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Open AccessJournal ArticleDOI

Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity

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TLDR
In this article, the authors show that every individual has a unique pattern of functional connections between brain regions, which act as a fingerprint that can accurately identify the individual from a large group.
Abstract
This study shows that every individual has a unique pattern of functional connections between brain regions. This functional connectivity profile acts as a ‘fingerprint’ that can accurately identify the individual from a large group. Furthermore, an individual's connectivity profile can predict his or her level of fluid intelligence.

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Posted ContentDOI

The diversity and multiplexity of edge communities within and between brain systems

TL;DR: This work reveals the pervasive overlap of edge communities across the cerebral cortex and characterizes their relationship with the brain’s system level architecture, and provides clear pathways for future research using edge-centric brain networks to investigate individual differences in behavior, development, and disease.
Journal ArticleDOI

Optimising neonatal fMRI data analysis: Design and validation of an extended dHCP preprocessing pipeline to characterise noxious-evoked brain activity in infants.

TL;DR: It is demonstrated that a substantial increase in spatial specificity and sensitivity to signal can be attained with a bespoke neonatal preprocessing pipeline through optimised motion and distortion correction, ICA‐based denoising, and haemodynamic modelling.
Posted ContentDOI

There is no single functional atlas even for a single individual: Parcellation of the human brain is state dependent

TL;DR: It is demonstrated that functional parcellations based on fMRI connectivity data reconfigure substantially and in a meaningful manner, according to brain state, to assess connections between parcels and extract network properties.
Proceedings ArticleDOI

Consistent Run Selection for Independent Component Analysis: Application to Fmri Analysis

TL;DR: This paper provides insight into the trade-offs between estimation accuracy and algorithmic consistency with or without deviations from the assumed model and assumptions such as the statistical independence, and proposes a new metric, cross inter-symbol interference, to quantify the consistency of an algorithm across different runs.
References
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Journal ArticleDOI

Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain

TL;DR: An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute was performed and it is believed that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain.
Journal ArticleDOI

Complex network measures of brain connectivity: uses and interpretations.

TL;DR: Construction of brain networks from connectivity data is discussed and the most commonly used network measures of structural and functional connectivity are described, which variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, and test resilience of networks to insult.
Journal ArticleDOI

The organization of the human cerebral cortex estimated by intrinsic functional connectivity

TL;DR: In this paper, the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI data from 1,000 subjects and a clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex.
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