Mapping how neurons are structurally wired into whole-brain networks can be challenging, particularly in larger brains where 3D microscopy is not available. Multi-modal datasets combining MRI and microscopy provide a solution, where high resolution but 2D microscopy can be complemented by whole-brain but low-resolution MRI. However, there lacks unified approaches to integrate and jointly analyse these multi-modal data in an insightful way. To address this gap, we introduce a data-fusion method for hybrid MRI-microscopy fibre orientation and connectome reconstruction. Specifically, we complement precise “in-plane” orientations from microscopy with “through-plane” information from MRI to construct 3D hybrid fibre orientations at resolutions far exceeding that of MRI whilst preserving microscopy's myelin specificity, resulting in superior fibre tracking. Our method is openly available, can be deployed on standard 2D microscopy, including different microscopy contrasts, and is species agnostic, facilitating neuroanatomical investigation in both animal models and human brains.

Acquisition of MRI and histology in the same ex-vivo tissue sample enables direct correlation between MR and histologically-derived metrics. Here, we analysed immunohistochemistry images of human visual cortex, anterior cingulate and hippocampus to produce stained area fraction maps for myelin, neurofilaments and microglia. We performed voxelwise correlations between MR parameters (FA, MD, R2*, R1) and histology maps to generally characterise the strength of relationships. We then used partial correlation to identify the unique variance in MR parameters explained by each histological feature, and multiple linear regression to explore how well multiple microstructural properties can together explain MR parameters.

Background Accurate registration between microscopy and MRI data is necessary for validating imaging biomarkers against neuropathology, and to disentangle complex signal dependencies in microstructural MRI. Existing registration methods often rely on serial histological sampling or significant manual input, providing limited scope to work with a large number of stand-alone histology sections. Here we present a customisable pipeline to automate the registration of stand-alone histology sections to whole-brain MRI data. Methods Our pipeline registers stained histology sections to whole-brain post-mortem MRI in 4 stages, with the help of two photographic intermediaries: a block face image (to undistort histology sections) and coronal brain slice photographs (to insert them into MRI space). Each registration stage is implemented as a configurable stand-alone Python script using our novel platform, Tensor Image Registration Library (TIRL), which provides flexibility for wider adaptation. We report our experience of registering 87 PLP-stained histology sections from 14 subjects and perform various experiments to assess the accuracy and robustness of each stage of the pipeline. Results All 87 histology sections were successfully registered to MRI. Histology-to-block registration (Stage 1) achieved 0.2-0.4 mm accuracy, better than commonly used existing methods. Block-to-slice matching (Stage 2) showed great robustness in automatically identifying and inserting small tissue blocks into whole brain slices with 0.2 mm accuracy. Simulations demonstrated sub-voxel level accuracy (0.13 mm) of the slice-to-volume registration (Stage 3) algorithm, which was observed in over 200 actual brain slice registrations, compensating 3D slice deformations up to 6.5 mm. Stage 4 combined the previous stages and generated refined pixelwise aligned multi-modal histology-MRI stacks. Conclusions Our open-source pipeline provides robust automation tools for registering stand-alone histology sections to MRI data with sub-voxel level precision, and the underlying framework makes it readily adaptable to a diverse range of microscopy-MRI studies. Highlights New software framework for prototyping bespoke image registration pipelines Automated pipeline to register stand-alone histology sections to whole-brain MRI Novel deformable slice-to-volume registration algorithm No strict necessity for serial histological sectioning for MRI-histology registration

We introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), a digital platform providing open access to curated, multimodal post-mortem neuroimaging datasets. Datasets span three themes; Digital Anatomist: datasets for neuroanatomical investigations; Digital Brain Zoo: datasets for comparative neuroanatomy; Digital Pathologist: datasets for neuropathology investigations. The first release includes 21 distinctive whole-brain diffusion MRI datasets, alongside microscopy and complementary MRI modalities. This includes one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired, whole-brain diffusion MRI in 14 non-human primate species, and one of the largest post-mortem whole-brain cohort imaging studies in neurodegeneration. Our resource facilitates incorporating post-mortem data into neuroimaging studies.

Understanding brain structure and function often requires combining data across different modalities and scales to link microscale cellular structures to macroscale features of whole brain organisation. Here we introduce the BigMac dataset, a resource combining in vivo MRI, extensive postmortem MRI and multi-contrast microscopy for multimodal characterisation of a single whole macaque brain. The data spans modalities (MRI and microscopy), tissue states (in vivo and postmortem), and four orders of spatial magnitude, from microscopy images with micrometre or sub-micrometre resolution, to MRI signals on the order of millimetres. Crucially, the MRI and microscopy images are carefully co-registered together to facilitate quantitative multimodal analyses. Here we detail the acquisition, curation, and first release of the data, that together make BigMac a unique, openly-disseminated resource available to researchers worldwide. Further, we demonstrate example analyses and opportunities afforded by the data, including improvement of connectivity estimates from ultra-high angular resolution diffusion MRI, neuroanatomical insight provided by polarised light imaging and myelin-stained histology, and the joint analysis of MRI and microscopy data for reconstruction of the microscopy-inspired connectome. All data and code are made openly available.

The acquisition of MRI and histology in the same post-mortem tissue sample enables direct correlation between MRI and histologically-derived parameters. However, there still lacks a standardised automated pipeline to process histology data, with most studies relying on manual intervention. Here, we introduce an automated pipeline to extract a quantitative histological measure for staining density (stain area fraction, SAF) from multiple immunohistochemical (IHC) stains. The pipeline is designed to directly address key IHC artefacts related to tissue staining and slide digitisation. Here, the pipeline was applied to post-mortem human brain data from multiple subjects, relating MRI parameters (FA, MD, RD, AD, R2*, R1) to IHC slides stained for myelin, neurofilaments, microglia and activated microglia. Utilising high-quality MRI-histology co-registrations, we then performed whole-slide voxelwise comparisons (simple correlations, partial correlations and multiple regression analyses) between multimodal MRI- and IHC-derived parameters. The pipeline was found to be reproducible, robust to artefacts and generalisable across multiple IHC stains. Our partial correlation results suggest that some simple MRI-SAF correlations should be interpreted with caution, due to the co-localisation of other tissue features (e.g., myelin and neurofilaments). Further, we find activated microglia-a generic biomarker of inflammation-to consistently be the strongest predictor of high DTI FA and low RD, which may suggest sensitivity of diffusion MRI to aspects of neuroinflammation related to microglial activation, even after accounting for other microstructural changes (demyelination, axonal loss and general microglia infiltration). Together, these results show the utility of this approach in carefully curating IHC data and performing multimodal analyses to better understand microstructural relationships with MRI.

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Appendix 1 Appendix 2 Appendix 3 Appendix 4 Data availability References Decision letter Author response Article and author information Metrics Abstract Post-mortem magnetic resonance imaging (MRI) provides the opportunity to acquire high-resolution datasets to investigate neuroanatomy and validate the origins of image contrast through microscopy comparisons. We introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), a data release platform providing open access to curated, multimodal post-mortem neuroimaging datasets. Datasets span three themes—Digital Neuroanatomist: datasets for detailed neuroanatomical investigations; Digital Brain Zoo: datasets for comparative neuroanatomy; and Digital Pathologist: datasets for neuropathology investigations. The first Digital Brain Bank data release includes 21 distinctive whole-brain diffusion MRI datasets for structural connectivity investigations, alongside microscopy and complementary MRI modalities. This includes one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired, whole-brain diffusion MRI in fourteen nonhuman primate species, and one of the largest post-mortem whole-brain cohort imaging studies in neurodegeneration. The Digital Brain Bank is the culmination of our lab's investment into post-mortem MRI methodology and MRI-microscopy analysis techniques. This manuscript provides a detailed overview of our work with post-mortem imaging to date, including the development of diffusion MRI methods to image large post-mortem samples, including whole, human brains. Taken together, the Digital Brain Bank provides cross-scale, cross-species datasets facilitating the incorporation of post-mortem data into neuroimaging studies. Editor's evaluation This paper describes a new open-access digital brain bank of post-mortem brains that have been scanned with high-resolution, multimodal magnetic resonance imaging and with select datasets accompanied by histological data. This valuable resource can be used to study healthy human brains, pathological human brains, and the brains of other species, opening new opportunities for comparative neuroanatomy and the biological validation of non-invasive neuroimaging signals. https://doi.org/10.7554/eLife.73153.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Magnetic resonance imaging (MRI) occupies a unique position in the neuroscience toolkit. In humans, MRI is used at the single-subject level diagnostically and is increasingly deployed at the population level in epidemiology (Marcus et al., 2007; Miller et al., 2016; Snoek et al., 2021; Van Essen et al., 2013). MRI is well-established in the context of imaging causal manipulations in experimental organisms ranging from mice (Denic et al., 2011; Thiessen et al., 2013) to nonhuman primates (Absinta et al., 2017; Klink et al., 2021) and provides precise measurements in cellular and tissue preparations (Wilhelm et al., 2012). This extensive landscape of overlap with the broader neuroscience toolkit creates the potential for MRI to facilitate integration between technologies and investigations. Although MRI hardware and acquisition protocols often need to be tailored to a specific domain, the underlying technology associated with all MRI measurements gives rise to a common set of signal forming mechanisms, facilitating cross-domain comparisons. There are few methods available to neuroscientists that span this breadth of domains. One challenge to the use of MRI as a bridging technology is the need for common measurements — for example, the same MRI measurements made across multiple species, or MRI and microscopy measurements in the same brain tissue (Mars et al., 2021). Post-mortem MRI provides unique opportunities for such common measurements. MRI in post-mortem tissue can be used to identify the origins of image contrast through integration with microscopy (Keren et al., 2015; Langkammer et al., 2012; Mollink et al., 2017), directly addressing concerns over the nonspecificity of MRI signals. In this context, post-mortem MRI data are important because they share common signal forming mechanisms with in vivo MRI and a common tissue state with microscopy, providing a framework for investigation across multiple spatial scales. Post-mortem MRI facilitates comparative anatomy investigations in species that are not traditionally accessible for in vivo imaging (Berns et al., 2015; Bhagwandin et al., 2017; Grewal et al., 2020; Heuer et al., 2019), including extinct species (Berns and Ashwell, 2017). Long post-mortem scans provide the opportunity to push the boundaries of spatial resolution, providing whole human brain coverage reaching voxel sizes of 100–500 μm (Edlow et al., 2019; Foxley et al., 2016; Fritz et al., 2019; Weigel et al., 2021), edging closer to microscopy techniques but benefitting from compatibility with in vivo imaging. As a nondestructive technique, post-mortem MRI enables the examination of tissue microstructure whilst preserving tissue, facilitating repeat MRI measurements with novel contrasts and technologies; and more generally, its integration with tools for post-mortem investigations (e.g., histopathology or proteomics). In this work, we introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), a data release platform resulting from a decade of post-mortem MRI research at the University of Oxford. The Digital Brain Bank provides open access to several post-mortem neuroimaging datasets spanning investigations into human neuroanatomy, cross-species neuroanatomy, and neuropathology. All datasets provide post-mortem MRI, including diffusion MRI, with complementary microscopy data (e.g., immunohistochemistry or PLI) included with some datasets. Our post-mortem imaging research has been specifically aimed at achieving whole-brain post-mortem MRI to support the investigation of multiple brain systems/regions and long-range connections (Foxley et al., 2014; Miller et al., 2011; Miller et al., 2012), and the first release to the Digital Brain Bank contains 21 distinct whole-brain post-mortem MRI datasets, including from whole human brains. All datasets are available to access, and prospective users of the Digital Brain Bank can explore a subset of data directly on the Digital Brain Bank website using Tview: a bespoke, open-source, and web-based image viewer. Tview has been developed for efficient browsing of imaging data spanning drastically different spatial scales, from submicron resolution microscopy to millimeter MRI acquisitions. It enables real-time visualization and interaction (zooming/panning) of both MRI and microscopy images, and with flexible overlays of different modalities. All datasets uploaded to the Digital Brain Bank are associated with researchers at the University of Oxford, or from close collaborators. Limited Derived Outputs from users of Digital Brain Bank datasets will also be considered for data upload. The first release to the Digital Brain Bank includes data from multiple published projects covering a breadth of neuroimaging research, including whole-brain diffusion MRI in 14 nonhuman primate species (Bryant et al., 2021; Roumazeilles et al., 2020; Roumazeilles et al., 2021), and one of the largest post-mortem whole-brain cohort imaging studies combining whole-brain MRI and microscopy in human neurodegeneration (Pallebage-Gamarallage et al., 2018). In addition, we present a previously unpublished project providing one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired (500 μm isotropic resolution). The Digital Brain Bank will continue to grow over the coming years, with a number of further datasets already at the early stages of curation (Howard et al., 2019a; Martins-Bach et al., 2021; Martins-Bach et al., 2020; Wu et al., 2021). Results The Digital Brain Bank is accessible at open.win.ox.ac.uk/DigitalBrainBank. Datasets have been organized into categories reflecting three predominant themes of post-mortem neuroimaging research: Digital Anatomist: datasets for detailed neuroanatomical investigations. Digital Brain Zoo: datasets for comparative neuroanatomy. Digital Pathologist: datasets for neuropathology investigations. Here, we provide an overview of each theme, with examples from available datasets in the first release to the Digital Brain Bank. A brief description of all the datasets provided with the first release, alongside relevant publications, is provided in Table 1. Table 1 Description of all datasets provided in the first release to the Digital Brain Bank. All Structural MRI datasets in the first release were acquired using a balanced SSFP (bSSFP) or T2-weighted sequence, which yields strong gray-white matter contrast in formalin-fixed post-mortem tissue. Diffusion MRI datasets were acquired using a combination of diffusion-weighted steady-state free precession (DW-SSFP) and diffusion-weighted spin-echo (DW-SE) sequences. Full details of the motivation behind the choice of sequences and available contrasts are described in the Discussion. †T2* and magnetic susceptibility maps are currently available in 9 out of 12 ALS brains and all control brains. The remaining datasets were either lost during scanner export, or are of insufficient data quality for public release. CategoryNameContents: MRIResolution (MRI)Contents: MicroscopyRelevant publicationsDigital AnatomistHuman High-Resolution Diffusion MRI-PLIWhole-brain diffusion MRI, structural MRI, quantitative T1 and T2 maps:– Control human brain: 1×Diffusion MRI: (500 μm, 1 and 2 mm iso.)Structural MRI: 312.5×312.5×500 μm3T1 map: (0.75×0.75×1.6 mm3)T2 map: (0.75×0.75×1.6 mm3)Polarised light imaging (4 μm in-plane) in the anterior commissure, corpus callosum, pons, thalamus, and visual cortex (same brain)Dataset described in this publication (Methodology in Appendix 1), Diffusion MRI processing described in Tendler et al., 2020b, T2 mapping described in Tendler et al., 2021Digital AnatomistHuman Callosum MRI-PLI-HistologyCorpus callosum diffusion MRI:– Excised control human corpus callosum samples: 3×Diffusion MRI: (400 μm iso.)Polarised light imaging (4 μm in-plane), bright-field microscopy images of immunohistochemistry stains (0.25 μm in-plane) for PLP (myelin) and GFAP (astrocyte) (same human corpus callosum samples)Mollink et al., 2017Whole-brain diffusion MRI and structural MRI (available in brains marked with a *):Bushbaby (Galago senegalensis): 1×Capuchin monkey (Sepajus apella): 1×Chimpanzee* (Pan troglodytes): 2×Colobus monkey (Colobus guereza): 1×Cotton-Top tamarin (Saguinus oedipus): 1×Golden Lion tamarin (Leontopithecus rosalia): 1×Hamadryas baboon* (Papio hamadryas): 1×Macaque monkey (Macaca mulatta): 3×Mangabey (Lophocebus albigena): 1×Night monkey, (Aotus lemurinus): 1×Ring-tailed lemur (Lemur catta): 3×Saki monkey (Pithecia pithecia): 1×Western Lowland gorilla* (Gorilla gorilla): 1×Woolly monkey (Lagothrix lagotricha): 1×Diffusion MRI:300 μm iso.: Bushbaby, Cotton-Top tamarin & Golden Lion Tamarin400 μm iso: Night monkey500 μm iso: Ring-tailed lemur and Saki monkey600 μm iso: Capuchin monkey, Chimpanzee, Colobus monkey, Hamadryas baboon, Macaque monkey, Mangabey, Western Lowland Gorilla and Woolly MonkeyStructural MRI200 μm iso: Western Lowland Gorilla220 μm iso: Hamadryas Baboon0.22×0.22×0.19 mm3: 1× Chimpanzee0.375×0.375×0.40 mm3: 1× ChimpanzeeNone1× Western Lowland gorilla and 1× Chimpanzee described in Roumazeilles et al., 2020, 3× Macaque monkey and 3× Ring-Tailed Lemur described in Roumazeilles et al., 2021. Hamadryas baboon, Cotton-Top tamarin and Golden Lion tamarin datasets described in this publication (Methodology in Appendix 1). All other datasets described in Bryant et al., 2021Digital Brain ZooMarsupialsWhole-brain diffusion MRI and structural MRI:Tasmanian devil (Sarcophilus harrisii): 2×Thylacine (Thylacinus cynocephalus): 2×Diffusion MRI:1 mm iso: 1× Tasmanian devil1.5 mm iso: 1× Tasmanian devil1.1 mm iso: 1× Thylacine1.0×1.1×0.8 mm3: 1× ThylacineStructural MRI330 μm iso: 1× Tasmanian devil and 1× Thylacine330×330×300 μm3: 1× Tasmanian devil500 μm iso: 1× ThylacineNoneBerns and Ashwell, 2017Digital Brain ZooCetaceansWhole-brain diffusion MRI and structural MRICommon dolphin (Delphinus delphis): 1×Pantropical dolphin (Stenella attenuata): 1×Diffusion MRI: (1.3 mm iso.)Structural MRI: (640×640×500 μm3)NoneBerns et al., 2015Digital Brain ZooCarnivoraWhole-brain diffusion MRI and structural MRI:– European wolf (Canis lupus): 1×Diffusion MRI: (600 μm iso.)Structural MRI: (220 μm iso.)NoneDataset described in this publication (Methodology in Appendix 1)Digital PathologistHuman ALS MRI-HistologyWhole-brain diffusion MRI, structural MRI, quantitative T1, T2, and T2* maps, magnetic susceptibility maps (selected brains†):Amyotrophic lateral sclerosis (ALS) human brains: 12×Control human brains: 3×Diffusion MRI: (850 μm iso.)Structural MRI: (230–250 μm in-plane; 270–500 μm slice)T1 map: (0.65–1 mm in-plane; 0.90–1.6 mm slice)T2 map: (0.65–1 mm in-plane; 0.90–1.6 mm slice)T2*/magnetic susceptibility maps: (0.5 mm in-plane; 1.1–1.3 mm slice)Bright-field microscopy immunohistochemistry stains (0.50 μm in-plane, exception pTDP43 – 0.25 μm in-plane): pTDP-43, IBA1 (pan microglia), CD68 (activated microglia/macrophages), PLP (myelin), SMI-312 (axonal phosphorylated neurofilaments), and ferritin (iron storage, subset of regions)Regions: Anterior cingulate cortex, corpus callosum, hippocampus, primary motor cortex, and visual cortex (same brains).Selected multimodal histology available in two brains (1× ALS and 1× Control), and multiregional PLP (available in 10 out of 12 ALS brains and all control brains, 5–8 regions per brain) in first data release – remaining histology being actively curated.Pallebage-Gamarallage et al., 2018, Magnetic susceptibility and T2* mapping protocol described in Wang et al., 2020, Diffusion MRI processing described in Tendler et al., 2020b, T2 mapping described in Tendler et al., 2021 Digital Anatomist Datasets within the Digital Anatomist provide a new direction for answering fundamental questions in neuroanatomy, through ultra-high resolution MRI data and complementary microscopy within the same sample in humans and model nonhuman species. The long scan times available in post-mortem MRI affords imaging at ultra-high spatial resolutions, facilitating the delineation of small tissue structures within the human brain, one of the key aims of the Digital Anatomist. Often, post-mortem investigations are limited to small sections of excised brain tissue that represent a limited anatomical region. However, our developments in whole-brain post-mortem diffusion imaging (Foxley et al., 2014; McNab et al., 2009; Miller et al., 2011; Miller et al., 2012; Tendler et al., 2020b) provide the opportunity to investigate structural connectivity and gross neuroanatomy, at scales that are unobtainable in vivo. These developments have culminated in the Human High-Resolution Diffusion MRI-PLI dataset, providing one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired (500 μm isotropic resolution), as shown in Figure 1. Companion datasets acquired at 1 mm and 2 mm (isotropic) provide a comparison at cutting-edge and conventional in vivo resolutions (Figure 1a). Figure 1 Download asset Open asset The Digital Anatomist. (a) Whole-brain diffusion MRI data available in the Human High-Resolution Diffusion MRI-PLI dataset reveals the wealth of information provided at increased spatial scales, one of the key aims of the Digital Anatomist. Here, the 500 μm dataset uncovers the information hidden at lower spatial resolutions, for example, visualizing the interdigitating transverse pontine fibers with the corticospinal tract or striations through the internal capsule. (b) Similarly, datasets across multiple spatial scales can inform us of the limitations at reduced imaging resolutions. Here, gyral tractography (occipital lobe) reveals an overall pattern of fibers turning into the gyral bank at 0.5 mm. At 1 mm, an underestimation of connectivity at the gyral banks is observed, known as the 'gyral bias' (Cottaar et al., 2021; Schilling et al., 2018). At 2 mm, tractography bears little resemblance to the expected architecture. Multimodal comparisons enable us to validate our findings, with complementary polarised light imaging (PLI) data at over 2 orders of magnitude increase in resolution (125×) revealing a similar pattern of gyral connectivity, and (c) excellent visual agreement with tractography across the pons. (a) displays diffusion tensor principal diffusion direction maps (modulated by fractional anisotropy). In addition to providing a new insight into human neuroanatomy, these data can be used to inform experimental design and the interpretation of results. Here, the Human High-Resolution Diffusion MRI-PLI dataset enables users to identify the resolution required to visualize certain brain structures (Figure 1a), and how spatial resolution impacts tractography performance (e.g., overcoming 'gyral bias'—Figure 1b; Cottaar et al., 2021; Schilling et al., 2018). Polarised light imaging (PLI) provides estimates of myelinated fiber orientation (Axer et al., 2011), and complementary PLI data acquired in a subset of brain regions (4 μm in-plane) facilitates cross-scale comparisons (Figure 1b and c). A further aim of the Digital Anatomist is to perform quantitative validations across modalities, relating MRI to microscopic measures. These kinds of analyses can only be achieved with accurately coregistered data, enabling pixel-wise comparisons across modalities acquired at drastically different spatial resolutions. This potential is most clearly seen in the Human Callosum MRI-PLI-Histology dataset, which provides diffusion MRI (400 μm isotropic), alongside complementary PLI (4 μm in-plane), and histology (myelin and astrocytes) (0.25 μm in-plane) in three excised human corpus callosum samples (Mollink et al., 2017). These data offer multiple pathways of investigation, including the identification of the origins of image contrast; validation of microstructural models of tissue (Mollink et al., 2017); and developing unique models explicitly linking MRI with microscopy (Howard et al., 2019b). Digital Brain Zoo The Digital Brain Zoo provides curated datasets to investigate neuroanatomy in nonhuman species and compare anatomy across species. Post-mortem MRI has enormous potential to inform comparative neuroanatomy for three reasons. First, it enables the scanning of species that would be extremely difficult or impossible to study in vivo. Second, samples can be imaged with minimal handling and without invasive procedures, enabling the study of rare specimens that would not be appropriate to dissect. Third, MRI investigations can be performed in whole-brain samples, rather than excised tissue sections. This makes post-mortem MRI ideally placed to characterize macroscopic brain structure, long-range structural connectivity, and tissue microstructure in species that are not traditional experimental models, and in particular rare species where very few brain samples may be available (Berns and Ashwell, 2017; Bhagwandin et al., 2017; Grewal et al., 2020; Mars et al., 2014). MRI data from multiple species allows one to formally compare brain organization, important for large-scale comparative neuroscience which has traditionally relied on very limited measures (e.g., whole or regional brain size measures of brain organization) (Mars et al., 2014). The ability to acquire data from whole brains opens up the possibility of elucidating principles of neural diversity across larger orders of mammalian species (Friedrich et al., 2021), and create between-species mappings to formally identify homologies and quantify unique aspects of any given brain (Mars et al., 2018). This also allows one to improve translational neuroscience by better understanding the relationship between the human brain and that of model species (e.g., macaque, marmoset, rat, and mouse). The Digital Brain Zoo provides access to post-mortem imaging datasets in nonhuman species covering multiple taxonomic ranks (Figure 2a), including nonhuman primate species (Bryant et al., 2021; Roumazeilles et al., 2020; Roumazeilles et al., 2021), Carnivora (Grewal et al., 2020), Marsupials (Berns and Ashwell, 2017), and Cetaceans (Berns et al., 2015). As with other collections in the Digital Brain Bank, the Digital Brain Zoo currently focuses primarily on whole-brain diffusion MRI. These datasets offer multiple pathways of investigation in comparative neuroanatomy, for example, through the examination of structural connections across brains (Figure 2b; Bryant et al., 2021). Furthermore, our developments in imaging large post-mortem samples have enabled us to acquire several high-quality post-mortem imaging datasets in species with brains that are too large to fit into specialized preclinical MRI systems, conventionally used to improve image quality in post-mortem MRI (see Discussion). Figure 2 Download asset Open asset The Digital Brain Zoo. (a) The first release of the Digital Brain Zoo provides whole-brain MRI datasets spanning multiple species and taxonomic ranks. Notably, we provide whole-brain diffusion MRI datasets from 14 nonhuman primate species, with samples selected for their high quality and to ensure sampling of all major branches of the primate evolutionary tree (Prosimian, New World monkey, Old World monkey, and Great Ape). (b) compares the relative volume of four tracts derived from nine nonhuman primate post-mortem datasets provided in the Digital Brain Zoo (Bryant et al., 2021), where increased distance from the centre corresponds to an increased volume. Digital Pathologist Datasets within the Digital Pathologist provide a new direction for examining neuropathology and MRI-pathology correlates in humans and established laboratory models. One of the biggest challenges in the use of MRI clinically is the lack of specificity to disease mechanisms. Many neurological diseases are characterized by changes at the cellular and subcellular level, which cannot be directly visualized with the limited resolution of MRI. Nevertheless, MRI contrast can be made sensitive to cellular-level phenomena that are relevant to disease. Acquisition of MRI and histology in the same tissue enables us to relate microscopic changes in the neural microenvironment to MRI image contrast. The primary aim of the Digital Pathologist is to facilitate these cross-scale comparisons, imaging brain tissue associated with a neurological disease. Such data are provided in the Human ALS MRI-Histology dataset (Figure 3a), which aims to identify how neuropathological changes in amyotrophic lateral sclerosis (ALS) give rise to altered MRI contrast, and answer specific questions related to ALS pathology. The Human ALS MRI-Histology dataset provides whole-brain multimodal MRI and selective histology in a cohort of 12 ALS (diagnosis during lifetime, confirmed ALS neuropathology) and 3 control (no known neuropathology) brains (Pallebage-Gamarallage et al., 2018) provided by the Oxford Brain Bank. MRI data includes diffusion, structural, quantitative susceptibility maps (via quantitative susceptibility mapping, QSM), and quantitative T1, T2, and T2* maps. Histology includes markers for proteinopathy (pTDP-43), microglia (CD68 and IBA1), myelin (PLP), neurofilaments (SMI-312), and iron (ferritin) in order to detect changes in a range of microstructures within cortical and subcortical regions (anterior cingulate cortex, corpus callosum, hippocampus, primary motor cortex, and visual cortex) associated with different proposed stages of ALS disease progression (Jucker and Walker, 2013). Figure 3 Download asset Open asset The Digital Pathologist. One of the key aims of the Digital Pathologist is the examination of neuropathological spread in neurological disease. The Human ALS MRI-Histology dataset (a) facilitates these investigations, combining whole-brain multimodal MRI and histology (selected brain regions) in a cohort of 12 ALS and 3 control brains. (b) Displays the reconstruction of five white matter pathways associated with different ALS stages in a single post-mortem brain (Kassubek et al., 2014). Comparisons between ALS and control brains over the corpus callosum of the cohort (c) reveals changes in fractional anisotropy (FA, normalized to Par/Temp/Occ lobe), with biggest changes associated with motor and prefrontal regions (Hofer and Frahm, 2006) (*=p<0.05; **=p<0.05 following multiple comparison correction) (full details of the corpus callosum analysis provided in Appendix 2). This reflects the anticipated changes in ALS with brain regions associated with motor function, in good agreement with a previous study (Chapman et al., 2014), which identified the greatest FA difference between ALS and controls in these regions. Accurate MRI-histology coregistrations facilitate cross-modality comparisons, and (d) displays an example of MRI-histology coregistration over the visual cortex of a single ALS brain achieved using the Tensor Image Registration Library (TIRL) (Huszar et al., 2019). V1=principal diffusion direction, FA=fractional anisotropy, MD=mean diffusivity, D⊥=radial diffusivity, MO=mode from diffusion tensor output, Dyad1=principal dyad orientation, f1=principal fiber fraction and D=diffusivity from Ball and Two-Stick output, swMRI=susceptibility-weighted MRI, χ=magnetic susceptibility. Details of stain contrasts in (b) and (d) are provided in Table 1. ALS, amyotrophic lateral sclerosis; MRI, magnetic resonance imaging. Different MRI modalities have known sensitivities to different components of the cellular environment. Combined with multimodal histology, these data provide the opportunity to relate neuropathologically induced changes in tissue microstructure to MRI image contrast. While these aims could be partially achieved by dissecting and scanning subregions of the brain, our approach of scanning whole post-mortem brains enables us to investigate neuropathological spread across the entire brain (Jucker and Walker, 2013). This facilitates investigations across long-range fiber-tracts associated with pathology (Figure 3b), or microstructural changes in multiple brain regions (Figure 3c). Notably, these analyses are being facilitated by accurate cross-modality image coregistrations (Huszar et al., 2019), enabling us to perform pixel-wise evaluations and integrate structural analyses to identify how pathology influences MR image contrast (Figure 3d) in a subset of brain regions associated with different proposed stages of ALS disease progression (Jucker and Walker, 2013). MRI data for the Human ALS MRI-Histology dataset for all 12 ALS and 3 control brains are immediately available to access, alongside a subset of histology data. Remaining histology data and MRI-histology coregistrations are being actively curated for future release to the Digital Brain Bank. Tview The Digital Brain Bank website enables users to browse a subset of data easily. A key feature of many datasets is that they contain both MRI and microscopy data. Few available viewers, whether downloadable or online, support both MRI and microscopy file formats, creating a barrier of entry for potential users. Moreover, 2D microscopy datasets are extremely high resolution: single images can exceed 10,000,000,000 pixels, running to gigabytes in size. We aim to provide online viewing of microscopy and MRI data using standard internet browsers, with a viewer that can handle data at very different spatial scales, provide flexible image overlays, and support color visualization for diffusion-derived measures, PLI, and multiple histological counterstains. Unable to identify existing software with these features, we developed a web-based image viewer, Tview. Tview is based on software originally used to display satellite imagery at multiple elevations, and enables real-time visualization, interaction (zooming/panning), and flexible overlays of different modalities in a single 2D plane of MRI and microscopy data. Visualization of multimodal (i.e., MRI and microscopy) datasets on the Digital Brain Bank website is achieved with Tview. An example Tview implementation is available at open.win.ox.ac.uk/DigitalBrainBank/#/tileviewer, where cross-modality coregistrations were performed using the Tensor Image Registration Library (TIRL) (Huszar et al., 2019) and FNIRT (Andersson et al., 2007; Jenkinson et al., 2012), both available as part of FSL. Code for Tview, the website, and server implementation are available at https://git.fmrib.ox.ac.uk/thanayik/dbb. The benefits of Tview are most readily realized with datasets incorporating MRI and microscopy images, enabling visualization of distinct contrasts over multiple spatial scales. However, many datasets provided in the first release to the Digital Brain Bank do not contain any microscopy data. For these datasets, a detailed static image is currently used for visualization on the Digital Brain Bank website. Requirements for data access and referencing datasets The Digital Brain Bank has been designed to minimize the burden on the user to access datasets, within ethical constraints. For many datasets, we have developed conditions of use terms via a material transfer agreement (MTA), which users agree to prior to access. The MTAs are primarily designed to ensure that datasets are used for research/educational purposes, to prevent misuse, and to satisfy funding requirements. For datasets restricted by MTAs, when possible, a subset of example data (e.

PURPOSE:Ex vivo imaging is a commonly used approach to investigate the biophysical mechanism of orientation-dependent signal phase evolution in white matter. Yet, how phase measurements are influenced by the structural alteration in the tissue after formalin fixation is not fully understood. Here, we study the effects on magnetic susceptibility, microstructural compartmentalization, and chemical exchange measurement with a postmortem formalin-fixed whole-brain human tissue.METHODS:A formalin-fixed, postmortem human brain specimen was scanned with multiple orientations to the main magnetic field direction for robust bulk magnetic susceptibility measurement with conventional quantitative susceptibility imaging models. White matter samples were subsequently excised from the whole-brain specimen and scanned in multiple rotations on an MRI scanner to measure the anisotropic magnetic susceptibility and microstructure-related contributions in the signal phase and to validate the findings of the whole-brain data.RESULTS:The bulk isotropic magnetic susceptibility of ex vivo whole-brain imaging is comparable to in vivo imaging, with noticeable enhanced nonsusceptibility contributions. The excised specimen experiment reveals that anisotropic magnetic susceptibility and compartmentalization phase effect were considerably reduced in the formalin-fixed white matter specimens.CONCLUSIONS:Formalin-fixed postmortem white matter exhibits comparable isotropic magnetic susceptibility to previous in vivo imaging findings. However, the measured phase and magnitude data of the fixed white matter tissue shows a significantly weaker orientation dependency and compartmentalization effect. Alternatives to formalin fixation are needed to better reproduce the in vivo microstructural effects in postmortem samples.

Post-mortem magnetic resonance imaging (MRI) provides the opportunity to acquire high-resolution datasets to investigate neuroanatomy and validate the origins of image contrast through microscopy comparisons. We introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), a data release platform providing open access to curated, multimodal post-mortem neuroimaging datasets. Datasets span three themes-Digital Neuroanatomist: datasets for detailed neuroanatomical investigations; Digital Brain Zoo: datasets for comparative neuroanatomy; and Digital Pathologist: datasets for neuropathology investigations. The first Digital Brain Bank data release includes 21 distinctive whole-brain diffusion MRI datasets for structural connectivity investigations, alongside microscopy and complementary MRI modalities. This includes one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired, whole-brain diffusion MRI in fourteen nonhuman primate species, and one of the largest post-mortem whole-brain cohort imaging studies in neurodegeneration. The Digital Brain Bank is the culmination of our lab's investment into post-mortem MRI methodology and MRI-microscopy analysis techniques. This manuscript provides a detailed overview of our work with post-mortem imaging to date, including the development of diffusion MRI methods to image large post-mortem samples, including whole, human brains. Taken together, the Digital Brain Bank provides cross-scale, cross-species datasets facilitating the incorporation of post-mortem data into neuroimaging studies.

To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model (d(parallel to) = 1.7 mu m(2)/ms). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of similar to 2 - 2.5 mu m(2)/ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.

Abstract Background: The rise of whole slide imaging systems has enabled pathologists to remotely view cases in high resolution to diagnose cancer, and efficiently archive images . The advent of machine learning techniques and their application in digital pathology have facilitated the identification of histological patterns for effective diagnosis of disease. Deep learning-based solutions have been developed to detect and recognize cancer types, and automatically grade and stage tumors through evaluation of pathological features and patterns. However, no image-based solutions have been able to replicate a multivariate test, such as a risk -of recurrence assay for early stage breast cancer. Traditionally, subtle differences in gene expression have been measured by microarray or NGS, however these changes may also be recognized phenotypically from hematoxylin and eosin stained (H&E) slides by digital biomarkers developed using novel machine learning techniques. The large repository of images with MammaPrint and BluePrint results may enable us to develop digital MammaPrint and digital BluePrint biomarkers that predict the risk of distant recurrences and the molecular subtypes of a tumor sample using only H&E stained digitized tumor slides. Methods: Using over70,000 H&E images of early stage breast cancer patients in combination with machine learning techniques, digital versions of MammaPrint and BluePrint were developed. In total 20,000 images were used for feasibility and algorithm optimization, another 50,000 images were used for further finetuning. MammaPrint indices and BluePrint scores and categorical results were used to train the system. After the algorithms were optimized, they were locked and validated in an independent set of 5000 H&E stained images. The MammaPrint and BluePrint predictions were compared to the original MammaPrint and BluePrint results obtained from the microarray assay. The finalized and locked algorithms were further validated for precision and reproducibility in a large data set of xx Images and processed multiple times. Multicenter clinical validation was performed in H&E stained images of multiple series with long term follow up (tbd), totaling ##k images. In this ##K cohort of patients, #% were HR+/HER2-, #% were clinically HER2+ and x% were triple negative. Results: Using an independent dataset of 5000 samples, we compared the MammaPrint and BluePrint predictions obtained from the H&E slides to the traditional versions of MammaPrint and BluePrint based on a microarray. The binary performance of the digital MammaPrint had a concordance of xx% (with an AUC of xx%),%NPA and %PPA when compared to the traditional MammaPrint high-low classification. For digital BluePrint, the system had a concordance of xx% and a classification accuracy of xx%. The analytical performance showed a precision and reproducibility of xx% In a multicenter clinical validation the DRFI was xx% in dMP low risk and yy% in dMP high risk groups. The xxx dataset the performance was similar to the microarray and better than compared to clinical parameters. Analyses will be available by the Placeholder Abstract deadline. Conclusions: The combination of machine learning and digital pathology has enabled development of rapid and highly accurate in silico versions of MammaPrint and BluePrint. Implementation of digital H&E based risk of recurrence and molecular subtyping could enable preservation of valuable diagnostic tissue, faster turnaround time for test results, and a more cost effective approach to treatment planning tools, especially in countries that do not allow send out of human tissue and this adoption of risk scoring is low. Final conclusions tbd Citation Format: Annuska M Glas, Jorge S Reis-Filho, Diederik Wehkamp, Belma Dodgas, Leonie Delahaye, Ran Godrich, Jeroen Mollink, Adam Casson, Anke Witteveen, Julian Viret, Donghun Lee, Matthew Lee, Hugo Horlings, Leo Grady, Thomas Fuchs, William Audeh, Christopher Kanan, Laura J van't Veer. Digital MammaPrint and BluePrint using machine learning and whole slide imaging [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD6-02.

Purpose Ex vivo imaging is a preferable method to study the biophysical mechanism of white matter orientation-dependent signal phase evolution. Yet, how formalin fixation, commonly used for tissue preservation, affects the phase measurement is not fully known. We, therefore, study the impacts of formalin fixation on magnetic susceptibility, microstructural compartmentalisation and chemical exchange measurement on human brain tissue. Methods A formalin-fixed, post-mortem human brain specimen was scanned with multiple orientations with respect to the main magnetic field direction for robust bulk magnetic susceptibility measurement with conventional quantitative susceptibility imaging models. Homogeneous white matter tissues were subsequently excised from the whole-brain specimen and scanned in multiple rotations on an MRI scanner to measure the anisotropic magnetic susceptibility and microstructure-related contributions in the signal phase. Electron microscopy was used to validate the MRI findings. Results The bulk isotropic magnetic susceptibility of ex vivo whole-brain imaging is comparable to in vivo imaging, with noticeable enhanced non-susceptibility contributions. The excised specimen experiment reveals that anisotropic magnetic susceptibility and compartmentalisation phase effect were considerably reduced in formalin-fixed white matter tissue. Conclusions Despite formalin-fixed white matter tissue has comparable bulk isotropic magnetic susceptibility to those measured via in vivo imaging, its orientation-dependent components in the signal phase related to the tissue microstructure is substantially weaker, making it less favourable in white matter microstructure studies using phase imaging.

Multi-echo gradient echo (ME-GRE) magnetic resonance signal evolution in white matter has a strong dependence on the orientation of myelinated axons with respect to the main static field. Although analytical solutions have been able to predict some of the white matter (WM) signal behaviour of the hollow cylinder model, it has been shown that realistic models of WM offer a better description of the signal behaviour observed. In this work, we present a pipeline to (i) generate realistic 2D WM models with their microstructure based on real axon morphology with adjustable fiber volume fraction (FVF) and g-ratio. We (ii) simulate their interaction with the static magnetic field to be able to simulate their MR signal. For the first time, we (iii) demonstrate that realistic 2D WM models can be used to simulate a MR signal that provides a good approximation of the signal obtained from a real 3D WM model derived from electron microscopy. We then (iv) demonstrate in silico that 2D WM models can be used to predict microstructural parameters in a robust way if ME-GRE multi-orientation data is available and the main fiber orientation in each pixel is known using DTI. A deep learning network was trained and characterized in its ability to recover the desired microstructural parameters such as FVF, g-ratio, free and bound water transverse relaxation and magnetic susceptibility. Finally, the network was trained to recover these micro-structural parameters from an ex vivo dataset acquired in 9 orientations with respect to the magnetic field and 12 echo times. We demonstrate that this is an overdetermined problem and that as few as 3 orientations can already provide comparable results for some of the decoded metrics.

Purpose Neuroscience methods working on widely different scales can complement and inform each other. At the macroscopic scale, magnetic resonance imaging methods that estimate microstructural measures have much to gain from ground truth validation and models based on accurate measurement of that microstructure. We present an approach to generate rich and accurate geometric models of white matter microstructure through dense segmentation of 3D electron microscopy (EM). Methods Volumetric data of the white matter of the genu of the corpus callosum of the adult mouse brain were acquired using serial blockface scanning electron microscopy (SBF-SEM). A segmentation pipeline was developed to separate the 3D EM data into compartments and individual cellular and subcellular constituents, making use of established tools as well as newly developed algorithms to achieve accurate segmentation of various compartments. Results The volume was segmented into six compartments comprising myelinated axons (axon, myelin sheath, nodes of Ranvier), oligodendrocytes, blood vessels, mitochondria, and unmyelinated axons. The myelinated axons had an average inner diameter of 0.56 μm and an average outer diameter of 0.87 μm. The diameter of unmyelinated axons was 0.43 μm. A mean g-ratio of 0.61 was found for myelinated axons, but the g-ratio was highly variable between as well as within axons. Conclusion The approach for segmentation of 3D EM data yielded a dense annotation of a range of white matter compartments that can be interrogated for their properties and used for in silico experiments of brain structure. We provide the resulting dense annotation as a resource to the neuroscience community.

Susceptibility weighted magnetic resonance imaging (MRI) is sensitive to the local concentration of iron and myelin. Here, we describe a robust image processing pipeline for quantitative susceptibility mapping (QSM) and R2* mapping of fixed post-mortem, whole-brain data. Using this pipeline, we compare the resulting quantitative maps in brains from patients with amyotrophic lateral sclerosis (ALS) and controls, with validation against iron and myelin histology. Twelve post-mortem brains were scanned with a multi-echo gradient echo sequence at 7T, from which susceptibility and R2* maps were generated. Semi-quantitative histological analysis for ferritin (the principal iron storage protein) and myelin proteolipid protein was performed in the primary motor, anterior cingulate and visual cortices. Magnetic susceptibility and R2* values in primary motor cortex were higher in ALS compared to control brains. Magnetic susceptibility and R2* showed positive correlations with both myelin and ferritin estimates from histology. Four out of nine ALS brains exhibited clearly visible hyperintense susceptibility and R2* values in the primary motor cortex. Our results demonstrate the potential for MRI-histology studies in whole, fixed post-mortem brains to investigate the biophysical source of susceptibility weighted MRI signals in neurodegenerative diseases like ALS.

Introduction Recently, an additional trigeminothalamic tract - the dorsal trigeminothalamic tract - has been described in human brainstems by our group next to the known ventral trigeminothalamic tract. As various elements of the trigeminal system are known to be organised in a somatotopic fashion, the question arose whether the fibres within the trigeminal root show specific distributions patterns in their contribution to the ventral trigeminothalamic tract and dorsal trigeminothalamic tract specifically. Methods This study investigated the arrangement of the fibres in the trigeminal root by combining various imaging methods in the pons of 11 post-mortem specimens. The pons were investigated by polarised light imaging (PLI) (n = 4; to quantify fibre orientation; 100 mu m interslice distance), histochemical staining methods (n = 3; to visualise the internal myeloarchitecture; 60 mu m) and ultra-high field, post-mortem magnetic resonance imaging (MRI) (n = 4; for tractography; 500 mu m interslice distance). Results This study shows that the fibres, from the point where the trigeminal root enters the brainstem, are distinctly arranged by their contribution to the ventral trigeminothalamic tract and dorsal trigeminothalamic tract. This finding is supported by both post-mortem, ultra-high dMRI and different light microscopy techniques. Conclusion The data from this study suggest that the fibres in the superior half of the root contribute mainly to the ventral trigeminothalamic tract, whereas the fibres in the inferior half mainly contribute to the dorsal trigeminothalamic tract. Such a somatotopic organisation could possibly create new insights into the anatomical origin of trigeminal neuralgia and the clinical relevance of this somatotopic organisation should therefore be further explored.

OBJECTIVE:The aim of this study was to test the hypothesis that white matter degeneration of the perforant path - as part of the Papez circuit - is a key feature of amyotrophic lateral sclerosis (ALS), even in the absence of frontotemporal dementia (FTD) or deposition of pTDP-43 inclusions in hippocampal granule cells. METHODS:We used diffusion Magnetic Resonance Imaging (dMRI), polarized light imaging (PLI) and immunohistochemical analysis of post mortem hippocampus specimens from controls (n = 5) and ALS patients (n = 14) to study white matter degeneration in the perforant path. RESULTS:diffusion Magnetic Resonance Imaging demonstrated a decrease in fractional anisotropy (P = 0.01) and an increase in mean diffusivity (P = 0.01) in the perforant path in ALS compared to controls. PLI-myelin density was lower in ALS (P = 0.05) and correlated with fractional anisotropy (r = 0.52, P = 0.03). These results were confirmed by immunohistochemistry; both myelin (proteolipid protein, P = 0.03) and neurofilaments (SMI-312, P = 0.02) were lower in ALS. Two out of the fourteen ALS cases showed pTDP-43 pathology in the dentate gyrus, but with comparable myelination levels in the perforant path to other ALS cases. CONCLUSION:We conclude that degeneration of the perforant path occurs in ALS patients and that this may occur before, or independent of, pTDP-43 aggregation in the dentate gyrus of the hippocampus. Future research should focus on correlating the degree of cognitive decline to the amount of white matter atrophy in the perforant path.

Microscopic features (that is, microstructure) of axons affect neural circuit activity through characteristics such as conduction speed. To what extent axonal microstructure in white matter relates to functional connectivity (synchrony) between brain regions is largely unknown. Using MRI data in 11,354 subjects, we constructed multivariate models that predict functional connectivity of pairs of brain regions from the microstructural signature of white matter pathways that connect them. Microstructure-derived models provided predictions of functional connectivity that explained 3.5% of cross-subject variance on average (ranging from 1–13%, or r = 0.1–0.36) and reached statistical significance in 90% of the brain regions considered. The microstructure–function relationships were associated with genetic variants, co-located with genes DAAM1 and LPAR1, that have previously been linked to neural development. Our results demonstrate that variation in white matter microstructure predicts a fraction of functional connectivity across individuals, and that this relationship is underpinned by genetic variability in certain brain areas. Mollink et al. establish a link between white matter microstructure and functional connectivity using MRI data from 11,354 individuals in the UK Biobank. GWAS identified genetic associations of these microstructure–function relationships.

Amyotrophic lateral sclerosis (ALS) is a progressive and incurable motor neuron disease. Some ALS patients are affected by a level of cognitive or behavioural decline that meets the criteria for frontotemporal dementia (FTD). ALS and FTD share genetic and pathological features; for example, the deposition of phosphorylated 43 kDa TAR DNA-binding protein (pTDP-43) in the brain. Spreading of pTDP-43 pathology in ALS towards brain areas that connect via the Papez circuit is a possible indicator of progression towards FTD. For example, pTDP-43 aggregates in the granule cells of the hippocampus correlate well with clinically manifest FTD. Here, we test the hypothesis that white matter degeneration of the perforant path – as part of the Papez circuit – in the hippocampus is a feature of ALS, even in the absence of fully developed FTD or deposition of pTDP-43 inclusions in hippocampal granule cells. We used diffusion MRI (dMRI), polarized light imaging (PLI) and immunohistochemical analysis of hippocampus sections from controls (n=5) and ALS patients (n=14) to perform an in-depth study of white matter in the perforant path. The dMRI results show a significant decrease in fractional anisotropy (p=0.01) and an increase in mean diffusivity (p=0.01), axial diffusivity (p=0.03) and radial diffusivity (p=0.03) in the perforant path in ALS patients compared to controls, possibly indicating a loss of white matter fibres. Myelin density (measured with PLI retardance) was lower in ALS patients compared to controls (p=0.05) and correlated with dMRI fractional anisotropy (r=0.52, p=0.03). The dMRI and PLI results were confirmed by the immunohistochemistry; both myelin (proteolipid protein, p=0.03) and neurofilaments (SMI-312, p=0.02) were lower in ALS patients. The activated microglial (CD68) density was similar in ALS and controls. Only two out of the fourteen ALS cases showed pTDP-43 pathology in the dentate gyrus; however, while these two ALS-FTD cases showed reduced myelination in the perforant path, the values were comparable to other ALS cases. We conclude that degeneration of the perforant path occurs in ALS patients and that this may occur before, or independent of, pTDP-43 aggregation in the dentate gyrus of the hippocampus. Future research should focus on correlating the degree of clinically observed cognitive decline to the amount of white matter atrophy in the perforant path.