The blessing of Dimensionality : feature selection outperforms functional connectivity-based feature transformation to classify ADHD subjects from EEG patterns of phase synchronisation

Functional connectivity (FC) characterizes brain activity from a multivariate set of N brain signals by means of an NxN matrix A, whose elements estimate the dependence within each possible pair of signals. Such matrix can be used as a feature vector for (un)supervised subject classification. Yet if N is large, A is highly dimensional. Little is known on the effect that different strategies to reduce its dimensionality may have on its classification ability. Here, we apply different machine learning algorithms to classify 33 children (age [6-14 years]) into two groups (healthy controls and Attention Deficit Hyperactivity Disorder patients) using EEG FC patterns obtained from two phase synchronisation indices. We found that the classification is highly successful (around 95%) if the whole matrix A is taken into account, and the relevant features are selected using machine learning methods. However, if FC algorithms are applied instead to transform A into a lower dimensionality matrix, the classification rate drops to less than 80%. We conclude that, for the purpose of pattern classification, the relevant features should be selected among the elements of A by using appropriate machine learning algorithms

Myelin and Modeling: Bootstrapping Cortical Microcircuits

Histological studies of myelin-stained sectioned cadaver brain and in vivo myelin-weighted magnetic resonance imaging (MRI) show that the cerebral cortex is organized into cortical areas with generally well-defined boundaries, which have consistent internal patterns of myelination. The process of myelination is largely driven by neural experience, in which the axonal passage of action potentials stimulates neighboring oligodendrocytes to perform their task. This bootstrapping process, such that the traffic of action potentials facilitates increased traffic, suggests the hypothesis that the specific pattern of myelination (myeloarchitecture) in each cortical area reveals the principal cortical microcircuits required for the function of that area. If this idea is correct, the observable sequential maturation of specific brain areas can provide evidence for models of the stages of cognitive development

Development of Microstructural Segmentation and 3D Reconstruction Method Using Serial Section of Tissue: 3D Educational Model of Human Hypothalamus

학위논문(석사)--서울대학교 대학원 :의과대학 의학과,2019. 8. 황영일최형진.INTRODUCTION: The 3D reconstruction technique of tissue staining images is very valuable in that it visualizes the microstructure information that Magnetic resonance imaging (MRI) and Computed tomography (CT) data cannot provide and is widely used for pathological diagnosis. Organizational 3D reconstruction needs the latest devices and software for each phase. However, in reality, it is not easy to equip all of the most brand-new equipment, and software, so the existing research has been done by only a limited number of people. For this reason, in this study, we tried to develop a 3D reconstruction method of the organization by using available laboratory equipment and highly accessible software. The human hypothalamus is relatively small compared to other brain structures, but it is the backbone of homeostasis regulation and an important structure directly linked to survival. It consists of more than 13 nuclei and microstructures, and many attempts have been made to identify them. However, most reported histology images were based on the 2D map, that cause researchers are experiencing difficulties in understanding spatial structure perception. In addition, most of the currently reported hypothalamic 3D maps are based on MRI data. This DICOM based medical image has the disadvantage that it is difficult to understand the detailed microstructure of the nucleus. In order to overcome these drawbacks, this study aims to develop a detailed 3D model of the human hypothalamus by using easily accessible devices and software. Since the 3D map of the human hypothalamus has not been reported so far, we have developed a method that allows a wider range of researchers to perform the 3D reconstruction of the tissue, which had previously been done by a limited number of people. We also tried to make the model created by the researchers easily accessible to the field. Methods: Nissl staining of human brain hypothalamus obtained by autopsy was converted to a digital image using a tissue slide scanner. The whole slide image was converted by using image processing software to adjust the resolution and extension. After that, segmentation of the hypothalamic microstructure was performed in the Adobe Photoshop software, and the missing slide images were prepared by manual interpolation. All the structure segmented images were transformed into black and white to produce a mask suitable for 3D reconstruction, and they were classified by structure. Then, the whole image was subjected to bit number correction and extension conversion suitable for 3D reconstruction using ImageJ software. Then 3D reconstruction software reconstructs the segmented structures into three dimensions and attempts 3D rendering. After transforming them into STL extensions, we tried to edit them using MeshMixer software. Through this process, 3D map was created with WebGL, and the 3D map education model of the human hypothesis was created. Results: A total of 100 staining images were obtained by Nissl staining using human brain hypothalamus. To make our results more clearly, hypothalamic 2D maps obtained in this study were compared with Allen atlas. A total of 23 segmentations were carried out including hypothalamic surrounding structures and nucleus distribution patterns. A total of 11 excluded slides were supplemented by manual interpolation. The hypothalamus 2D map was used to reconstruct the human hypothalamus as a 3D reconstructed volume model and a 3D reconstructed surface model. The 3D reconstruction surface model was obtained by using MeshMixer to complement the smoothing and the outlier point of each structure. Then, I created a hypothalamus 3D reconstruction education model using WebGL service to make possible for anyone to easily access and learn without the constraint of time and space. Discussion: In this study, I developed a method for producing 2D map and 3D reconstructed images of Nissl stained using hypothalamus tissue. This is the first 3D reconstruction model based on the hypothalamus, which is meant to help other researchers and medical personnel in education and research. Previous studies have shown that the spacing of the slices of the hypothalamus tissue was not constant, but this study succeeded in acquiring the results of the staining of the hypothalamus tissue at 100 ㎛ intervals as the basic data for 3D reconstruction. Many other types of missing images were found due to the lack of consideration of various variables that occurred during the reconstruction process. The anatomical structure and various parameters were considered and corrected for more satisfactory results. In addition, existing image-based software provides automatic segmentation function considering only the distinctive features of shaded images, so it is very inappropriate to classify subtle clustering patterns such as nucleus and structures in the human hypothalamus. It is significant that the progress process is segmented, and the separate software suitable for each process is applied, and the process of working with them is compatible with each other. Most software is free, low cost and easy to learn and use, so it provides a way to easily create an organization 3D image without expensive software or equipment. The existing hypothalamus training data were mostly 2D illustrations, but the 3D reconstructed images produced in this study are easy to grasp the positional relationship of structures more space. In particular, since the hypothalamus does not contain data showing nuclear reconstruction as a 3D reconstructed image, the educational model of this study will be of great help to many hypothalamus researchers. And the 3D WebGL education model has pedagogical value because it enables free access and access through users personal device, enabling ubiquitous learning that is not restricted by time and space. Conclusions: Through this study, I have established a method for producing 2D map and 3D reconstruction using human hypothalamus. Through the 3D reconstruction image and the education model, the positional relation of the human hypothalamus can be recognized by spatial perception. This result is pedagogically worthy because it can be used as U-learning material to help researchers self-directed learning by opening it to open source WebGL for easy use by anyone.서론: 조직 염색 영상의 3차원 재구성 기술은 MRI와 CT가 제공하지 못하는 미세구조를 시각화 하여 3차원 조직학에 활용된다는 점에서 가치가 있다. 조직 3차원 재구성에는 각 단계에 적합한 기기와 소프트웨어가 사용된다. 그러나 고가의 기기와 소프트웨어를 전부 갖추기란 쉽지 않으므로 기존의 연구는 제한적인 소수에 의해 이루어져 왔다. 사람 시상하부는 다른 뇌 구조물에 비해 상대적으로 크기는 작지만 항상성 조절의 중추이며 생존과 직결된 신체 활동을 조절하는 중요한 기관이다. 기존 시상하부에 대한 시각적 연구는 조직학 영상 기반의 2차원 지도 중심으로 이루어져 구조를 공간지각적으로 이해하는 데 많은 어려움이 있었다. 또한 현재 발표된 대부분의 사람 시상하부 3차원 지도는 MRI 데이터를 기반으로 작성되어 있어 신경핵 단위의 미세구조 정보를 충분히 전달하지 못한다. 따라서 본 연구에서는 두가지 목표를 달성하고자 하였다. 첫째로, 접근성이 좋은 장비와 소프트웨어를 활용하여 보다 넓은 범위의 연구자들이 수행할 수 있는 조직의 3차원 재구성 방법을 개발하고자 하였다. 둘째로, 위의 과정을 통해 확립한 방법을 시상하부에 적용하여 해당 분야 연구자들이 학습 및 교육에 활용할 수 있는 3차원 모델을 제작하고자 하였다. 연구 대상 및 방법: 사람 시상하부 전체와 시각로가 포함된 조직을 대상으로 조직 3차원 재구성을 시도하였다. 염색된 각 조직절편을 슬라이드 스캐너를 이용해 디지털 영상으로 변환하였고 ZEN을 사용하여 해상도 조절과 확장자 변환을 시행하였다. 전체 영상을 Adobe Photoshop을 이용하여 시각로와 미세혈관, 안쪽후각겉질의 외곽선을 기준으로 정합 하였다. 이 후 미세조직 구역화, 소실된 슬라이드 영상의 수동 보간법 적용, 전체 구역화 영상의 흑백 변환, 마스크 제작, 구조물 별 분류를 시행하였다. 또한 전체 영상을 ImageJ를 사용하여 bit수 교정 및 확장자 변환을 수행하였다. 이 후 MEDIP에서 구역화 한 구조물 영상의 3차원 재구성, STL 확장자 변환 및 내보내기를 시행하였다. 이 후 MeshMixer를 사용하여 표면의 요철과 이상점을 교정한 뒤 webGL 교육모델로 제작하였다. 이렇게 수립된 protocol을 사람 시상하부에 적용하여 내부 신경핵과 미세구조를 3차원으로 재구성하였다. 결과: Zen, Adobe Photoshop, ImageJ를 사용하여 조직 염색 영상, 시각로 2차원 지도, 색면 레이어, 패스영역 레이어, 흑백변환 영상, 흑백 반전 영상, Raw data mask를 생성하였다. 이 후 MEDIP과 Meshmixer 를 사용하여 전체조직과 시각로 부피모델, 표면모델, 교육모델을 생성하였다. 이를 사람 시상하부 미세구조의 시각화에 적용하여 조직 염색 영상, 2차원 지도, 미세구조와 신경핵 구역화 색면 레이어, 패스영역 레이어, 흑백 변환 영상, 흑백 반전 영상, 3차원 Raw data mask, 3차원 재구성 부피 모델, 표면 모델, 3차원 교육모델을 생성하였다. 고찰: 본 연구에서는 Zen, Adobe Photoshop, ImageJ, MEDIP, Meshmixer로 이어지는 조직의 3차원 재구성 제작 protocol을 개발하였다. 본 연구는 기존의 3차원 재구성 방법론과 비교했을 때 외곽선이 뚜렷하지 않은 구조물의 영상 위에 수동으로 구역화를 수행했다는 점에서 차별성이 있다. 또한 기존의 조직 3차원 재구성 방법론과 비교했을 때 각 단계에 사용되는 고가의 소프트웨어와 기기를 여러 개의 접근성이 높은 소프트웨어로 분할 및 적용했다는 점에서 기존 연구와 다르다. 본 연구의 2차원 지도는 Allen atlas가 제공하는 사람 뇌 2차원 지도와 비교했을 때 보다 촘촘한 100 ㎛의 영상을 일정한 간격으로 획득했다는 점에서 차별성이 있다. 또한 MRI 기반 3차원 재구성 모델이 제공하지 못하는 여러 미세구조와 신경핵을 시각화 했다는 점에서 가치가 있다. 기존의 2차원 신경해부학 교육자료와 비교했을 때 본 연구에서 제작한 3차원 교육모델은 구조물들의 위치관계를 공간지각적으로 보다 쉽게 파악할 수 있다는 장점이 있다. 또한 본 연구의 결과물은 기존의 3차원 신경해부학 교육자료와 달리 실험을 통해 얻은 실물 자료를 기반으로 제작되었다는 점에서 가치가 있다. 결론: 본 연구에서는 사람 시상하부 조직을 매개로 조직의 3차원 재구성 protocol을 확립하였다. 이를 통해 사람 시상하부 내부의 미세구조와 신경핵의 위치관계를 공간지각적으로 파악할 수 있는 부피모델과 표면모델을 생성할 수 있었다. 이 결과물들은 의료인 교육에 적합한 교육모델로 변환하여 공개 자료로 제공되었다.초 록 ……………………………………………………………ⅰ 목 차 …………………………………………………………… ⅳ 표 목록 ……………………………………………………… v 그림 목록…………………………………………………… vi 서 론…………………………………………………………… 9 본 론 …………………………………………………………… 15 Chapter 1. 조직의 3차원 재구성 제작 protocol 개발...... 15 Chapter 2. 사람 시상하부 조직의 3차원 재구성.............. 52 결 론…………………………………………………………… 95 참고문헌…………………………………………………………96 Abstract………………………………………………………101Maste

Building connectomes using diffusion MRI: why, how and but

Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically-relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments

Microstructural grey matter parcellation and its relevance for connectome analyses.

The human brain connectome is closely linked to the anatomical framework provided by the structural segregation of the cortex into distinct cortical areas. Therefore, a thorough anatomical reference for the analysis and interpretation of connectome data is indispensable to understand the structure and function of different regions of the cortex, the white matter fibre architecture connecting them, and the interplay between these different entities. This article focuses on parcellation schemes of the cerebral grey matter and their relevance for connectome analyses. In particular, benefits and limitations of using different available atlases and parcellation schemes are reviewed. It is furthermore discussed how atlas information is currently used in connectivity analyses with major focus on seed-based and seed-target analyses, connectivity-based parcellation results, and the robust anatomical interpretation of connectivity data. Particularly this last aspect opens the possibility of integrating connectivity information into given anatomical frameworks, paving the way to multi-modal atlases of the human brain for a thorough understanding of structure-function relationships

Development and application of a human cortical brain atlas on MRI considering phylogeny = Développement et emploi d'un atlas du cortex cérébral humain réalisé sur IRM et tenant compte de la phylogénie

Le cortex cérébral est une structure en couches complexe qui remplit différents types de fonctions. Au cours de l’histoire des neurosciences, plusieurs atlas corticaux ont été développés pour classifier différentes régions du cortex en tant que zones aux caractéristiques structurelles ou fonctionnelles communes, afin d'étudier et de quantifier les changements aux états sain et pathologique. Cependant, il n'existe pas d'atlas suivant une approche phylogénétique, c'est-à-dire, basée sur les critères d'évolution communs. Ce mémoire présente les étapes de création d'un nouvel atlas dans un modèle d’imagerie par résonance magnétique (IRM) en espace standard (pseudo-Talairach) : le PAN-Atlas, basé sur l'origine phylogénétique commune de chaque zone corticale, et son application sur des scans d’IRM de dix individus pour évaluer sa performance. D’abord, nous avons regroupé les différentes régions corticales en cinq régions d'intérêt (RdI) d'origine phylogénétique connue (archicortex, paléocortex, périarchicortex, proïsocortex, isocortex ou néocortex) sur la base de protocoles de segmentation validés histologiquement par d'autres groupes de chercheurs. Puis, nous avons segmenté ces régions manuellement sur le modèle d’IRM cérébrale moyen MNI-ICBM 2009c, en formant des masques. Par la suite, on a utilisé un pipeline multi-étapes de traitement des images pour réaliser le recalage des masques de notre atlas aux scans pondérés T1 de dix participants sains, en obtenant ainsi des masques automatiques pour chaque RdI. Les masques automatiques ont été évalués après une correction manuelle par le biais de l’indice Dice-kappa, qui quantifie la colocalisation des voxels de chaque masque automatique vs. le masque corrigé manuellement. L’indice a montré une très bonne à excellente performance de notre atlas. Cela a permis l’évaluation et comparaison des volumes corticales de chaque région et la quantification des valeurs de transfert de magnétisation (ITM), qui sont sensibles à la quantité de myéline présente dans le tissu. Ce travail montre que la division régionale du cortex en IRM avec une approche phylogénétique est réalisable à l'aide de notre PAN-Atlas en espace standard et que les masques peuvent être utilisés pour différents types de quantifications, comme les volumes corticaux, ou l’estimation des valeurs de ITM. Notre atlas pourrait éventuellement servir à évaluer les différences entre personnes saines et celles atteintes par des maladies neurodégénératives ou d’autres maladies neurologiques.The cerebral cortex is a complex layered structure that performs different types of functions. Throughout the history of neuroscience, several cortical atlases have been developed to classify/divide different regions of the cortex into areas with common structural or functional characteristics, to then study and quantify changes in healthy and pathological states. However, to date, there is no atlas following a phylogenetic approach, i.e. based on the common evolution criteria. This thesis presents the steps of creation of a new atlas corresponding to a standard MRI template: the PAN-Atlas, based on the common phylogenetic origin of each cortical zone, and its application on MRI scans of ten healthy participants to assess its performance. First, we grouped the different cortical regions into five regions of interest (ROI) of known phylogenetic origin (archicortex, paleocortex, periarchicortex, proisocortex, isocortex or neocortex) based on MRI protocols previously validated through histology by other groups of researchers. Then, we manually segmented these ROIs on the MNI-ICBM 2009c average brain MRI template, creating corresponding masks. We then used a multi-step image processing pipeline to register the atlas’ masks to T1 weighted images of ten healthy participants, generating automatic masks for each scan. The accuracy of these automatic atlas’ masks was assessed after manual correction using Dice-kappa similarity index, to quantify the colocalization of the automatic vs. the manually corrected masks. The Dice-kappa values showed a very good to excellent performance of the automatic atlas’ masks. This allowed the evaluation and comparison of cortical volumes of each ROI, as well as the quantification of magnetization transfer ratio (MTR) values, which are sensitive to myelin content. This work shows that the division of the cortex on MRI following a phylogenetic approach is feasible using our PAN Atlas, and that the masks of the atlas can be used to perform different types of quantifications, such as the ones presented here (cortical volume and MTR per ROI). Our atlas could similarly be used to assess differences between the cortex of healthy individuals and people affected by neurodegenerative diseases and other neurological disorders