Do We Need a Human post mortem Whole-Brain Anatomical Ground Truth in in vivo Magnetic Resonance Imaging?

Non-invasive in vivo neuroimaging techniques provide a wide array of possibilities to study human brain function. A number of approaches are available that improve our understanding of the anatomical location of brain activation patterns, including the development of probabilistic conversion tools to register individual in vivo data to population based neuroanatomical templates. Two elegant examples were published by Horn et al. (2017) in which a method was described to warp DBS electrode coordinates, and histological data to MNI-space (Ewert et al., 2017). The conversion of individual brain scans to a standard space is done assuming that individual anatomical scans provide a reliable image of the underlying neuroanatomy. It is unclear to what extent spatial distortions related to tissue properties, or MRI artifacts exist in these scans. Therefore, the question rises whether the anatomical information from the individual scans can be considered a real ground truth. To accommodate the knowledge-gap as a result of limited anatomical information, generative brain models have been developed circumventing these challenges through the application of assumption sets without recourse to any ground truth. We would like to argue that, although these efforts are valuable, the definition of an anatomical ground truth is preferred. Its definition requires a system in which non-invasive approaches can be validated using invasive methods of investigation. We argue that the application of post mortem MRI studies in combination with microscopy analyses brings an anatomical ground truth for the human brain within reach, which is of importance for all research within the human in vivo neuroimaging field

Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases

Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for brain mapping in postmortem MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high resolution of 135 postmortem human brain tissue specimens imaged at 0.3 mm$^{3}$ isotropic using a T2w sequence on a 7T whole-body MRI scanner. We developed a deep learning pipeline to segment the cortical mantle by benchmarking the performance of nine deep neural architectures, followed by post-hoc topological correction. We then segment four subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities, and the normal appearing white matter. We show generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm^3 and 0.16 mm^3 isotropic T2*w FLASH sequence at 7T. We then compute localized cortical thickness and volumetric measurements across key regions, and link them with semi-quantitative neuropathological ratings. Our code, Jupyter notebooks, and the containerized executables are publicly available at: https://pulkit-khandelwal.github.io/exvivo-brain-upennComment: Preprint submitted to NeuroImage Project website: https://pulkit-khandelwal.github.io/exvivo-brain-upen

A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI.

AbstractAutomated analysis of MRI data of the subregions of the hippocampus requires computational atlases built at a higher resolution than those that are typically used in current neuroimaging studies. Here we describe the construction of a statistical atlas of the hippocampal formation at the subregion level using ultra-high resolution, ex vivo MRI. Fifteen autopsy samples were scanned at 0.13mm isotropic resolution (on average) using customized hardware. The images were manually segmented into 13 different hippocampal substructures using a protocol specifically designed for this study; precise delineations were made possible by the extraordinary resolution of the scans. In addition to the subregions, manual annotations for neighboring structures (e.g., amygdala, cortex) were obtained from a separate dataset of in vivo, T1-weighted MRI scans of the whole brain (1mm resolution). The manual labels from the in vivo and ex vivo data were combined into a single computational atlas of the hippocampal formation with a novel atlas building algorithm based on Bayesian inference. The resulting atlas can be used to automatically segment the hippocampal subregions in structural MRI images, using an algorithm that can analyze multimodal data and adapt to variations in MRI contrast due to differences in acquisition hardware or pulse sequences. The applicability of the atlas, which we are releasing as part of FreeSurfer (version 6.0), is demonstrated with experiments on three different publicly available datasets with different types of MRI contrast. The results show that the atlas and companion segmentation method: 1) can segment T1 and T2 images, as well as their combination, 2) replicate findings on mild cognitive impairment based on high-resolution T2 data, and 3) can discriminate between Alzheimer's disease subjects and elderly controls with 88% accuracy in standard resolution (1mm) T1 data, significantly outperforming the atlas in FreeSurfer version 5.3 (86% accuracy) and classification based on whole hippocampal volume (82% accuracy)

Imaging-based parcellations of the human brain

A defining aspect of brain organization is its spatial heterogeneity, which gives rise to multiple topographies at different scales. Brain parcellation — defining distinct partitions in the brain, be they areas or networks that comprise multiple discontinuous but closely interacting regions — is thus fundamental for understanding brain organization and function. The past decade has seen an explosion of in vivo MRI-based approaches to identify and parcellate the brain on the basis of a wealth of different features, ranging from local properties of brain tissue to long-range connectivity patterns, in addition to structural and functional markers. Given the high diversity of these various approaches, assessing the convergence and divergence among these ensuing maps is a challenge. Inter-individual variability adds to this challenge but also provides new opportunities when coupled with cross-species and developmental parcellation studies

Microstructural imaging of the human brain with a 'super-scanner': 10 key advantages of ultra-strong gradients for diffusion MRI

The key component of a microstructural diffusion MRI 'super-scanner' is a dedicated high-strength gradient system that enables stronger diffusion weightings per unit time compared to conventional gradient designs. This can, in turn, drastically shorten the time needed for diffusion encoding, increase the signal-to-noise ratio, and facilitate measurements at shorter diffusion times. This review, written from the perspective of the UK National Facility for In Vivo MR Imaging of Human Tissue Microstructure, an initiative to establish a shared 300 mT/m-gradient facility amongst the microstructural imaging community, describes ten advantages of ultra-strong gradients for microstructural imaging. Specifically, we will discuss how the increase of the accessible measurement space compared to a lower-gradient systems (in terms of Δ, b-value, and TE) can accelerate developments in the areas of 1) axon diameter distribution mapping; 2) microstructural parameter estimation; 3) mapping micro-vs macroscopic anisotropy features with gradient waveforms beyond a single pair of pulsed-gradients; 4) multi-contrast experiments, e.g. diffusion-relaxometry; 5) tractography and high-resolution imaging in vivo and 6) post mortem; 7) diffusion-weighted spectroscopy of metabolites other than water; 8) tumour characterisation; 9) functional diffusion MRI; and 10) quality enhancement of images acquired on lower-gradient systems. We finally discuss practical barriers in the use of ultra-strong gradients, and provide an outlook on the next generation of 'super-scanners'

Serial sectioning PSOCT and 2PM for imaging post-mortem human brain and neurodegeneration

The study of aging and neurodegenerative processes in the human brain necessitates a comprehensive understanding of its myeloarchitectonic, cytoarchitectonic, and vascular structures. While recent computational advances have enabled volumetric reconstruction of the human brain using stained slices, the standard histological processing methods have often led to tissue distortions and loss, making deformation-free reconstruction challenging. Therefore, the development of a multi-scale and volumetric imaging technique that can accurately measure multiple structures within the intact brain would be a significant technical breakthrough. In this work, we present the development of an integrated approach that combines serial sectioning Polarization Sensitive Optical Coherence Tomography (PSOCT) and Two Photon Microscopy (2PM) to provide label-free multi-contrast imaging of human brain tissue. Our method allows for the simultaneous visualization of scattering, birefringence, and autofluorescence properties of the post-mortem human brain. By utilizing high-throughput reconstruction of 4x4x2cm3 sample blocks and simple registration of PSOCT and 2PM images, we enable comprehensive analysis of myelin content, cellular information, and vascular structure. PSOCT provides mesoscopic images and enables quantitative measurement of those brain structures, while 2PM provide complementary microscopic validation and enrichment of cellular and capillary information. This combined approach reveals myelin density and structure maps of the whole brain block and supplies intricate vessel and capillary networks as well as lipofuscin-filled cell soma across cortical regions, providing insights into the myeloarchitectural, cellular and vascular changes associated with neurodegenerative diseases such as Alzheimer's disease (AD) and Chronic Traumatic Encephalopathy (CTE)

Applications of Gradient Representations in Resting-State fMRI

Classical models of brain organization have often considered the brain to be made up of a mosaic of patches that are demarcated by discrete boundaries, often defined histologically. In contrast, emerging views have pointed towards an alternative paradigm – referred to as gradients – by conceptualizing brain organization as sets of organizational axes that characterizes spatial variation of differing connectivity principles over the extent of a region. Such organizational axes provide a well-suited framework for elucidating underpinnings of brain connectivity and has garnered widespread attention across various domains of neuroimaging. This work seeks to explore various applications of gradient estimation techniques, in combination with resting-state functional connectivity data, across the fields of basic, comparative, and clinical neuroscience. First, gradient estimation was performed on resting-state functional connectivity (RSFC) patterns of the primary somatosensory cortex to unveil a secondary organizational axis that spans the region’s anterior-posterior axis, akin to circuitry fundamental to sensory cortical information processing. Second, gradient techniques were used in a cross-species comparison study to unify connectivity principles of humans and marmosets by mapping them simultaneously onto a set of organizational axes. In doing so, this provided a systematic framework to compare the functional architecture of both species, facilitating novel insight of a well-integrated default-mode network in humans, compared to marmosets. Third, connectivity gradients, along with a myriad of other resting-state fMRI features were used to explore the implications of focal lesion pathophysiology on functional organization of the thalamus in individuals with Multiple Sclerosis. A lack of focal changes to resting-state related features was observed suggesting the limited role of focal thalamic lesions to functional organization in MS. Together, these different avenues of research highlight the capacity for a gradient-centric view in neuroimaging to provide profound insights into brain organization, and its utility across the applications of basic, comparative, and clinical neuroscience