Timothy J. Hendrickson
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Timothy J. Hendrickson has a total of 40 co-authors. Here are the top co-authors by number of shared publications:
- Bryon A. Mueller
Department of Psychiatry and Behavioral Science, University of Minnesota Medical School, Minneapolis, USA
2 shared publications - Mark Fiecas
Division of Biostatistics and Health Data Science University of Minnesota Minneapolis Minnesota USA
2 shared publications Alice Graham
Oregon Health & Science University, USA
1 shared publicationAmanda R. Rueter
Department of Neurology, University of Minnesota, USA
1 shared publicationAnders J. Perrone
Masonic Institute for the Developing Brain, University of Minnesota, USA
1 shared publication- Ann Frances Haynos
Virginia Commonwealth University
1 shared publication
Latest Publications
- Top Cited PapersOpen Access
- 9 March 2026
- Published by Elsevier in Developmental Cognitive Neuroscience
- Vol. 79, 101706
BIBSNet: A deep learning baby image brain segmentation network for MRI scans
- 9 March 2026
- Published by Elsevier in Developmental Cognitive Neuroscience
- Vol. 79, 101706
Top Cited PapersOpen AccessBIBSNet: A deep learning baby image brain segmentation network for MRI scans
- 1 January 2023
- Published by International Press of Boston in Statistics and Its Interface
- Vol. 16 (2) , 259-277
Approximate hidden semi-Markov models for dynamic connectivity analysis in resting-state fMRI
International Press of Boston - publishers of scholarly mathematical and scientific journals and books
- 1 January 2023
- Published by International Press of Boston in Statistics and Its Interface
- Vol. 16 (2) , 259-277
Approximate hidden semi-Markov models for dynamic connectivity analysis in resting-state fMRI
International Press of Boston - publishers of scholarly mathematical and scientific journals and books
- 20 October 2021
- Published by Oxford University Press (OUP) in Cerebral Cortex
- Vol. 32 (12) , 2688-2702
Resting Hypoconnectivity of Theoretically Defined Addiction Networks during Early Abstinence Predicts Subsequent Relapse in Alcohol Use Disorder
Theoretical models of addiction suggest that alterations in addiction domains including incentive salience, negative emotionality, and executive control lead to relapse in alcohol use disorder (AUD). To determine whether the functional organization of neural networks underlying these domains predict subsequent relapse, we generated theoretically defined addiction networks. We collected resting functional magnetic resonance imaging data from 45 individuals with AUD during early abstinence (number of days abstinent M = 25.40, SD = 16.51) and calculated the degree of resting-state functional connectivity (RSFC) within these networks. Regression analyses determined whether the RSFC strength in domain-defined addiction networks measured during early abstinence predicted subsequent relapse (dichotomous or continuous relapse metrics). RSFC within each addiction network measured during early abstinence was significantly lower in those that relapsed (vs. abstained) and predicted subsequent time to relapse. Lower incentive salience RSFC during early abstinence increased the odds of relapsing. Neither RSFC in a control network nor clinical self-report measures predicted relapse. The association between low incentive salience RSFC and faster relapse highlights the need to design timely interventions that enhance RSFC in AUD individuals at risk of relapsing faster.
- 20 October 2021
- Published by Oxford University Press (OUP) in Cerebral Cortex
- Vol. 32 (12) , 2688-2702
Resting Hypoconnectivity of Theoretically Defined Addiction Networks during Early Abstinence Predicts Subsequent Relapse in Alcohol Use Disorder
Theoretical models of addiction suggest that alterations in addiction domains including incentive salience, negative emotionality, and executive control lead to relapse in alcohol use disorder (AUD). To determine whether the functional organization of neural networks underlying these domains predict subsequent relapse, we generated theoretically defined addiction networks. We collected resting functional magnetic resonance imaging data from 45 individuals with AUD during early abstinence (number of days abstinent M = 25.40, SD = 16.51) and calculated the degree of resting-state functional connectivity (RSFC) within these networks. Regression analyses determined whether the RSFC strength in domain-defined addiction networks measured during early abstinence predicted subsequent relapse (dichotomous or continuous relapse metrics). RSFC within each addiction network measured during early abstinence was significantly lower in those that relapsed (vs. abstained) and predicted subsequent time to relapse. Lower incentive salience RSFC during early abstinence increased the odds of relapsing. Neither RSFC in a control network nor clinical self-report measures predicted relapse. The association between low incentive salience RSFC and faster relapse highlights the need to design timely interventions that enhance RSFC in AUD individuals at risk of relapsing faster.