186 related articles for article (PubMed ID: 29414494)
1. Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.
Sepehrband F; Lynch KM; Cabeen RP; Gonzalez-Zacarias C; Zhao L; D'Arcy M; Kesselman C; Herting MM; Dinov ID; Toga AW; Clark KA
Neuroimage; 2018 May; 172():217-227. PubMed ID: 29414494
[TBL] [Abstract][Full Text] [Related]
2. Use of Machine Learning to Determine Deviance in Neuroanatomical Maturity Associated With Future Psychosis in Youths at Clinically High Risk.
Chung Y; Addington J; Bearden CE; Cadenhead K; Cornblatt B; Mathalon DH; McGlashan T; Perkins D; Seidman LJ; Tsuang M; Walker E; Woods SW; McEwen S; van Erp TGM; Cannon TD;
JAMA Psychiatry; 2018 Sep; 75(9):960-968. PubMed ID: 29971330
[TBL] [Abstract][Full Text] [Related]
3. Defining multivariate normative rules for healthy aging using neuroimaging and machine learning: an application to Alzheimer's disease.
Andrade de Oliveira A; Carthery-Goulart MT; Oliveira Júnior PP; Carrettiero DC; Sato JR
J Alzheimers Dis; 2015; 43(1):201-12. PubMed ID: 25079801
[TBL] [Abstract][Full Text] [Related]
4. Neuroanatomical spatial patterns in Turner syndrome.
Marzelli MJ; Hoeft F; Hong DS; Reiss AL
Neuroimage; 2011 Mar; 55(2):439-47. PubMed ID: 21195197
[TBL] [Abstract][Full Text] [Related]
5. Freesurfer cortical normative data for adults using Desikan-Killiany-Tourville and ex vivo protocols.
Potvin O; Dieumegarde L; Duchesne S;
Neuroimage; 2017 Aug; 156():43-64. PubMed ID: 28479474
[TBL] [Abstract][Full Text] [Related]
6. The effect of gender on the neuroanatomy of children with autism spectrum disorders: a support vector machine case-control study.
Retico A; Giuliano A; Tancredi R; Cosenza A; Apicella F; Narzisi A; Biagi L; Tosetti M; Muratori F; Calderoni S
Mol Autism; 2016; 7():5. PubMed ID: 26788282
[TBL] [Abstract][Full Text] [Related]
7. Local label learning (LLL) for subcortical structure segmentation: application to hippocampus segmentation.
Hao Y; Wang T; Zhang X; Duan Y; Yu C; Jiang T; Fan Y;
Hum Brain Mapp; 2014 Jun; 35(6):2674-97. PubMed ID: 24151008
[TBL] [Abstract][Full Text] [Related]
8. Multivariate models of brain volume for identification of children and adolescents with fetal alcohol spectrum disorder.
Little G; Beaulieu C
Hum Brain Mapp; 2020 Apr; 41(5):1181-1194. PubMed ID: 31737980
[TBL] [Abstract][Full Text] [Related]
9. A comparison of three brain atlases for MCI prediction.
Ota K; Oishi N; Ito K; Fukuyama H;
J Neurosci Methods; 2014 Jan; 221():139-50. PubMed ID: 24140118
[TBL] [Abstract][Full Text] [Related]
10. Age-Related Effects and Sex Differences in Gray Matter Density, Volume, Mass, and Cortical Thickness from Childhood to Young Adulthood.
Gennatas ED; Avants BB; Wolf DH; Satterthwaite TD; Ruparel K; Ciric R; Hakonarson H; Gur RE; Gur RC
J Neurosci; 2017 May; 37(20):5065-5073. PubMed ID: 28432144
[TBL] [Abstract][Full Text] [Related]
11. Combined structural and functional patterns discriminating upper limb motor disability in multiple sclerosis using multivariate approaches.
Zhong J; Chen DQ; Nantes JC; Holmes SA; Hodaie M; Koski L
Brain Imaging Behav; 2017 Jun; 11(3):754-768. PubMed ID: 27146291
[TBL] [Abstract][Full Text] [Related]
12. View-centralized multi-atlas classification for Alzheimer's disease diagnosis.
Liu M; Zhang D; Shen D;
Hum Brain Mapp; 2015 May; 36(5):1847-65. PubMed ID: 25624081
[TBL] [Abstract][Full Text] [Related]
13. A human brain atlas derived via n-cut parcellation of resting-state and task-based fMRI data.
James GA; Hazaroglu O; Bush KA
Magn Reson Imaging; 2016 Feb; 34(2):209-18. PubMed ID: 26523655
[TBL] [Abstract][Full Text] [Related]
14. Multivariate pattern analysis reveals subtle brain anomalies relevant to the cognitive phenotype in neurofibromatosis type 1.
Duarte JV; Ribeiro MJ; Violante IR; Cunha G; Silva E; Castelo-Branco M
Hum Brain Mapp; 2014 Jan; 35(1):89-106. PubMed ID: 22965669
[TBL] [Abstract][Full Text] [Related]
15. Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer's disease.
Ota K; Oishi N; Ito K; Fukuyama H; ;
J Neurosci Methods; 2015 Dec; 256():168-83. PubMed ID: 26318777
[TBL] [Abstract][Full Text] [Related]
16. Extreme learning machine-based classification of ADHD using brain structural MRI data.
Peng X; Lin P; Zhang T; Wang J
PLoS One; 2013; 8(11):e79476. PubMed ID: 24260229
[TBL] [Abstract][Full Text] [Related]
17. Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements.
Tustison NJ; Cook PA; Klein A; Song G; Das SR; Duda JT; Kandel BM; van Strien N; Stone JR; Gee JC; Avants BB
Neuroimage; 2014 Oct; 99():166-79. PubMed ID: 24879923
[TBL] [Abstract][Full Text] [Related]
18. Linking contemporary high resolution magnetic resonance imaging to the von Economo legacy: A study on the comparison of MRI cortical thickness and histological measurements of cortical structure.
Scholtens LH; de Reus MA; van den Heuvel MP
Hum Brain Mapp; 2015 Aug; 36(8):3038-46. PubMed ID: 25988402
[TBL] [Abstract][Full Text] [Related]
19. Analysis of structural brain MRI and multi-parameter classification for Alzheimer's disease.
Zhang Y; Liu S
Biomed Tech (Berl); 2018 Jul; 63(4):427-437. PubMed ID: 28622141
[TBL] [Abstract][Full Text] [Related]
20. SVM recursive feature elimination analyses of structural brain MRI predicts near-term relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis.
Wottschel V; Chard DT; Enzinger C; Filippi M; Frederiksen JL; Gasperini C; Giorgio A; Rocca MA; Rovira A; De Stefano N; Tintoré M; Alexander DC; Barkhof F; Ciccarelli O;
Neuroimage Clin; 2019; 24():102011. PubMed ID: 31734524
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]