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1.
Cereb Cortex ; 34(9)2024 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-39264753

RESUMO

Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Building on recent advancements in ultra-high-resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 $\mu $m, we propose a Multi-resolution U-Nets framework that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.8 for supra- and infragranular layers. This enables surface modeling, atlas construction, anomaly detection in disease states, and cross-modality validation while also paving the way for finer layer segmentation. Our approach offers a powerful tool for comprehensive neuroanatomical investigations and holds promise for advancing our mechanistic understanding of progression of neurodegenerative diseases.


Assuntos
Córtex Cerebral , Imageamento por Ressonância Magnética , Humanos , Imageamento por Ressonância Magnética/métodos , Córtex Cerebral/diagnóstico por imagem , Processamento de Imagem Assistida por Computador/métodos , Feminino , Masculino , Idoso , Pessoa de Meia-Idade , Adulto
2.
bioRxiv ; 2024 Sep 10.
Artigo em Inglês | MEDLINE | ID: mdl-39314357

RESUMO

The increasing prevalence of multi-site diffusion-weighted magnetic resonance imaging (dMRI) studies potentially offers enhanced statistical power for investigating brain structure. However, these studies face challenges due to variations in scanner hardware and acquisition protocols. While several methods exist for dMRI data harmonization, few specifically address structural brain connectivity. We introduce a new distribution-matching approach to harmonizing structural brain connectivity across different sites and scanners. We evaluate our method using structural brain connectivity data from two distinct datasets of OASIS-3 and ADNI-2, comparing its performance to the widely used ComBat method. Our approach is meant to align the statistical properties of connectivity data from these two datasets. We examine the impact of harmonization on the correlation of brain connectivity with the Mini-Mental State Examination score and age. Our results demonstrate that our distribution-matching technique more effectively harmonizes structural brain connectivity, often producing stronger and more significant correlations compared to ComBat. Qualitative assessments illustrate the desired distributional alignment of ADNI-2 with OASIS-3, while quantitative evaluations confirm robust performance. This work contributes to the growing field of dMRI harmonization, potentially improving the reliability and comparability of structural connectivity studies that combine data from different sources in neuroscientific and clinical research.

3.
Res Sq ; 2024 Aug 07.
Artigo em Inglês | MEDLINE | ID: mdl-39149445

RESUMO

The accurate measurement of three-dimensional (3D) fiber orientation in the brain is crucial for reconstructing fiber pathways and studying their involvement in neurological diseases. Comprehensive reconstruction of axonal tracts and small fascicles requires high-resolution technology beyond the ability of current in vivo imaging (e.g. diffusion magnetic resonance imaging). Optical imaging methods such as polarization-sensitive optical coherence tomography (PS-OCT) and polarization microscopy can quantify fiber orientation at micrometer resolution but have been limited to two-dimensional in-plane orientation or thin slices, preventing the comprehensive study of connectivity in 3D. In this work we present a novel method to quantify volumetric 3D orientation in full angular space with PS-OCT. We measure the polarization contrasts of the brain sample from two illumination angles of 0 and 15 degrees and apply a computational method that yields the 3D optic axis orientation and true birefringence. We further present 3D fiber orientation maps of entire coronal cerebrum sections and brainstem with 10 µm in-plane resolution, revealing unprecedented details of fiber configurations. We envision that our method will open a promising avenue towards large-scale 3D fiber axis mapping in the human brain as well as other complex fibrous tissues at microscopic level.

4.
Med Image Anal ; 98: 103292, 2024 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-39173411

RESUMO

Surface-based cortical registration is an important topic in medical image analysis and facilitates many downstream applications. Current approaches for cortical registration are mainly driven by geometric features, such as sulcal depth and curvature, and often assume that registration of folding patterns leads to alignment of brain function. However, functional variability of anatomically corresponding areas across subjects has been widely reported, particularly in higher-order cognitive areas. In this work, we present JOSA, a novel cortical registration framework that jointly models the mismatch between geometry and function while simultaneously learning an unbiased population-specific atlas. Using a semi-supervised training strategy, JOSA achieves superior registration performance in both geometry and function to the state-of-the-art methods but without requiring functional data at inference. This learning framework can be extended to any auxiliary data to guide spherical registration that is available during training but is difficult or impossible to obtain during inference, such as parcellations, architectonic identity, transcriptomic information, and molecular profiles. By recognizing the mismatch between geometry and function, JOSA provides new insights into the future development of registration methods using joint analysis of brain structure and function.


Assuntos
Imageamento por Ressonância Magnética , Humanos , Imageamento por Ressonância Magnética/métodos , Encéfalo/diagnóstico por imagem , Encéfalo/anatomia & histologia , Algoritmos , Imageamento Tridimensional/métodos , Processamento de Imagem Assistida por Computador/métodos , Atlas como Assunto
5.
Imaging Neurosci (Camb) ; 2: 1-33, 2024 Jun 25.
Artigo em Inglês | MEDLINE | ID: mdl-39015335

RESUMO

Affine image registration is a cornerstone of medical-image analysis. While classical algorithms can achieve excellent accuracy, they solve a time-consuming optimization for every image pair. Deep-learning (DL) methods learn a function that maps an image pair to an output transform. Evaluating the function is fast, but capturing large transforms can be challenging, and networks tend to struggle if a test-image characteristic shifts from the training domain, such as the resolution. Most affine methods are agnostic to the anatomy the user wishes to align, meaning the registration will be inaccurate if algorithms consider all structures in the image. We address these shortcomings with SynthMorph, a fast, symmetric, diffeomorphic, and easy-to-use DL tool for joint affine-deformable registration of any brain image without preprocessing. First, we leverage a strategy that trains networks with widely varying images synthesized from label maps, yielding robust performance across acquisition specifics unseen at training. Second, we optimize the spatial overlap of select anatomical labels. This enables networks to distinguish anatomy of interest from irrelevant structures, removing the need for preprocessing that excludes content which would impinge on anatomy-specific registration. Third, we combine the affine model with a deformable hypernetwork that lets users choose the optimal deformation-field regularity for their specific data, at registration time, in a fraction of the time required by classical methods. This framework is applicable to learning anatomy-aware, acquisition-agnostic registration of any anatomy with any architecture, as long as label maps are available for training. We analyze how competing architectures learn affine transforms and compare state-of-the-art registration tools across an extremely diverse set of neuroimaging data, aiming to truly capture the behavior of methods in the real world. SynthMorph demonstrates high accuracy and is available at https://w3id.org/synthmorph, as a single complete end-to-end solution for registration of brain magnetic resonance imaging (MRI) data.

6.
Elife ; 122024 Jun 19.
Artigo em Inglês | MEDLINE | ID: mdl-38896568

RESUMO

We present open-source tools for three-dimensional (3D) analysis of photographs of dissected slices of human brains, which are routinely acquired in brain banks but seldom used for quantitative analysis. Our tools can: (1) 3D reconstruct a volume from the photographs and, optionally, a surface scan; and (2) produce a high-resolution 3D segmentation into 11 brain regions per hemisphere (22 in total), independently of the slice thickness. Our tools can be used as a substitute for ex vivo magnetic resonance imaging (MRI), which requires access to an MRI scanner, ex vivo scanning expertise, and considerable financial resources. We tested our tools on synthetic and real data from two NIH Alzheimer's Disease Research Centers. The results show that our methodology yields accurate 3D reconstructions, segmentations, and volumetric measurements that are highly correlated to those from MRI. Our method also detects expected differences between post mortem confirmed Alzheimer's disease cases and controls. The tools are available in our widespread neuroimaging suite 'FreeSurfer' (https://surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools).


Every year, thousands of human brains are donated to science. These brains are used to study normal aging, as well as neurological diseases like Alzheimer's or Parkinson's. Donated brains usually go to 'brain banks', institutions where the brains are dissected to extract tissues relevant to different diseases. During this process, it is routine to take photographs of brain slices for archiving purposes. Often, studies of dead brains rely on qualitative observations, such as 'the hippocampus displays some atrophy', rather than concrete 'numerical' measurements. This is because the gold standard to take three-dimensional measurements of the brain is magnetic resonance imaging (MRI), which is an expensive technique that requires high expertise ­ especially with dead brains. The lack of quantitative data means it is not always straightforward to study certain conditions. To bridge this gap, Gazula et al. have developed an openly available software that can build three-dimensional reconstructions of dead brains based on photographs of brain slices. The software can also use machine learning methods to automatically extract different brain regions from the three-dimensional reconstructions and measure their size. These data can be used to take precise quantitative measurements that can be used to better describe how different conditions lead to changes in the brain, such as atrophy (reduced volume of one or more brain regions). The researchers assessed the accuracy of the method in two ways. First, they digitally sliced MRI-scanned brains and used the software to compute the sizes of different structures based on these synthetic data, comparing the results to the known sizes. Second, they used brains for which both MRI data and dissection photographs existed and compared the measurements taken by the software to the measurements obtained with MRI images. Gazula et al. show that, as long as the photographs satisfy some basic conditions, they can provide good estimates of the sizes of many brain structures. The tools developed by Gazula et al. are publicly available as part of FreeSurfer, a widespread neuroimaging software that can be used by any researcher working at a brain bank. This will allow brain banks to obtain accurate measurements of dead brains, allowing them to cheaply perform quantitative studies of brain structures, which could lead to new findings relating to neurodegenerative diseases.


Assuntos
Doença de Alzheimer , Encéfalo , Imageamento Tridimensional , Aprendizado de Máquina , Humanos , Imageamento Tridimensional/métodos , Doença de Alzheimer/diagnóstico por imagem , Doença de Alzheimer/patologia , Encéfalo/diagnóstico por imagem , Encéfalo/patologia , Fotografação/métodos , Dissecação , Imageamento por Ressonância Magnética/métodos , Neuropatologia/métodos , Neuroimagem/métodos
7.
Alzheimers Dement ; 20(7): 4649-4662, 2024 07.
Artigo em Inglês | MEDLINE | ID: mdl-38877668

RESUMO

INTRODUCTION: The entorhinal cortex (EC) and perirhinal cortex (PC) are vulnerable to Alzheimer's disease. A triggering factor may be the interaction of vascular dysfunction and tau pathology. METHODS: We imaged post mortem human tissue at 100 µm3 with 7 T magnetic resonance imaging and manually labeled individual blood vessels (mean = 270 slices/case). Vessel density was quantified and compared per EC subfield, between EC and PC, and in relation to tau and TAR DNA-binding protein 43 (TDP-43) semiquantitative scores. RESULTS: PC was more vascularized than EC and vessel densities were higher in posterior EC subfields. Tau and TDP-43 strongly correlated with vasculature density and subregions with severe tau at the preclinical stage had significantly greater vessel density than those with low tau burden. DISCUSSION: These data impact cerebrovascular maps, quantification of subfield vasculature, and correlation of vasculature and pathology at early stages. The ordered association of vessel density, and tau or TDP-43 pathology, may be exploited in a predictive context. HIGHLIGHTS: Vessel density correlates with phosphorylated tau (p-tau) burden in entorhinal and perirhinal cortices. Perirhinal area 35 and posterior entorhinal cortex showed greatest p-tau burden but also the highest vessel density in the preclinical phase of Alzheimer's disease. We combined an ex vivo magnetic resonance imaging model and histopathology to demonstrate the 3D reconstruction of intracortical vessels and its spatial relationship to the pathology.


Assuntos
Doença de Alzheimer , Proteínas de Ligação a DNA , Córtex Entorrinal , Proteínas tau , Humanos , Córtex Entorrinal/patologia , Córtex Entorrinal/metabolismo , Proteínas tau/metabolismo , Proteínas de Ligação a DNA/metabolismo , Feminino , Masculino , Fosforilação , Doença de Alzheimer/patologia , Doença de Alzheimer/metabolismo , Idoso , Idoso de 80 Anos ou mais , Imageamento por Ressonância Magnética , Vasos Sanguíneos/patologia , Vasos Sanguíneos/metabolismo
8.
Sci Transl Med ; 16(745): eadj4303, 2024 May.
Artigo em Inglês | MEDLINE | ID: mdl-38691619

RESUMO

Consciousness is composed of arousal (i.e., wakefulness) and awareness. Substantial progress has been made in mapping the cortical networks that underlie awareness in the human brain, but knowledge about the subcortical networks that sustain arousal in humans is incomplete. Here, we aimed to map the connectivity of a proposed subcortical arousal network that sustains wakefulness in the human brain, analogous to the cortical default mode network (DMN) that has been shown to contribute to awareness. We integrated data from ex vivo diffusion magnetic resonance imaging (MRI) of three human brains, obtained at autopsy from neurologically normal individuals, with immunohistochemical staining of subcortical brain sections. We identified nodes of the proposed default ascending arousal network (dAAN) in the brainstem, hypothalamus, thalamus, and basal forebrain. Deterministic and probabilistic tractography analyses of the ex vivo diffusion MRI data revealed projection, association, and commissural pathways linking dAAN nodes with one another and with DMN nodes. Complementary analyses of in vivo 7-tesla resting-state functional MRI data from the Human Connectome Project identified the dopaminergic ventral tegmental area in the midbrain as a widely connected hub node at the nexus of the subcortical arousal and cortical awareness networks. Our network-based autopsy methods and connectivity data provide a putative neuroanatomic architecture for the integration of arousal and awareness in human consciousness.


Assuntos
Tronco Encefálico , Estado de Consciência , Imageamento por Ressonância Magnética , Vigília , Humanos , Tronco Encefálico/diagnóstico por imagem , Tronco Encefálico/fisiologia , Vigília/fisiologia , Estado de Consciência/fisiologia , Imageamento por Ressonância Magnética/métodos , Imagem Multimodal/métodos , Conectoma , Vias Neurais/fisiologia , Masculino , Feminino , Imagem de Difusão por Ressonância Magnética , Adulto , Nível de Alerta/fisiologia
9.
Front Neurosci ; 18: 1375530, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38774790

RESUMO

The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly available imaging databases for training automatic LC segmentation models take advantage of specialized contrast-enhancing (e.g., neuromelanin-sensitive) MRI. Segmentation models developed with such image contrasts, however, are not readily applicable to existing datasets with conventional MRI sequences. In this work, we evaluate the feasibility of using non-contrast neuroanatomical information to geometrically approximate the LC region from standard 3-Tesla T1-weighted images of 20 subjects from the Human Connectome Project (HCP). We employ this dataset to train and internally/externally evaluate two automatic localization methods, the Expected Label Value and the U-Net. For out-of-sample segmentation, we compare the results with atlas-based segmentation, as well as test the hypothesis that using the phase image as input can improve the robustness. We then apply our trained models to a larger subset of HCP, while exploratorily correlating LC imaging variables and structural connectivity with demographic and clinical data. This report provides an evaluation of computational methods estimating neural structure.

10.
Artigo em Inglês | MEDLINE | ID: mdl-38665679

RESUMO

We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain-connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple heads. The proposed network is a simple design that employs different heads involving graph convolutions focused on edges and nodes, thoroughly capturing representations from the input data. To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification. This quantifies the degree to which the connectome varies depending on the sex, which is important for improving our understanding of health and disease in both sexes. We show experiments on two publicly available datasets: PREVENT-AD (347 subjects) and OASIS3 (771 subjects). The proposed model demonstrates the highest performance compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning. We provide a detailed analysis of each component of our model.

11.
Proc Natl Acad Sci U S A ; 121(19): e2313568121, 2024 May 07.
Artigo em Inglês | MEDLINE | ID: mdl-38648470

RESUMO

United States (US) Special Operations Forces (SOF) are frequently exposed to explosive blasts in training and combat, but the effects of repeated blast exposure (RBE) on SOF brain health are incompletely understood. Furthermore, there is no diagnostic test to detect brain injury from RBE. As a result, SOF personnel may experience cognitive, physical, and psychological symptoms for which the cause is never identified, and they may return to training or combat during a period of brain vulnerability. In 30 active-duty US SOF, we assessed the relationship between cumulative blast exposure and cognitive performance, psychological health, physical symptoms, blood proteomics, and neuroimaging measures (Connectome structural and diffusion MRI, 7 Tesla functional MRI, [11C]PBR28 translocator protein [TSPO] positron emission tomography [PET]-MRI, and [18F]MK6240 tau PET-MRI), adjusting for age, combat exposure, and blunt head trauma. Higher blast exposure was associated with increased cortical thickness in the left rostral anterior cingulate cortex (rACC), a finding that remained significant after multiple comparison correction. In uncorrected analyses, higher blast exposure was associated with worse health-related quality of life, decreased functional connectivity in the executive control network, decreased TSPO signal in the right rACC, and increased cortical thickness in the right rACC, right insula, and right medial orbitofrontal cortex-nodes of the executive control, salience, and default mode networks. These observations suggest that the rACC may be susceptible to blast overpressure and that a multimodal, network-based diagnostic approach has the potential to detect brain injury associated with RBE in active-duty SOF.


Assuntos
Traumatismos por Explosões , Militares , Humanos , Traumatismos por Explosões/diagnóstico por imagem , Adulto , Masculino , Estados Unidos , Imageamento por Ressonância Magnética , Feminino , Tomografia por Emissão de Pósitrons , Cognição/fisiologia , Encéfalo/diagnóstico por imagem , Encéfalo/metabolismo , Adulto Jovem
12.
ArXiv ; 2024 Feb 26.
Artigo em Inglês | MEDLINE | ID: mdl-38463507

RESUMO

Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing. However, the broad range of neuroanatomical variation in the developing brain, combined with additional challenges such as high motion levels, as well as shoulder and chest signal in the images, leaves many adult-specific tools ill-suited for pediatric skull-stripping. Building on an existing framework for robust and accurate skull-stripping, we propose developmental SynthStrip (d-SynthStrip), a skull-stripping model tailored to pediatric images. This framework exposes networks to highly variable images synthesized from label maps. Our model substantially outperforms pediatric baselines across scan types and age cohorts. In addition, the <1-minute runtime of our tool compares favorably to the fastest baselines. We distribute our model at https://w3id.org/synthstrip.

13.
bioRxiv ; 2024 Jan 24.
Artigo em Inglês | MEDLINE | ID: mdl-38328208

RESUMO

The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly available imaging databases for training automatic LC segmentation models take advantage of specialized contrast-enhancing (e.g., neuromelanin-sensitive) MRI. Segmentation models developed with such image contrasts, however, are not readily applicable to existing datasets with conventional MRI sequences. In this work, we evaluate the feasibility of using non-contrast neuroanatomical information to geometrically approximate the LC region from standard 3-Tesla T1-weighted images of 20 subjects from the Human Connectome Project (HCP). We employ this dataset to train and internally/externally evaluate two automatic localization methods, the Expected Label Value and the U-Net. We also test the hypothesis that using the phase image as input can improve the robustness of out-of-sample segmentation. We then apply our trained models to a larger subset of HCP, while exploratorily correlating LC imaging variables and structural connectivity with demographic and clinical data. This report contributes and provides an evaluation of two computational methods estimating neural structure.

14.
bioRxiv ; 2024 Jan 30.
Artigo em Inglês | MEDLINE | ID: mdl-37333251

RESUMO

We present open-source tools for 3D analysis of photographs of dissected slices of human brains, which are routinely acquired in brain banks but seldom used for quantitative analysis. Our tools can: (i) 3D reconstruct a volume from the photographs and, optionally, a surface scan; and (ii) produce a high-resolution 3D segmentation into 11 brain regions per hemisphere (22 in total), independently of the slice thickness. Our tools can be used as a substitute for ex vivo magnetic resonance imaging (MRI), which requires access to an MRI scanner, ex vivo scanning expertise, and considerable financial resources. We tested our tools on synthetic and real data from two NIH Alzheimer's Disease Research Centers. The results show that our methodology yields accurate 3D reconstructions, segmentations, and volumetric measurements that are highly correlated to those from MRI. Our method also detects expected differences between post mortem confirmed Alzheimer's disease cases and controls. The tools are available in our widespread neuroimaging suite "FreeSurfer" ( https://surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools ).

15.
bioRxiv ; 2023 Dec 08.
Artigo em Inglês | MEDLINE | ID: mdl-38106176

RESUMO

Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Leveraging recent advancements in ultra-high resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 µm, we propose a multi-resolution U-Nets framework (MUS) that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.8 for supra- and infragranular layers. This enables surface modeling, atlas construction, anomaly detection in disease states, and cross-modality validation, while also paving the way for finer layer segmentation. Our approach offers a powerful tool for comprehensive neuroanatomical investigations and holds promise for advancing our mechanistic understanding of progression of neurodegenerative diseases.

16.
Alzheimers Dement (Amst) ; 15(4): e12511, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-38111597

RESUMO

Introduction: Discovery of the associations between brain structural connectivity and clinical and demographic variables can help to better understand the vulnerability and resilience of the brain architecture to neurodegenerative diseases and to discover biomarkers. Methods: We used four diffusion-MRI databases, three related to Alzheimer's disease (AD), to exploratorily correlate structural connections between 85 brain regions with non-MRI variables, while stringently correcting the significance values for multiple testing and ruling out spurious correlations via careful visual inspection. We repeated the analysis with brain connectivity augmented with multi-synaptic neural pathways. Results: We found 85 and 101 significant relationships with direct and augmented connectivity, respectively, which were generally stronger for the latter. Age was consistently linked to decreased connectivity, and healthier clinical scores were generally linked to increased connectivity. Discussion: Our findings help to elucidate which structural brain networks are affected in AD and aging and highlight the importance of including indirect connections.

17.
bioRxiv ; 2023 Oct 23.
Artigo em Inglês | MEDLINE | ID: mdl-37961162

RESUMO

The accurate measurement of three-dimensional (3D) fiber orientation in the brain is crucial for reconstructing fiber pathways and studying their involvement in neurological diseases. Optical imaging methods such as polarization-sensitive optical coherence tomography (PS-OCT) provide important tools to directly quantify fiber orientation at micrometer resolution. However, brain imaging based on the optic axis by PS-OCT so far has been limited to two-dimensional in-plane orientation, preventing the comprehensive study of connectivity in 3D. In this work, we present a novel method to obtain the 3D fiber orientation in full angular space with only two illumination angles. We measure the optic axis orientation and the apparent birefringence by PS-OCT from a normal and a 15 deg tilted illumination, and then apply a computational method yielding the 3D optic axis orientation and true birefringence. We verify that our method accurately recovers a large range of through-plane orientations from -85 deg to 85 deg with a high angular precision. We further present 3D fiber orientation maps of entire coronal sections of human cerebrum and brainstem with 10 µm in-plane resolution, revealing unprecedented details of fiber configurations. We envision that further development of our method will open a promising avenue towards large-scale 3D fiber axis mapping in the human brain and other complex fibrous tissues at microscopic level.

18.
Adv Sci (Weinh) ; 10(35): e2303381, 2023 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-37882348

RESUMO

The study of aging and neurodegenerative processes in the human brain requires a comprehensive understanding of cytoarchitectonic, myeloarchitectonic, and vascular structures. Recent computational advances have enabled volumetric reconstruction of the human brain using thousands of stained slices, however, tissue distortions and loss resulting from standard histological processing have hindered deformation-free reconstruction. Here, the authors describe an integrated serial sectioning polarization-sensitive optical coherence tomography (PSOCT) and two photon microscopy (2PM) system to provide label-free multi-contrast imaging of intact brain structures, including scattering, birefringence, and autofluorescence of human brain tissue. The authors demonstrate high-throughput reconstruction of 4 × 4 × 2cm3 sample blocks and simple registration between PSOCT and 2PM images that enable comprehensive analysis of myelin content, vascular structure, and cellular information. The high-resolution 2PM images provide microscopic validation and enrichment of the cellular information provided by the PSOCT optical properties on the same sample, revealing the densely packed fibers, capillaries, and lipofuscin-filled cell bodies in the cortex and white matter. It is  shown that the imaging system enables quantitative characterization of various pathological features in aging process, including myelin degradation, lipofuscin accumulation, and microvascular changes, which opens up numerous opportunities in the study of neurodegenerative diseases in the future.


Assuntos
Microscopia , Tomografia de Coerência Óptica , Humanos , Tomografia de Coerência Óptica/métodos , Microscopia/métodos , Lipofuscina , Encéfalo/diagnóstico por imagem , Neuroimagem
19.
Sci Adv ; 9(41): eadg3844, 2023 10 13.
Artigo em Inglês | MEDLINE | ID: mdl-37824623

RESUMO

Brain cells are arranged in laminar, nuclear, or columnar structures, spanning a range of scales. Here, we construct a reliable cell census in the frontal lobe of human cerebral cortex at micrometer resolution in a magnetic resonance imaging (MRI)-referenced system using innovative imaging and analysis methodologies. MRI establishes a macroscopic reference coordinate system of laminar and cytoarchitectural boundaries. Cell counting is obtained with a digital stereological approach on the 3D reconstruction at cellular resolution from a custom-made inverted confocal light-sheet fluorescence microscope (LSFM). Mesoscale optical coherence tomography enables the registration of the distorted histological cell typing obtained with LSFM to the MRI-based atlas coordinate system. The outcome is an integrated high-resolution cellular census of Broca's area in a human postmortem specimen, within a whole-brain reference space atlas.


Assuntos
Área de Broca , Córtex Cerebral , Humanos , Encéfalo/diagnóstico por imagem , Imageamento por Ressonância Magnética/métodos , Mapeamento Encefálico
20.
J Spec Oper Med ; 23(4): 47-56, 2023 Dec 29.
Artigo em Inglês | MEDLINE | ID: mdl-37851859

RESUMO

United States Special Operations Forces (SOF) personnel are frequently exposed to explosive blasts in training and combat. However, the effects of repeated blast exposure on the human brain are incompletely understood. Moreover, there is currently no diagnostic test to detect repeated blast brain injury (rBBI). In this "Human Performance Optimization" article, we discuss how the development and implementation of a reliable diagnostic test for rBBI has the potential to promote SOF brain health, combat readiness, and quality of life.


Assuntos
Traumatismos por Explosões , Militares , Humanos , Estados Unidos , Qualidade de Vida , Encéfalo/diagnóstico por imagem , Traumatismos por Explosões/diagnóstico , Traumatismos por Explosões/terapia , Explosões
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