paper-with-me

홈 › Papers

How to Direct the Edges of the Connectomes: Dynamics of the Consensus Connectomes and the Development of the Connections in the Human Brain

2016-03-13

The human connectome is the object of an intensive research today. In these graphs, the vertices correspond to the small areas of the gray matter, and two vertices are connected by an edge, if a diffusion-MRI based workflow finds connections between those areas. One main question of the field is discovering the directions of the edges. In a previous work we have reported the construction of the Budapest Reference Connectome Server http://connectome.pitgroup.org from the data recorded in the Human Connectome Project of the NIH. After the server had been published, we recognized a surprising and unforeseen property of it: The server can generate the braingraph of connections that are present in at least $k$ graphs out of the 418, for any value of $k=1,2,...,418$. When the value of $k$ is changed from $k=418$ through 1 by moving a slider at the webserver from right to left, more and more edges appear in the consensus graph. The astonishing observation is that the appearance of the new edges is not random: it is similar to a growing tree. We hypothesize that this movement of the slider in the webserver may copy the development of the connections in the human brain in the following sense: the connections that are present in all subjects are the oldest ones, and those that are present in a decreasing fraction of subjects are gradually the newer connections in the individual brain development. An animation on the phenomenon is available at https://youtu.be/EnWwIf_HNjw. Based on this hypothesis, we can assign directions to the edges of the connectome as follows: Let $G_i$ denote the consensus connectome where each edge is present in at least $i$ graphs. Suppose that vertex $v$ is isolated in $G_{k+1}$, and becomes connected to a vertex $u$ in $G_k$, where $u$ was connected to other vertices already in $G_{k+1}$. Then we direct this $(v,u)$ edge from $v$ to $u$.

📄 PDF Abstract BibTeX arXiv:1509.05703

Code (0)

등록된 구현이 없습니다.

Tasks

Diffusion MRI

Similar Papers 제목 키워드 기반

High-Resolution Directed Human Connectomes and the Consensus Connectome Dynamics

2016-09-28

Here we show a method of directing the edges of the connectomes, prepared from diffusion tensor imaging (DTI) datasets from the human brain. Before the present work, no high-definition directed braingraphs (or connectome…

Vocal Bursts Intensity Prediction

Parameterizable Consensus Connectomes from the Human Connectome Project: The Budapest Reference Connectome Server v3.0

2016-02-15

Connections of the living human brain, on a macroscopic scale, can be mapped by a diffusion MR imaging based workflow. Since the same anatomic regions can be corresponded between distinct brains, one can compare the pres…

The Robustness and the Doubly-Preferential Attachment Simulation of the Consensus Connectome Dynamics of the Human Brain

2016-10-14

The increasing quantity and quality of the publicly available human cerebral diffusion MRI data make possible the study of the brain as it was unimaginable before. The Consensus Connectome Dynamics (CCD) is a remarkable …

Diffusion MRI

DeepMultiConnectome: Deep Multi-Task Prediction of Structural Connectomes Directly from Diffusion MRI Tractography

2025-05-27 · Marcus J. Vroemen, Yuqian Chen, Yui Lo, Tengfei Xue 외

Diffusion MRI (dMRI) tractography enables in vivo mapping of brain structural connections, but traditional connectome generation is time-consuming and requires gray matter parcellation, posing challenges for large-scale …

Diffusion MRIMulti-Task Learning

Data-Efficient Neural Training with Dynamic Connectomes

2025-08-09 · Yutong Wu, Peilin He, Tananun Songdechakraiwut arxiv

The study of dynamic functional connectomes has provided valuable insights into how patterns of brain activity change over time. Neural networks process information through artificial neurons, conceptually inspired by pa…