Adalı T - Search Results

1

A Shared Vision for Machine Learning in Neuroscience.

Vu MT, Adalı T, Ba D, Buzsáki G, Carlson D, Heller K, Liston C, Rudin C, Sohal VS, Widge AS, Mayberg HS, Sapiro G, Dzirasa K. Vu MT, et al. Among authors: adali t. J Neurosci. 2018 Feb 14;38(7):1601-1607. doi: 10.1523/JNEUROSCI.0508-17.2018. Epub 2018 Jan 26. J Neurosci. 2018. PMID: 29374138 Free PMC article. Review.

2

Learning Spatiotemporal Brain Dynamics in Adolescents via Multimodal MEG and fMRI Data Fusion Using Joint Tensor/Matrix Decomposition.

Belyaeva I, Gabrielson B, Wang YP, Wilson TW, Calhoun VD, Stephen JM, Adali T. Belyaeva I, et al. Among authors: adali t. IEEE Trans Biomed Eng. 2024 Jul;71(7):2189-2200. doi: 10.1109/TBME.2024.3364704. Epub 2024 Jun 19. IEEE Trans Biomed Eng. 2024. PMID: 38345949

3

Reproducibility in Matrix and Tensor Decompositions: Focus on Model Match, Interpretability, and Uniqueness.

Adali T, Kantar F, Akhonda MABS, Strother S, Calhoun VD, Acar E. Adali T, et al. IEEE Signal Process Mag. 2022 Jul;39(4):8-24. doi: 10.1109/msp.2022.3163870. Epub 2022 Jun 28. IEEE Signal Process Mag. 2022. PMID: 36337436 Free PMC article. No abstract available.

4

A review of multivariate methods for multimodal fusion of brain imaging data.

Sui J, Adali T, Yu Q, Chen J, Calhoun VD. Sui J, et al. Among authors: adali t. J Neurosci Methods. 2012 Feb 15;204(1):68-81. doi: 10.1016/j.jneumeth.2011.10.031. Epub 2011 Nov 11. J Neurosci Methods. 2012. PMID: 22108139 Free PMC article. Review.

5

Capturing subject variability in fMRI data: A graph-theoretical analysis of GICA vs. IVA.

Laney J, Westlake KP, Ma S, Woytowicz E, Calhoun VD, Adalı T. Laney J, et al. Among authors: adali t. J Neurosci Methods. 2015 May 30;247:32-40. doi: 10.1016/j.jneumeth.2015.03.019. Epub 2015 Mar 20. J Neurosci Methods. 2015. PMID: 25797843 Free PMC article.

6

The role of diversity in complex ICA algorithms for fMRI analysis.

Du W, Levin-Schwartz Y, Fu GS, Ma S, Calhoun VD, Adalı T. Du W, et al. Among authors: adali t. J Neurosci Methods. 2016 May 1;264:129-135. doi: 10.1016/j.jneumeth.2016.03.012. Epub 2016 Mar 15. J Neurosci Methods. 2016. PMID: 26993820 Free PMC article.

7

A method to compare the discriminatory power of data-driven methods: Application to ICA and IVA.

Levin-Schwartz Y, Calhoun VD, Adalı T. Levin-Schwartz Y, et al. Among authors: adali t. J Neurosci Methods. 2019 Jan 1;311:267-276. doi: 10.1016/j.jneumeth.2018.10.008. Epub 2018 Oct 30. J Neurosci Methods. 2019. PMID: 30389489 Free PMC article.

8

An ICA based approach for steady-state and transient analysis of task fMRI data: Application to study of thermal pain response.

Song X, Bhinge S, Quiton RL, Adalı T. Song X, et al. Among authors: adali t. J Neurosci Methods. 2019 Oct 1;326:108356. doi: 10.1016/j.jneumeth.2019.108356. Epub 2019 Jul 13. J Neurosci Methods. 2019. PMID: 31310824

9

Graph-theoretical analysis identifies transient spatial states of resting-state dynamic functional network connectivity and reveals dysconnectivity in schizophrenia.

Long Q, Bhinge S, Calhoun VD, Adali T. Long Q, et al. Among authors: adali t. J Neurosci Methods. 2021 Feb 15;350:109039. doi: 10.1016/j.jneumeth.2020.109039. Epub 2020 Dec 25. J Neurosci Methods. 2021. PMID: 33370561 Free PMC article.

10

Disjoint subspaces for common and distinct component analysis: Application to the fusion of multi-task FMRI data.

Akhonda MABS, Gabrielson B, Bhinge S, Calhoun VD, Adali T. Akhonda MABS, et al. Among authors: adali t. J Neurosci Methods. 2021 Jul 1;358:109214. doi: 10.1016/j.jneumeth.2021.109214. Epub 2021 May 3. J Neurosci Methods. 2021. PMID: 33957159 Free PMC article.

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