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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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Citations
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Journal ArticleDOI

Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI

TL;DR: The results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data.
Journal ArticleDOI

Precision Functional Mapping of Individual Human Brains

TL;DR: A novel MRI dataset containing 5 hr of RSFC data, 6 hour of task fMRI, multiple structural MRIs, and neuropsychological tests from each of ten adults generated ten high-fidelity, individual-specific functional connectomes, revealing several new types of spatial and organizational variability in brain networks.
Journal ArticleDOI

A neuromarker of sustained attention from whole-brain functional connectivity.

TL;DR: It is demonstrated that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention, and predicts a clinical measure of attention—symptoms of attention deficit hyperactivity disorder—from resting-state connectivity in an independent sample of children and adolescents.
Journal ArticleDOI

Using connectome-based predictive modeling to predict individual behavior from brain connectivity.

TL;DR: This protocol includes the following steps: feature selection, feature summarization, model building, and assessment of prediction significance, and it has been demonstrated that the CPM protocol performs as well as or better than many of the existing approaches in brain-behavior prediction.
Posted ContentDOI

Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI

TL;DR: The results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data.
References
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Journal ArticleDOI

The WU-Minn Human Connectome Project: An Overview

TL;DR: Progress made during the first half of the Human Connectome Project project in refining the methods for data acquisition and analysis provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings.
Journal ArticleDOI

The minimal preprocessing pipelines for the Human Connectome Project.

TL;DR: The minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space are described.
Journal ArticleDOI

Automatically Parcellating the Human Cerebral Cortex

TL;DR: A technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set is presented, comparable in accuracy to manual labeling.
Journal ArticleDOI

Functional network organization of the human brain

TL;DR: In this article, the authors studied functional brain organization in healthy adults using resting state functional connectivity MRI and proposed two novel brain wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships.
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