The future of FMRI connectivity

By Stephen M. Smith. NeuroImage, 2012

369 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Networks and Communications, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5895a70f83de90a14f9aa027ca4d21e251978bf2

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In Cognitive Neuroscience

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Advancing functional connectivity research from association to causation (2019, background): “This improves causal inferences in the case of confounders (Box 2 However, partial correlation does not improve causal inferences in the case of colliders 5,88 (Box 2 Figure C).”
Understanding DCM: Ten simple rules for the clinician (2013, neutral): “The coupling parameters (intrinsic or extrinsic) are estimated in the same manner, and models can be similarly compared with BMS. Importantly, given the additional complexity of the generative models employed, these DCMs can furnish estimates of much more subtle parameters than those estimated in…”
The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition (2016, background): “These results underscore the human brain’s ability to reconfigure network organization selectively and adaptively when confronted with changing cognitive demands to achieve an optimal balance between segregation and integration. mands (Smith, 2012; Buckner et al., 2013).”
Multimodal population brain imaging in the UK Biobank prospective epidemiological study (2016, neutral): “While this might not provide statistical certainty for a diagnosis or interventional recommendation, it should allow for single-patient imaging to be used in a similar way to current state-of-the-art patient-tuned genetic testing.”
Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples (2012, neutral): “All doi:10.1016/j.neuroimage.2012.06.026 a b s t r a c t a r t i c l e i n f o”
A symmetric multivariate leakage correction for MEG connectomes (2015, positive): “…ROIs are then treated as nodes in a network, and the relationships between them can be analysed leading to graphs of the functional connections between brain regions (connectomes), and comparisons of connection strengths between populations of interest Smith (2012), Woolrich and Stephan (2013).”
Structure and function of complex brain networks (2013, background): “Functional connectivity is generally derived from time series observations, and describes patterns of statistical dependence among neural elements.(12) Time series data may be derived with a variety of techniques, including electroencephalography (EEG), magnetoencephalography (MEG), and functional m”
The WU-Minn Human Connectome Project: An Overview (2013, background): “…entities; however, as alluded to above, neither should be regarded as an explicit, validated indicator of direct anatomical connectivity, although significant values in the partial correlation matrix will hopefully have a high probability of reflecting genuine connections (Smith, 2012).”
Influences on the Test–Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility (2017, background): “Given the promise of resting-state functional connectivity as a research and clinical tool (Smith 2012), characterizing its test– retest reliability is an active line of research and has resulted in estimates of test–retest reliability ranging from poor to good (Shehzad et al. 2009; Van Dijk et al.…”