Aki Tsuchiyagaito, Ph.D.Research Associate, Laureate Institute for Brain Research
Email: atsuchiyagaito@laureateinstitute.org Research ApproachDr. Tsuchiyagaito’s main research interests include developing novel neuroscience-based treatments for young adults, adolescents and youth to provide early intervention or prevention for mental disorders as well as determining which patient populations and traditional therapies might benefit from the addition of such treatments.
The primary methods Dr. Tsuchiyagaito employs in her research are real-time fMRI neurofeedback (rtfMRI-nf) and EEG. Her research aims to (1) understand clinically relevant brain mechanisms related to rumination, worry, and repetitive negative thinking (RNT) in young adults with mood disorders, and (2) to develop clinical applications of rtfMRI-nf and its future translation into EEG-neurofeedback (EEG-nf). |
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Research Program Highlights
Main Questions
Can we use rtfMRI-nf to reduce repetitive negative thinking (RNT)? Can rtfMRI-nf induce acute change of brain circuit related to RNT? If so, how can we improve rtfMRI-nf to induce stable change of brain circuit related to RNT? Will the combination of rtfMRI-nf and any other treatment modalities (i.e., pharmacotherapy, talk therapy such as cognitive behavioral therapy (CBT), brain stimulation) help to maintain the stable change of brain plasticity? By modulating RNT related circuit, can we reduce depression severity, and can we prevent future onset of depression? Can we use RNT related brain circuit as a monitoring marker of depression treatment? Who can benefit from rtfMRI-nf approach?
Approach
We employ rtfMRI-nf to modulate the target brain circuit. rtfMRI-nf can provide us insights of: (1) how modulating the target brain circuit influences neural and behavioral markers such as fMRI, structural MRI, EEG, heart rate variability, cognitive bias, and daily mood changes, etc; and (2) how the human brain can learn how to regulate its own neurological signals.
Future Directions
rtfMRI-nf is not solely the brain modulation techniques but is utilizing one’s own capacity to control and adjust their brain. Our rtfMRI-nf approach is to train the brain to make effective connections and regulate itself, and to help each person understand when their brain is in the desired state (i.e., free from stuck mind such as rumination and worry). Understanding potential predictors or monitoring markers of successful rtfMRI-nf to reduce RNT or rumination will enable us to consider potential collaboration of other treatment modalities (i.e., pharmacotherapy, CBT, brain stimulation) in order to contribute to the advancement of precision medicine. Finally, established knowledge of fMRI-EEG inter-relationships will enable us to translate rtfMRI-nf findings to more cost-effective EEG-nf tools.
Can we use rtfMRI-nf to reduce repetitive negative thinking (RNT)? Can rtfMRI-nf induce acute change of brain circuit related to RNT? If so, how can we improve rtfMRI-nf to induce stable change of brain circuit related to RNT? Will the combination of rtfMRI-nf and any other treatment modalities (i.e., pharmacotherapy, talk therapy such as cognitive behavioral therapy (CBT), brain stimulation) help to maintain the stable change of brain plasticity? By modulating RNT related circuit, can we reduce depression severity, and can we prevent future onset of depression? Can we use RNT related brain circuit as a monitoring marker of depression treatment? Who can benefit from rtfMRI-nf approach?
Approach
We employ rtfMRI-nf to modulate the target brain circuit. rtfMRI-nf can provide us insights of: (1) how modulating the target brain circuit influences neural and behavioral markers such as fMRI, structural MRI, EEG, heart rate variability, cognitive bias, and daily mood changes, etc; and (2) how the human brain can learn how to regulate its own neurological signals.
Future Directions
rtfMRI-nf is not solely the brain modulation techniques but is utilizing one’s own capacity to control and adjust their brain. Our rtfMRI-nf approach is to train the brain to make effective connections and regulate itself, and to help each person understand when their brain is in the desired state (i.e., free from stuck mind such as rumination and worry). Understanding potential predictors or monitoring markers of successful rtfMRI-nf to reduce RNT or rumination will enable us to consider potential collaboration of other treatment modalities (i.e., pharmacotherapy, CBT, brain stimulation) in order to contribute to the advancement of precision medicine. Finally, established knowledge of fMRI-EEG inter-relationships will enable us to translate rtfMRI-nf findings to more cost-effective EEG-nf tools.
Scientific Background
Dr. Tsuchiyagaito received her Bachelor’s degree from the University of Tokyo in 2005. After several years of serving in the Japan Ground Self Defense Force, she subsequently completed Master’s degree in Clinical Psychology at Health Sciences University of Hokkaido and obtained the license of Clinical Psychologist in 2016. She worked as a Clinical Psychologist at several medical universities and hospitals providing Cognitive Behavioral Therapy (CBT) treating depression and anxiety disorders. Her doctoral work in Child Development investigated the efficacy of CBT for obsessive-compulsive disorder (OCD) and comorbid autism spectrum disorder (ASD) using structural MRI. Dr. Tsuchiyagaito next completed a postdoctoral fellowship awarded by Japan Society of the Promotion Science (JSPS) and she joined the LIBR as a Post-doctoral Associate in 2018. She worked closely with Dr. Jerzy Borduka, and learned how to design, conduct, and analyze rtfMRI-nf for individuals with mental disorders. Dr. Tsuchiyagaito started a position as a Research Associate in 2021, and focuses on how to translate a novel brain modulation approach to younger populations, and how to disseminate neuroscience-informed interventions.
Selected Publications
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Tsuchiyagaito A, Misaki M, Zoubi OA, Paulus M, Bodurka J. Prevent breaking bad: A proof of concept study of rebalancing the brain's rumination circuit with real-time fMRI functional connectivity neurofeedback. Hum Brain Mapp. 2021 Mar;42(4):922-940. doi: 10.1002/hbm.25268. Epub 2020 Nov 10. PubMed PMID: 33169903; PubMed Central PMCID: PMC7856643.
Mayeli A, Misaki M, Zotev V, Tsuchiyagaito A, Al Zoubi O, Phillips R, Smith J, Stewart JL, Refai H, Paulus MP, Bodurka J. Self-regulation of ventromedial prefrontal cortex activation using real-time fMRI neurofeedback-Influence of default mode network. Hum Brain Mapp. 2020 Feb 1;41(2):342-352. doi: 10.1002/hbm.24805. Epub 2019 Oct 21. PubMed PMID: 31633257; PubMed Central PMCID: PMC7267960.
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Tsuchiyagaito A, Smith JL, El-Sabbagh N, Zotev V, Misaki M, Al Zoubi O, Kent Teague T, Paulus MP, Bodurka J, Savitz J. Real-time fMRI neurofeedback amygdala training may influence kynurenine pathway metabolism in major depressive disorder. Neuroimage Clin. 2021;29:102559. doi: 10.1016/j.nicl.2021.102559. Epub 2021 Jan 19. PubMed PMID: 33516062; PubMed Central PMCID: PMC7847971.
Tsuchiyagaito A, Hirano Y, Asano K, Oshima F, Nagaoka S, Takebayashi Y, Matsumoto K, Masuda Y, Iyo M, Shimizu E, Nakagawa A. Cognitive-Behavioral Therapy for Obsessive-Compulsive Disorder with and without Autism Spectrum Disorder: Gray Matter Differences Associated with Poor Outcome. Front Psychiatry. 2017;8:143. doi: 10.3389/fpsyt.2017.00143. eCollection 2017. PubMed PMID: 28861007; PubMed Central PMCID: PMC5559438.
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Misaki M, Tsuchiyagaito A, Al Zoubi O, Paulus M, Bodurka J. Connectome-wide search for functional connectivity locus associated with pathological rumination as a target for real-time fMRI neurofeedback intervention. Neuroimage Clin. 2020;26:102244. doi: 10.1016/j.nicl.2020.102244. Epub 2020 Mar 12. PubMed PMID: 32193171; PubMed Central PMCID: PMC7082218.
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