The study's objective is to explore the distinctions in the functional brain
network connectivity between Alzheimer's Disease (AD) patients and normal
controls using Functional Magnetic Resonance Imaging (fMRI). The study included
590 individuals, with 175 having AD dementia and 415 age-, gender-, and
handedness-matched normal controls. The connectivity of functional brain
networks was measured using ROI-to-ROI and ROI-to-Voxel connectivity analyses.
The findings reveal a general decrease in functional connectivity among the AD
group in comparison to the normal control group. These results advance our
comprehension of AD pathophysiology and could assist in identifying AD
biomarkers.

使用功能磁共振成像研究了阿尔茨海默病患者和正常控制组之间的功能性脑网络连通性差异，结果发现 AD 组整体的功能连接度降低，这一成果有助于我们深入理解 AD 的病理生理学并找出 AD 的生物标志物。

与阿尔茨海默病相关的异常功能性脑网络连接

Abnormal Functional Brain Network Connectivity Associated with  Alzheimer's Disease

Recently, there has been increased interest in fusing multimodal imaging to
better understand brain organization. Specifically, accounting for knowledge of
anatomical pathways connecting brain regions should lead to desirable outcomes
such as increased accuracy in functional brain network estimates and greater
reproducibility of topological features across scanning sessions. Despite the
clear merits, major challenges persist in integrative analyses including an
incomplete understanding of the structure-function relationship and
inaccuracies in mapping anatomical structures due to deficiencies in existing
imaging technology. Clearly advanced network modeling tools are needed to
appropriately incorporate anatomical structure in constructing brain functional
networks. We propose a hierarchical Bayesian Gaussian graphical modeling
approach that estimates the functional networks via sparse precision matrices
whose degree of edge-specific shrinkage is informed by anatomical structure and
an independent baseline component. The approach flexibly identifies functional
connections supported by structural connectivity knowledge. This enables robust
brain network estimation even in the presence of mis-specified anatomical
knowledge, while accommodating heterogeneity in the structure-function
relationship. We implement the approach via an efficient optimization algorithm
yielding maximum a posteriori estimates. Extensive numerical studies reveal the
clear advantages of our approach over competing methods in accurately
estimating brain functional connectivity, even when the anatomical knowledge is
mis-specified. An application of the approach to the Philadelphia
Neurodevelopmental Cohort (PNC) study reveals gender based connectivity
differences across multiple age groups, and higher reproducibility in the
estimation of network metrics compared to alternative methods.

通过采用分层贝叶斯高斯图模型，该研究方法能够利用结构连接知识来估计功能网络，从而提高了功能大脑网络估计的准确性和拓扑特征在扫描会话间的可重复性。

结合解剖学知识的大脑功能网络集成贝叶斯分析

Integrative Bayesian Analysis of Brain Functional Networks Incorporating  Anatomical Knowledge

Complex functional brain network analyses have exploded over the last eight
years, gaining traction due to their profound clinical implications. The
application of network science (an interdisciplinary offshoot of graph theory)
has facilitated these analyses and enabled examining the brain as an integrated
system that produces complex behaviors. While the field of statistics has been
integral in advancing activation analyses and some connectivity analyses in
functional neuroimaging research, it has yet to play a commensurate role in
complex network analyses. Fusing novel statistical methods with network-based
functional neuroimage analysis will engender powerful analytical tools that
will aid in our understanding of normal brain function as well as alterations
due to various brain disorders. Here we survey widely used statistical and
network science tools for analyzing fMRI network data and discuss the
challenges faced in filling some of the remaining methodological gaps. When
applied and interpreted correctly, the fusion of network scientific and
statistical methods has a chance to revolutionize the understanding of brain
function.

通过结合网络科学和统计方法，将在功能神经影像研究中产生强大的分析工具，以促进我们对大脑功能及不同脑疾病的理解。