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Connectomics and new approaches for analyzing human brain functional connectivity.

Craddock RC, Tungaraza RL, Milham MP - Gigascience (2015)

Bottom Line: The number and complexity of functional interactions within the connectome and the large amounts of data required to study them position functional connectivity research as a "big data" problem.This review describes several outstanding problems in brain functional connectomics with the goal of engaging researchers from a broad spectrum of data sciences to help solve these problems.Additionally it provides information about open science resources consisting of raw and preprocessed data to help interested researchers get started.

View Article: PubMed Central - PubMed

Affiliation: Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, 10962 New York USA ; Center for the Developing Brain, Child Mind Institute, 445 Park Ave, New York, 10022 New York USA.

ABSTRACT
Estimating the functional interactions between brain regions and mapping those connections to corresponding inter-individual differences in cognitive, behavioral and psychiatric domains are central pursuits for understanding the human connectome. The number and complexity of functional interactions within the connectome and the large amounts of data required to study them position functional connectivity research as a "big data" problem. Maximizing the degree to which knowledge about human brain function can be extracted from the connectome will require developing a new generation of neuroimaging analysis algorithms and tools. This review describes several outstanding problems in brain functional connectomics with the goal of engaging researchers from a broad spectrum of data sciences to help solve these problems. Additionally it provides information about open science resources consisting of raw and preprocessed data to help interested researchers get started.

No MeSH data available.


Parcellation of the brain into functionally homogenous brain regions (A) and the resulting connectome (B). Community detection identifies seven different modules, which are indicated by the color of the nodes in B.
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Fig1: Parcellation of the brain into functionally homogenous brain regions (A) and the resulting connectome (B). Community detection identifies seven different modules, which are indicated by the color of the nodes in B.

Mentions: In 2005 Sporns [19] and Hagmann [20] independently and in parallel coined the term the human connectome, which embodies the notion that the set of all connections within the human brain can be represented and understood as graphs. In the context of iFC, graphs provide a mathematical representation of the functional interactions between brain areas: nodes in the graph represent brain areas and edges indicate their functional connectivity (as illustrated in Figure 1). While general graphs can have multiple edges between two nodes, brain graphs tend to be simple graphs with a single undirected edge between pairs of nodes (i.e. the direction of influence between nodes is unknown). Additionally edges in graphs of brain function tend to be weighted - annotated with a value indicating the similarity between nodes. Analyzing functional connectivity involves 1) preprocessing the data to remove confounding variation and to make it comparable across datasets, 2) specification of brain areas to be used as nodes, 3) identification of edges from the iFC between nodes, and 4) analysis of the graph (i.e. the structure and edges) to identify relationships with inter- or intra- individual variability. All of these steps have been well covered in the literature by other reviews [12,17,21] and repeating that information provides little value. Instead we will focus on exciting areas in the functional connectomics literature that we believe provide the greatest opportunities for data scientists in this quickly advancing field.Figure 1


Connectomics and new approaches for analyzing human brain functional connectivity.

Craddock RC, Tungaraza RL, Milham MP - Gigascience (2015)

Parcellation of the brain into functionally homogenous brain regions (A) and the resulting connectome (B). Community detection identifies seven different modules, which are indicated by the color of the nodes in B.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4373299&req=5

Fig1: Parcellation of the brain into functionally homogenous brain regions (A) and the resulting connectome (B). Community detection identifies seven different modules, which are indicated by the color of the nodes in B.
Mentions: In 2005 Sporns [19] and Hagmann [20] independently and in parallel coined the term the human connectome, which embodies the notion that the set of all connections within the human brain can be represented and understood as graphs. In the context of iFC, graphs provide a mathematical representation of the functional interactions between brain areas: nodes in the graph represent brain areas and edges indicate their functional connectivity (as illustrated in Figure 1). While general graphs can have multiple edges between two nodes, brain graphs tend to be simple graphs with a single undirected edge between pairs of nodes (i.e. the direction of influence between nodes is unknown). Additionally edges in graphs of brain function tend to be weighted - annotated with a value indicating the similarity between nodes. Analyzing functional connectivity involves 1) preprocessing the data to remove confounding variation and to make it comparable across datasets, 2) specification of brain areas to be used as nodes, 3) identification of edges from the iFC between nodes, and 4) analysis of the graph (i.e. the structure and edges) to identify relationships with inter- or intra- individual variability. All of these steps have been well covered in the literature by other reviews [12,17,21] and repeating that information provides little value. Instead we will focus on exciting areas in the functional connectomics literature that we believe provide the greatest opportunities for data scientists in this quickly advancing field.Figure 1

Bottom Line: The number and complexity of functional interactions within the connectome and the large amounts of data required to study them position functional connectivity research as a "big data" problem.This review describes several outstanding problems in brain functional connectomics with the goal of engaging researchers from a broad spectrum of data sciences to help solve these problems.Additionally it provides information about open science resources consisting of raw and preprocessed data to help interested researchers get started.

View Article: PubMed Central - PubMed

Affiliation: Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, 10962 New York USA ; Center for the Developing Brain, Child Mind Institute, 445 Park Ave, New York, 10022 New York USA.

ABSTRACT
Estimating the functional interactions between brain regions and mapping those connections to corresponding inter-individual differences in cognitive, behavioral and psychiatric domains are central pursuits for understanding the human connectome. The number and complexity of functional interactions within the connectome and the large amounts of data required to study them position functional connectivity research as a "big data" problem. Maximizing the degree to which knowledge about human brain function can be extracted from the connectome will require developing a new generation of neuroimaging analysis algorithms and tools. This review describes several outstanding problems in brain functional connectomics with the goal of engaging researchers from a broad spectrum of data sciences to help solve these problems. Additionally it provides information about open science resources consisting of raw and preprocessed data to help interested researchers get started.

No MeSH data available.