Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns
Reads0
Chats0
TLDR
It is highlighted that the chronnectome captures inherent functional dynamics of individual brain networks and provides implications for individualized characterization of health and disease.Abstract:
The human brain is a large, interacting dynamic network, and its architecture of coupling among brain regions varies across time (termed the “chronnectome”). However, very little is known about whether and how the dynamic properties of the chronnectome can characterize individual uniqueness, such as identifying individuals as a “fingerprint” of the brain. Here, we employed multiband resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 105) and a sliding time-window dynamic network analysis approach to systematically examine individual time-varying properties of the chronnectome. We revealed stable and remarkable individual variability in three dynamic characteristics of brain connectivity (i.e., strength, stability, and variability), which was mainly distributed in three higher order cognitive systems (i.e., default mode, dorsal attention, and fronto-parietal) and in two primary systems (i.e., visual and sensorimotor). Intriguingly, the spatial patterns of these dynamic characteristics of brain connectivity could successfully identify individuals with high accuracy and could further significantly predict individual higher cognitive performance (e.g., fluid intelligence and executive function), which was primarily contributed by the higher order cognitive systems. Together, our findings highlight that the chronnectome captures inherent functional dynamics of individual brain networks and provides implications for individualized characterization of health and disease.read more
Citations
More filters
Journal ArticleDOI
Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises.
TL;DR: An overview of recent studies that utilize machine learning approaches to identify neuroimaging predictors over the past decade is provided and connectome-based predictive modeling, which has grown in popularity in recent years is highlighted.
Journal ArticleDOI
Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies.
Angus Ho Ching Fong,Kwangsun Yoo,Monica D. Rosenberg,Sheng Zhang,Chiang-Shan R. Li,Dustin Scheinost,R. Todd Constable,Marvin M. Chun +7 more
TL;DR: DFC predicts attention performance across individuals by considering temporal changes in network structure and combining DFC and static FC features numerically improves predictions over either model alone, but the improvement was not statistically significant.
Journal ArticleDOI
Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder.
Dongmei Zhi,Vince D. Calhoun,Luxian Lv,Xiaohong Ma,Qing Ke,Zening Fu,Yuhui Du,Yuhui Du,Yongfeng Yang,Xiao Yang,Miao Pan,Shile Qi,Rongtao Jiang,Qingbao Yu,Jing Sui +14 more
TL;DR: This study is the first attempt to investigate the dynamic functional abnormalities in MDD in a Chinese population using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions inMDD, which might underscore the impaired cognitive functions in this mental disorder.
Journal ArticleDOI
Static and dynamic connectomics differentiate between depressed patients with and without suicidal ideation.
TL;DR: It is revealed that combining static and dynamic connectomics could differentiate between SI and NSI, offering new insight into the physiopathological mechanisms underlying SI.
Journal ArticleDOI
Endless Fluctuations: Temporal Dynamics of the Amplitude of Low Frequency Fluctuations
Wei Liao,Huafu Chen,Jiao Li,Gong-Jun Ji,Guo-Rong Wu,Zhiliang Long,Qiang Xu,Xujun Duan,Qian Cui,Bharat B. Biswal +9 more
TL;DR: It is found that the heteromodal association cortex had the most variable dynamics while the limbic regions had the least, consistent with previous findings, and the temporal variability of dynamic ALFF depended on EEG power fluctuations.
References
More filters
Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Journal ArticleDOI
Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain
Nathalie Tzourio-Mazoyer,B. Landeau,D. Papathanassiou,Fabrice Crivello,Octave Etard,Nicolas Delcroix,Bernard Mazoyer,Marc Joliot +7 more
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
Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion
Jonathan D. Power,Kelly Anne Barnes,Abraham Z. Snyder,Bradley L. Schlaggar,Steven E. Petersen +4 more
TL;DR: The results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.
Journal ArticleDOI
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
B.T. Thomas Yeo,Fenna M. Krienen,Jorge Sepulcre,Jorge Sepulcre,Mert R. Sabuncu,Mert R. Sabuncu,Danial Lashkari,Marisa O. Hollinshead,Marisa O. Hollinshead,Joshua L. Roffman,Jordan W. Smoller,Lilla Zöllei,Jonathan R. Polimeni,Bruce Fischl,Bruce Fischl,Hesheng Liu,Randy L. Buckner +16 more
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.
Journal ArticleDOI
Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control
William W. Seeley,Vinod Menon,Alan F. Schatzberg,Jennifer Keller,Gary H. Glover,Heather A. Kenna,Allan L. Reiss,Michael D. Greicius +7 more
TL;DR: Two distinct networks typically coactivated during functional MRI tasks are identified, anchored by dorsal anterior cingulate and orbital frontoinsular cortices with robust connectivity to subcortical and limbic structures, and an “executive-control network” that links dorsolateral frontal and parietal neocortices.
Related Papers (5)
Dynamic functional connectivity: Promise, issues, and interpretations
R. Matthew Hutchison,Thilo Womelsdorf,Elena A. Allen,Elena A. Allen,Peter A. Bandettini,Vince D. Calhoun,Vince D. Calhoun,Maurizio Corbetta,Maurizio Corbetta,Stefania Della Penna,Jeff H. Duyn,Gary H. Glover,Javier Gonzalez-Castillo,Daniel A. Handwerker,Shella D. Keilholz,Vesa Kiviniemi,David A. Leopold,Francesco de Pasquale,Olaf Sporns,Martin Walter,Martin Walter,Catie Chang +21 more