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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

Connectome-based individualized prediction of loneliness.

TL;DR: The results revealed that individual loneliness could be predicted by within- and between-network connectivity of prefrontal, limbic and temporal systems, which are involved in cognitive control, emotional processing and social perceptions and communications, respectively.
Journal ArticleDOI

Activity in the fronto-parietal multiple-demand network is robustly associated with individual differences in working memory and fluid intelligence.

TL;DR: It is demonstrated that a core component of individual differences variance in executive abilities and fluid intelligence is selectively and robustly positively associated with the level of activity in the MD network, a result that aligns well with lesion studies.
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Clinical utility of resting-state functional connectivity magnetic resonance imaging for mood and cognitive disorders.

TL;DR: In this paper, the authors reviewed the basic methodology and analysis techniques relevant to clinical studies, and the clinical applications of the technique for examining neuropsychiatric disorders, focusing on mood disorders (major depressive disorder and bipolar disorder) and dementia (Alzheimer's disease and mild cognitive impairment).
Journal ArticleDOI

BrainPrint: EEG biometric identification based on analyzing brain connectivity graphs

TL;DR: The results demonstrate that the graph-based method proposed in this study is effective in improving the recognition rate and inter-state stability of EEG-based biometric identification systems.
Journal ArticleDOI

Individualized functional networks reconfigure with cognitive state.

TL;DR: It is demonstrated that the brain's functional networks are not spatially fixed, but that many nodes change their network membership as a function of cognitive state, and these reconfigurations are highly robust and reliable to the extent that they can be used to predict cognitive state with up to 97% accuracy.
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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