The DOI matches its Crossref record; 36 of the 37 references it cites were found in a registry or an index.
Elucidating a Magnetic Resonance Imaging-Based Neuroanatomic Biomarker for Psychosis: Classification Analysis Using Probabilistic Brain Atlas and Machine Learning Algorithms
Open accessAbstract
Background No objective diagnostic biomarkers or laboratory tests have yet been developed for psychotic illness. Magnetic resonance imaging (MRI) studies consistently find significant abnormalities in multiple brain structures in psychotic patients relative to healthy control subjects, but these abnormalities show substantial overlap with anatomic variation that is in the normal range and therefore nondiagnostic. Recently, efforts have been made to discriminate psychotic patients from healthy individuals using machine-learning-based pattern classification methods on MRI data.Methods Three-dimensional cortical gray matter density (GMD) maps were generated for 36 patients with recent-onset psychosis and 36 sex- and age-matched control subjects using a cortical pattern matching method. Between-group differences in GMD were evaluated. Second, the sparse multinomial logistic regression classifier included in the Multivariate Pattern Analysis in Python machine-learning package was applied to the cortical GMD maps to discriminate psychotic patients from control subjects.Results Patients showed significantly lower GMD, particularly in prefrontal, cingulate, and lateral temporal brain regions. Pattern classification analysis achieved 86.1% accuracy in discriminating patients from controls using leave-one-out cross-validation.Conclusions These results suggest that even at the early stage of illness, psychotic patients present distinct patterns of regional cortical gray matter changes that can be discriminated from the normal pattern. These findings indicate that we can detect complex patterns of brain abnormality in early stages of psychotic illness, which has critical implications for early identification and intervention in individuals at ultra-high risk for developing psychosis/schizophrenia.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“imaging studies have demonstrated relationships between cognitive deficits and structural or functional brain abnormalities.9–13”
From Clinical trials of potential cognitive-enhancing drugs in schizophrenia: what have we learned so far? (2013) · cites this paper as [10]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Zhang, Y., Brady, M., & Smith, S. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906424 · Cited by 7,628
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.668698 · Cited by 4,731
Distributed and overlapping representations of faces and objects in ventral temporal cortex
Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001) · Science · doi:10.1126/science.1063736 · Cited by 3,940
Structured Clinical Interview for DSM-IV Axis I Disorders
Spitzer, R. L., First, M. B., & Gibbon, M. (1997) · Medical Entomology and Zoology · Cited by 3,582
Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space
Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C. (1994) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199403000-00005 · Cited by 3,431
Regression Analysis by Example.
Kmenta, J., Chatterjee, S., & Price, B. P. (1980) · Journal of the American Statistical Association · doi:10.2307/2287213 · Cited by 3,223
A review of MRI findings in schizophrenia
Shenton, M. E., Dickey, C. C., Frumin, M., & McCarley, R. W. (2001) · Schizophrenia Research · doi:10.1016/s0920-9964(01)00163-3 · Cited by 2,346
Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration
Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G. E., Collins, D. L., Gee, J., Hellier, P., et al. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.037 · Cited by 2,227
Self-citationA Probabilistic Atlas of the Human Brain: Theory and Rationale for Its Development
Mazziotta, J. C., Toga, A. W., Evans, A., Fox, P. T., & Lancaster, J. L. (1995) · NeuroImage · doi:10.1006/nimg.1995.1012 · Cited by 1,578
Self-citationIdentifying natural images from human brain activity
Kay, K., Naselaris, T., Prenger, R., & Gallant, J. L. (2008) · Nature · doi:10.1038/nature06713 · Cited by 1,272
Detecting activations in PET and fMRI: levels of inference and power
Friston, K. J., Holmes, A., Poline, J.-B., Price, C. J., & Frith, C. D. (1996) · NeuroImage · doi:10.1006/nimg.1996.0074 · Cited by 1,148 · cited 1×
“This approach has been used in many prior studies and is comparable to an approach called set-level inference in functional brain imaging (28).”
MethodSparse multinomial logistic regression: fast algorithms and generalization bounds
Krishnapuram, B., Carin, L., Figueiredo, M. A. T., & Hartemink, A. J. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.127 · Cited by 883
Miller, T. J., McGlashan, T. H., Rosen, J. L., Somjee, L., Markovich, P. J., Stein, K., & Woods, S. W. (2002) · American Journal of Psychiatry · doi:10.1176/appi.ajp.159.5.863 · Cited by 872
Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI
MacDonald, D. J., Kabani, N. J., Avis, D., & Evans, A. C. (2000) · NeuroImage · doi:10.1006/nimg.1999.0534 · Cited by 850
Training and quality assurance with the structured clinical interview for DSM-iV (SCID-I/P)
Ventura, J., Liberman, R. P., Green, M. F., Shaner, A., & Mintz, J. (1998) · Psychiatry Research · doi:10.1016/s0165-1781(98)00038-9 · Cited by 591 · cited 3×
“Interrater reliability was calculated by comparing trainee ratings with gold standard ratings, as described in Ventura et al. (20); trainees are required to achieve a sensitivity kappa of .75 or greater and a specificity kappa of at least .75 regarding SCID-I/P symptoms, as well as 90% agreement on all diagnostic clas…”
BackgroundMethodPantelis, C. (2005) · Schizophrenia Bulletin · doi:10.1093/schbul/sbi034 · Cited by 573
Regression Analysis by Example
(2007) · Technometrics · doi:10.1198/tech.2007.s498 · Cited by 548
PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data
Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V., & Pollmann, S. (2009) · Neuroinformatics · doi:10.1007/s12021-008-9041-y · Cited by 482
Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia
Thompson, P. M., Hayashi, K. M., Sowell, E. R., Gogtay, N., Giedd, J. N., Rapoport, J. L., de Zubicaray, G. I., Janke, A. L., Rose, S., Semple, J. M., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.071 · Cited by 396
Self-citationFan, Y., Resnick, S. M., Wu, X., & Davatzikos, C. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.02.043 · Cited by 288
Davatzikos, C. A., Shen, D. G., Gur, R. C., Wu, X., Liu, D., Yong Fan, A., Hughett, P., Turetsky, B. I., & Gur, R. E. (2005) · Archives of General Psychiatry · doi:10.1001/archpsyc.62.11.1218 · Cited by 270
Cortical variability and asymmetry in normal aging and Alzheimer's disease
Thompson, P. M. (1998) · Cerebral Cortex · doi:10.1093/cercor/8.6.492 · Cited by 264
Self-citationKawasaki, Y., Suzuki, M., Kherif, F., Takahashi, T., Zhou, S.-Y., Nakamura, K., Matsui, M., Sumiyoshi, T., Seto, H., & Kurachi, M. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.08.018 · Cited by 180
Narr, K. L., Thompson, P. M., Sharma, T., Moussai, J., Zoumalan, C. I., Rayman, J., & Toga, A. W. (2001) · American Journal of Psychiatry · doi:10.1176/appi.ajp.158.2.244 · Cited by 148
Self-citationMotion Artifact in Magnetic Resonance Imaging: Implications for Automated Analysis
Blumenthal, J. D., Zijdenbos, A. P., Molloy, E. A., & Giedd, J. N. (2002) · NeuroImage · doi:10.1006/nimg.2002.1076 · Cited by 137
Generative and recognition models for neuroanatomy
Friston, K. J., & Ashburner, J. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.04.021 · Cited by 126
McGlashan, T. H., Miller, T. J., & Woods, S. W. (2001) · Schizophrenia Bulletin · doi:10.1093/oxfordjournals.schbul.a006896 · Cited by 124
The Psychosis Prodrome in Adolescent Patients Viewed Through the Lens of DSM-IV
MEYER, S. E., Bearden, C. E., Lux, S. R., Gordon, J. L., Johnson, J. K., O’Brien, M. P., Niendam, T. A., Loewy, R. L., Ventura, J., & Cannon, T. D. (2005) · Journal of Child and Adolescent Psychopharmacology · doi:10.1089/cap.2005.15.434 · Cited by 121
Self-citationYong Fan, A., Gur, R. E., Gur, R. C., Wu, X., Shen, D. G., Calkins, M. E., & Davatzikos, C. A. (2007) · Biological Psychiatry · doi:10.1016/j.biopsych.2007.03.015 · Cited by 119
PyMVPA: a unifying approach to the analysis of neuroscientific data
Hanke, M. (2009) · Frontiers in Neuroinformatics · doi:10.3389/neuro.11.003.2009 · Cited by 110
Yoon, U., Lee, J., Im, K., Shin, Y., Cho, B. H., Kim, I. Y., Kwon, J. S., & Kim, S. I. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.11.021 · Cited by 101
Caprihan, A., Pearlson, G. D., & Calhoun, V. D. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.04.255 · Cited by 83
fMRI pattern classification using neuroanatomically constrained boosting
Martínez‐Ramón, M., Koltchinskii, V. I., Heileman, G. L., & Posse, S. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.022 · Cited by 59
Ventricular shape biomarkers for Alzheimer's disease in clinical MR images
Ferrarini, L., Palm, W. M., Olofsen, H., van der Landen, R., van Buchem, M. A., Reiber, J. H. C., & Behloul, F. (2008) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21471 · Cited by 55
Automatic Subcortical Segmentation Using a Contextual Model
Morra, J. H., Tu, Z., Apostolova, L. G., Green, A. E., Toga, A. W., & Thompson, P. M. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85988-8_24 · Cited by 48
Self-citationA method to classify schizophrenia using inter-task spatial correlations of functional brain images
Michael, A. M., Calhoun, V. D., Andreasen, N. C., & Baum, S. A. (2008) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2008.4650462 · Cited by 17
Cited by
Sorted by citations
Orrù, G., Pettersson-Yeo, W., Marquand, A. F., Sartori, G., & Mechelli, A. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2012.01.004 · Cited by 1,012
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Arbabshirani, M. R., Plis, S., Sui, J., & Calhoun, V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.079 · Cited by 912
Wolfers, T., Buitelaar, J. K., Beckmann, C. F., Franke, B., & Marquand, A. F. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.08.001 · Cited by 290
Diagnostic neuroimaging across diseases
Klöppel, S., Abdulkadir, A., Jack, C. R., Koutsouleris, N., Mourão-Miranda, J., & Vemuri, P. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.002 · Cited by 279
Voxelwise genome-wide association study (vGWAS)
Stein, J. L., Hua, X., Lee, S., Ho, A. J., Leow, A., Toga, A. W., Saykin, A. J., Shen, L., Foroud, T., Pankratz, N., et al. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.02.032 · Cited by 276
Self-citation
Metadata
- Authors
- Sun, D., van Erp, T. G. M., Thompson, P. M., Bearden, C. E., Daley, M., Kushan, L., Hardt, M. E., Nuechterlein, K. H., Toga, A. W., & Cannon, T. D.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Cannon, Tyrone D.
- ORCID
- Venue
- Biological Psychiatry
- Publisher
- Elsevier BV
- Published
- 2009-12
- Volume
- 66
- Issue
- 11
- Pages
- 1055-1060
- ISSN
- 0006-3223 (ISSN-L)
- PMC
- PMC3192809
- OpenAlex
- W2039397313
- Cited by
- 148
- References
- 37
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 2 · 2022: 5 · 2021: 9 · 2020: 8 · 2019: 5 · 2018: 8 · 2017: 12 · 2016: 10 · 2015: 16 · 2014: 12 · 2013: 18 · 2012: 13
- FWCI
- 8.25
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Biological Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Public Health Service
- National Institute on Aging (R01AG020098, R56AG020098, AG020098)
- National Institute of Mental Health (MH037705, K23MH079028, R01MH065079, R01MH037705, P50MH066286, MH65079)
- National Institute of Biomedical Imaging and Bioengineering (R01EB008432, R01EB008281, R01EB007813, EB008281, EB 007813, EB008432)
- National Center for Research Resources (P41RR013642, U54RR021813)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (HD 050735, R01HD050735)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- psychosis, schizophrenia, probabilistic brain atlas, multivariate pattern analysis, sparse multinomial logistic regression, recent-onset psychosis, prefrontal cortex, cingulate cortex, lateral temporal cortex, clinical high risk for psychosis, early detection of psychosis
- MeSH terms
- Adult, Algorithms, Atlases as Topic, Brain Mapping, Cerebral Cortex, Female, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Predictive Value of Tests, Psychotic Disorders, Statistics as Topic, Biomarkers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
