scholarly journals Network structural dependency in the human connectome across the life-span

2019 ◽  
Vol 3 (3) ◽  
pp. 792-806 ◽  
Author(s):  
Markus D. Schirmer ◽  
Ai Wern Chung ◽  
P. Ellen Grant ◽  
Natalia S. Rost
Keyword(s):  
Life Span ◽  
Age Groups ◽  
Model Fitting ◽  
Gaussian Mixture ◽  
Global Functioning ◽  
Rich Club ◽  
Network Measure ◽  
The Rich ◽  
Cortical Regions ◽  
Dependency Index

Principles of network topology have been widely studied in the human connectome. Of particular interest is the modularity of the human brain, where the connectome is divided into subnetworks from which changes with development, aging or disease can be investigated. We present a weighted network measure, the Network Dependency Index (NDI), to identify an individual region’s importance to the global functioning of the network. Importantly, we utilize NDI to differentiate four subnetworks (Tiers) in the human connectome following Gaussian mixture model fitting. We analyze the topological aspects of each subnetwork with respect to age and compare it to rich club-based subnetworks (rich club, feeder, and seeder). Our results first demonstrate the efficacy of NDI to identify more consistent, central nodes of the connectome across age groups, when compared with the rich club framework. Stratifying the connectome by NDI led to consistent subnetworks across the life-span, revealing distinct patterns associated with age where, for example, the key relay nuclei and cortical regions are contained in a subnetwork with highest NDI. The divisions of the human connectome derived from our data-driven NDI framework have the potential to reveal topological alterations described by network measures through the life-span.

scholarly journals Task-related effective connectivity reveals that the cortical rich club gates cortex-wide communication

2017 ◽  
Author(s):  
Mario Senden ◽  
Niels Reuter ◽  
Martijn P. van den Heuvel ◽  
Rainer Goebel ◽  
Gustavo Deco ◽  
...  
Keyword(s):  
Information Exchange ◽  
Effective Connectivity ◽  
Functional Integration ◽  
Brain Regions ◽  
Rich Club ◽  
State Dependent ◽  
Gating Mechanism ◽  
The Rich ◽  
Cortical Regions ◽  
Peripheral Regions

AbstractHigher cognition may require the globally coordinated integration of specialized brain regions into functional networks. A collection of structural cortical hubs - referred to as the rich club - has been hypothesized to support task-specific functional integration. In the present paper, we use a whole-cortex model to estimate directed interactions between 68 cortical regions from fMRI activity for four different tasks (reflecting different cognitive domains) and resting state. We analyze the state-dependent input and output effective connectivity of the structural rich club and relate these to whole-cortex dynamics and network reconfigurations. We find that the cortical rich club exhibits an increase in outgoing effective connectivity during task performance as compared to rest while incoming connectivity remains constant. Increased outgoing connectivity targets a sparse set of peripheral regions with specific regions strongly overlapping between tasks. At the same time, community detection analyses reveal massive reorganizations of interactions among peripheral regions, including those serving as target of increased rich club output. This suggests that while peripheral regions may play a role in several tasks, their concrete interplay might nonetheless be task-specific. Furthermore, we observe that whole-cortex dynamics are faster during task as compared to rest. The decoupling effects usually accompanying faster dynamics appear to be counteracted by the increased rich club outgoing effective connectivity. Together our findings speak to a gating mechanism of the rich club that supports fast-paced information exchange among relevant peripheral regions in a task-specific and goal-directed fashion, while constantly listening to the whole network.

scholarly journals Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140165 ◽  
Author(s):  
Leonardo L. Gollo ◽  
Andrew Zalesky ◽  
R. Matthew Hutchison ◽  
Martijn van den Heuvel ◽  
Michael Breakspear
Keyword(s):  
Spatial Scales ◽  
Brain Regions ◽  
Brain Network ◽  
Morphological Properties ◽  
Brain Organization ◽  
Rich Club ◽  
Guiding Principle ◽  
The Rich ◽  
Cortical Regions ◽  
The Brain

For more than a century, cerebral cartography has been driven by investigations of structural and morphological properties of the brain across spatial scales and the temporal/functional phenomena that emerge from these underlying features. The next era of brain mapping will be driven by studies that consider both of these components of brain organization simultaneously—elucidating their interactions and dependencies. Using this guiding principle, we explored the origin of slowly fluctuating patterns of synchronization within the topological core of brain regions known as the rich club, implicated in the regulation of mood and introspection. We find that a constellation of densely interconnected regions that constitute the rich club (including the anterior insula, amygdala and precuneus) play a central role in promoting a stable, dynamical core of spontaneous activity in the primate cortex. The slow timescales are well matched to the regulation of internal visceral states, corresponding to the somatic correlates of mood and anxiety. In contrast, the topology of the surrounding ‘feeder’ cortical regions shows unstable, rapidly fluctuating dynamics likely to be crucial for fast perceptual processes. We discuss these findings in relation to psychiatric disorders and the future of connectomics.

Conflict Monitoring Across the Life Span

2014 ◽  
Vol 28 (3) ◽  
pp. 124-135 ◽  
Author(s):  
Daniela Czernochowski
Keyword(s):  
Older Adults ◽  
Life Span ◽  
Age Groups ◽  
Conflict Detection ◽  
Response Conflict ◽  
Conflict Monitoring ◽  
Subsequent Performance ◽  
Additional Control ◽  
Performance Response ◽  
Conflict Detection And Resolution

Errors can play a major role for optimizing subsequent performance: Response conflict associated with (near) errors signals the need to recruit additional control resources to minimize future conflict. However, so far it remains open whether children and older adults also adjust their performance as a function of preceding response conflict. To examine the life span development of conflict detection and resolution, response conflict was elicited during a task-switching paradigm. Electrophysiological correlates of conflict detection for correct and incorrect responses and behavioral indices of post-error adjustments were assessed while participants in four age groups were asked to focus on either speed or accuracy. Despite difficulties in resolving response conflict, the ability to detect response conflict as indexed by the Ne/ERN component was expected to mature early and be preserved in older adults. As predicted, reliable Ne/ERN peaks were detected across age groups. However, only for adults Ne/ERN amplitudes associated with errors were larger compared to Nc/CRN amplitudes for correct trials under accuracy instructions, suggesting an ongoing maturation in the ability to differentiate levels of response conflict. Behavioral interference costs were considerable in both children and older adults. Performance for children and older adults deteriorated rather than improved following errors, in line with intact conflict detection, but impaired conflict resolution. Thus, participants in all age groups were able to detect response conflict, but only young adults successfully avoided subsequent conflict by up-regulating control.

scholarly journals Effect of Multishell Diffusion MRI Acquisition Strategy and Parcellation Scale on Rich-Club Organization of Human Brain Structural Networks

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 970
Author(s):  
Maedeh Khalilian ◽  
Kamran Kazemi ◽  
Mahshid Fouladivanda ◽  
Malek Makki ◽  
Mohammad Sadegh Helfroush ◽  
...  
Keyword(s):  
Human Brain ◽  
Clustering Coefficient ◽  
Quality Data ◽  
B Values ◽  
Rich Club ◽  
Global Efficiency ◽  
Acquisition Strategy ◽  
The Rich ◽  
Structural Networks ◽  
Single Shell

The majority of network studies of human brain structural connectivity are based on single-shell diffusion-weighted imaging (DWI) data. Recent advances in imaging hardware and software capabilities have made it possible to acquire multishell (b-values) high-quality data required for better characterization of white-matter crossing-fiber microstructures. The purpose of this study was to investigate the extent to which brain structural organization and network topology are affected by the choice of diffusion magnetic resonance imaging (MRI) acquisition strategy and parcellation scale. We performed graph-theoretical network analysis using DWI data from 35 Human Connectome Project subjects. Our study compared four single-shell (b = 1000, 3000, 5000, 10,000 s/mm2) and multishell sampling schemes and six parcellation scales (68, 200, 400, 600, 800, 1000 nodes) using five graph metrics, including small-worldness, clustering coefficient, characteristic path length, modularity and global efficiency. Rich-club analysis was also performed to explore the rich-club organization of brain structural networks. Our results showed that the parcellation scale and imaging protocol have significant effects on the network attributes, with the parcellation scale having a substantially larger effect. Regardless of the parcellation scale, the brain structural networks exhibited a rich-club organization with similar cortical distributions across the parcellation scales involving at least 400 nodes. Compared to single b-value diffusion acquisitions, the deterministic tractography using multishell diffusion imaging data consisting of shells with b-values higher than 5000 s/mm2 resulted in significantly improved fiber-tracking results at the locations where fiber bundles cross each other. Brain structural networks constructed using the multishell acquisition scheme including high b-values also exhibited significantly shorter characteristic path lengths, higher global efficiency and lower modularity. Our results showed that both parcellation scale and sampling protocol can significantly impact the rich-club organization of brain structural networks. Therefore, caution should be taken concerning the reproducibility of connectivity results with regard to the parcellation scale and sampling scheme.

Growth and spatiotemporal distribution of juvenile shortfin mako (Isurus oxyrinchus) in the western and central North Pacific

2015 ◽  
Vol 66 (12) ◽  
pp. 1176 ◽  
Author(s):  
M. Kai ◽  
K. Shiozaki ◽  
S. Ohshimo ◽  
K. Yokawa
Keyword(s):  
North Pacific ◽  
Linear Models ◽  
North Pacific Ocean ◽  
Age Groups ◽  
Growth Curves ◽  
Growth Function ◽  
Gaussian Mixture ◽  
Isurus Oxyrinchus ◽  
Shortfin Mako ◽  
Central North Pacific

This paper presents an estimation of growth curves and spatiotemporal distributions of juvenile shortfin mako shark (Isurus oxyrinchus) in the western and central North Pacific Ocean using port sampling data collected from 2005 to 2013. The monthly length compositions show a clear transition of three modes in the size range of smaller than 150-cm precaudal length (PCL), which were believed to represent the growth of age-0 to age-2 classes, and they were then decomposed into age groups by fitting a Gaussian mixture distribution. Simulation data of lengths at monthly ages were generated from the mean and standard deviation of each distribution, and fit with a von Bertalanffy growth function. Parameters of the estimated growth curves for males and females were 274.4 and 239.4cm PCL for the asymptotic length and 0.19 and 0.25 year–1 for the growth coefficient indicating apparently faster growth than previously reported. Generalised linear models were applied to age-0 to explore the seasonal changes of PCL by area. They were born during late autumn and winter off the coast of north-eastern Japan, an area known to have relatively high productivity compared with other pelagic areas, and gradually expanded their habitat eastward and northward with the seasons as they grew.

Abstract 14655: Diffusion Neuroimaging of Adults With D-Transposition of the Great Arteries Reveal White Matter Alterations in the Connectomic Rich Club

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Ai Wern Chung ◽  
Borjan Gagoski ◽  
Jane W Newburger ◽  
P. Ellen Grant ◽  
Michelle GURVITZ
Keyword(s):  
White Matter ◽  
Structural Integrity ◽  
Circulatory Arrest ◽  
A Priori ◽  
Group Differences ◽  
High Angular Resolution ◽  
Central System ◽  
Rich Club ◽  
Apparent Diffusion ◽  
Cortical Regions

Introduction: The population of adults with d-transposition of the great arteries (TGA) continue to grow. As this group has underlying neurocognitive impairment and longer-term neurovascular damage, advanced neuroimaging to identify markers for treatment is required. Diffusion (d)MRI tractography quantifies the structural integrity of white matter (WM) pathways in the brain - where lower FA (fractional anisotropy) and higher ADC (apparent diffusion coefficient) typify WM damage. The brain’s structural backbone is its rich club (RC), a set of highly interconnected regions established before birth and vital for effective cognitive function. Moreover, there are Feeder and Seeder subnetworks peripheral to the RC, which are thought to form later and may be more adaptive. Hypothesis: We hypothesize that adults with TGA have alterations in both the brain’s structural RC and in peripheral connections. Methods: Subjects were TGA adults from the Boston Circulatory Arrest Study (n = 25, mean age 28.46 ± 1.14yr) and Controls (n = 13, 28.35 ± 1.70). Multi-shell, high-angular resolution dMRI data were acquired and fitted with a multi-fiber model (Fig). After tractography, a connectome of the number of tracts connecting pairwise cortical regions was computed. A priori RC regions were bilateral superior frontal and parietal frontal gyri, precuneus, posterior cingulate and insular regions. Connections were grouped into subnetworks and mean FA and ADC computed. Results: Cohorts were age-matched (p=0.801, unpaired t-test). Overall, patients had lower FA and greater ADC than controls in all subnetworks. Group differences (unpaired t-tests) were significant in the RC (ADC p=0.029), Feeder subnetwork (FA p=0.041; ADC p=0.042), with trends in Seeder subnetworks (FA p=0.061; ADC p=0.062). Conclusions: Widespread WM alterations exist in adults with TGA not only in the brain’s most central system, but also connections feeding into the RC suggesting prenatal and adaptive changes.

scholarly journals MGMM: An R Package for fitting Gaussian Mixture Models on Incomplete Data

2019 ◽  
Author(s):  
Zachary R. McCaw ◽  
Hanna Julienne ◽  
Hugues Aschard
Keyword(s):  
Missing Data ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Model Fitting ◽  
Simulated Data ◽  
R Package ◽  
Gaussian Mixture ◽  
Parameter Estimates ◽  
Cluster Assignment ◽  
Underlying Distribution

AbstractAlthough missing data are prevalent in applications, existing implementations of Gaussian mixture models (GMMs) require complete data. Standard practice is to perform complete case analysis or imputation prior to model fitting. Both approaches have serious drawbacks, potentially resulting in biased and unstable parameter estimates. Here we present MGMM, an R package for fitting GMMs in the presence of missing data. Using three case studies on real and simulated data sets, we demonstrate that, when the underlying distribution is near-to a GMM, MGMM is more effective at recovering the true cluster assignments than state of the art imputation followed by standard GMM. Moreover, MGMM provides an accurate assessment of cluster assignment uncertainty even when the generative distribution is not a GMM. This assessment may be used to identify unassignable observations. MGMM is available as an R package on CRAN: https://CRAN.R-project.org/package=MGMM.

scholarly journals Identification of key regulators in prostate cancer from gene expression datasets of patients

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Irengbam Rocky Mangangcha ◽  
Md. Zubbair Malik ◽  
Ömer Küçük ◽  
Shakir Ali ◽  
R. K. Brojen Singh
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Regulatory Pathways ◽  
Scale Free ◽  
Topological Parameters ◽  
Rich Club ◽  
Self Organized ◽  
Mutational Status ◽  
Interactome Network ◽  
The Rich

Abstract Identification of key regulators and regulatory pathways is an important step in the discovery of genes involved in cancer. Here, we propose a method to identify key regulators in prostate cancer (PCa) from a network constructed from gene expression datasets of PCa patients. Overexpressed genes were identified using BioXpress, having a mutational status according to COSMIC, followed by the construction of PCa Interactome network using the curated genes. The topological parameters of the network exhibited power law nature indicating hierarchical scale-free properties and five levels of organization. Highest degree hubs (k ≥ 65) were selected from the PCa network, traced, and 19 of them was identified as novel key regulators, as they participated at all network levels serving as backbone. Of the 19 hubs, some have been reported in literature to be associated with PCa and other cancers. Based on participation coefficient values most of these are connector or kinless hubs suggesting significant roles in modular linkage. The observation of non-monotonicity in the rich club formation suggested the importance of intermediate hubs in network integration, and they may play crucial roles in network stabilization. The network was self-organized as evident from fractal nature in topological parameters of it and lacked a central control mechanism.

Development of the Life Story in Early Adolescence

2016 ◽  
Vol 38 (2) ◽  
pp. 125-138 ◽  
Author(s):  
Kristina L. Steiner ◽  
David B. Pillemer
Keyword(s):  
Life Span ◽  
Early Adolescence ◽  
Life Stories ◽  
Developmental Psychology ◽  
Life Story ◽  
Age Groups ◽  
Single Event ◽  
Life Narrative ◽  
Novel Method ◽  
Causal Coherence

Life span developmental psychology proposes that the ability to create a coherent life narrative does not develop until early adolescence. Using a novel methodology, 10-, 12-, and 14-year-old participants were asked to tell their life stories aloud to a researcher. Later, participants separated their transcribed narratives into self-identified chapters. When life stories were assessed with measures of temporal and causal coherence, most participants in all age groups were able to tell a linear and coherent narrative. The 10-year-olds were more likely to start their narratives after birth and to use single event chapters in their stories, but they did not differ significantly from older participants in terms of the coherence or chronology of their chapters. This novel method for analyzing life narratives both supports and extends prior research on the development of life stories in adolescence.

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