Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher signal-to-noise ratio and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts. Another major advantage of ex vivo dMRI is the direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a 3-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We give general considerations and foundational knowledge that must be considered when designing experiments. We describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

The arcuate fascicle (AF) is the main fibre tract in the brain for human language. It connects frontal and temporal language areas in the superior and middle temporal gyrus (MTG). The AF's connection to the MTG was considered unique to humans and has influenced theories of the evolution of language. Here, using high-resolution diffusion MRI of post-mortem brains, we demonstrate that both wild and captive chimpanzees have a direct AF connection into the MTG, albeit weaker than in humans. This finding challenges the notion of a strictly human-specific AF morphology and suggests that language-related neural specialisation in humans likely evolved through gradual evolutionary strengthening of a pre-existing connection, rather than arising de novo. It is likely that this neural architecture supporting complex communication was already present in the last common ancestor of hominins and chimpanzees 7 million years ago, enabling the evolution of language processes in the human lineage.

Diffusion-weighted MRI (DWI) is widely used for assessing tissue microstructure, with echo-planar imaging (EPI) sequences being the preferred acquisition method due to their fast speed. However, EPI-based DWI is highly sensitive to field inhomogeneities, leading to susceptibility-induced distortions that compromise image quality. Traditional correction methods, such as TOPUP, estimate displacement fields from a pair of reversed phase-encoding (reversedPE) images to mitigate these distortions. While effective, these approaches suffer from high computational cost, limiting their clinical utility. In this study, we propose an unsupervised learning method for susceptibility artifact correction in EPI. A transformer-style convolutional network enhanced with deformable convolutions is developed to estimate the displacement field from a pair of reversed-PE images, followed by image unwarping and intensity modulation to generate the distortion-free images. This approach surpasses the performance of conventional U-Net-based methods in accuracy. Additionally, a spatially weighted smoothness loss is introduced to enhance robustness against noise in the input data so that the predicted displacement fields from a pair of low b-value DWI can be applied to correct other images with different b-values and diffusion directions from the same subject, optimizing acquisition and computational efficiency. A single model was trained and evaluated on large datasets from multiple organs, acquired with diverse imaging sequences and parameters, at both 1.5T and 3T. Our results demonstrate that the proposed approach achieves generalizable high-quality distortion correction while significantly reducing processing time compared to TOPUP, highlighting its potential for clinical translation.

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open-source software and databases specific to small animal and ex vivo imaging.

Comparing brain connectivity between chimpanzees and humans is a means of understanding human cognition and evolution. To address the scarcity of chimpanzee neuroimaging data, we introduce a high-quality MRI resource that reveals previously unseen anatomical details, offering valuable insights into human brain evolution.

Long-standing questions about human brain evolution may only be resolved through comparisons with close living evolutionary relatives, such as chimpanzees. This applies in particular to structural white matter (WM) connectivity, which continuously expanded throughout evolution. However, due to legal restrictions on chimpanzee research, neuroscience research currently relies largely on data with limited detail or on comparisons with evolutionarily distant monkeys. Here, we present a detailed magnetic resonance imaging resource to study structural WM connectivity in the chimpanzee. This open-access resource contains (1) WM reconstructions of a postmortem chimpanzee brain, using the highest-quality diffusion magnetic resonance imaging data yet acquired from great apes; (2) an optimized and validated method for high-quality fiber orientation reconstructions; and (3) major fiber tract segmentations for cross-species morphological comparisons. This dataset enabled us to identify phylogenetically relevant details of the chimpanzee connectome, and we anticipate that it will substantially contribute to understanding human brain evolution. This resource presents a high-resolution diffusion MRI dataset of a chimpanzee brain, which should be useful for evolutionary studies of primate brain evolution.

Chimpanzee brain maturation provides an invaluable framework for understanding the evolution of the human brain. We performed ultra-high resolution quantitative magnetic resonance imaging (qMRI) with histological validation on post mortem brains from captive and wild chimpanzees with a broad age range. We mapped developmental myelination and age-related iron accumulation across regions and layers of the neocortex. Compared to humans, chimpanzees showed more myelination and iron deposition in motor and premotor cortices, while the auditory cortex was more strongly myelinated in humans. Our model suggests that chimpanzees' cortical myelination was largely completed by the age of nine years, while iron accumulation continued throughout the lifespan. The regions with highest adult levels of myelin and iron took the longest to mature, challenging the widespread assumption that highly myelinated regions complete their development first. The reported maps and developmental curves provide a foundation for comparative neuroscience research and understanding of human brain evolution. ### Competing Interest Statement The Max Planck Institute for Human Cognitive and Brain Sciences has an institutional research agreement with Siemens Healthcare. Nikolaus Weiskopf holds a patent on acquisition of MRI data during spoiler gradients (US 10401453 B2). Nikolaus Weiskopf was a speaker at an event organized by Siemens Healthcare and was reimbursed for the travel expenses.

Language is bounded to the left hemisphere in the adult brain and the functional lateralization can already be observed early during development. Here we investigate whether this is paralleled by a lateralization of the white matter structural language network. We analyze the strength and microstructural properties of language-related fiber tracts connecting temporal and frontal cortices with a separation of two dorsal tracts, one targeting the posterior Broca’s area (BA44) and one targeting the precentral gyrus (BA6). In a large sample of young children (3-6 years), we demonstrate that, in contrast to the BA6-targeting tract, the microstructural asymmetry of the BA44-targeting fiber tract significantly correlates locally with different aspects of development. While the asymmetry in its anterior segment reflects age, the asymmetry in its posterior segment is associated with the children’s language skills. These findings demonstrate a fine-grained structure-to-function mapping in the lateralized network and go beyond our current view of language-related human brain maturation.

To decipher the evolution of the hominoid brain and its functions, it is essential to conduct comparative studies in primates, including our closest living relatives. However, strong ethical concerns preclude in vivo neuroimaging of great apes. We propose a responsible and multidisciplinary alternative approach that links behavior to brain anatomy in non-human primates from diverse ecological backgrounds. The brains of primates observed in the wild or in captivity are extracted and fixed shortly after natural death, and then studied using advanced MRI neuroimaging and histology to reveal macro- and microstructures. By linking detailed neuroanatomy with observed behavior within and across primate species, our approach provides new perspectives on brain evolution. Combined with endocranial brain imprints extracted from computed tomographic scans of the skulls these data provide a framework for decoding evolutionary changes in hominin fossils. This approach is poised to become a key resource for investigating the evolution and functional differentiation of hominoid brains.

Developmental dyslexia (DD) is one of the most common learning disorders, affecting millions of children and adults worldwide. To date, scientific research has attempted to explain DD primarily based on pathophysiological alterations in the cerebral cortex. In contrast, several decades ago, pioneering research on five post-mortem human brains suggested that a core characteristic of DD might be morphological alterations in a specific subdivision of the visual thalamus – the magnocellular LGN (M-LGN). However, due to considerable technical challenges in investigating LGN subdivisions non-invasively in humans, this finding was never confirmed in-vivo, and its relevance for DD pathology remained highly controversial. Here, we leveraged recent advances in high-resolution magnetic resonance imaging (MRI) at high field strength (7 Tesla) to investigate the M-LGN in DD in-vivo. Using a case-control design, we acquired data from a large sample of young adults with DD (n = 26; age 28 ± 7 years; 13 females) and matched control participants (n = 28; age 27 ± 6 years; 15 females). Each participant completed a comprehensive diagnostic behavioral test battery and participated in two MRI sessions, including three functional MRI experiments and one structural MRI acquisition. We measured blood-oxygen-level-dependent responses and longitudinal relaxation rates to compare both groups on LGN subdivision function and myelination. Based on previous research, we hypothesized that the M-LGN is altered in DD and that these alterations are associated with a key DD diagnostic score, i.e., rapid letter and number naming (RANln). The results showed aberrant responses of the M-LGN in DD compared to controls, which was reflected in a different functional lateralization of this subdivision between groups. These alterations were associated with RANln performance, specifically in male DD. We also found lateralization differences in the longitudinal relaxation rates of the M-LGN in DD relative to controls. Conversely, the other main subdivision of the LGN, the parvocellular LGN (P-LGN), showed comparable blood-oxygen-level-dependent responses and longitudinal relaxation rates between groups. The present study is the first to unequivocally show that M-LGN alterations are a hallmark of DD, affecting both the function and microstructure of this subdivision. It further provides a first functional interpretation of M-LGN alterations and a basis for a better understanding of sex-specific differences in DD with implications for prospective diagnostic and treatment strategies.

We present a novel approach to estimate fiber Orientation Distribution Functions (ODFs) by applying the generalized Constant Solid Angle (CSA) method to High Angular Resolution Susceptibility Imaging (HARSI) data from post-mortem chimpanzee brain. The acquisition details and analytical pipelines are presented and derived susceptibility tensor metrics and ODFs are compared to metrics derived from traditional High Angular Resolution Diffusion Imaging (HARDI). The ODFs estimated from susceptibility data indicate comparable efficiency in resolving intersecting fiber orientations compared to HARDI-ODFs and increased sensitivity to secondary direction. This suggests a potential to obtain complementary information on brain white matter microstructural properties.

We present Local Spherical Deconvolution (LSD), a diffusion MRI deconvolution method to reconstruct crossing fiber directions in the brain. In contrast to previous approaches which assumed a single deconvolution kernel for the entire brain, LSD utilizes information theory to identify an optimal kernel in each image voxel. Using a high-resolution post-mortem chimpanzee brain, we show that fibers are reconstructed with LSD with increased precision and a reduced number of false peaks compared to conventional methods. A test-retest analysis supports the stability and accuracy of LSD.

Human language is supported by a cortical network involving Broca's area, which comprises Brodmann Areas 44 and 45 (BA44 and BA45). While cytoarchitectonic homolog areas have been identified in nonhuman primates, it remains unknown how these regions evolved to support human language. Here, we use histological data and advanced cortical registration methods to precisely compare the morphology of BA44 and BA45 in humans and chimpanzees. We found a general expansion of Broca's areas in humans, with the left BA44 enlarging the most, growing anteriorly into a region known to process syntax. Together with recent functional and receptorarchitectural studies, our findings support the conclusion that BA44 evolved from an action-related region to a bipartite system, with a posterior portion supporting action and an anterior portion supporting syntactic processes. Our findings add novel insights to the longstanding debate on the relationship between language and action, and the evolution of Broca's area.

We introduce a computationally efficient fODF-weighted graph structure where shortest-paths through white matter compute the probability of connection while naturally limiting the angle of propagation between steps. Connectivity matrices obtained from this structure maintain many properties of probabilistic streamline count connectomes while avoiding the sampling bias of tractography.

Uncovering brain-tissue microstructure including axonal characteristics is a major neuroimaging research focus. Within this scope, anisotropic properties of magnetic susceptibility in white matter have been successfully employed to estimate primary axonal trajectories using mono-tensorial models. However, anisotropic susceptibility has not yet been considered for modeling more complex fiber structures within a voxel, such as intersecting bundles, or an estimation of orientation distribution functions (ODFs). This information is routinely obtained by high angular resolution diffusion imaging (HARDI) techniques. In applications to fixed tissue, however, diffusion-weighted imaging suffers from an inherently low signal-to-noise ratio and limited spatial resolution, leading to high demands on the performance of the gradient system in order to mitigate these limitations. In the current work, high angular resolution susceptibility imaging (HARSI) is proposed as a novel, phase-based methodology to estimate ODFs. A multiple gradient-echo dataset was acquired in an entire fixed chimpanzee brain at 61 orientations by reorienting the specimen in the magnetic field. The constant solid angle method was adapted for estimating phase-based ODFs. HARDI data were also acquired for comparison. HARSI yielded information on whole-brain fiber architecture, including identification of peaks of multiple bundles that resembled features of the HARDI results. Distinct differences between both methods suggest that susceptibility properties may offer complementary microstructural information. These proof-of-concept results indicate a potential to study the axonal organization in post-mortem primate and human brain at high resolution.

The selection pressures that drove dramatic encephalisation processes through the mammal lineage remain elusive, as does knowledge of brain structure reorganisation through this process. In particular, considerable structural brain changes are present across the primate lineage, culminating in the complex human brain that allows for unique behaviours such as language and sophisticated tool use. To understand this evolution, a diverse sample set of humans' closest relatives with varying socio‐ecologies is needed. However, current brain banks predominantly curate brains from primates that died in zoological gardens. We try to address this gap by establishing a field pipeline mitigating the challenges associated with brain extractions of wild primates in their natural habitat. The success of our approach is demonstrated by our ability to acquire a novel brain sample of deceased primates with highly variable socio‐ecological exposure and a particular focus on wild chimpanzees. Methods in acquiring brain tissue from wild settings are comprehensively explained, highlighting the feasibility of conducting brain extraction procedures under strict biosafety measures by trained veterinarians in field sites. Brains are assessed at a fine‐structural level via high‐resolution MRI and state‐of‐the‐art histology. Analyses confirm that excellent tissue quality of primate brains sourced in the field can be achieved with a comparable tissue quality of brains acquired from zoo‐living primates. Our field methods are noninvasive, here defined as not harming living animals, and may be applied to other mammal systems than primates. In sum, the field protocol and methodological pipeline validated here pose a major advance for assessing the influence of socio‐ecology on medium to large mammal brains, at both macro‐ and microstructural levels as well as aiding with the functional annotation of brain regions and neuronal pathways via specific behaviour assessments.

Developmental dyslexia (DD) is a reading disorder with a prevalence of 5-10%. Neuroscientific research has typically focused on explaining DD symptoms based on pathophysiological changes in the cerebral cortex. However, DD might also be associated with alterations in sensory thalami – central subcortical stations of sensory pathways. A post-mortem study on the visual sensory thalamus (lateral geniculate nucleus, LGN) showed histopathological changes in the magnocellular (M-LGN), but not in the parvocellular (P-LGN), subdivisions. M-LGN and P-LGN have different functional properties and belong to two different visual systems. Whether M-LGN alterations also exist in DD in-vivo is unclear. Also, the potential relevance of M-LGN alterations to DD symptoms is unknown. This lack of knowledge is partly due to considerable technical challenges in investigating LGN subdivisions non-invasively in humans. Here, we employed recent advances in high-field 7 Tesla functional magnetic resonance imaging (fMRI) to map the M- and P-LGN in-vivo in DD adults (n=26) and matched controls (n=28). We show that (i) M-LGN responses differ between DD and control participants, (ii) these differences are more pronounced in male than in female DD participants, and (iii) M-LGN alterations predict a core symptom of DD in male DD participants only, i.e., rapid naming ability. Our results provide a first functional interpretation of M-LGN changes in DD and support DD theories that propose a direct relevance of sensory thalamus alterations for DD symptoms. In addition, the sex-specific behavioral relevance of M-LGN alterations within DD calls for taking sex differences into account when planning brain-based therapeutic interventions. Significance Statement Developmental dyslexia (DD) is one of the most common learning disorders affecting millions of children and adults world-wide. Several decades ago, pioneering research in five DD post-mortem brains suggested that DD is characterized not only by alterations of the cerebral cortex, but also by changes in a subsection of the visual sensory thalamus – the so-called M-LGN. The relevance of these findings for DD remained highly controversial. Using recent developments in high-resolution functional neuroimaging, we now discovered that M-LGN alterations are present also in DD in-vivo and predict a core symptom of DD in males. Our results provide a first functional interpretation of M-LGN alterations in DD and provide a basis for better understanding sex-specific differences in DD.

Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength diffusion MRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for diffusion MRI and where the field is headed in the coming years.