NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders
[Display omitted] •Propose a new pipeline to link brain changes among different datasets, studies, and disorders.•Identify reproducible biomarkers in schizophrenia using independent data.•Find both common and unique brain impairments in schizophrenia and autism.•Reveal gradual changes from healthy c...
Saved in:
| Published in | NeuroImage clinical Vol. 28; p. 102375 |
|---|---|
| Main Authors | , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier Inc
01.01.2020
Elsevier |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | [Display omitted]
•Propose a new pipeline to link brain changes among different datasets, studies, and disorders.•Identify reproducible biomarkers in schizophrenia using independent data.•Find both common and unique brain impairments in schizophrenia and autism.•Reveal gradual changes from healthy controls to mild cognitive impairment to Alzheimer’s disease.•Obtain high classification accuracy (~90%) between bipolar disorder and major depressive disorder.
Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to which unique and similar changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. However, it is still an open question on replicating and translating findings across studies. Standardized approaches for capturing reproducible and comparable imaging markers are greatly needed. Here, we propose a pipeline based on the priori-driven independent component analysis, NeuroMark, which is capable of estimating brain functional network measures from functional magnetic resonance imaging (fMRI) data that can be used to link brain network abnormalities among different datasets, studies, and disorders. NeuroMark automatically estimates features adaptable to each individual subject and comparable across datasets/studies/disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer’s disease, bipolar disorder, and major depressive disorder) to evaluate validity of the proposed pipeline from different perspectives (replication of brain abnormalities, cross-study comparison, identification of subtle brain changes, and multi-disorder classification using identified biomarkers). Our results highlight that NeuroMark effectively identified replicated brain network abnormalities of schizophrenia across different datasets; revealed interesting neural clues on the overlap and specificity between autism and schizophrenia; demonstrated brain functional impairments present to varying degrees in mild cognitive impairments and Alzheimer's disease; and captured biomarkers that achieved good performance in classifying bipolar disorder and major depressive disorder. |
|---|---|
| AbstractList | •
Propose a new pipeline to link brain changes among different datasets, studies, and disorders.
•
Identify reproducible biomarkers in schizophrenia using independent data.
•
Find both common and unique brain impairments in schizophrenia and autism.
•
Reveal gradual changes from healthy controls to mild cognitive impairment to Alzheimer’s disease.
•
Obtain high classification accuracy (~90%) between bipolar disorder and major depressive disorder.
Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to which unique and similar changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. However, it is still an open question on replicating and translating findings across studies. Standardized approaches for capturing reproducible and comparable imaging markers are greatly needed. Here, we propose a pipeline based on the priori-driven independent component analysis, NeuroMark, which is capable of estimating brain functional network measures from functional magnetic resonance imaging (fMRI) data that can be used to link brain network abnormalities among different datasets, studies, and disorders. NeuroMark automatically estimates features adaptable to each individual subject and comparable across datasets/studies/disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer’s disease, bipolar disorder, and major depressive disorder) to evaluate validity of the proposed pipeline from different perspectives (replication of brain abnormalities, cross-study comparison, identification of subtle brain changes, and multi-disorder classification using identified biomarkers). Our results highlight that NeuroMark effectively identified replicated brain network abnormalities of schizophrenia across different datasets; revealed interesting neural clues on the overlap and specificity between autism and schizophrenia; demonstrated brain functional impairments present to varying degrees in mild cognitive impairments and Alzheimer's disease; and captured biomarkers that achieved good performance in classifying bipolar disorder and major depressive disorder. Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to which unique and similar changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. However, it is still an open question on replicating and translating findings across studies. Standardized approaches for capturing reproducible and comparable imaging markers are greatly needed. Here, we propose a pipeline based on the priori-driven independent component analysis, NeuroMark, which is capable of estimating brain functional network measures from functional magnetic resonance imaging (fMRI) data that can be used to link brain network abnormalities among different datasets, studies, and disorders. NeuroMark automatically estimates features adaptable to each individual subject and comparable across datasets/studies/disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer's disease, bipolar disorder, and major depressive disorder) to evaluate validity of the proposed pipeline from different perspectives (replication of brain abnormalities, cross-study comparison, identification of subtle brain changes, and multi-disorder classification using identified biomarkers). Our results highlight that NeuroMark effectively identified replicated brain network abnormalities of schizophrenia across different datasets; revealed interesting neural clues on the overlap and specificity between autism and schizophrenia; demonstrated brain functional impairments present to varying degrees in mild cognitive impairments and Alzheimer's disease; and captured biomarkers that achieved good performance in classifying bipolar disorder and major depressive disorder.Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to which unique and similar changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. However, it is still an open question on replicating and translating findings across studies. Standardized approaches for capturing reproducible and comparable imaging markers are greatly needed. Here, we propose a pipeline based on the priori-driven independent component analysis, NeuroMark, which is capable of estimating brain functional network measures from functional magnetic resonance imaging (fMRI) data that can be used to link brain network abnormalities among different datasets, studies, and disorders. NeuroMark automatically estimates features adaptable to each individual subject and comparable across datasets/studies/disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer's disease, bipolar disorder, and major depressive disorder) to evaluate validity of the proposed pipeline from different perspectives (replication of brain abnormalities, cross-study comparison, identification of subtle brain changes, and multi-disorder classification using identified biomarkers). Our results highlight that NeuroMark effectively identified replicated brain network abnormalities of schizophrenia across different datasets; revealed interesting neural clues on the overlap and specificity between autism and schizophrenia; demonstrated brain functional impairments present to varying degrees in mild cognitive impairments and Alzheimer's disease; and captured biomarkers that achieved good performance in classifying bipolar disorder and major depressive disorder. [Display omitted] •Propose a new pipeline to link brain changes among different datasets, studies, and disorders.•Identify reproducible biomarkers in schizophrenia using independent data.•Find both common and unique brain impairments in schizophrenia and autism.•Reveal gradual changes from healthy controls to mild cognitive impairment to Alzheimer’s disease.•Obtain high classification accuracy (~90%) between bipolar disorder and major depressive disorder. Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to which unique and similar changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. However, it is still an open question on replicating and translating findings across studies. Standardized approaches for capturing reproducible and comparable imaging markers are greatly needed. Here, we propose a pipeline based on the priori-driven independent component analysis, NeuroMark, which is capable of estimating brain functional network measures from functional magnetic resonance imaging (fMRI) data that can be used to link brain network abnormalities among different datasets, studies, and disorders. NeuroMark automatically estimates features adaptable to each individual subject and comparable across datasets/studies/disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer’s disease, bipolar disorder, and major depressive disorder) to evaluate validity of the proposed pipeline from different perspectives (replication of brain abnormalities, cross-study comparison, identification of subtle brain changes, and multi-disorder classification using identified biomarkers). Our results highlight that NeuroMark effectively identified replicated brain network abnormalities of schizophrenia across different datasets; revealed interesting neural clues on the overlap and specificity between autism and schizophrenia; demonstrated brain functional impairments present to varying degrees in mild cognitive impairments and Alzheimer's disease; and captured biomarkers that achieved good performance in classifying bipolar disorder and major depressive disorder. Graphical abstract Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to which unique and similar changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. However, it is still an open question on replicating and translating findings across studies. Standardized approaches for capturing reproducible and comparable imaging markers are greatly needed. Here, we propose a pipeline based on the priori-driven independent component analysis, NeuroMark, which is capable of estimating brain functional network measures from functional magnetic resonance imaging (fMRI) data that can be used to link brain network abnormalities among different datasets, studies, and disorders. NeuroMark automatically estimates features adaptable to each individual subject and comparable across datasets/studies/disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer's disease, bipolar disorder, and major depressive disorder) to evaluate validity of the proposed pipeline from different perspectives (replication of brain abnormalities, cross-study comparison, identification of subtle brain changes, and multi-disorder classification using identified biomarkers). Our results highlight that NeuroMark effectively identified replicated brain network abnormalities of schizophrenia across different datasets; revealed interesting neural clues on the overlap and specificity between autism and schizophrenia; demonstrated brain functional impairments present to varying degrees in mild cognitive impairments and Alzheimer's disease; and captured biomarkers that achieved good performance in classifying bipolar disorder and major depressive disorder. |
| ArticleNumber | 102375 |
| Author | Salman, Mustafa Lin, Dongdong Hong, L. Elliot Calhoun, Vince D. Fu, Zening Sui, Jing Rahaman, Md Abdur Xing, Ying Osuch, Elizabeth A. Kochunov, Peter Gao, Shuang Du, Yuhui Abrol, Anees Chen, Jiayu |
| Author_xml | – sequence: 1 givenname: Yuhui surname: Du fullname: Du, Yuhui email: duyuhui@sxu.edu.cn organization: School of Computer and Information Technology, Shanxi University, Taiyuan, China – sequence: 2 givenname: Zening surname: Fu fullname: Fu, Zening organization: Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA – sequence: 3 givenname: Jing surname: Sui fullname: Sui, Jing organization: Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA – sequence: 4 givenname: Shuang surname: Gao fullname: Gao, Shuang organization: Chinese Academy of Sciences (CAS) Centre for Excellence in Brain Science and Intelligence Technology, China – sequence: 5 givenname: Ying surname: Xing fullname: Xing, Ying organization: School of Computer and Information Technology, Shanxi University, Taiyuan, China – sequence: 6 givenname: Dongdong surname: Lin fullname: Lin, Dongdong organization: Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA – sequence: 7 givenname: Mustafa orcidid: 0000-0002-3545-3195 surname: Salman fullname: Salman, Mustafa organization: Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA – sequence: 8 givenname: Anees surname: Abrol fullname: Abrol, Anees organization: Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA – sequence: 9 givenname: Md Abdur surname: Rahaman fullname: Rahaman, Md Abdur organization: Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA – sequence: 10 givenname: Jiayu surname: Chen fullname: Chen, Jiayu organization: Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA – sequence: 11 givenname: L. Elliot surname: Hong fullname: Hong, L. Elliot organization: University of Maryland, Center for Brain Imaging Research, Baltimore, USA – sequence: 12 givenname: Peter surname: Kochunov fullname: Kochunov, Peter organization: University of Maryland, Center for Brain Imaging Research, Baltimore, USA – sequence: 13 givenname: Elizabeth A. surname: Osuch fullname: Osuch, Elizabeth A. organization: Lawson Health Research Institute, London Health Sciences Centre, London, Canada – sequence: 14 givenname: Vince D. surname: Calhoun fullname: Calhoun, Vince D. organization: Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32961402$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFkltr3DAQhU1JadI0f6APRY992VSSbdkOJRBCLwtJC708i5E0SrXxSo5kL-y_r9xNShJoahA2oznfsUbnZbHng8eieM3oMaNMvFsde6f7Y075XOBlUz8rDjhn5YLVLd-7971fHKW0ovlpKW2EeFHsl7wTrKL8oBi-4BTDJcTrE3LmCUxjWMOIhoDPy8Awug2S5fkZUZByeXAD9s4jGQNxBv3o7JZEHGIwk3aqR2Ivvy3JOgMxJhIsURGcJ8alEE0uvSqeW-gTHt2-D4ufHz_8OP-8uPj6KdtcLLTgzbiwSgvVlKqm0DCh27pV0LRlBaVW3Ahbd3kJpVldNhY5YIVUg22Uhq6xJS0Pi-WOawKs5BBd_qWtDODkn0KIVxLimEeI0lrRGNS05tBVorNtA8roknOwXV1XXWad7ljDpNZodD52hP4B9OGOd7_kVdjIpqYdbVkGvL0FxHAzYRrl2iWNfQ8ew5Qkr6q64m0lRG59c9_rr8ndleWGdtegY0gpopXajTC6MFu7XjIq54DIlZwDIueAyF1AspQ_kt7RnxS934kw39bGYZRJO_QajYuoxzxO97T89JFc5_w4Df01bjGtwhR9zoFkMnFJ5fc5tnNqOS0pZ3weyMm_Af9z_w1igv2V |
| CitedBy_id | crossref_primary_10_1002_hbm_26483 crossref_primary_10_1016_j_biopsych_2023_12_019 crossref_primary_10_1038_s41598_022_15539_2 crossref_primary_10_1093_schbul_sbac088 crossref_primary_10_1002_brb3_70427 crossref_primary_10_1007_s11042_023_17187_8 crossref_primary_10_52294_001c_118576 crossref_primary_10_3389_fpsyt_2022_804440 crossref_primary_10_1016_j_nicl_2023_103363 crossref_primary_10_1093_schbul_sbae142 crossref_primary_10_3389_fnins_2023_1078995 crossref_primary_10_3389_fnsys_2021_724805 crossref_primary_10_1016_j_schres_2024_06_045 crossref_primary_10_3389_fnins_2022_895637 crossref_primary_10_1109_MSP_2022_3163870 crossref_primary_10_1001_jamanetworkopen_2023_12810 crossref_primary_10_1002_hbm_26077 crossref_primary_10_1002_hbm_26472 crossref_primary_10_1002_hbm_26479 crossref_primary_10_1016_j_biopsych_2023_12_002 crossref_primary_10_1002_hbm_26234 crossref_primary_10_1002_dad2_12393 crossref_primary_10_1162_netn_a_00398 crossref_primary_10_1016_j_bpsc_2021_07_004 crossref_primary_10_1016_j_nicl_2020_102534 crossref_primary_10_1016_j_pscychresns_2022_111563 crossref_primary_10_1016_j_neuroimage_2021_118048 crossref_primary_10_3389_fnhum_2021_689488 crossref_primary_10_1089_brain_2020_0896 crossref_primary_10_1371_journal_pone_0282707 crossref_primary_10_1016_j_neuroimage_2024_120617 crossref_primary_10_1177_13872877241313056 crossref_primary_10_1093_cercor_bhac189 crossref_primary_10_3389_fninf_2023_1207721 crossref_primary_10_1002_hbm_26783 crossref_primary_10_1038_s44220_023_00151_8 crossref_primary_10_3389_fnimg_2023_1216494 crossref_primary_10_1016_j_brs_2023_12_005 crossref_primary_10_15212_RADSCI_2023_0008 crossref_primary_10_1007_s11682_024_00901_x crossref_primary_10_1109_TBME_2024_3432273 crossref_primary_10_1109_JBHI_2024_3352075 crossref_primary_10_1093_cercor_bhab423 crossref_primary_10_1093_cercor_bhac512 crossref_primary_10_1002_hbm_25205 crossref_primary_10_1097_j_pain_0000000000003152 crossref_primary_10_1016_j_media_2022_102430 crossref_primary_10_1162_imag_a_00187 crossref_primary_10_1016_j_biopsych_2023_09_017 crossref_primary_10_1016_j_dcn_2025_101523 crossref_primary_10_1016_j_neuroimage_2022_119013 crossref_primary_10_1093_braincomms_fcac270 crossref_primary_10_3389_fnimg_2022_971201 crossref_primary_10_1371_journal_pone_0295984 crossref_primary_10_1016_j_isci_2024_109319 crossref_primary_10_1002_mco2_70002 crossref_primary_10_1002_hbm_26251 crossref_primary_10_1186_s13195_022_01006_7 crossref_primary_10_1016_j_nicl_2023_103434 crossref_primary_10_1038_s41380_024_02683_6 crossref_primary_10_1016_j_neuroimage_2024_120839 crossref_primary_10_1089_brain_2023_0040 crossref_primary_10_1089_brain_2020_0950 crossref_primary_10_1371_journal_pone_0293053 crossref_primary_10_3389_fnimg_2023_1097523 crossref_primary_10_1111_adb_13395 crossref_primary_10_1016_j_neuroimage_2022_119250 crossref_primary_10_1002_hbm_26200 crossref_primary_10_1007_s12021_022_09604_4 crossref_primary_10_1016_j_neuroimage_2024_120674 crossref_primary_10_3389_fnins_2021_682110 crossref_primary_10_1016_j_biopsych_2024_01_006 crossref_primary_10_1162_netn_a_00247 crossref_primary_10_1038_s41537_025_00593_2 crossref_primary_10_1093_schbul_sbae069 crossref_primary_10_1097_WCO_0000000000001081 crossref_primary_10_1016_j_pscychresns_2023_111655 crossref_primary_10_3390_s24030814 crossref_primary_10_1016_j_compbiomed_2023_107005 crossref_primary_10_1177_02698811241257877 crossref_primary_10_1016_j_nicl_2024_103719 crossref_primary_10_1212_WNL_0000000000209800 crossref_primary_10_1002_cpe_6855 crossref_primary_10_1109_TBME_2024_3423703 crossref_primary_10_3389_fpsyt_2024_1165424 crossref_primary_10_1002_hbm_25862 crossref_primary_10_1109_JBHI_2022_3212479 crossref_primary_10_1089_brain_2021_0079 crossref_primary_10_1007_s12021_022_09617_z crossref_primary_10_1002_hbm_26273 crossref_primary_10_1007_s11682_024_00857_y crossref_primary_10_3389_fncir_2021_649417 crossref_primary_10_1093_psyrad_kkad026 crossref_primary_10_3389_fnagi_2023_1159054 crossref_primary_10_1016_j_jneumeth_2022_109537 crossref_primary_10_1080_01621459_2024_2370593 crossref_primary_10_1016_j_biopsych_2022_09_024 crossref_primary_10_1093_brain_awab452 crossref_primary_10_3390_s23063264 crossref_primary_10_1002_hbm_25890 crossref_primary_10_1093_cercor_bhaf024 crossref_primary_10_1007_s42979_024_03019_5 crossref_primary_10_1002_brb3_3554 crossref_primary_10_1016_j_ijpsycho_2024_112354 crossref_primary_10_3389_fnimg_2024_1390409 crossref_primary_10_1038_s41398_023_02312_w crossref_primary_10_1016_j_eclinm_2024_102876 crossref_primary_10_3389_fnins_2024_1467446 crossref_primary_10_1016_j_biopsych_2023_03_025 crossref_primary_10_2139_ssrn_3862492 crossref_primary_10_3389_fncir_2020_593263 crossref_primary_10_1002_hbm_26456 crossref_primary_10_1038_s44220_024_00377_0 crossref_primary_10_1007_s12021_021_09550_7 crossref_primary_10_3389_fneur_2024_1490763 crossref_primary_10_1073_pnas_2221533120 crossref_primary_10_3390_e26070545 crossref_primary_10_1007_s12264_025_01349_9 crossref_primary_10_1016_j_dcn_2024_101496 crossref_primary_10_1016_j_jneumeth_2025_110403 crossref_primary_10_1016_j_nicl_2022_103056 crossref_primary_10_1093_schbul_sbae110 crossref_primary_10_1186_s42649_021_00058_7 crossref_primary_10_1002_hbm_70005 crossref_primary_10_1016_j_tins_2024_05_011 crossref_primary_10_1371_journal_pone_0299753 crossref_primary_10_3389_fphar_2023_1102413 crossref_primary_10_1038_s42003_021_02592_2 crossref_primary_10_52294_001c_129695 |
| Cites_doi | 10.1176/appi.ajp.2010.09091379 10.1016/j.neuroimage.2017.09.035 10.1002/wps.20631 10.3389/fnins.2018.00525 10.1016/j.neuroimage.2018.06.003 10.1016/j.neuroimage.2016.04.051 10.1073/pnas.0809141106 10.1016/j.neuroimage.2007.10.012 10.1016/j.nicl.2017.11.002 10.1073/pnas.0905267106 10.1002/hbm.23086 10.1016/j.nicl.2019.101747 10.1038/srep05549 10.1016/j.tics.2010.04.004 10.1097/WCO.0b013e32833782d4 10.1016/j.trac.2016.04.021 10.1111/acps.12945 10.1093/brain/121.5.889 10.1002/hbm.20540 10.1016/j.neuroimage.2017.10.022 10.1038/nn.3818 10.1016/j.schres.2015.11.021 10.1038/s41591-020-0793-8 10.1016/j.neuroimage.2009.12.011 10.1016/j.neuroimage.2014.09.007 10.1109/RBME.2012.2211076 10.1016/j.pscychresns.2010.04.008 10.1093/schbul/sby008 10.1176/appi.ajp.2012.12010056 10.1002/hbm.23346 10.1093/schbul/sby046 10.1002/hbm.1048 10.1109/TAMD.2015.2440298 10.1002/hbm.20919 10.1016/S1053-8119(09)71511-3 10.1002/hbm.24591 10.1176/appi.ajp.2019.19050480 10.1007/s10548-017-0603-x 10.1093/cercor/bhs352 10.1073/pnas.0812686106 10.1016/j.neuroimage.2017.09.020 10.1117/12.911248 10.1016/j.nicl.2014.07.003 10.1016/j.nicl.2017.05.024 10.1038/sdata.2014.49 10.1162/neco.1995.7.6.1129 10.3389/fnins.2015.00254 10.1016/j.paid.2017.01.033 10.1093/schbul/sbu188 10.3389/fnhum.2014.00897 10.1109/MLSP.2014.6958889 10.1016/j.jad.2014.04.054 10.1016/j.schres.2017.12.008 10.1002/hbm.22058 10.1038/nn.4478 10.1093/oxfordjournals.schbul.a033321 10.1016/j.neuroimage.2012.11.008 10.1111/sltb.12471 10.1016/j.jneumeth.2017.08.007 10.1016/j.pscychresns.2015.03.003 10.1001/jamapsychiatry.2015.0101 10.1016/j.conb.2003.09.012 10.1093/brain/awaa025 10.1109/TBME.2011.2167149 10.1186/1741-7015-11-126 10.1002/hbm.24064 10.1001/jamapsychiatry.2015.0505 10.1016/j.neuroimage.2003.12.030 10.1016/j.neuron.2013.10.044 10.1186/1741-7015-11-127 10.1093/cercor/bht165 10.3389/fninf.2012.00009 10.3389/fnins.2017.00267 10.1038/nn.4499 10.1371/journal.pone.0117029 10.1016/j.jad.2012.04.013 10.1126/science.1194144 10.1016/j.nic.2017.06.012 10.1006/nimg.2001.0978 10.1093/brain/awn018 |
| ContentType | Journal Article |
| Copyright | 2020 The Author(s) The Author(s) Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved. 2020 The Author(s) 2020 |
| Copyright_xml | – notice: 2020 The Author(s) – notice: The Author(s) – notice: Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved. – notice: 2020 The Author(s) 2020 |
| CorporateAuthor | for the Alzheimer's Disease Neuroimaging Initiative Alzheimer's Disease Neuroimaging Initiative |
| CorporateAuthor_xml | – name: for the Alzheimer's Disease Neuroimaging Initiative – name: Alzheimer's Disease Neuroimaging Initiative |
| DBID | 6I. AAFTH AAYXX CITATION NPM 7X8 5PM DOA |
| DOI | 10.1016/j.nicl.2020.102375 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef PubMed MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 2213-1582 |
| EndPage | 102375 |
| ExternalDocumentID | oai_doaj_org_article_ff67dec052a9469f87abdc322af95549 PMC7509081 32961402 10_1016_j_nicl_2020_102375 S2213158220302126 1_s2_0_S2213158220302126 |
| Genre | Research Support, U.S. Gov't, Non-P.H.S Research Support, Non-U.S. Gov't Journal Article Research Support, N.I.H., Extramural |
| GrantInformation_xml | – fundername: NCRR NIH HHS grantid: P20 RR021938 – fundername: NIH HHS grantid: S10 OD023696 – fundername: CIHR – fundername: NIBIB NIH HHS grantid: R01 EB020407 – fundername: NIGMS NIH HHS grantid: P20 GM103472 – fundername: NIBIB NIH HHS grantid: R01 EB015611 – fundername: NIA NIH HHS grantid: U01 AG024904 |
| GroupedDBID | .1- .FO 0R~ 1P~ 457 53G 5VS AAEDT AAEDW AAIKJ AALRI AAXUO AAYWO ABMAC ACGFS ACVFH ADBBV ADCNI ADEZE ADRAZ ADVLN AEUPX AEXQZ AFJKZ AFPUW AFRHN AFTJW AGHFR AIGII AITUG AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ AOIJS APXCP BAWUL BCNDV DIK EBS EJD FDB GROUPED_DOAJ HYE HZ~ IPNFZ IXB KQ8 M41 M48 M~E O-L O9- OK1 RIG ROL RPM SSZ Z5R 0SF 6I. AACTN AAFTH AFCTW NCXOZ AAYXX CITATION NPM 7X8 5PM |
| ID | FETCH-LOGICAL-c627t-fbc6b73b50a716c858ba7834a3cb2d6f596f56bc1537fe2ae4e0caf7bca97f303 |
| IEDL.DBID | M48 |
| ISSN | 2213-1582 |
| IngestDate | Wed Aug 27 01:26:28 EDT 2025 Thu Aug 21 18:17:34 EDT 2025 Fri Jul 11 11:16:41 EDT 2025 Thu Jan 02 22:57:18 EST 2025 Tue Jul 01 01:09:46 EDT 2025 Thu Apr 24 23:10:26 EDT 2025 Tue Jul 25 21:07:00 EDT 2023 Sun Feb 23 10:19:27 EST 2025 Tue Aug 26 16:33:07 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | fMRI NeuroMark Brain disorders Reproducible and comparable biomarkers Independent component analysis |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c627t-fbc6b73b50a716c858ba7834a3cb2d6f596f56bc1537fe2ae4e0caf7bca97f303 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Data in Study 3 used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf Co-first authors: Yuhui Du and Zening Fu. |
| ORCID | 0000-0002-3545-3195 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1016/j.nicl.2020.102375 |
| PMID | 32961402 |
| PQID | 2445428466 |
| PQPubID | 23479 |
| PageCount | 1 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_ff67dec052a9469f87abdc322af95549 pubmedcentral_primary_oai_pubmedcentral_nih_gov_7509081 proquest_miscellaneous_2445428466 pubmed_primary_32961402 crossref_citationtrail_10_1016_j_nicl_2020_102375 crossref_primary_10_1016_j_nicl_2020_102375 elsevier_sciencedirect_doi_10_1016_j_nicl_2020_102375 elsevier_clinicalkeyesjournals_1_s2_0_S2213158220302126 elsevier_clinicalkey_doi_10_1016_j_nicl_2020_102375 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2020-01-01 |
| PublicationDateYYYYMMDD | 2020-01-01 |
| PublicationDate_xml | – month: 01 year: 2020 text: 2020-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Netherlands |
| PublicationPlace_xml | – name: Netherlands |
| PublicationTitle | NeuroImage clinical |
| PublicationTitleAlternate | Neuroimage Clin |
| PublicationYear | 2020 |
| Publisher | Elsevier Inc Elsevier |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier |
| References | Woodward, Karbasforoushan, Heckers (b0395) 2012; 169 Fusar‐Poli, Solmi, Brondino, Davies, Chae, Politi, Borgwardt, Lawrie, Parnas, McGuire (b0210) 2019; 18 Bell, Sejnowski (b0035) 1995; 7 Calhoun, Adali, Pearlson, Pekar (b0060) 2001; 14 Yang, Xu, Xu, Hoy, Handwerker, Chen, Northoff, Zuo, Bandettini (b0415) 2014; 4 Bergsland, Horakova, Dwyer, Uher, Vaneckova, Tyblova, Seidl, Krasensky, Havrdova, Zivadinov (b0040) 2018; 17 Du, Lin, Yu, Sui, Chen, Rachakonda, Adali, Calhoun (b0165) 2017; 11 Anticevic, Cole, Repovs, Murray, Brumbaugh, Winkler, Savic, Krystal, Pearlson, Glahn (b0020) 2014; 24 Cosgrove, Suppes (b0100) 2013; 11 Xu, Groth, Pearlson, Schretlen, Calhoun (b0410) 2009; 30 Chand, G.B., Dwyer, D.B., Erus, G., Sotiras, A., Varol, E., Srinivasan, D., Doshi, J., Pomponio, R., Pigoni, A., Dazzan, P., Kahn, R.S., Schnack, H.G., Zanetti, M.V., Meisenzahl, E., Busatto, G.F., Crespo-Facorro, B., Pantelis, C., Wood, S.J., Zhuo, C., Shinohara, R.T., Shou, H., Fan, Y., Gur, R.C., Gur, R.E., Satterthwaite, T.D., Koutsouleris, N., Wolf, D.H., Davatzikos, C., 2020. Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain. Schreiner, Klimes-Dougan, Cullen (b0350) 2019; 49 Bressler, Menon (b0045) 2010; 14 Jie, Zhu, Ma, Osuch, Wammes, Theberge, Li, Zhang, Jiang, Sui, Calhoun (b0250) 2015; 7 Wang, Hermens, Hickie, Lagopoulos (b0380) 2012; 142 Himberg, J., Hyvarinen, A., 2003. ICASSO: software for investigating the reliability of ICA estimates by clustering and visualization. In: 2003 IEEE XIII Workshop on Neural Networks for Signal Processing – Nnsp'03, pp. 259–268. Allen, E.A., Damaraju, E., Plis, S.M., Erhardt, E.B., Eichele, T., Calhoun, V.D., 2014. Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 24(3), 663–676. Du, Y., Li, H.M., Wu, H., Fan, Y., 2012. Identification of subject specific and functional consistent ROIs using semi-supervised learning. Proc. SPIE, Medical Imag. 2012: Imag. Proc. 8314. Damaraju, Allen, Belger, Ford, McEwen, Mathalon, Mueller, Pearlson, Potkin, Preda, Turner, Vaidya, van Erp, Calhoun (b0120) 2014; 5 Ford, Apputhurai, Meyer, Crewther (b0185) 2017; 110 Beckmann, Mackay, Filippini, Smith (b0030) 2009; 47 Du, Fryer, Fu, Lin, Sui, Chen, Damaraju, Mennigen, Stuart, Loewy, Mathalon, Calhoun (b0140) 2018; 180 Noble, Scheinost, Constable (b0300) 2019; 116157 Leonardi, Van De Ville (b0255) 2015; 104 McKeown, Hansen, Sejnowsk (b0290) 2003; 13 Cohen, Daw, Engelhardt, Hasson, Li, Niv, Norman, Pillow, Ramadge, Turk-Browne, Willke (b0095) 2017; 20 Damaraju, Allen, Belger, Ford, McEwen, Mathalon, Mueller, Pearlson, Potkin, Preda (b0115) 2014; 5 Salman, Du, Lin, Fu, Fedorov, Damaraju, Sui, Chen, Mayer, Rosse, Mathalon, Ford, Erp, Calhoun (b0345) 2019; 22 Öngür, Lundy, Greenhouse, Shinn, Menon, Cohen, Renshaw (b0305) 2010; 183 Minshew, Keller (b0295) 2010; 23 Woo, Chang, Lindquist, Wager (b0390) 2017; 20 Du, Pearlson, Yu, He, Lin, Sui, Wu, Calhoun (b0170) 2016; 170 Huster, Raud (b0235) 2018; 31 Marquand, Wolfers, Mennes, Buitelaar, Beckmann (b0280) 2016; 1 Whitfield-Gabrieli, Thermenos, Milanovic, Tsuang, Faraone, McCarley, Shenton, Green, Nieto-Castanon, LaViolette, Wojcik, Gabrieli, Seidman (b0385) 2009; 106 Poldrack, Gorgolewski (b0320) 2014; 17 Tzourio-Mazoyer, Landeau, Papathanassiou, Crivello, Etard, Delcroix, Mazoyer, Joliot (b0375) 2002; 15 Hommer, Swedo (b0220) 2015; 41 Xia, M., Womer, F.Y., Chang, M., Zhu, Y., Zhou, Q., Edmiston, E.K., Jiang, X., Wei, S., Duan, J., Xu, K., Tang, Y., He Y., Wang, F., 2019. Shared and distinct functional architectures of brain networks across psychiatric disorders. Schizophr. Bull. 45(2) 450–463. Favre, Baciu, Pichat, Bougerol, Polosan (b0180) 2014; 165 Fu, Tu, Di, Du, Pearlson, Turner, Biswal, Zhang, Calhoun (b0195) 2018; 180 Fu, Tu, Di, Du, Sui, Biswal, Zhang, de Lacy, Calhoun (b0200) 2018 Bailey, Luthert, Dean, Harding, Janota, Montgomery, Rutter, Lantos (b0025) 1998; 121 Du, Fan (b0150) 2013; 69 Rashid, Arbabshirani, Damaraju, Cetin, Miller, Pearlson, Calhoun (b0330) 2016; 134 Smith, Fox, Miller, Glahn, Fox, Mackay, Filippini, Watkins, Toro, Laird, Beckmann (b0365) 2009; 106 Peng, Liddle, Iwabuchi, Zhang, Wu, Liu, Jiang, Xu, Liddle, Palaniyappan, Fang (b0315) 2015; 232 Zang, Jiang, Lu, He, Tian (b0425) 2004; 22 Andreasen, Paradiso, O'Leary (b0015) 1998; 24 Buckner (b0050) 2013; 80 Yu, Du, Chen, He, Sui, Pearlson, Calhoun (b0420) 2017; 291 Demirci, Clark, Calhoun (b0130) 2008; 39 Sun, H., Lui, S., Yao, L., Deng, W., Xiao, Y., Zhang, W., Huang, X., Hu, J., Bi, F., Li, T., Sweeney, J.A., Gong, Q., 2015. Two patterns of white matter abnormalities in medication-naive patients with first-episode schizophrenia revealed by diffusion tensor imaging and cluster analysis. JAMA Psychiatry 72(7), 678–686. Xu, Calhoun, Worhunsky, Xiang, Li, Wall, Pearlson, Potenza (b0405) 2015; 10 Li, Zalesky, Yue, Howes, Yan, Liu, Fan, Whitaker, Xu, Rao, Li, Liu, Wang, Sun, Song, Li, Chen, Chen, Wang, Liu, Li, Yang, Guo, Wan, Lv, Lu, Yan, Song, Wang, Zhang, Wu, Ning, Du, Cheng, Xu, Xu, Zhang, Wang, Jiang, Liu (b0260) 2020; 26 Fu, Caprihan, Chen, Du, Adair, Sui, Rosenberg, Calhoun (b0190) 2019; 40 Honnorat, Dong, Meisenzahl-Lechner, Koutsouleris, Davatzikos (b0225) 2019; 214 Cerliani, Mennes, Thomas, Di Martino, Thioux, Keysers (b0075) 2015; 72 Cuadros-Rodriguez, Perez-Castano, Ruiz-Samblas (b0105) 2016; 80 Silva, R.F., Castro, E., Gupta, C.N., C. M., Arbabshirani, M., Potluru, V.K., Plis, S.M., Calhoun, V.D., 2014. The tenth annual MLSP competition: schizophrenia classification challenge. In: 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6. Hutchison, Womelsdorf, Gati, Everling, Menon (b0240) 2013; 34 Lin, Liu, Zheng, Liang, Calhoun (b0265) 2010; 31 Insel, Cuthbert, Garvey, Heinssen, Pine, Quinn, Sanislow, Wang (b0245) 2010; 167 Dwyer, D.B., Cabral, C., Kambeitz-Ilankovic, L., Sanfelici, R., Kambeitz, J., Calhoun, V.D., Falkai, P., Pantelis, C., Meisenzahl, E., Koutsouleris, N., 2018. Brain subtyping enhances the neuroanatomical discrimination of schizophrenia, Schizophr. Bull. 44(5) 1060–1069. Poline, Breeze, Ghosh, Gorgolewski, Halchenko, Hanke, Haselgrove, Helmer, Keator, Marcus, Poldrack, Schwartz, Ashburner, Kennedy (b0325) 2012; 6 Calhoun, Adali (b0055) 2012; 5 Rashid, Damaraju, Pearlson, Calhoun (b0340) 2014; 8 Chen, Ye, Jin, Zhu, Wang (b0090) 2019; 9 Du, Y., Fu, Z.N., Calhoun, V.D., 2018. Classification and prediction of brain disorders using functional connectivity: promising but challenging, Front. Neurosci. 12. Huster, Plis, Calhoun (b0230) 2015; 9 Chang, Glover (b0085) 2010; 50 Ma, Correa, Li, Eichele, Calhoun, Adali (b0275) 2011; 58 Abrol, Damaraju, Miller, Stephen, Claus, Mayer, Calhoun (b0005) 2017; 163 Dosenbach, Nardos, Cohen, Fair, Power, Church, Nelson, Wig, Vogel, Lessov-Schlaggar, Barnes, Dubis, Feczko, Coalson, Pruett, Barch, Petersen, Schlaggar (b0135) 2010; 329 Osuch, Gao, Wammes, Théberge, Williamson, Neufeld, Du, Sui, Calhoun (b0310) 2018; 138 Fu, Tu, Di, Du, Sui, Biswal, Zhang, de Lacy, Calhoun (b0205) 2019; 190 Sheline, Barch, Price, Rundle, Vaishnavi, Snyder, Mintun, Wang, Coalson, Raichle (b0355) 2009; 106 de Lacy, Doherty, King, Rachakonda, Calhoun (b0125) 2017; 15 Cuthbert, Insel (b0110) 2013; 11 Rashid, Blanken, Muetzel, Miller, Damaraju, Arbabshirani, Erhardt, Verhulst, van der Lugt, Jaddoe, Tiemeier, White, Calhoun (b0335) 2018; 39 Calhoun, de Lacy (b0065) 2017; 27 Du, Allen, He, Sui, Wu, Calhoun (b0145) 2016; 37 Marusak, Calhoun, Brown, Crespo, Sala‐Hamrick, Gotlib, Thomason (b0285) 2017; 38 Zuo, Anderson, Bellec, Birn, Biswal, Blautzik, Breitner, Buckner, Calhoun, Castellanos, Chen, Chen, Chen, Chen, Colcombe, Courtney, Craddock, Di Martino, Dong, Fu, Gong, Gorgolewski, Han, He, He, Ho, Holmes, Hou, Huckins, Jiang, Jiang, Kelley, Kelly, King, LaConte, Lainhart, Lei, Li, Li, Li, Lin, Liu, Liu, Liu, Liu, Lu, Lu, Luna, Luo, Lurie, Mao, Margulies, Mayer, Meindl, Meyerand, Nan, Nielsen, O'Connor, Paulsen, Prabhakaran, Qi, Qiu, Shao, Shehzad, Tang, Villringer, Wang, Wang, Wei, Wei, Weng, Wu, Xu, Yang, Yang, Zang, Zhang, Zhang, Zhang, Zhang, Zhao, Zhen, Zhou, Zhu, Milham (b0430) 2014; 1 Cao, Cannon (b0070) 2019; 176 Liu, Liang, Zhou, He, Hao, Song, Yu, Liu, Liu, Jiang (b0270) 2008; 131 Du (10.1016/j.nicl.2020.102375_b0165) 2017; 11 Calhoun (10.1016/j.nicl.2020.102375_b0065) 2017; 27 Damaraju (10.1016/j.nicl.2020.102375_b0115) 2014; 5 Marquand (10.1016/j.nicl.2020.102375_b0280) 2016; 1 10.1016/j.nicl.2020.102375_b0155 Yang (10.1016/j.nicl.2020.102375_b0415) 2014; 4 Cohen (10.1016/j.nicl.2020.102375_b0095) 2017; 20 Fu (10.1016/j.nicl.2020.102375_b0200) 2018 Hutchison (10.1016/j.nicl.2020.102375_b0240) 2013; 34 Liu (10.1016/j.nicl.2020.102375_b0270) 2008; 131 Favre (10.1016/j.nicl.2020.102375_b0180) 2014; 165 Yu (10.1016/j.nicl.2020.102375_b0420) 2017; 291 McKeown (10.1016/j.nicl.2020.102375_b0290) 2003; 13 Schreiner (10.1016/j.nicl.2020.102375_b0350) 2019; 49 Minshew (10.1016/j.nicl.2020.102375_b0295) 2010; 23 Du (10.1016/j.nicl.2020.102375_b0145) 2016; 37 Cuadros-Rodriguez (10.1016/j.nicl.2020.102375_b0105) 2016; 80 Du (10.1016/j.nicl.2020.102375_b0140) 2018; 180 Salman (10.1016/j.nicl.2020.102375_b0345) 2019; 22 Du (10.1016/j.nicl.2020.102375_b0170) 2016; 170 Bell (10.1016/j.nicl.2020.102375_b0035) 1995; 7 Jie (10.1016/j.nicl.2020.102375_b0250) 2015; 7 10.1016/j.nicl.2020.102375_b0400 Noble (10.1016/j.nicl.2020.102375_b0300) 2019; 116157 Zang (10.1016/j.nicl.2020.102375_b0425) 2004; 22 10.1016/j.nicl.2020.102375_b0360 Xu (10.1016/j.nicl.2020.102375_b0405) 2015; 10 Bressler (10.1016/j.nicl.2020.102375_b0045) 2010; 14 10.1016/j.nicl.2020.102375_b0160 Xu (10.1016/j.nicl.2020.102375_b0410) 2009; 30 10.1016/j.nicl.2020.102375_b0080 Osuch (10.1016/j.nicl.2020.102375_b0310) 2018; 138 Lin (10.1016/j.nicl.2020.102375_b0265) 2010; 31 Honnorat (10.1016/j.nicl.2020.102375_b0225) 2019; 214 Whitfield-Gabrieli (10.1016/j.nicl.2020.102375_b0385) 2009; 106 Leonardi (10.1016/j.nicl.2020.102375_b0255) 2015; 104 Woo (10.1016/j.nicl.2020.102375_b0390) 2017; 20 Tzourio-Mazoyer (10.1016/j.nicl.2020.102375_b0375) 2002; 15 Andreasen (10.1016/j.nicl.2020.102375_b0015) 1998; 24 Fu (10.1016/j.nicl.2020.102375_b0205) 2019; 190 Hommer (10.1016/j.nicl.2020.102375_b0220) 2015; 41 Smith (10.1016/j.nicl.2020.102375_b0365) 2009; 106 Peng (10.1016/j.nicl.2020.102375_b0315) 2015; 232 de Lacy (10.1016/j.nicl.2020.102375_b0125) 2017; 15 10.1016/j.nicl.2020.102375_b0215 Rashid (10.1016/j.nicl.2020.102375_b0335) 2018; 39 Wang (10.1016/j.nicl.2020.102375_b0380) 2012; 142 Insel (10.1016/j.nicl.2020.102375_b0245) 2010; 167 Anticevic (10.1016/j.nicl.2020.102375_b0020) 2014; 24 10.1016/j.nicl.2020.102375_b0010 Chang (10.1016/j.nicl.2020.102375_b0085) 2010; 50 10.1016/j.nicl.2020.102375_b0175 Rashid (10.1016/j.nicl.2020.102375_b0340) 2014; 8 Poline (10.1016/j.nicl.2020.102375_b0325) 2012; 6 10.1016/j.nicl.2020.102375_b0370 Cerliani (10.1016/j.nicl.2020.102375_b0075) 2015; 72 Buckner (10.1016/j.nicl.2020.102375_b0050) 2013; 80 Fusar‐Poli (10.1016/j.nicl.2020.102375_b0210) 2019; 18 Marusak (10.1016/j.nicl.2020.102375_b0285) 2017; 38 Ford (10.1016/j.nicl.2020.102375_b0185) 2017; 110 Dosenbach (10.1016/j.nicl.2020.102375_b0135) 2010; 329 Du (10.1016/j.nicl.2020.102375_b0150) 2013; 69 Fu (10.1016/j.nicl.2020.102375_b0190) 2019; 40 Bergsland (10.1016/j.nicl.2020.102375_b0040) 2018; 17 Chen (10.1016/j.nicl.2020.102375_b0090) 2019; 9 Sheline (10.1016/j.nicl.2020.102375_b0355) 2009; 106 Calhoun (10.1016/j.nicl.2020.102375_b0055) 2012; 5 Cosgrove (10.1016/j.nicl.2020.102375_b0100) 2013; 11 Ma (10.1016/j.nicl.2020.102375_b0275) 2011; 58 Fu (10.1016/j.nicl.2020.102375_b0195) 2018; 180 Beckmann (10.1016/j.nicl.2020.102375_b0030) 2009; 47 Demirci (10.1016/j.nicl.2020.102375_b0130) 2008; 39 Woodward (10.1016/j.nicl.2020.102375_b0395) 2012; 169 Bailey (10.1016/j.nicl.2020.102375_b0025) 1998; 121 Huster (10.1016/j.nicl.2020.102375_b0235) 2018; 31 Poldrack (10.1016/j.nicl.2020.102375_b0320) 2014; 17 Cao (10.1016/j.nicl.2020.102375_b0070) 2019; 176 Zuo (10.1016/j.nicl.2020.102375_b0430) 2014; 1 Rashid (10.1016/j.nicl.2020.102375_b0330) 2016; 134 Li (10.1016/j.nicl.2020.102375_b0260) 2020; 26 Calhoun (10.1016/j.nicl.2020.102375_b0060) 2001; 14 Damaraju (10.1016/j.nicl.2020.102375_b0120) 2014; 5 Huster (10.1016/j.nicl.2020.102375_b0230) 2015; 9 Öngür (10.1016/j.nicl.2020.102375_b0305) 2010; 183 Cuthbert (10.1016/j.nicl.2020.102375_b0110) 2013; 11 Abrol (10.1016/j.nicl.2020.102375_b0005) 2017; 163 |
| References_xml | – volume: 1 start-page: 433 year: 2016 end-page: 447 ident: b0280 article-title: Beyond lumping and splitting: a review of computational approaches for stratifying psychiatric disorders publication-title: Biol. Psychiatry Cogn. Neurosci. Neuroimag. – volume: 80 start-page: 807 year: 2013 end-page: 815 ident: b0050 article-title: The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging publication-title: Neuron – volume: 40 start-page: 3203 year: 2019 end-page: 3221 ident: b0190 article-title: Altered static and dynamic functional network connectivity in Alzheimer's disease and subcortical ischemic vascular disease: shared and specific brain connectivity abnormalities publication-title: Hum. Brain Mapp. – volume: 14 start-page: 140 year: 2001 end-page: 151 ident: b0060 article-title: A method for making group inferences from functional MRI data using independent component analysis publication-title: Hum. Brain Mapp. – volume: 121 start-page: 889 year: 1998 end-page: 905 ident: b0025 article-title: A clinicopathological study of autism publication-title: Brain – reference: Silva, R.F., Castro, E., Gupta, C.N., C. M., Arbabshirani, M., Potluru, V.K., Plis, S.M., Calhoun, V.D., 2014. The tenth annual MLSP competition: schizophrenia classification challenge. In: 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6. – volume: 10 start-page: e0117029 year: 2015 ident: b0405 article-title: Functional network overlap as revealed by fMRI using sICA and its potential relationships with functional heterogeneity, balanced excitation and inhibition, and sparseness of neuron activity publication-title: PLoS One – volume: 165 start-page: 182 year: 2014 end-page: 189 ident: b0180 article-title: fMRI evidence for abnormal resting-state functional connectivity in euthymic bipolar patients publication-title: J. Affect Disord. – volume: 18 start-page: 192 year: 2019 end-page: 207 ident: b0210 article-title: Transdiagnostic psychiatry: a systematic review publication-title: World Psychiatry – volume: 13 start-page: 620 year: 2003 end-page: 629 ident: b0290 article-title: Independent component analysis of functional MRI: what is signal and what is noise? publication-title: Curr. Opin. Neurobiol. – volume: 232 start-page: 250 year: 2015 end-page: 256 ident: b0315 article-title: Dissociated large-scale functional connectivity networks of the precuneus in medication-naive first-episode depression publication-title: Psychiatry Res – volume: 329 start-page: 1358 year: 2010 end-page: 1361 ident: b0135 article-title: Prediction of individual brain maturity using fMRI publication-title: Science – volume: 31 start-page: 1076 year: 2010 end-page: 1088 ident: b0265 article-title: Semiblind spatial ICA of fMRI using spatial constraints publication-title: Hum. Brain Mapp. – volume: 180 start-page: 619 year: 2018 end-page: 631 ident: b0195 article-title: Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: an application to schizophrenia publication-title: Neuroimage – volume: 5 start-page: 298 year: 2014 end-page: 308 ident: b0120 article-title: Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia publication-title: Neuroimage Clin. – volume: 69 start-page: 157 year: 2013 end-page: 197 ident: b0150 article-title: Group information guided ICA for fMRI data analysis publication-title: Neuroimage – volume: 6 start-page: 9 year: 2012 ident: b0325 article-title: Data sharing in neuroimaging research publication-title: Front. Neuroinform – volume: 110 start-page: 80 year: 2017 end-page: 84 ident: b0185 article-title: Confirmatory factor analysis of autism and schizophrenia spectrum traits publication-title: Personality Individual Diff. – volume: 58 start-page: 3406 year: 2011 end-page: 3417 ident: b0275 article-title: Automatic identification of functional clusters in FMRI data using spatial dependence publication-title: IEEE Trans. Biomed. Eng. – volume: 20 start-page: 365 year: 2017 end-page: 377 ident: b0390 article-title: Building better biomarkers: brain models in translational neuroimaging publication-title: Nat. Neurosci. – reference: Du, Y., Fu, Z.N., Calhoun, V.D., 2018. Classification and prediction of brain disorders using functional connectivity: promising but challenging, Front. Neurosci. 12. – volume: 138 start-page: 472 year: 2018 end-page: 482 ident: b0310 article-title: Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients publication-title: Acta Psychiatr. Scand. – reference: Dwyer, D.B., Cabral, C., Kambeitz-Ilankovic, L., Sanfelici, R., Kambeitz, J., Calhoun, V.D., Falkai, P., Pantelis, C., Meisenzahl, E., Koutsouleris, N., 2018. Brain subtyping enhances the neuroanatomical discrimination of schizophrenia, Schizophr. Bull. 44(5) 1060–1069. – reference: Himberg, J., Hyvarinen, A., 2003. ICASSO: software for investigating the reliability of ICA estimates by clustering and visualization. In: 2003 IEEE XIII Workshop on Neural Networks for Signal Processing – Nnsp'03, pp. 259–268. – volume: 26 start-page: 558 year: 2020 end-page: 565 ident: b0260 article-title: A neuroimaging biomarker for striatal dysfunction in schizophrenia publication-title: Nat. Med. – year: 2018 ident: b0200 article-title: Transient increased thalamic-sensory connectivity and decreased whole-brain dynamism in autism publication-title: NeuroImage – reference: Sun, H., Lui, S., Yao, L., Deng, W., Xiao, Y., Zhang, W., Huang, X., Hu, J., Bi, F., Li, T., Sweeney, J.A., Gong, Q., 2015. Two patterns of white matter abnormalities in medication-naive patients with first-episode schizophrenia revealed by diffusion tensor imaging and cluster analysis. JAMA Psychiatry 72(7), 678–686. – volume: 23 start-page: 124 year: 2010 end-page: 130 ident: b0295 article-title: The nature of brain dysfunction in autism: functional brain imaging studies publication-title: Curr. Opin. Neurol. – volume: 8 start-page: 897 year: 2014 ident: b0340 article-title: Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects publication-title: Front. Hum. Neurosci. – volume: 106 start-page: 13040 year: 2009 end-page: 13045 ident: b0365 article-title: Correspondence of the brain's functional architecture during activation and rest publication-title: Proc. Natl. Acad. Sci. – volume: 49 start-page: 899 year: 2019 end-page: 913 ident: b0350 article-title: Neural correlates of suicidality in adolescents with major depression: resting-state functional connectivity of the precuneus and posterior cingulate cortex publication-title: Suicide Life Threat Behav. – volume: 34 start-page: 2154 year: 2013 end-page: 2177 ident: b0240 article-title: Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques: Dynamic Functional Connectivity publication-title: Hum. Brain Mapp. – volume: 176 start-page: 498 year: 2019 end-page: 500 ident: b0070 article-title: Cerebellar dysfunction and schizophrenia: from “Cognitive Dysmetria” to a potential therapeutic target publication-title: Am. J. Psychiatry – volume: 4 start-page: 5549 year: 2014 ident: b0415 article-title: Brain network informed subject community detection in early-onset schizophrenia publication-title: Sci. Rep. – volume: 72 start-page: 767 year: 2015 ident: b0075 article-title: Increased functional connectivity between subcortical and cortical resting-state networks in autism spectrum disorder publication-title: JAMA Psychiatry – volume: 50 start-page: 81 year: 2010 end-page: 98 ident: b0085 article-title: Time–frequency dynamics of resting-state brain connectivity measured with fMRI publication-title: NeuroImage – volume: 17 start-page: 444 year: 2018 end-page: 451 ident: b0040 article-title: Gray matter atrophy patterns in multiple sclerosis: a 10-year source-based morphometry study publication-title: Neuroimage Clin. – volume: 37 start-page: 1005 year: 2016 end-page: 1025 ident: b0145 article-title: Artifact removal in the context of group ICA: a comparison of single-subject and group approaches: artifact removal in the context of group ICA publication-title: Hum. Brain Mapp. – volume: 291 start-page: 61 year: 2017 end-page: 68 ident: b0420 article-title: Comparing brain graphs in which nodes are regions of interest or independent components: a simulation study publication-title: J. Neurosci. Methods – reference: Xia, M., Womer, F.Y., Chang, M., Zhu, Y., Zhou, Q., Edmiston, E.K., Jiang, X., Wei, S., Duan, J., Xu, K., Tang, Y., He Y., Wang, F., 2019. Shared and distinct functional architectures of brain networks across psychiatric disorders. Schizophr. Bull. 45(2) 450–463. – volume: 116157 year: 2019 ident: b0300 article-title: A decade of test-retest reliability of functional connectivity: a systematic review and meta-analysis publication-title: Neuroimage – volume: 39 start-page: 3127 year: 2018 end-page: 3142 ident: b0335 article-title: Connectivity dynamics in typical development and its relationship to autistic traits and autism spectrum disorder publication-title: Hum. Brain. Mapp. – volume: 24 start-page: 203 year: 1998 end-page: 218 ident: b0015 article-title: Cognitive dysmetria“ as an integrative theory of schizophrenia: a dysfunction in cortical-subcortical-cerebellar circuitry? publication-title: Schizophr. Bull. – volume: 11 start-page: 127 year: 2013 ident: b0100 article-title: Informing DSM-5: biological boundaries between bipolar I disorder, schizoaffective disorder, and schizophrenia publication-title: BMC Med. – volume: 11 start-page: 267 year: 2017 ident: b0165 article-title: Comparison of IVA and GIG-ICA in brain functional network estimation using fMRI data publication-title: Front. Neurosci. – volume: 142 start-page: 6 year: 2012 end-page: 12 ident: b0380 article-title: A systematic review of resting-state functional-MRI studies in major depression publication-title: J. Affect. Disord. – volume: 7 start-page: 1129 year: 1995 end-page: 1159 ident: b0035 article-title: An information-maximization approach to blind separation and blind deconvolution publication-title: Neural Comput. – volume: 5 start-page: 298 year: 2014 end-page: 308 ident: b0115 article-title: Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia publication-title: NeuroImage: Clin. – volume: 9 start-page: 254 year: 2015 ident: b0230 article-title: Group-level component analyses of EEG: validation and evaluation publication-title: Front. Neurosci. – reference: Du, Y., Li, H.M., Wu, H., Fan, Y., 2012. Identification of subject specific and functional consistent ROIs using semi-supervised learning. Proc. SPIE, Medical Imag. 2012: Imag. Proc. 8314. – volume: 106 start-page: 1279 year: 2009 end-page: 1284 ident: b0385 article-title: Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia publication-title: Proc. Natl. Acad. Sci. – volume: 170 start-page: 55 year: 2016 end-page: 65 ident: b0170 article-title: Interaction among subsystems within default mode network diminished in schizophrenia patients: a dynamic connectivity approach publication-title: Schizophrenia Res. – volume: 9 year: 2019 ident: b0090 article-title: Association between thalamocortical functional connectivity abnormalities and cognitive deficits in schizophrenia publication-title: Sci. Rep. – volume: 20 start-page: 304 year: 2017 end-page: 313 ident: b0095 article-title: Computational approaches to fMRI analysis publication-title: Nat. Neurosci. – volume: 80 start-page: 612 year: 2016 end-page: 624 ident: b0105 article-title: Quality performance metrics in multivariate classification methods for qualitative analysis publication-title: Trac-Trends Anal. Chem. – volume: 15 start-page: 513 year: 2017 end-page: 524 ident: b0125 article-title: Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum publication-title: Neuroimage Clin. – volume: 11 start-page: 126 year: 2013 ident: b0110 article-title: Toward the future of psychiatric diagnosis: the seven pillars of RDoC publication-title: BMC Med. – reference: Allen, E.A., Damaraju, E., Plis, S.M., Erhardt, E.B., Eichele, T., Calhoun, V.D., 2014. Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 24(3), 663–676. – volume: 167 start-page: 748 year: 2010 end-page: 751 ident: b0245 article-title: Research Domain Criteria (RDoC): toward a new classification framework for research on mental disorders publication-title: AJP – reference: Chand, G.B., Dwyer, D.B., Erus, G., Sotiras, A., Varol, E., Srinivasan, D., Doshi, J., Pomponio, R., Pigoni, A., Dazzan, P., Kahn, R.S., Schnack, H.G., Zanetti, M.V., Meisenzahl, E., Busatto, G.F., Crespo-Facorro, B., Pantelis, C., Wood, S.J., Zhuo, C., Shinohara, R.T., Shou, H., Fan, Y., Gur, R.C., Gur, R.E., Satterthwaite, T.D., Koutsouleris, N., Wolf, D.H., Davatzikos, C., 2020. Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain. – volume: 22 start-page: 101747 year: 2019 ident: b0345 article-title: Group ICA for identifying biomarkers in Schizophrenia: 'Adaptive' networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression publication-title: Neuroimage Clin – volume: 15 start-page: 273 year: 2002 end-page: 289 ident: b0375 article-title: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain publication-title: NeuroImage – volume: 30 start-page: 711 year: 2009 end-page: 724 ident: b0410 article-title: Source-based morphometry: the use of independent component analysis to identify gray matter differences with application to schizophrenia publication-title: Hum. Brain Mapp. – volume: 134 start-page: 645 year: 2016 end-page: 657 ident: b0330 article-title: Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity publication-title: Neuroimage – volume: 163 start-page: 160 year: 2017 end-page: 176 ident: b0005 article-title: Replicability of time-varying connectivity patterns in large resting state fMRI samples publication-title: Neuroimage – volume: 180 start-page: 632 year: 2018 end-page: 645 ident: b0140 article-title: Dynamic functional connectivity impairments in early schizophrenia and clinical high-risk for psychosis publication-title: Neuroimage – volume: 41 start-page: 313 year: 2015 end-page: 314 ident: b0220 article-title: Schizophrenia and autism--related disorders publication-title: Schizophr. Bull. – volume: 104 start-page: 430 year: 2015 end-page: 436 ident: b0255 article-title: On spurious and real fluctuations of dynamic functional connectivity during rest publication-title: Neuroimage – volume: 7 start-page: 320 year: 2015 end-page: 331 ident: b0250 article-title: Discriminating bipolar disorder from major depression based on SVM-FoBa: efficient feature selection with multimodal brain imaging data publication-title: IEEE Trans. Auton. Mental Dev. – volume: 1 start-page: 140049 year: 2014 ident: b0430 article-title: An open science resource for establishing reliability and reproducibility in functional connectomics publication-title: Sci Data – volume: 27 start-page: 561 year: 2017 end-page: 579 ident: b0065 article-title: Ten key observations on the analysis of resting-state functional MR imaging data using independent component analysis publication-title: Neuroimaging Clin. North Am. – volume: 131 start-page: 945 year: 2008 end-page: 961 ident: b0270 article-title: Disrupted small-world networks in schizophrenia publication-title: Brain – volume: 17 start-page: 1510 year: 2014 end-page: 1517 ident: b0320 article-title: Making big data open: data sharing in neuroimaging publication-title: Nat. Neurosci. – volume: 106 start-page: 1942 year: 2009 end-page: 1947 ident: b0355 article-title: The default mode network and self-referential processes in depression publication-title: PNAS – volume: 24 start-page: 3116 year: 2014 end-page: 3130 ident: b0020 article-title: Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness publication-title: Cerebral Cortex – volume: 190 start-page: 191 year: 2019 end-page: 204 ident: b0205 article-title: Transient increased thalamic-sensory connectivity and decreased whole-brain dynamism in autism publication-title: Neuroimage – volume: 169 start-page: 1092 year: 2012 end-page: 1099 ident: b0395 article-title: Thalamocortical dysconnectivity in schizophrenia publication-title: AJP – volume: 47 start-page: S148 year: 2009 ident: b0030 article-title: Group comparison of resting-state FMRI data using multi-subject ICA and dual regression publication-title: Neuroimage – volume: 183 start-page: 59 year: 2010 end-page: 68 ident: b0305 article-title: Default mode network abnormalities in bipolar disorder and schizophrenia publication-title: Psychiatry Res. Neuroimag. – volume: 22 start-page: 394 year: 2004 end-page: 400 ident: b0425 article-title: Regional homogeneity approach to fMRI data analysis publication-title: NeuroImage – volume: 39 start-page: 1774 year: 2008 end-page: 1782 ident: b0130 article-title: A projection pursuit algorithm to classify individuals using fMRI data: application to schizophrenia publication-title: NeuroImage – volume: 214 start-page: 43 year: 2019 end-page: 50 ident: b0225 article-title: Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods publication-title: Schizophr. Res. – volume: 14 start-page: 277 year: 2010 end-page: 290 ident: b0045 article-title: Large-scale brain networks in cognition: emerging methods and principles publication-title: Trends Cogn. Sci. – volume: 38 start-page: 97 year: 2017 end-page: 108 ident: b0285 article-title: Dynamic functional connectivity of neurocognitive networks in children publication-title: Hum. Brain Mapp. – volume: 31 start-page: 3 year: 2018 end-page: 16 ident: b0235 article-title: A tutorial review on multi-subject decomposition of EEG publication-title: Brain Topogr. – volume: 5 start-page: 60 year: 2012 end-page: 73 ident: b0055 article-title: Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery publication-title: IEEE Rev. Biomed. Eng. – volume: 167 start-page: 748 issue: 7 year: 2010 ident: 10.1016/j.nicl.2020.102375_b0245 article-title: Research Domain Criteria (RDoC): toward a new classification framework for research on mental disorders publication-title: AJP doi: 10.1176/appi.ajp.2010.09091379 – volume: 180 start-page: 619 issue: Pt B year: 2018 ident: 10.1016/j.nicl.2020.102375_b0195 article-title: Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: an application to schizophrenia publication-title: Neuroimage doi: 10.1016/j.neuroimage.2017.09.035 – volume: 18 start-page: 192 issue: 2 year: 2019 ident: 10.1016/j.nicl.2020.102375_b0210 article-title: Transdiagnostic psychiatry: a systematic review publication-title: World Psychiatry doi: 10.1002/wps.20631 – ident: 10.1016/j.nicl.2020.102375_b0155 doi: 10.3389/fnins.2018.00525 – volume: 116157 year: 2019 ident: 10.1016/j.nicl.2020.102375_b0300 article-title: A decade of test-retest reliability of functional connectivity: a systematic review and meta-analysis publication-title: Neuroimage – volume: 190 start-page: 191 year: 2019 ident: 10.1016/j.nicl.2020.102375_b0205 article-title: Transient increased thalamic-sensory connectivity and decreased whole-brain dynamism in autism publication-title: Neuroimage doi: 10.1016/j.neuroimage.2018.06.003 – volume: 134 start-page: 645 year: 2016 ident: 10.1016/j.nicl.2020.102375_b0330 article-title: Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity publication-title: Neuroimage doi: 10.1016/j.neuroimage.2016.04.051 – volume: 106 start-page: 1279 issue: 4 year: 2009 ident: 10.1016/j.nicl.2020.102375_b0385 article-title: Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.0809141106 – volume: 39 start-page: 1774 issue: 4 year: 2008 ident: 10.1016/j.nicl.2020.102375_b0130 article-title: A projection pursuit algorithm to classify individuals using fMRI data: application to schizophrenia publication-title: NeuroImage doi: 10.1016/j.neuroimage.2007.10.012 – volume: 17 start-page: 444 year: 2018 ident: 10.1016/j.nicl.2020.102375_b0040 article-title: Gray matter atrophy patterns in multiple sclerosis: a 10-year source-based morphometry study publication-title: Neuroimage Clin. doi: 10.1016/j.nicl.2017.11.002 – volume: 106 start-page: 13040 issue: 31 year: 2009 ident: 10.1016/j.nicl.2020.102375_b0365 article-title: Correspondence of the brain's functional architecture during activation and rest publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.0905267106 – volume: 37 start-page: 1005 issue: 3 year: 2016 ident: 10.1016/j.nicl.2020.102375_b0145 article-title: Artifact removal in the context of group ICA: a comparison of single-subject and group approaches: artifact removal in the context of group ICA publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.23086 – volume: 22 start-page: 101747 year: 2019 ident: 10.1016/j.nicl.2020.102375_b0345 article-title: Group ICA for identifying biomarkers in Schizophrenia: 'Adaptive' networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression publication-title: Neuroimage Clin doi: 10.1016/j.nicl.2019.101747 – volume: 9 issue: 1 year: 2019 ident: 10.1016/j.nicl.2020.102375_b0090 article-title: Association between thalamocortical functional connectivity abnormalities and cognitive deficits in schizophrenia publication-title: Sci. Rep. – volume: 4 start-page: 5549 year: 2014 ident: 10.1016/j.nicl.2020.102375_b0415 article-title: Brain network informed subject community detection in early-onset schizophrenia publication-title: Sci. Rep. doi: 10.1038/srep05549 – volume: 14 start-page: 277 issue: 6 year: 2010 ident: 10.1016/j.nicl.2020.102375_b0045 article-title: Large-scale brain networks in cognition: emerging methods and principles publication-title: Trends Cogn. Sci. doi: 10.1016/j.tics.2010.04.004 – volume: 23 start-page: 124 issue: 2 year: 2010 ident: 10.1016/j.nicl.2020.102375_b0295 article-title: The nature of brain dysfunction in autism: functional brain imaging studies publication-title: Curr. Opin. Neurol. doi: 10.1097/WCO.0b013e32833782d4 – volume: 80 start-page: 612 year: 2016 ident: 10.1016/j.nicl.2020.102375_b0105 article-title: Quality performance metrics in multivariate classification methods for qualitative analysis publication-title: Trac-Trends Anal. Chem. doi: 10.1016/j.trac.2016.04.021 – volume: 1 start-page: 433 issue: 5 year: 2016 ident: 10.1016/j.nicl.2020.102375_b0280 article-title: Beyond lumping and splitting: a review of computational approaches for stratifying psychiatric disorders publication-title: Biol. Psychiatry Cogn. Neurosci. Neuroimag. – volume: 138 start-page: 472 issue: 5 year: 2018 ident: 10.1016/j.nicl.2020.102375_b0310 article-title: Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients publication-title: Acta Psychiatr. Scand. doi: 10.1111/acps.12945 – volume: 121 start-page: 889 issue: Pt 5 year: 1998 ident: 10.1016/j.nicl.2020.102375_b0025 article-title: A clinicopathological study of autism publication-title: Brain doi: 10.1093/brain/121.5.889 – volume: 30 start-page: 711 issue: 3 year: 2009 ident: 10.1016/j.nicl.2020.102375_b0410 article-title: Source-based morphometry: the use of independent component analysis to identify gray matter differences with application to schizophrenia publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.20540 – volume: 180 start-page: 632 issue: Pt B year: 2018 ident: 10.1016/j.nicl.2020.102375_b0140 article-title: Dynamic functional connectivity impairments in early schizophrenia and clinical high-risk for psychosis publication-title: Neuroimage doi: 10.1016/j.neuroimage.2017.10.022 – ident: 10.1016/j.nicl.2020.102375_b0215 – volume: 17 start-page: 1510 issue: 11 year: 2014 ident: 10.1016/j.nicl.2020.102375_b0320 article-title: Making big data open: data sharing in neuroimaging publication-title: Nat. Neurosci. doi: 10.1038/nn.3818 – volume: 170 start-page: 55 issue: 1 year: 2016 ident: 10.1016/j.nicl.2020.102375_b0170 article-title: Interaction among subsystems within default mode network diminished in schizophrenia patients: a dynamic connectivity approach publication-title: Schizophrenia Res. doi: 10.1016/j.schres.2015.11.021 – volume: 26 start-page: 558 issue: 4 year: 2020 ident: 10.1016/j.nicl.2020.102375_b0260 article-title: A neuroimaging biomarker for striatal dysfunction in schizophrenia publication-title: Nat. Med. doi: 10.1038/s41591-020-0793-8 – volume: 50 start-page: 81 issue: 1 year: 2010 ident: 10.1016/j.nicl.2020.102375_b0085 article-title: Time–frequency dynamics of resting-state brain connectivity measured with fMRI publication-title: NeuroImage doi: 10.1016/j.neuroimage.2009.12.011 – volume: 104 start-page: 430 year: 2015 ident: 10.1016/j.nicl.2020.102375_b0255 article-title: On spurious and real fluctuations of dynamic functional connectivity during rest publication-title: Neuroimage doi: 10.1016/j.neuroimage.2014.09.007 – volume: 5 start-page: 60 year: 2012 ident: 10.1016/j.nicl.2020.102375_b0055 article-title: Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery publication-title: IEEE Rev. Biomed. Eng. doi: 10.1109/RBME.2012.2211076 – volume: 183 start-page: 59 issue: 1 year: 2010 ident: 10.1016/j.nicl.2020.102375_b0305 article-title: Default mode network abnormalities in bipolar disorder and schizophrenia publication-title: Psychiatry Res. Neuroimag. doi: 10.1016/j.pscychresns.2010.04.008 – ident: 10.1016/j.nicl.2020.102375_b0175 doi: 10.1093/schbul/sby008 – volume: 169 start-page: 1092 issue: 10 year: 2012 ident: 10.1016/j.nicl.2020.102375_b0395 article-title: Thalamocortical dysconnectivity in schizophrenia publication-title: AJP doi: 10.1176/appi.ajp.2012.12010056 – volume: 38 start-page: 97 issue: 1 year: 2017 ident: 10.1016/j.nicl.2020.102375_b0285 article-title: Dynamic functional connectivity of neurocognitive networks in children publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.23346 – ident: 10.1016/j.nicl.2020.102375_b0400 doi: 10.1093/schbul/sby046 – volume: 14 start-page: 140 issue: 3 year: 2001 ident: 10.1016/j.nicl.2020.102375_b0060 article-title: A method for making group inferences from functional MRI data using independent component analysis publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.1048 – volume: 7 start-page: 320 issue: 4 year: 2015 ident: 10.1016/j.nicl.2020.102375_b0250 article-title: Discriminating bipolar disorder from major depression based on SVM-FoBa: efficient feature selection with multimodal brain imaging data publication-title: IEEE Trans. Auton. Mental Dev. doi: 10.1109/TAMD.2015.2440298 – volume: 31 start-page: 1076 issue: 7 year: 2010 ident: 10.1016/j.nicl.2020.102375_b0265 article-title: Semiblind spatial ICA of fMRI using spatial constraints publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.20919 – volume: 47 start-page: S148 issue: Supplement 1 year: 2009 ident: 10.1016/j.nicl.2020.102375_b0030 article-title: Group comparison of resting-state FMRI data using multi-subject ICA and dual regression publication-title: Neuroimage doi: 10.1016/S1053-8119(09)71511-3 – volume: 40 start-page: 3203 issue: 11 year: 2019 ident: 10.1016/j.nicl.2020.102375_b0190 article-title: Altered static and dynamic functional network connectivity in Alzheimer's disease and subcortical ischemic vascular disease: shared and specific brain connectivity abnormalities publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.24591 – volume: 176 start-page: 498 issue: 7 year: 2019 ident: 10.1016/j.nicl.2020.102375_b0070 article-title: Cerebellar dysfunction and schizophrenia: from “Cognitive Dysmetria” to a potential therapeutic target publication-title: Am. J. Psychiatry doi: 10.1176/appi.ajp.2019.19050480 – volume: 31 start-page: 3 issue: 1 year: 2018 ident: 10.1016/j.nicl.2020.102375_b0235 article-title: A tutorial review on multi-subject decomposition of EEG publication-title: Brain Topogr. doi: 10.1007/s10548-017-0603-x – ident: 10.1016/j.nicl.2020.102375_b0010 doi: 10.1093/cercor/bhs352 – volume: 106 start-page: 1942 issue: 6 year: 2009 ident: 10.1016/j.nicl.2020.102375_b0355 article-title: The default mode network and self-referential processes in depression publication-title: PNAS doi: 10.1073/pnas.0812686106 – volume: 163 start-page: 160 year: 2017 ident: 10.1016/j.nicl.2020.102375_b0005 article-title: Replicability of time-varying connectivity patterns in large resting state fMRI samples publication-title: Neuroimage doi: 10.1016/j.neuroimage.2017.09.020 – ident: 10.1016/j.nicl.2020.102375_b0160 doi: 10.1117/12.911248 – volume: 5 start-page: 298 year: 2014 ident: 10.1016/j.nicl.2020.102375_b0120 article-title: Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia publication-title: Neuroimage Clin. doi: 10.1016/j.nicl.2014.07.003 – volume: 15 start-page: 513 year: 2017 ident: 10.1016/j.nicl.2020.102375_b0125 article-title: Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum publication-title: Neuroimage Clin. doi: 10.1016/j.nicl.2017.05.024 – year: 2018 ident: 10.1016/j.nicl.2020.102375_b0200 article-title: Transient increased thalamic-sensory connectivity and decreased whole-brain dynamism in autism publication-title: NeuroImage – volume: 1 start-page: 140049 year: 2014 ident: 10.1016/j.nicl.2020.102375_b0430 article-title: An open science resource for establishing reliability and reproducibility in functional connectomics publication-title: Sci Data doi: 10.1038/sdata.2014.49 – volume: 7 start-page: 1129 issue: 6 year: 1995 ident: 10.1016/j.nicl.2020.102375_b0035 article-title: An information-maximization approach to blind separation and blind deconvolution publication-title: Neural Comput. doi: 10.1162/neco.1995.7.6.1129 – volume: 9 start-page: 254 year: 2015 ident: 10.1016/j.nicl.2020.102375_b0230 article-title: Group-level component analyses of EEG: validation and evaluation publication-title: Front. Neurosci. doi: 10.3389/fnins.2015.00254 – volume: 110 start-page: 80 year: 2017 ident: 10.1016/j.nicl.2020.102375_b0185 article-title: Confirmatory factor analysis of autism and schizophrenia spectrum traits publication-title: Personality Individual Diff. doi: 10.1016/j.paid.2017.01.033 – volume: 41 start-page: 313 issue: 2 year: 2015 ident: 10.1016/j.nicl.2020.102375_b0220 article-title: Schizophrenia and autism--related disorders publication-title: Schizophr. Bull. doi: 10.1093/schbul/sbu188 – volume: 8 start-page: 897 year: 2014 ident: 10.1016/j.nicl.2020.102375_b0340 article-title: Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects publication-title: Front. Hum. Neurosci. doi: 10.3389/fnhum.2014.00897 – ident: 10.1016/j.nicl.2020.102375_b0360 doi: 10.1109/MLSP.2014.6958889 – volume: 165 start-page: 182 year: 2014 ident: 10.1016/j.nicl.2020.102375_b0180 article-title: fMRI evidence for abnormal resting-state functional connectivity in euthymic bipolar patients publication-title: J. Affect Disord. doi: 10.1016/j.jad.2014.04.054 – volume: 214 start-page: 43 year: 2019 ident: 10.1016/j.nicl.2020.102375_b0225 article-title: Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods publication-title: Schizophr. Res. doi: 10.1016/j.schres.2017.12.008 – volume: 34 start-page: 2154 issue: 9 year: 2013 ident: 10.1016/j.nicl.2020.102375_b0240 article-title: Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques: Dynamic Functional Connectivity publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.22058 – volume: 20 start-page: 365 issue: 3 year: 2017 ident: 10.1016/j.nicl.2020.102375_b0390 article-title: Building better biomarkers: brain models in translational neuroimaging publication-title: Nat. Neurosci. doi: 10.1038/nn.4478 – volume: 24 start-page: 203 issue: 2 year: 1998 ident: 10.1016/j.nicl.2020.102375_b0015 article-title: Cognitive dysmetria“ as an integrative theory of schizophrenia: a dysfunction in cortical-subcortical-cerebellar circuitry? publication-title: Schizophr. Bull. doi: 10.1093/oxfordjournals.schbul.a033321 – volume: 69 start-page: 157 year: 2013 ident: 10.1016/j.nicl.2020.102375_b0150 article-title: Group information guided ICA for fMRI data analysis publication-title: Neuroimage doi: 10.1016/j.neuroimage.2012.11.008 – volume: 49 start-page: 899 issue: 3 year: 2019 ident: 10.1016/j.nicl.2020.102375_b0350 article-title: Neural correlates of suicidality in adolescents with major depression: resting-state functional connectivity of the precuneus and posterior cingulate cortex publication-title: Suicide Life Threat Behav. doi: 10.1111/sltb.12471 – volume: 291 start-page: 61 year: 2017 ident: 10.1016/j.nicl.2020.102375_b0420 article-title: Comparing brain graphs in which nodes are regions of interest or independent components: a simulation study publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2017.08.007 – volume: 232 start-page: 250 issue: 3 year: 2015 ident: 10.1016/j.nicl.2020.102375_b0315 article-title: Dissociated large-scale functional connectivity networks of the precuneus in medication-naive first-episode depression publication-title: Psychiatry Res doi: 10.1016/j.pscychresns.2015.03.003 – volume: 72 start-page: 767 issue: 8 year: 2015 ident: 10.1016/j.nicl.2020.102375_b0075 article-title: Increased functional connectivity between subcortical and cortical resting-state networks in autism spectrum disorder publication-title: JAMA Psychiatry doi: 10.1001/jamapsychiatry.2015.0101 – volume: 13 start-page: 620 issue: 5 year: 2003 ident: 10.1016/j.nicl.2020.102375_b0290 article-title: Independent component analysis of functional MRI: what is signal and what is noise? publication-title: Curr. Opin. Neurobiol. doi: 10.1016/j.conb.2003.09.012 – ident: 10.1016/j.nicl.2020.102375_b0080 doi: 10.1093/brain/awaa025 – volume: 58 start-page: 3406 issue: 12 year: 2011 ident: 10.1016/j.nicl.2020.102375_b0275 article-title: Automatic identification of functional clusters in FMRI data using spatial dependence publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2011.2167149 – volume: 11 start-page: 126 year: 2013 ident: 10.1016/j.nicl.2020.102375_b0110 article-title: Toward the future of psychiatric diagnosis: the seven pillars of RDoC publication-title: BMC Med. doi: 10.1186/1741-7015-11-126 – volume: 39 start-page: 3127 issue: 8 year: 2018 ident: 10.1016/j.nicl.2020.102375_b0335 article-title: Connectivity dynamics in typical development and its relationship to autistic traits and autism spectrum disorder publication-title: Hum. Brain. Mapp. doi: 10.1002/hbm.24064 – ident: 10.1016/j.nicl.2020.102375_b0370 doi: 10.1001/jamapsychiatry.2015.0505 – volume: 22 start-page: 394 issue: 1 year: 2004 ident: 10.1016/j.nicl.2020.102375_b0425 article-title: Regional homogeneity approach to fMRI data analysis publication-title: NeuroImage doi: 10.1016/j.neuroimage.2003.12.030 – volume: 80 start-page: 807 issue: 3 year: 2013 ident: 10.1016/j.nicl.2020.102375_b0050 article-title: The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging publication-title: Neuron doi: 10.1016/j.neuron.2013.10.044 – volume: 11 start-page: 127 year: 2013 ident: 10.1016/j.nicl.2020.102375_b0100 article-title: Informing DSM-5: biological boundaries between bipolar I disorder, schizoaffective disorder, and schizophrenia publication-title: BMC Med. doi: 10.1186/1741-7015-11-127 – volume: 24 start-page: 3116 issue: 12 year: 2014 ident: 10.1016/j.nicl.2020.102375_b0020 article-title: Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness publication-title: Cerebral Cortex doi: 10.1093/cercor/bht165 – volume: 6 start-page: 9 year: 2012 ident: 10.1016/j.nicl.2020.102375_b0325 article-title: Data sharing in neuroimaging research publication-title: Front. Neuroinform doi: 10.3389/fninf.2012.00009 – volume: 11 start-page: 267 year: 2017 ident: 10.1016/j.nicl.2020.102375_b0165 article-title: Comparison of IVA and GIG-ICA in brain functional network estimation using fMRI data publication-title: Front. Neurosci. doi: 10.3389/fnins.2017.00267 – volume: 20 start-page: 304 issue: 3 year: 2017 ident: 10.1016/j.nicl.2020.102375_b0095 article-title: Computational approaches to fMRI analysis publication-title: Nat. Neurosci. doi: 10.1038/nn.4499 – volume: 10 start-page: e0117029 issue: 2 year: 2015 ident: 10.1016/j.nicl.2020.102375_b0405 article-title: Functional network overlap as revealed by fMRI using sICA and its potential relationships with functional heterogeneity, balanced excitation and inhibition, and sparseness of neuron activity publication-title: PLoS One doi: 10.1371/journal.pone.0117029 – volume: 142 start-page: 6 issue: 1–3 year: 2012 ident: 10.1016/j.nicl.2020.102375_b0380 article-title: A systematic review of resting-state functional-MRI studies in major depression publication-title: J. Affect. Disord. doi: 10.1016/j.jad.2012.04.013 – volume: 329 start-page: 1358 issue: 5997 year: 2010 ident: 10.1016/j.nicl.2020.102375_b0135 article-title: Prediction of individual brain maturity using fMRI publication-title: Science doi: 10.1126/science.1194144 – volume: 27 start-page: 561 issue: 4 year: 2017 ident: 10.1016/j.nicl.2020.102375_b0065 article-title: Ten key observations on the analysis of resting-state functional MR imaging data using independent component analysis publication-title: Neuroimaging Clin. North Am. doi: 10.1016/j.nic.2017.06.012 – volume: 15 start-page: 273 issue: 1 year: 2002 ident: 10.1016/j.nicl.2020.102375_b0375 article-title: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain publication-title: NeuroImage doi: 10.1006/nimg.2001.0978 – volume: 5 start-page: 298 year: 2014 ident: 10.1016/j.nicl.2020.102375_b0115 article-title: Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia publication-title: NeuroImage: Clin. doi: 10.1016/j.nicl.2014.07.003 – volume: 131 start-page: 945 issue: Pt 4 year: 2008 ident: 10.1016/j.nicl.2020.102375_b0270 article-title: Disrupted small-world networks in schizophrenia publication-title: Brain doi: 10.1093/brain/awn018 |
| SSID | ssj0000800766 |
| Score | 2.610706 |
| Snippet | [Display omitted]
•Propose a new pipeline to link brain changes among different datasets, studies, and disorders.•Identify reproducible biomarkers in... Graphical abstract Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to... • Propose a new pipeline to link brain changes among different datasets, studies, and disorders. • Identify reproducible biomarkers in schizophrenia using... |
| SourceID | doaj pubmedcentral proquest pubmed crossref elsevier |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 102375 |
| SubjectTerms | Brain disorders fMRI Independent component analysis NeuroMark Radiology Regular Reproducible and comparable biomarkers |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQD4gL4s3ykpG4oYjESeyE21JRtUjlAFTqzfJTpGqTqMke-PfM2M5qF1B74ZDLbpzHzOzMN-uZbwh513ILWlZthrEvq1rOMlWoImONVdqXmjETqi2-8uOz6st5fb4z6gtrwiI9cBTcB--5sM7kNVMtpHK-EUpbA2aofAuhMLTuQczbSaYuEg4SYaOSsaLMirphqWMmFnch6ywkhyxSF2CR4U5UCuT9e8Hpb_D5Zw3lTlA6ekDuJzRJ1_EtHpI7rn9E7p6m_fLHZAzcG9iP85Gue6o28wAI1VmqejisGtHZ0ZPDNcVoZunYjdif7ug80C608PpfFHkvkRa205eO-tNvJ_QKa3quJzp4qnHEBLWJw3N6Qs6OPv84PM7SjIXMcCbmzGvDtSh1nSvInExTN1rh7A1VGs0s93ULB9cGHKPwjilXudwoL7RRrfAQ_56Sg37o3XNCK9whLA14AFFVoOWWMQfxr-G51oIXdkWKRcbSJAJynINxKZdKswuJepGoFxn1siLvt2vGSL9x49mfUHXbM5E6O3wABiWTQcnbDGpFykXxculOBX8KF-puvLX41yo3JZcwyUJOTObyOxok2iMD9wq4ga9IvV2ZUE9EM7fe8e1ilRJcAu7zqN4Nm0kCYqshq6w4XPtZtNKtSErWAiDLGTzvnv3uyWz_m777GWjHEVsCgHzxP4T8ktzDV4n_Zb0iB_P1xr0GdDfrN-GH_BvtE0rt priority: 102 providerName: Directory of Open Access Journals – databaseName: ScienceDirect Open Access Journals (Elsevier) dbid: IXB link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3Pj5QwFG42ezBejL8df6Um3gwZKNBSb7MTN7sm60HdZG5NW1rFrEAG5uB_73tQyOKaNfHAYRgKTd_je1_pe18JeSt5CVbWMsLYF2WSs0gnOolYUWrjU8OYHbItPvGzy-zjLt8dke1UC4NplQH7R0wf0DqcWYfRXLdVtf7CWJImOQQ48FMAYJTdTrNiKOLbnczfWZARiWHJEq-PsEGonRnTvFB_FqaJbBQxwHTDa_FpkPFfhKmbNPTPbMpr4en0PrkXeCXdjF1_QI5c_ZDcuQgr549IO6hwYGXOe7qpqT70DXBVV1Jdw1HqFmGPnm83FONaSduqxUp1R_uGVkMxr_9FUQETBWIrc-Wov_h8Tn9ids--o42nBjeboGVQ8-wek8vTD1-3Z1HYbSGynIk-8sZyI1KTxxrmULbIC6NxFw6dWsNK7nMJBzcWIFJ4x7TLXGy1F8ZqKTxEwifkuG5q94zQDNcKUwtYILIM7C0ZcxAJCx4bI3hSrkgyjbGyQYocd8S4UlPO2Q-FdlFoFzXaZUXezW3aUYjj1qtP0HTzlSiiPZxo9t9U8CLlPRels3HOtMy49IXQprSAcNpLYFlyRdLJ8GqqUwVkhRtVtz5a_K2V6wI4dCpRHVOxuuHAK5LPLRfvwD-f-GbySgXggCs-unbNoVPA3XKYX2Yc7v109NJ5SFImgZrFDPq78N_FmC3_qavvgwA5skygks__s78vyF38NX7IekmO-_3BvQJq15vXw7v7G6ymSnA priority: 102 providerName: Elsevier |
| Title | NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S2213158220302126 https://www.clinicalkey.es/playcontent/1-s2.0-S2213158220302126 https://dx.doi.org/10.1016/j.nicl.2020.102375 https://www.ncbi.nlm.nih.gov/pubmed/32961402 https://www.proquest.com/docview/2445428466 https://pubmed.ncbi.nlm.nih.gov/PMC7509081 https://doaj.org/article/ff67dec052a9469f87abdc322af95549 |
| Volume | 28 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3fi9QwEA7nCeKL-Ns99Yjgm1TatE0aQWTv8LgV1gd1Yd9CkibaY23X7S7c_ffOtOnq6nKCD13YtknLzGTmSzP5hpCXkpegZS0jjH1RJjmLdKKTiBWlNj41jNku2-IjP59lH-b5_IAM5Y6CANu9UzusJzVbLV5f_rh6BwP-7a9cLSSRhbke65kIRH6D3ITIJLCiwTTA_YuAjkS3fMlYkkZJXrCwj2Z_NzuxqqP03wlZf0PSPzMrfwtVZ3fJnYAx6bg3invkwNX3ya1pWEV_QJYdIwfu0nlDxzXVm3UDuNWVVNdwlHqJLpBOTscUY1xJl9USd607um5o1W3s9VcU2TCRLLYyC0f99NOEfsdMn1VLG08NFp6gZWD2bB-S2dn7L6fnUai8EFnOxDryxnIjUpPHGuZTtsgLo7Eih06tYSX3uYSDGwvuUnjHtMtcbLUXxmopPETFR-Swbmr3hNAM1w1TC35BZBnoXjLmICoWPDZG8KQckWSQsbKBlhyrYyzUkH92oVAvCvWier2MyKttm2VPynHt3Seouu2dSKjdnWhWX1UYn8p7Lkpn45xpmXHpC6FNacHbaS8BcckRSQfFq2HPKnhZ6Ki69tFiXyvXDnauEtUyFavPaJBojwycLqAJPiL5tmXAQj3G-ecTXwxWqcBR4OqPrl2zaRXguBzmmhmHvh_3VroVScokwLSYwfvu2O-OzHav1NW3jowcESfAyqP_ks9Tchv_9Z-0npHD9WrjngPIW5vj7uMI_E7mJ8fdKP4JXFJPWg |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELZKkYAL4s3yNBKcULSJk9gJEocWqHZptwdopb0Z27EhqCTRZleov4s_yEzirLoUFQmph1yS2LFmxt98jmfGhLzMeQFaVnmAvi9Ics4CFakoYFmhtIs1Y6aLtjjkk-Pk4zydb5FfQy4MhlV67O8xvUNrf2fspTluynL8mbEojlJwcGCnAMDcR1bu29OfsG5r307fg5JfMbb34ejdJPBHCwSGM7EMnDZci1inoYIFg8nSTCs8ckLFRrOCuzSHi2sDeCCcZcomNjTKCW1ULhzAPvR7hVwF9iEQDabz3fWPHaRgotsjxQEGOEKfrNPHlWHBW1iXsr5qAsY3nnGI3bkBG37xPO_9M3zzjD_cu0VueiJLd3pZ3SZbtrpDrs38Vv1d0nRlPzAV6A3dqahaLWsgx7agqoKrUA3iLAVhUXSkBW3KBlPjLV3WtOyyh90pxZKbWJG21CeWutmnKf2B4USLltaOajzdgha-fGh7jxxfig7uk-2qruxDQhPcnIwNgI9IEjCwnDELrjfjodaCR8WIRIOMpfG1z_EIjhM5BLl9l6gXiXqRvV5G5PW6TdNX_rjw7V1U3fpNrNrd3agXX6U3W-kcF4U1YcpUnvDcZULpwgCkKpcDrctHJB4UL4fEWIBy6Ki88NPib61s69GolZFsmQzluRkzIum65cak--cXXwxWKQGNcItJVbZetRLIYgoL2oRD3w96K12LJGY5cMGQwXg37HdDZptPqvJbV_EcaS1w10f_Od7n5PrkaHYgD6aH-4_JDXzS_0V7QraXi5V9CrxyqZ9185iSL5cNHL8BkNqJTg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=NeuroMark%3A+An+automated+and+adaptive+ICA+based+pipeline+to+identify+reproducible+fMRI+markers+of+brain+disorders&rft.jtitle=NeuroImage+clinical&rft.au=Du%2C+Yuhui&rft.au=Fu%2C+Zening&rft.au=Sui%2C+Jing&rft.au=Gao%2C+Shuang&rft.date=2020-01-01&rft.pub=Elsevier+Inc&rft.issn=2213-1582&rft.eissn=2213-1582&rft.volume=28&rft_id=info:doi/10.1016%2Fj.nicl.2020.102375&rft.externalDocID=S2213158220302126 |
| thumbnail_m | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fcdn.clinicalkey.com%2Fck-thumbnails%2F22131582%2FS2213158220X00040%2Fcov150h.gif |