual system

Science

Non-invasive assessment of axonal fiber connectivity in the human brain via diffusion tensor MRI

Magn. Reson. Med.

Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging

Magn. Reson. Med.

Regular Article
Initial Demonstration of in Vivo Tracing of Axonal Projections in the Macaque Brain and Comparison with the Human Brain Using Diffusion Tensor Imaging and Fast Marching Tractography