Brain mapping at high resolutions: Challenges and opportunities

Milligan, Kyle; Balwani, Aishwarya H.; Dyer, Eva L.

doi:10.1016/j.cobme.2019.10.009

Cited by 12 publications

(14 citation statements)

References 28 publications

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“…Moreover, since specific staining agents and immunohistochemistry techniques can enhance different microscopic details, the approach described here opens the door to draw new multimodal maps of the human brain 88 , in a scalable and reproducible way. With the advancement of microscopy-oriented techniques (e.g., 50 , 82 , 89 – 92 ), we believe that closing the gap with macroscopic modalities is crucial.…”

Section: Discussionmentioning

confidence: 99%

A multimodal computational pipeline for 3D histology of the human brain

Mancini

Casamitjana

Peter

et al. 2020

Sci Rep

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Ex vivo imaging enables analysis of the human brain at a level of detail that is not possible in vivo with MRI. In particular, histology can be used to study brain tissue at the microscopic level, using a wide array of different stains that highlight different microanatomical features. Complementing MRI with histology has important applications in ex vivo atlas building and in modeling the link between microstructure and macroscopic MR signal. However, histology requires sectioning tissue, hence distorting its 3D structure, particularly in larger human samples. Here, we present an open-source computational pipeline to produce 3D consistent histology reconstructions of the human brain. The pipeline relies on a volumetric MRI scan that serves as undistorted reference, and on an intermediate imaging modality (blockface photography) that bridges the gap between MRI and histology. We present results on 3D histology reconstruction of whole human hemispheres from two donors. Motivation. One of the major challenges in the quest to understand the human brain as a complex system is characterizing its multiscale organization. From a macroscopic anatomical perspective, the brain is subdivided into distinct structures and presents well-defined landmarks, for instance its gyri and sulci. At a much finer scale, neurons are interconnected to form complex circuits. These circuits give rise to features at coarser scales, e.g., the laminar organization of the cortex 1. Although these distinct levels of organization may seem separate, they are, in fact, deeply linked and mutually influential. One clear example is given by Alzheimer's disease, which can be described by the interactions between proteinopathy at the microscale and distributed, network-level disruptions at the macroscale 2. Understanding these multi-scale mechanisms requires a multi-scale map of the brain. However, the current concept of brain mapping is closely linked to the specific tool used to construct cartographic representations, and thus to the spatial scale of the tool. As a result, the different organizational principles can only be observed in a scale-specific fashion with dedicated tools 3. Multi-scale imaging of the human brain is therefore necessarily multimodal, as different modalities are needed to study different scales. While a macroscopic anatomical picture of the brain is easily acquired non invasively or ex vivo with magnetic resonance imaging (MRI), finer characterization requires histological procedures and microscopy. Histology and MRI are highly complementary modalities: histology produces excellent contrast at the microscopic scale using dedicated stains that target different microanatomical or cytoarchitectural features, but it is a 2D modality that also inevitably introduces distortions in the tissue during blocking and sectioning. MRI does not yield microscopic resolution, but produces undistorted 3D volumes. Therefore, the combination of these two modalities offers a solution to the problem of imaging the human brain at high resolution in 3D.

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Section: Discussionmentioning

confidence: 99%

A multimodal computational pipeline for 3D histology of the human brain

Mancini

Casamitjana

Peter

et al. 2020

Sci Rep

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“…Furthermore, obtaining measurements through microscopy presents its own difficulties: tissue handling, contrast, stain density, dissection, lighting, etc. [2].…”

Section: Introductionmentioning

confidence: 99%

An agent based model (ABM) to reproduce the boolean logic behaviour of neuronal self organized communities through pulse delay modulation and generation of logic gates

Irastorza-Valera,

Benitez,

Montáns

et al. 2023

Preprint

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The human brain is arguably the most complex “machine” to ever exist. Its detailed functioning is yet to be fully understood, let alone modeled. Neurological processes have logical signal-processing aspects and biophysical aspects, and both affect the brain structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence approaches inspired by its logical functioning. In this article, we present an approach to model some aspects of the brain learning and signal processing, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model, to demonstrate how dynamic neuroplasticity, neural inhibition and neurons migration can remodel the brain logical connectivity to syncronize signal processing and obtain target latencies. This work demonstrates the importance of dynamic logical and biophysical remodelling in brain plasticity.

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“…Consequently, methods in neuroimaging have been making steady progress towards pipelines that can capture multi-area and even whole-brain volumes at high resolutions [4,5,6]. The analyses of such large-scale, diversified neural datasets however, bring with them a host of problems [7,8,9] such as limited or partial views of the data, imaging and physical artifacts, as well as the need for huge amounts of compute and automation. Furthermore, the heterogeneity in the underlying neuronal components within and across brain areas, in terms of both structure and distribution, makes the characterisation of the individual components as well as that of the various regions they constitute incredibly challenging.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Modeling of Neural Structure in X-Ray Imagery

Balwani

Miano

Liu

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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Methods for resolving the brain's microstructure are rapidly improving, allowing us to image large brain volumes at high resolutions. As a result, the interrogation of samples spanning multiple diversified brain regions is becoming increasingly common. Understanding these samples often requires multiscale processing: segmentation of the detailed microstructure and large-scale modelling of the macrostructure. Current brain mapping algorithms often analyze data only at a single scale, and optimization for each scale occurs independently, potentially limiting the consistency, performance, and interpretability. In this work we introduce a deep learning framework for segmentation of brain structure at multiple scales. We leverage a modified U-Net architecture with a multi-task learning objective and unsupervised pre-training to simultaneously model both the micro and macro architecture of the brain. We successfully apply our methods to a heterogeneous, three-dimensional, X-ray micro-CT dataset spanning multiple regions in the mouse brain, and show that our approach consistently outperforms another multi-task architecture, and is competitive with strong single-task baselines at both scales.

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Brain mapping at high resolutions: Challenges and opportunities

Cited by 12 publications

References 28 publications

A multimodal computational pipeline for 3D histology of the human brain

A multimodal computational pipeline for 3D histology of the human brain

An agent based model (ABM) to reproduce the boolean logic behaviour of neuronal self organized communities through pulse delay modulation and generation of logic gates

Multi-Scale Modeling of Neural Structure in X-Ray Imagery

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