Format
Sort by
Items per page

Send to

Choose Destination

Links from PubMed

Items: 1 to 20 of 100

1.

Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code.

Zhao X, Rangaprakash D, Denney TS Jr, Katz JS, Dretsch MN, Deshpande G.

Data Brief. 2018 Feb 2;22:570-573. doi: 10.1016/j.dib.2018.01.080. eCollection 2019 Feb.

2.

Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning.

Zhao X, Rangaprakash D, Yuan B, Denney TS Jr, Katz JS, Dretsch MN, Deshpande G.

Front Appl Math Stat. 2018 Sep;4. pii: 25. doi: 10.3389/fams.2018.00025. Epub 2018 Sep 25.

3.

Unsupervised classification of major depression using functional connectivity MRI.

Zeng LL, Shen H, Liu L, Hu D.

Hum Brain Mapp. 2014 Apr;35(4):1630-41. doi: 10.1002/hbm.22278. Epub 2013 Apr 24.

PMID:
23616377
4.

Clinical utility of resting-state functional connectivity magnetic resonance imaging for mood and cognitive disorders.

Takamura T, Hanakawa T.

J Neural Transm (Vienna). 2017 Jul;124(7):821-839. doi: 10.1007/s00702-017-1710-2. Epub 2017 Mar 23. Review.

PMID:
28337552
5.

Visualization and unsupervised predictive clustering of high-dimensional multimodal neuroimaging data.

Mwangi B, Soares JC, Hasan KM.

J Neurosci Methods. 2014 Oct 30;236:19-25. doi: 10.1016/j.jneumeth.2014.08.001. Epub 2014 Aug 10.

PMID:
25117552
6.

Functional connectivity of neural motor networks is disrupted in children with developmental coordination disorder and attention-deficit/hyperactivity disorder.

McLeod KR, Langevin LM, Goodyear BG, Dewey D.

Neuroimage Clin. 2014 Mar 26;4:566-75. doi: 10.1016/j.nicl.2014.03.010. eCollection 2014.

7.

Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory.

Khazaee A, Ebrahimzadeh A, Babajani-Feremi A.

Clin Neurophysiol. 2015 Nov;126(11):2132-41. doi: 10.1016/j.clinph.2015.02.060. Epub 2015 Apr 1.

PMID:
25907414
8.

Resting-State Connectivity Biomarkers of Cognitive Performance and Social Function in Individuals With Schizophrenia Spectrum Disorder and Healthy Control Subjects.

Viviano JD, Buchanan RW, Calarco N, Gold JM, Foussias G, Bhagwat N, Stefanik L, Hawco C, DeRosse P, Argyelan M, Turner J, Chavez S, Kochunov P, Kingsley P, Zhou X, Malhotra AK, Voineskos AN; Social Processes Initiative in Neurobiology of the Schizophrenia(s) Group.

Biol Psychiatry. 2018 Nov 1;84(9):665-674. doi: 10.1016/j.biopsych.2018.03.013. Epub 2018 Apr 13.

PMID:
29779671
9.

Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging.

Du Y, Fu Z, Calhoun VD.

Front Neurosci. 2018 Aug 6;12:525. doi: 10.3389/fnins.2018.00525. eCollection 2018. Review.

10.

Predicting and Grouping Digitized Paintings by Style using Unsupervised Feature Learning.

Gultepe E, Conturo TE, Makrehchi M.

J Cult Herit. 2018 May-Jun;31:13-23. doi: 10.1016/j.culher.2017.11.008. Epub 2017 Dec 20.

PMID:
30034259
11.

Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders.

Zhang J, Cheng W, Liu Z, Zhang K, Lei X, Yao Y, Becker B, Liu Y, Kendrick KM, Lu G, Feng J.

Brain. 2016 Aug;139(Pt 8):2307-21. doi: 10.1093/brain/aww143. Epub 2016 Jul 14.

PMID:
27421791
12.

Data-Driven Phenotypic Categorization for Neurobiological Analyses: Beyond DSM-5 Labels.

Van Dam NT, O'Connor D, Marcelle ET, Ho EJ, Cameron Craddock R, Tobe RH, Gabbay V, Hudziak JJ, Xavier Castellanos F, Leventhal BL, Milham MP.

Biol Psychiatry. 2017 Mar 15;81(6):484-494. doi: 10.1016/j.biopsych.2016.06.027. Epub 2016 Jul 19.

13.

Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method.

Guo X, Dominick KC, Minai AA, Li H, Erickson CA, Lu LJ.

Front Neurosci. 2017 Aug 21;11:460. doi: 10.3389/fnins.2017.00460. eCollection 2017.

14.

Insights into multimodal imaging classification of ADHD.

Colby JB, Rudie JD, Brown JA, Douglas PK, Cohen MS, Shehzad Z.

Front Syst Neurosci. 2012 Aug 16;6:59. doi: 10.3389/fnsys.2012.00059. eCollection 2012.

15.

A general prediction model for the detection of ADHD and Autism using structural and functional MRI.

Sen B, Borle NC, Greiner R, Brown MRG.

PLoS One. 2018 Apr 17;13(4):e0194856. doi: 10.1371/journal.pone.0194856. eCollection 2018.

16.

A New Approach to Investigate the Association between Brain Functional Connectivity and Disease Characteristics of Attention-Deficit/Hyperactivity Disorder: Topological Neuroimaging Data Analysis.

Kyeong S, Park S, Cheon KA, Kim JJ, Song DH, Kim E.

PLoS One. 2015 Sep 9;10(9):e0137296. doi: 10.1371/journal.pone.0137296. eCollection 2015.

17.

Perfusion magnetic resonance imaging in psychiatry.

Th├ęberge J.

Top Magn Reson Imaging. 2008 Apr;19(2):111-30. doi: 10.1097/RMR.0b013e3181808140. Review.

PMID:
19363433
18.

Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets.

Yoo K, Rosenberg MD, Hsu WT, Zhang S, Li CR, Scheinost D, Constable RT, Chun MM.

Neuroimage. 2018 Feb 15;167:11-22. doi: 10.1016/j.neuroimage.2017.11.010. Epub 2017 Nov 6.

PMID:
29122720
19.

Fusion of fMRI and non-imaging data for ADHD classification.

Riaz A, Asad M, Alonso E, Slabaugh G.

Comput Med Imaging Graph. 2018 Apr;65:115-128. doi: 10.1016/j.compmedimag.2017.10.002. Epub 2017 Oct 19.

PMID:
29137838
20.

Dissecting psychiatric spectrum disorders by generative embedding.

Brodersen KH, Deserno L, Schlagenhauf F, Lin Z, Penny WD, Buhmann JM, Stephan KE.

Neuroimage Clin. 2013 Nov 16;4:98-111. doi: 10.1016/j.nicl.2013.11.002. eCollection 2014. Review.

Supplemental Content

Support Center