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Open AccessJournal ArticleDOI

Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity

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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Citations
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Journal ArticleDOI

Functional connectome fingerprinting: Identifying individuals and predicting cognitive functions via autoencoder.

TL;DR: In this article, the authors proposed to enhance the uniqueness of individual connectome based on an autoencoder network, which can be regarded as "brain fingerprinting" to identify an individual from a pool of subjects.
Journal ArticleDOI

Enhancing Intelligence: From the Group to the Individual.

TL;DR: The training program was based on the adaptive dual n-back task, and participants completed a comprehensive battery measuring fluid and crystallized ability, along with working memory and attention control, before and after training, which revealed positive effects for visuospatial processing across cognitive domains.
Proceedings ArticleDOI

Controlling a confound in predictive models with a test set minimizing its effect

TL;DR: This work introduces a non-sparametric approach to control for a confounding effect in a predictive model, based on crafting a test set on which the effect of interest is independent from the confounding effect, and demonstrates the approach with a large sample resting-state fMRI and psychometric data of healthy aging subjects.
Journal ArticleDOI

A Gaussian Process Model of Human Electrocorticographic Data.

TL;DR: The approach makes the simplifying assumptions that different people’s brains exhibit similar correlational structure, and that activity and correlation patterns vary smoothly over space, and provides a person- and task-general means of inferring high spatiotemporal resolution full-brain neural dynamics from standard low-density intracranial recordings.
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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