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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

Resting-state Functional Connectivity and Deception: Exploring Individualized Deceptive Propensity by Machine Learning.

TL;DR: The potential of using RSFC as a task-independent neural trait for predicting deceptive propensity is suggested, and light is shed on using machine-learning approaches in deception detection.
Journal ArticleDOI

Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping

TL;DR: The BOLD perturbation model allows us to separate fMRI phase signal (by complex division) and to perform inverse mapping for pure BOLD δχ reconstruction for intrinsic functional χ mapping and to computationally separate dynamic brain functional BOLD responses from static background in a brain functional activity.
Journal ArticleDOI

Individual differences in (dis)honesty are represented in the brain's functional connectivity at rest.

TL;DR: In this article, the authors used connectome-based predictive modelling (CPM) on resting state functional connectivity patterns in combination with a novel task which inconspicuously measures voluntary cheating to gain access to the neurocognitive determinants of (dis)honesty.
Posted Content

Uncovering differential identifiability in network properties of human brain functional connectomes.

TL;DR: This work explores the differential identifiability profiles of network measures when 𝕀f is applied on the functional connectomes, and directly on derived network measurements, and finds that applying the framework, either way, increases task sensitivity of network properties.
Journal ArticleDOI

Brain parcellation driven by dynamic functional connectivity better capture intrinsic network dynamics.

TL;DR: The findings shed new light on the functional organization of resting brains at the timescale of seconds and emphasized the significance of a dFC‐driven and voxel‐wise functional homogeneous parcellation for network dynamics analyses in neuroscience.
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.
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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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