Resting-state functional connectivity in neuropsychiatric disorders

By Michael D. Greicius. Current Opinion in Neurology, 2008

1,155 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4c0e0753f856c01b38344bcd0aae416003cebeba

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Graph-Based Network Analysis of Resting-State Functional MRI (2010, neutral): “…trait variability and behavioral characteristics (Hampson et al., 2006; Fox et al., 2007; Hesselmann et al., 2008; Kelly et al., 2008; Di Martino et al., 2009; Yan et al., 2009a), as well as various clinical populations (for reviews, see Greicius, 2008; Broyd et al., 2009; Zhang and Raichle, 2010).”
Can structure predict function in the human brain? (2010, neutral): “The literature on changes in SC and FC in neuropsychiatric conditions is a complex and expanding field, and we refer the reader to reviews by Buckner et al. (2008), Greicius (2008) and Bassett and Bullmore (2009) for more complete coverage.”
The role of the posterior cingulate cortex in cognition and disease. (2014, background): “There is surprisingly little neuropsychological research about the cognitive consequences of focal lesions on the PCC. Strokes around the posteromedial cortex produce an amnestic syndrome, which may result in part from damage to both the retrosplenial cortex and PCC (Valenstein et al., 1987).”
Impact of Mindfulness-Based Stress Reduction Training on Intrinsic Brain Connectivity (2011, methodology): “Intrinsic connectivity fMRI has been used to characterize neural networks in healthy and patient populations (Buckner et al., 2008; Greicius, 2008), and it may be also particularly relevant for the study of changes in functional connectivity that are hypothesized to result from regular practice of m”
CLASSIFICATION OF SCHIZOPHRENIA AND BIPOLAR PATIENTS USING STATIC AND DYNAMIC RESTING-STATE FMRI BRAIN CONNECTIVITY (2016, background): “The majority of FNC studies are primarily based on the assumption that FNC is stationary throughout the entire scan session (or at least stationary during a given task or resting-state condition) (Camchong et al., 2011; Greicius, 2008; Meda et al., 2012; Sorg et al., 2013).”
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia (2015, neutral): “, 2002), including schizophrenia (SZ) (Greicius, 2008; Jafri et al., 2008; Liang et al., 2006; Liu et al., 2008; Mingoia et al., 2012; Yu et al., 2012; Zhou et al., 2007).”
The role of physiological noise in resting-state functional connectivity (2012, neutral): “Finally, low frequency fluctuations in the fMRI signal during rest have been linked to individual differences in behavior or pathology (Di Martino et al., 2009; Fox et al., 2007; Garrity et al., 2007; Greicius, 2008; Kennedy and Courchesne, 2008; Kim et al., 2010).”
Reliable Intrinsic Connectivity Networks: Test-Retest Evaluation Using ICA and Dual Regression Approach (2009, neutral): “, 2009), seed-based correlation analyses are commonly used to identify abnormalities in ICNs related to psychopathology (Greicius, 2008; Broyd et al., 2009), as well as relationships between RSFC measures and individual differences in behavioral (Kelly et al.”
Can brain state be manipulated to emphasize individual differences in functional connectivity? (2017, background): “Rest, the default condition for functional connectivity research, has undeniable benefits (Greicius, 2008; Smith et al., 2013).”
Measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS) (2012, background): “Leveraging these advantages, significant resting state fcMRI abnormalities have been identified across almost every major neurological and psychiatric disease (for reviews see Fox and Greicius, 2010; Greicius, 2008; Zhang and Raichle, 2010).”
Benchmarking functional connectome‐based predictive models for resting‐state fMRI (2019, neutral): “As it can easily be acquired in many different individuals, rest-fMRI is a promising candidate for markers of brain function (Biswal et al., 2010; Greicius, 2008).”
Health, USA Reviewed by: (2010, neutral): “The abnormal RSNs were in accordance with functional deficits typical of each disorder with evidence that the RSNs are selectively vulnerable to neuropathology, measured by focal atrophy (Greicius, 2008; Supekar et al., 2008; Seeley et al., 2009).”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, positive): “…Driesen, Skudlarski, Gore, & Constable, 2006) and clinical conditions (e.g., psychiatric and neurological disorders; Fox & Greicius, 2010; M. Greicius, 2008), and can be used to predict behavioral performance (e.g., M. D. Rosenberg et al., 2016) and individual identity (e.g., Finn et al.,…”
Multi-level bootstrap analysis of stable clusters in resting-state fMRI (2009, methodology): “Deriving stable individual counterparts to the group-level RSNs would be of particular interest in clinical applications, where the RSNs could serve of biomarkers for various diseases (Greicius, 2008).”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, background): “…Fransson, 2006; Sun et al., 2007), learning (Albert et al., 2009; Bassett et al., 2011; Lewis et al., 2009; Tambini et al., 2010), and large state transitions such as sleep (Horovitz et al., 2008, 2009), sedation (Greicius et al., 2008), and anesthesia (Boveroux et al., 2010; Peltier et al., 2005).”
Predicting brain-age from multimodal imaging data captures cognitive impairment (2016, positive): “…functional connectivity based on resting-state fMRI data (Craddock et al., 2013) also has the potential to provide clinically-relevant biomarkers (Craddock et al., 2009; Castellanos et al., 2013), as the data is easily acquired in a clinical setting (Greicius, 2008; Damoiseaux et al., 2012).”
Addiction related alteration in resting-state brain connectivity (2010, neutral): “A number of groups have begun to study the resting-state connectivity in a variety of neuropsychiatric disorders such as Alzheimer's disease, depression, and schizophrenia (Greicius, 2008; Liu et al., 2007; Wang et al., 2007; Zhou et al., 2008).”