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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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Machine learning in resting-state fMRI analysis

TL;DR: A taxonomy of machine learning methods in resting-state functional Magnetic Resonance Imaging (rs-fMRI) data can be found in this paper, where the authors identify three major divisions of unsupervised learning methods with regard to their applications to rs-FMRI, based on whether they discover principal modes of variation across space, time or population.
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Which multiband factor should you choose for your resting-state fMRI study?

TL;DR: In this paper, the authors evaluate MB factors of 2, 3, 4, 6, 8, 9, and 12 with 2mm isotropic voxels, and additionally 2mm and 3.3mm single-band acquisitions, on a 32-channel head coil.
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Towards precise resting-state fMRI biomarkers in psychiatry: synthesizing developments in transdiagnostic research, dimensional models of psychopathology, and normative neurodevelopment

TL;DR: In this article, the authors discuss three avenues of research that are overcoming this limitation: (i) the adoption of transdiagnostic research designs, which involve studying and explicitly comparing multiple disorders from distinct diagnostic axes of psychiatry; (ii) dimensional models of psychopathology that map the full spectrum of symptomatology and that cut across traditional disorder boundaries; and (iii) modeling individuals' unique functional connectomes throughout development.
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Connectivity-based change point detection for large-size functional networks.

TL;DR: The proposed covariance-based change point detection method is not only efficient in detecting change points in large samples of large-size networks but also is less sensitive to the window size selection and provides the consequent identification of the changed edges.
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Reliability and Individual Specificity of EEG Microstate Characteristics.

TL;DR: Findings reveal that EEG microstate characteristics are reliably unique in single subjects and possess abundant inter-individual variability.
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.
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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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