We present a pipeline for unbiased and robust multimodal registration of
neuroimaging modalities with minimal pre-processing. While typical multimodal
studies need to use multiple independent processing pipelines, with diverse
options and hyperparameters, we propose a single and structured framework to
jointly process different image modalities. The use of state-of-the-art
learning-based techniques enables fast inferences, which makes the presented
method suitable for large-scale and/or multi-cohort datasets with a diverse
number of modalities per session. The pipeline currently works with structural
MRI, resting state fMRI and amyloid PET images. We show the predictive power of
the derived biomarkers using in a case-control study and study the cross-modal
relationship between different image modalities. The code can be found in
https: //github.com/acasamitjana/JUMP.

利用最少的预处理提供无偏差和稳健的多模式神经影像注册，通过单一结构化框架联合处理不同图像模式，使用先进的基于学习的技术实现快速推论，适用于大规模和 / 或多队列数据集，目前可处理结构 MRI、静息状态 fMRI 和淀粉样 PET 图像，通过案例对照研究证明了所得生物标志物的预测能力，并研究了不同图像模式之间的跨模式关系。

JUMP: 一种用于神经影像的联合多模态注册流程的最小预处理方法

JUMP: A joint multimodal registration pipeline for neuroimaging with  minimal preprocessing

We propose a novel algorithm called Unique Brain Network Identification
Number (UBNIN) for encoding brain networks of individual subject. To realize
this objective, we employed T1-weighted structural MRI of 180 Parkinson's
disease (PD) patients from National Institute of Mental Health and
Neurosciences, India. We parcellated each subject's brain volume and
constructed individual adjacency matrix using correlation between grey matter
(GM) volume of every pair of regions. The unique code is derived from values
representing connections of every node (i), weighted by a factor of 2^-(i-1).
The numerical representation UBNIN was observed to be distinct for each
individual brain network, which may also be applied to other neuroimaging
modalities. This model may be implemented as neural signature of a person's
unique brain connectivity, thereby useful for brainprinting applications.
Additionally, we segregated the above dataset into five age-cohorts:
A:22-32years, B:33-42years, C:43-52years, D:53-62years and E:63-72years to
study the variation in network topology over age. Sparsity was adopted as the
threshold estimate to binarize each age-based correlation matrix. Connectivity
metrics were obtained using Brain Connectivity toolbox-based MATLAB functions.
For each age-cohort, a decreasing trend was observed in mean clustering
coefficient with increasing sparsity. Significantly different clustering
coefficient was noted between age-cohort B and C (sparsity: 0.63,0.66), C and E
(sparsity: 0.66,0.69). Our findings suggest network connectivity patterns
change with age, indicating network disruption due to the underlying
neuropathology. Varying clustering coefficient for different cohorts indicate
that information transfer between neighboring nodes change with age. This
provides evidence on age-related brain shrinkage and network degeneration.

本文提出了一种新的算法，Unique Brain Network Identification Number (UBNIN)，用于编码个体的大脑网络，并使用这种算法对帕金森病患者进行研究。结果表明，UBNIN 的数值对于每个大脑网络都是独特的，这可以作为人类大脑连接的神经表征，同时该算法方法可以用于其他神经影像学模态。在年龄层面上，该研究发现随着稀疏度的增加，在不同年龄组之间的平均聚类系数是不同的，这提供了关于年龄相关性脑萎缩和网络退化的证据。