Identifying functional network changing patterns in individuals at clinical high-risk for psychosis and patients with early illness schizophrenia: A group ICA study
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Vince D. Calhoun | Daniel H. Mathalon | Dongdong Lin | Jiayu Chen | Yuhui Du | Qingbao Yu | Jing Sui | V. Calhoun | D. Mathalon | Qingbao Yu | Jiayu Chen | Yuhui Du | D. Lin | J. Sui | B. Stuart | S. Fryer | R. Loewy | Susanna L. Fryer | Barbara Stuart | Rachel L. Loewy | Yuhui Du | Y. Du | Susanna Fryer
[1] Yuhui Du,et al. Identification of subject specific and functional consistent ROIs using semi-supervised learning , 2012, Medical Imaging.
[2] E. Bullmore,et al. Human brain networks in health and disease , 2009, Current opinion in neurology.
[3] Justin T. Baker,et al. Functional connectivity of left Heschl's gyrus in vulnerability to auditory hallucinations in schizophrenia , 2013, Schizophrenia Research.
[4] Hao He,et al. Artifact removal in the context of group ICA: A comparison of single‐subject and group approaches , 2016, Human brain mapping.
[5] V. Calhoun,et al. Aberrant "default mode" functional connectivity in schizophrenia. , 2007, The American journal of psychiatry.
[6] O. Tervonen,et al. The effect of model order selection in group PICA , 2010, Human brain mapping.
[7] Daniel H Mathalon,et al. Reduced Amplitude of Low-Frequency Brain Oscillations in the Psychosis Risk Syndrome and Early Illness Schizophrenia , 2016, Neuropsychopharmacology.
[8] Hao He,et al. Semi-supervised learning of brain functional networks , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).
[9] Jingyu Liu,et al. A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders , 2015, NeuroImage.
[10] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[11] V. Calhoun,et al. Interaction among subsystems within default mode network diminished in schizophrenia patients: A dynamic connectivity approach , 2016, Schizophrenia Research.
[12] Tianming Liu. A few thoughts on brain ROIs , 2011, Brain Imaging and Behavior.
[13] M. V. D. Heuvel,et al. Exploring the brain network: A review on resting-state fMRI functional connectivity , 2010, European Neuropsychopharmacology.
[14] Xi-Nian Zuo,et al. Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach , 2010, NeuroImage.
[15] Vince D. Calhoun,et al. Automatic Identification of Functional Clusters in fMRI Data Using Spatial Dependence , 2011, IEEE Transactions on Biomedical Engineering.
[16] O. Sporns. Contributions and challenges for network models in cognitive neuroscience , 2014, Nature Neuroscience.
[17] Nadim Joni Shah,et al. Increased neural response related to neutral faces in individuals at risk for psychosis , 2008, NeuroImage.
[18] V. Calhoun,et al. Functional Brain Networks in Schizophrenia: A Review , 2009, Front. Hum. Neurosci..
[19] J. Shimony,et al. Resting-State fMRI: A Review of Methods and Clinical Applications , 2013, American Journal of Neuroradiology.
[20] Tae Young Lee,et al. Altered Fronto-Temporal Functional Connectivity in Individuals at Ultra-High-Risk of Developing Psychosis , 2015, PloS one.
[21] Julia M. Sheffield,et al. Neuroscience and Biobehavioral Reviews Cognition and Resting-state Functional Connectivity in Schizophrenia , 2022 .
[22] Godfrey D Pearlson,et al. Prodromal assessment with the structured interview for prodromal syndromes and the scale of prodromal symptoms: predictive validity, interrater reliability, and training to reliability. , 2003, Schizophrenia bulletin.
[23] S. Woods,et al. Chlorpromazine equivalent doses for the newer atypical antipsychotics. , 2003, The Journal of clinical psychiatry.
[24] Yuhui Du,et al. Group information guided ICA for fMRI data analysis , 2013, NeuroImage.
[25] S. Bressler,et al. Large-scale brain networks in cognition: emerging methods and principles , 2010, Trends in Cognitive Sciences.
[26] M. Kempton,et al. Predicting Psychosis: Meta-analysis of Transition Outcomes in Individuals at High Clinical Risk , 2013 .
[27] M. P. van den Heuvel,et al. Normalized Cut Group Clustering of Resting-State fMRI Data , 2008, PloS one.
[28] Tyrone D. Cannon,et al. Validity of the prodromal risk syndrome for first psychosis: findings from the North American Prodrome Longitudinal Study. , 2009, Schizophrenia bulletin.
[29] Ann K. Shinn,et al. Default mode network abnormalities in bipolar disorder and schizophrenia , 2010, Psychiatry Research: Neuroimaging.
[30] Daniel C. Javitt,et al. Auditory dysfunction in schizophrenia: integrating clinical and basic features , 2015, Nature Reviews Neuroscience.
[31] Tyrone D. Cannon. How Schizophrenia Develops: Cognitive and Brain Mechanisms Underlying Onset of Psychosis , 2015, Trends in Cognitive Sciences.
[32] Ioana L. Coman,et al. Atypical functional connectivity in resting-state networks of individuals with 22q11.2 deletion syndrome: associations with neurocognitive and psychiatric functioning , 2016, Journal of Neurodevelopmental Disorders.
[33] Vince D. Calhoun,et al. Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI Data , 2017, Front. Neurosci..
[34] Stephen M Smith,et al. Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.
[35] Tülay Adali,et al. Comparison of multi‐subject ICA methods for analysis of fMRI data , 2010, Human brain mapping.
[36] Dongdong Lin,et al. Dynamic functional connectivity impairments in early schizophrenia and clinical high-risk for psychosis , 2017, NeuroImage.
[37] J. Gabrieli,et al. Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia , 2009, Proceedings of the National Academy of Sciences.
[38] Kaiming Li,et al. Review of methods for functional brain connectivity detection using fMRI , 2009, Comput. Medical Imaging Graph..
[39] Eswar Damaraju,et al. Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.
[40] Susanne Walitza,et al. Aberrant coupling within and across the default mode, task-positive, and salience network in subjects at risk for psychosis. , 2014, Schizophrenia bulletin.
[41] V. Calhoun,et al. Exploring the Psychosis Functional Connectome: Aberrant Intrinsic Networks in Schizophrenia and Bipolar Disorder , 2012, Front. Psychiatry.
[42] B. Biswal,et al. Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.
[43] J. Klosterkötter,et al. Diagnosing schizophrenia in the initial prodromal phase , 2000, Schizophrenia Research.
[44] Feng Liu,et al. Patients with first-episode, drug-naive schizophrenia and subjects at ultra-high risk of psychosis shared increased cerebellar-default mode network connectivity at rest , 2016, Scientific Reports.
[45] A. Mechelli,et al. Dysconnectivity in schizophrenia: Where are we now? , 2011, Neuroscience & Biobehavioral Reviews.
[46] S. Kay,et al. The positive and negative syndrome scale (PANSS) for schizophrenia. , 1987, Schizophrenia bulletin.
[47] Dongdong Lin,et al. Identifying dynamic functional connectivity biomarkers using GIG‐ICA: Application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder , 2017, Human brain mapping.
[48] Hao He,et al. Identifying brain dynamic network states via GIG-ICA: Application to schizophrenia, bipolar and schizoaffective disorders , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).
[49] Vince D. Calhoun,et al. Is Aberrant Functional Connectivity A Psychosis Endophenotype? A Resting State Functional Magnetic Resonance Imaging Study , 2013, Biological Psychiatry.
[50] J. Pekar,et al. A method for making group inferences from functional MRI data using independent component analysis , 2001, Human brain mapping.
[51] Paolo Fusar-Poli,et al. At risk for schizophrenic or affective psychoses? A meta-analysis of DSM/ICD diagnostic outcomes in individuals at high clinical risk. , 2013, Schizophrenia bulletin.
[52] J. Kwon,et al. Regional Brain Atrophy and Functional Disconnection in Broca’s Area in Individuals at Ultra-High Risk for Psychosis and Schizophrenia , 2012, PloS one.
[53] Thomas E. Nichols,et al. Non‐parametric combination and related permutation tests for neuroimaging , 2016, Human brain mapping.
[54] A. Yung,et al. Mapping the Onset of Psychosis: The Comprehensive Assessment of At-Risk Mental States , 2005 .
[55] V. Calhoun,et al. Multisubject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery , 2012, IEEE Reviews in Biomedical Engineering.
[56] M. Kubicki,et al. Review of functional and anatomical brain connectivity findings in schizophrenia , 2013, Current opinion in psychiatry.
[57] Tyrone D. Cannon,et al. Association of Thalamic Dysconnectivity and Conversion to Psychosis in Youth and Young Adults at Elevated Clinical Risk. , 2015, JAMA psychiatry.
[58] Aapo Hyvärinen,et al. Validating the independent components of neuroimaging time series via clustering and visualization , 2004, NeuroImage.
[59] Yuan Zhou,et al. Prefrontal cortex and the dysconnectivity hypothesis of schizophrenia , 2015, Neuroscience Bulletin.
[60] P. Roland,et al. Estimation of the Probabilities of 3D Clusters in Functional Brain Images , 1998, NeuroImage.
[61] M. Kendall. Statistical Methods for Research Workers , 1937, Nature.
[62] G. Venkatasubramanian,et al. Current perspectives on chlorpromazine equivalents: Comparing apples and oranges! , 2013, Indian journal of psychiatry.
[63] Vince D. Calhoun,et al. Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia , 2014, Proceedings of the National Academy of Sciences.
[64] Yuan Zhou,et al. Functional disintegration in paranoid schizophrenia using resting-state fMRI , 2007, Schizophrenia Research.
[65] Jungsu S. Oh,et al. Altered resting-state connectivity in subjects at ultra-high risk for psychosis: an fMRI study , 2010, Behavioral and Brain Functions.
[66] Tyrone D. Cannon,et al. Prediction of psychosis in youth at high clinical risk: a multisite longitudinal study in North America. , 2008, Archives of general psychiatry.