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For: Kang SG, Cho SE. Neuroimaging Biomarkers for Predicting Treatment Response and Recurrence of Major Depressive Disorder. Int J Mol Sci 2020;21:E2148. [PMID: 32245086 DOI: 10.3390/ijms21062148] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
Number Citing Articles
1 Lin E, Lin CH, Lane HY. Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments. Clin Psychopharmacol Neurosci 2021;19:577-88. [PMID: 34690113 DOI: 10.9758/cpn.2021.19.4.577] [Reference Citation Analysis]
2 Suh JS, Fiori LM, Ali M, Harkness KL, Ramonas M, Minuzzi L, Hassel S, Strother SC, Zamyadi M, Arnott SR, Farzan F, Foster JA, Lam RW, MacQueen GM, Milev R, Müller DJ, Parikh SV, Rotzinger S, Sassi RB, Soares CN, Uher R, Kennedy SH, Turecki G, Frey BN. Hypothalamus volume and DNA methylation of stress axis genes in major depressive disorder: A CAN-BIND study report. Psychoneuroendocrinology 2021;132:105348. [PMID: 34229186 DOI: 10.1016/j.psyneuen.2021.105348] [Reference Citation Analysis]
3 Yang Z, Jian L, Qiu H, Zhang C, Cheng S, Ji J, Li T, Wang Y, Li J, Li K. Understanding complex functional wiring patterns in major depressive disorder through brain functional connectome. Transl Psychiatry 2021;11:526. [PMID: 34645783 DOI: 10.1038/s41398-021-01646-7] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
4 Zhao S, Bao Z, Zhao X, Xu M, Li MD, Yang Z. Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods. Front Neurosci 2021;15:645998. [PMID: 34220416 DOI: 10.3389/fnins.2021.645998] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Lin E, Lin CH, Lane HY. Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease. Int J Mol Sci 2021;22:7911. [PMID: 34360676 DOI: 10.3390/ijms22157911] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Datta S, Suryadevara U, Cheong J. Mood Disorders. Continuum (Minneap Minn) 2021;27:1712-37. [PMID: 34881733 DOI: 10.1212/CON.0000000000001051] [Reference Citation Analysis]
7 Kelley ME, Choi KS, Rajendra JK, Craighead WE, Rakofsky JJ, Dunlop BW, Mayberg HS. Establishing Evidence for Clinical Utility of a Neuroimaging Biomarker in Major Depressive Disorder: Prospective Testing and Implementation Challenges. Biol Psychiatry 2021;90:236-42. [PMID: 33896622 DOI: 10.1016/j.biopsych.2021.02.966] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
8 Bassa A, Sagués T, Porta-Casteràs D, Serra P, Martínez-Amorós E, Palao DJ, Cano M, Cardoner N. The Neurobiological Basis of Cognitive Side Effects of Electroconvulsive Therapy: A Systematic Review. Brain Sci 2021;11:1273. [PMID: 34679338 DOI: 10.3390/brainsci11101273] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
9 Ballester PL, Suh JS, Nogovitsyn N, Hassel S, Strother SC, Arnott SR, Minuzzi L, Sassi RB, Lam RW, Milev R, Müller DJ, Taylor VH, Kennedy SH, Frey BN; CAN-BIND Investigator Team. Accelerated brain aging in major depressive disorder and antidepressant treatment response: A CAN-BIND report. Neuroimage Clin 2021;32:102864. [PMID: 34710675 DOI: 10.1016/j.nicl.2021.102864] [Reference Citation Analysis]
10 Khadka B, Lee JY, Park EK, Kim KT, Bae JS. Impacts of Drug Interactions on Pharmacokinetics and the Brain Transporters: A Recent Review of Natural Compound-Drug Interactions in Brain Disorders. Int J Mol Sci 2021;22:1809. [PMID: 33670407 DOI: 10.3390/ijms22041809] [Reference Citation Analysis]
11 Korda AI, Andreou C, Borgwardt S. Pattern classification as decision support tool in antipsychotic treatment algorithms. Exp Neurol 2021;339:113635. [PMID: 33548218 DOI: 10.1016/j.expneurol.2021.113635] [Reference Citation Analysis]
12 Cho SE, Park CA, Na KS, Chung C, Ma HJ, Kang CK, Kang SG. Left-right asymmetric and smaller right habenula volume in major depressive disorder on high-resolution 7-T magnetic resonance imaging. PLoS One 2021;16:e0255459. [PMID: 34343199 DOI: 10.1371/journal.pone.0255459] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Koyanagi M, Yamada M, Higashi T, Mitsunaga W, Moriuchi T, Tsujihata M. The Usefulness of Functional Near-Infrared Spectroscopy for the Assessment of Post-Stroke Depression. Front Hum Neurosci 2021;15:680847. [PMID: 34239431 DOI: 10.3389/fnhum.2021.680847] [Reference Citation Analysis]
14 Ya Y, Ji L, Jia Y, Zou N, Jiang Z, Yin H, Mao C, Luo W, Wang E, Fan G. Machine Learning Models for Diagnosis of Parkinson’s Disease Using Multiple Structural Magnetic Resonance Imaging Features. Front Aging Neurosci 2022;14:808520. [DOI: 10.3389/fnagi.2022.808520] [Reference Citation Analysis]
15 Mosabbir AA, Braun Janzen T, Al Shirawi M, Rotzinger S, Kennedy SH, Farzan F, Meltzer J, Bartel L. Investigating the Effects of Auditory and Vibrotactile Rhythmic Sensory Stimulation on Depression: An EEG Pilot Study. Cureus 2022;14:e22557. [PMID: 35371676 DOI: 10.7759/cureus.22557] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]