[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Journal of Statistical Mechanics: theory and experiment

介绍Plos Computational Biology主编Philip E．Bourne关于论文发表的10务简单法则，包括：读很多论文，客观看待自身工作，选择好的编辑，提高英语水平，忍受退稿，提高论文质量等。以期对医学生和研究人员发表文章提供有益帮助。

Plos Computational Biology主编关于论文获得发表的10条简单法则的评析

Resting-state functional connectivity is typically modeled as the correlation structure of whole-brain regional activity. It is studied widely, both to gain insight into the brain’s intrinsic organization but also to develop markers sensitive to changes in an individual’s cognitive, clinical, and developmental state. Despite this, the origins and drivers of functional connectivity, especially at the level of densely sampled individuals, remain elusive. Here, we leverage novel methodology to decompose functional connectivity into its precise framewise contributions. Using two dense sampling datasets, we investigate the origins of individualized functional connectivity, focusing specifically on the role of brain network “events” – short-lived and peaked patterns of high-amplitude cofluctuations. Here, we develop a statistical test to identify events in empirical recordings. We show that the patterns of cofluctuation expressed during events are repeated across multiple scans of the same individual and represent idiosyncratic variants of template patterns that are expressed at the group level. Lastly, we propose a simple model of functional connectivity based on event cofluctuations, demonstrating that group-averaged cofluctuations are suboptimal for explaining participant-specific connectivity. Our work complements recent studies implicating brief instants of high-amplitude cofluctuations as the primary drivers of static, whole-brain functional connectivity. Our work also extends those studies, demonstrating that cofluctuations during events are individualized, positing a dynamic basis for functional connectivity.

/pdf/individualized-event-structure-drives-individual-differences-4ai82c4ueu.pdf

Individualized event structure drives individual differences in whole-brain functional connectivity

Null models in network neuroscience

Modularity maximization as a flexible and generic framework for brain network exploratory analysis.

The modular structure of brain networks supports specialized information processing, complex dynamics, and cost-efficient spatial embedding. Inter-individual variation in modular structure has been linked to differences in performance, disease, and development. There exist many data-driven methods for detecting and comparing modular structure, the most popular of which is modularity maximization. Although modularity maximization is a general framework that can be modified and reparamaterized to address domain-specific research questions, its application to neuroscientific datasets has, thus far, been narrow. Here, we highlight several strategies in which the ``out-of-the-box'' version of modularity maximization can be extended to address questions specific to neuroscience. First, we present approaches for detecting ``space-independent'' modules and for applying modularity maximization to signed matrices. Next, we show that the modularity maximization frame is well-suited for detecting task- and condition-specific modules. Finally, we highlight the role of multi-layer models in detecting and tracking modules across time, tasks, subjects, and modalities. In summary, modularity maximization is a flexible and general framework that can be adapted to detect modular structure resulting from a wide range of hypotheses. This article highlights opens multiple frontiers for future research and applications.

Modularity maximization as a flexible and generic framework for brain network exploratory analysis

Many studies have shown that the human endocrine system modulates brain function, reporting associations between fluctuations in hormone concentrations and both brain activity and connectivity. However, how hormonal fluctuations impact fast changes in brain network structure over short timescales remains unknown. Here, we leverage ``edge time series99 analysis to investigate the relationship between high-amplitude network states and quotidian variation in sex steroid and gonadotropic hormones in a single individual sampled over the course of two endocrine states, across a natural menstrual cycle and under a hormonal regimen. We find that the frequency of high-amplitude network states are associated with follicle-stimulating and luteinizing hormone, but not the sex hormones estradiol and progesterone. Nevertheless, we show that scan-to-scan variation in the co-fluctuation patterns expressed during network states are robustly linked with the concentration of all four hormones, positing a network-level target of hormonal control. We conclude by speculating on the role of hormones in shaping ongoing brain dynamics.

/pdf/high-amplitude-network-co-fluctuations-linked-to-variation-6bvmtdtvx1.pdf

High-amplitude network co-fluctuations linked to variation in hormone concentrations over menstrual cycle

Edge time series decompose FC into its framewise contributions. Previous studies have focused on characterizing the properties of high-amplitude frames, including their cluster structure. Less is known about middle- and low-amplitude co-fluctuations. Here, we address those questions directly, using data from two dense-sampling studies: the MyConnectome project and Midnight Scan Club. We develop a hierarchical clustering algorithm to group peak co-fluctuations of all magnitudes into nested and multi-scale clusters based on their pairwise concordance. At a coarse scale, we find evidence of three large clusters that, collectively, engage virtually all canonical brain systems. At finer scales, however, each cluster is dissolved, giving way to increasingly refined patterns of co-fluctuations involving specific sets of brain systems. We also find an increase in global co-fluctuation magnitude with hierarchical scale. Finally, we comment on the amount of data needed to estimate co-fluctuation pattern clusters and implications for brain-behavior studies. Collectively, the findings reported here fill several gaps in current knowledge concerning the heterogeneity and richness of co-fluctuation patterns as estimated with edge time series while providing some practical guidance for future studies.

Sarah Greenwell

Papers

Individualized event structure drives individual differences in whole-brain functional connectivity

Modularity maximization as a flexible and generic framework for brain network exploratory analysis.

Modularity maximization as a flexible and generic framework for brain network exploratory analysis

High-amplitude network co-fluctuations linked to variation in hormone concentrations over menstrual cycle

Hierarchical organization of spontaneous co-fluctuations in densely-sampled individuals using fMRI