Search results for "Human connectome"
showing 5 items of 5 documents
Synergetic and redundant information flow detected by unnormalized Granger causality: application to resting state fMRI
2015
Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficult to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently we introduce a pairwise index of synergy which is zero when two in…
Insight into Disrupted Spatial Patterns of Human Connectome in Alzheimer’s Disease via Subgraph Mining
2012
Alzheimer’s disease (AD) is the most common cause of age-related dementia, which prominently affects the human connectome. In this paper, the authors focus on the question how they can identify disrupted spatial patterns of the human connectome in AD based on a data mining framework. Using diffusion tractography, the human connectomes for each individual subject were constructed based on two diffusion derived attributes: fiber density and fractional anisotropy, to represent the structural brain connectivity patterns. After frequent subgraph mining, the abnormal score was finally defined to identify disrupted subgraph patterns in patients. Experiments demonstrated that our data-driven approa…
Normative vs. patient-specific brain connectivity in Deep Brain Stimulation
2020
AbstractBrain connectivity profiles seeding from deep brain stimulation (DBS) electrodes have emerged as informative tools to estimate outcome variability across DBS patients. Given the limitations of acquiring and processing patient-specific diffusion-weighted imaging data, most studies have employed normative atlases of the human connectome. To date, it remains unclear whether patient-specific connectivity information would strengthen the accuracy of such analyses. Here, we compared similarities and differences between patient-specific, disease-matched and normative structural connectivity data and retrospective estimation of clinical improvement that they may generate.Data from 33 patien…
Normative vs. patient-specific brain connectivity in deep brain stimulation
2020
Abstract Brain connectivity profiles seeding from deep brain stimulation (DBS) electrodes have emerged as informative tools to estimate outcome variability across DBS patients. Given the limitations of acquiring and processing patient-specific diffusion-weighted imaging data, a number of studies have employed normative atlases of the human connectome. To date, it remains unclear whether patient-specific connectivity information would strengthen the accuracy of such analyses. Here, we compared similarities and differences between patient-specific, disease-matched and normative structural connectivity data and their ability to predict clinical improvement. Data from 33 patients suffering from…
Discovering Aberrant Patterns of Human Connectome in Alzheimer's Disease via Subgraph Mining
2012
Alzheimer's disease (AD) is the most common cause of age-related dementia, which prominently affects the human connectome. Diffusion weighted imaging (DWI) provides a promising way to explore the organization of white matter fiber tracts in the human brain in a non-invasive way. However, the immense amount of data from millions of voxels of a raw diffusion map prevent an easy way to utilizable knowledge. In this paper, we focus on the question how we can identify disrupted spatial patterns of the human connectome in AD based on a data mining framework. Using diffusion tractography, the human connectomes for each individual subject were constructed based on two diffusion derived attributes: …