Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training
Neurofeedback (NF) aptitude, which refers to an individual's ability to change brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical applications to screen patients suitable for NF tre...
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Elsevier
2021
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oai:doaj.org-article:733db5073d48444ca77555d5c56e370a2021-11-28T04:29:04ZResting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training1095-957210.1016/j.neuroimage.2021.118733https://doaj.org/article/733db5073d48444ca77555d5c56e370a2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1053811921010053https://doaj.org/toc/1095-9572Neurofeedback (NF) aptitude, which refers to an individual's ability to change brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical applications to screen patients suitable for NF treatment. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude, independent of NF-targeting brain regions. We combined the data from fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the multiple regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Subsequently, the reproducibility of the prediction model was validated using independent test data from another site. The identified FC model revealed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting that NF aptitude may be involved in the attentional mode-orientation modulation system's characteristics in task-free resting-state brain activity.Takashi NakanoMasahiro TakamuraHaruki NishimuraMaro G. MachizawaNaho IchikawaAtsuo YoshinoGo OkadaYasumasa OkamotoShigeto YamawakiMakiko YamadaTetsuya SuharaJunichiro YoshimotoElsevierarticleNeurofeedback with functional MRIPrediction of neurofeedback aptitude, Resting-state functional connectivityPartial least square regressionGeneralization to independent test dataNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENNeuroImage, Vol 245, Iss , Pp 118733- (2021) |
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DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Neurofeedback with functional MRI Prediction of neurofeedback aptitude, Resting-state functional connectivity Partial least square regression Generalization to independent test data Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 |
| spellingShingle |
Neurofeedback with functional MRI Prediction of neurofeedback aptitude, Resting-state functional connectivity Partial least square regression Generalization to independent test data Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Takashi Nakano Masahiro Takamura Haruki Nishimura Maro G. Machizawa Naho Ichikawa Atsuo Yoshino Go Okada Yasumasa Okamoto Shigeto Yamawaki Makiko Yamada Tetsuya Suhara Junichiro Yoshimoto Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training |
| description |
Neurofeedback (NF) aptitude, which refers to an individual's ability to change brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical applications to screen patients suitable for NF treatment. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude, independent of NF-targeting brain regions. We combined the data from fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the multiple regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Subsequently, the reproducibility of the prediction model was validated using independent test data from another site. The identified FC model revealed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting that NF aptitude may be involved in the attentional mode-orientation modulation system's characteristics in task-free resting-state brain activity. |
| format |
article |
| author |
Takashi Nakano Masahiro Takamura Haruki Nishimura Maro G. Machizawa Naho Ichikawa Atsuo Yoshino Go Okada Yasumasa Okamoto Shigeto Yamawaki Makiko Yamada Tetsuya Suhara Junichiro Yoshimoto |
| author_facet |
Takashi Nakano Masahiro Takamura Haruki Nishimura Maro G. Machizawa Naho Ichikawa Atsuo Yoshino Go Okada Yasumasa Okamoto Shigeto Yamawaki Makiko Yamada Tetsuya Suhara Junichiro Yoshimoto |
| author_sort |
Takashi Nakano |
| title |
Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training |
| title_short |
Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training |
| title_full |
Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training |
| title_fullStr |
Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training |
| title_full_unstemmed |
Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training |
| title_sort |
resting-state brain activity can predict target-independent aptitude in fmri-neurofeedback training |
| publisher |
Elsevier |
| publishDate |
2021 |
| url |
https://doaj.org/article/733db5073d48444ca77555d5c56e370a |
| work_keys_str_mv |
AT takashinakano restingstatebrainactivitycanpredicttargetindependentaptitudeinfmrineurofeedbacktraining AT masahirotakamura restingstatebrainactivitycanpredicttargetindependentaptitudeinfmrineurofeedbacktraining AT harukinishimura restingstatebrainactivitycanpredicttargetindependentaptitudeinfmrineurofeedbacktraining AT marogmachizawa restingstatebrainactivitycanpredicttargetindependentaptitudeinfmrineurofeedbacktraining AT nahoichikawa restingstatebrainactivitycanpredicttargetindependentaptitudeinfmrineurofeedbacktraining AT atsuoyoshino restingstatebrainactivitycanpredicttargetindependentaptitudeinfmrineurofeedbacktraining AT gookada restingstatebrainactivitycanpredicttargetindependentaptitudeinfmrineurofeedbacktraining AT yasumasaokamoto restingstatebrainactivitycanpredicttargetindependentaptitudeinfmrineurofeedbacktraining AT shigetoyamawaki restingstatebrainactivitycanpredicttargetindependentaptitudeinfmrineurofeedbacktraining AT makikoyamada restingstatebrainactivitycanpredicttargetindependentaptitudeinfmrineurofeedbacktraining AT tetsuyasuhara restingstatebrainactivitycanpredicttargetindependentaptitudeinfmrineurofeedbacktraining AT junichiroyoshimoto restingstatebrainactivitycanpredicttargetindependentaptitudeinfmrineurofeedbacktraining |
| _version_ |
1718408385410367488 |