Optimized partial-coverage functional analysis pipeline (OPFAP): a semi-automated pipeline for skull stripping and co-registration of partial-coverage, ultra-high-field functional images

Yoo, Peter E.; Cleary, Jon O.; Kolbe, Scott; Ordidge, Roger J.; O’Brien, Terence J.; Opie, Nicholas L.; John, Sam E.; Oxley, Thomas J.; Moffat, Bradford A.

doi:10.1007/s10334-018-0690-z

Cited by 3 publications

(4 citation statements)

References 47 publications

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“…We used a customized analysis pipeline due to the partial-coverage 7T-fMRI data (i.e., optimized partial-coverage functional analysis pipeline OPFAP 54 ). OPFAP consists of many steps and sub-pipelines, thus, the details are beyond the scope of this study.…”

Section: Methodsmentioning

confidence: 99%

“…OPFAP consists of many steps and sub-pipelines, thus, the details are beyond the scope of this study. We recommend referring to our previous work for technical information 54 .…”

Section: Methodsmentioning

confidence: 99%

“…The ROIs were manually defined in a study-specific template brain space, then were transformed into each participant’s functional space to avoid potential human error or bias of defining inconsistent ROIs across individuals. We opted to define the ROIs manually, instead of utilizing atlas-based approach, as the partial-coverage GE-EPI images acquired at 7T did not co-register well with FSL’s default standard brain space, Montreal Neurological Institute space (e.g., MNI_152) 54 .…”

Section: Methodsmentioning

confidence: 99%

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Feasibility of identifying the ideal locations for motor intention decoding using unimodal and multimodal classification at 7T-fMRI

Yoo

Oxley

John

et al. 2018

Sci Rep

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Invasive Brain-Computer Interfaces (BCIs) require surgeries with high health-risks. The risk-to-benefit ratio of the procedure could potentially be improved by pre-surgically identifying the ideal locations for mental strategy classification. We recorded high-spatiotemporal resolution blood-oxygenation-level-dependent (BOLD) signals using functional MRI at 7 Tesla in eleven healthy participants during two motor imagery tasks. BCI diagnostic task isolated the intent to imagine movements, while BCI simulation task simulated the neural states that may be yielded in a real-life BCI-operation scenario. Imagination of movements were classified from the BOLD signals in sub-regions of activation within a single or multiple dorsal motor network regions. Then, the participant’s decoding performance during the BCI simulation task was predicted from the BCI diagnostic task. The results revealed that drawing information from multiple regions compared to a single region increased the classification accuracy of imagined movements. Importantly, systematic unimodal and multimodal classification revealed the ideal combination of regions that yielded the best classification accuracy at the individual-level. Lastly, a given participant’s decoding performance achieved during the BCI simulation task could be predicted from the BCI diagnostic task. These results show the feasibility of 7T-fMRI with unimodal and multimodal classification being utilized for identifying ideal sites for mental strategy classification.

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Section: Methodsmentioning

confidence: 99%

“…OPFAP consists of many steps and sub-pipelines, thus, the details are beyond the scope of this study. We recommend referring to our previous work for technical information 54 .…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Feasibility of identifying the ideal locations for motor intention decoding using unimodal and multimodal classification at 7T-fMRI

Yoo

Oxley

John

et al. 2018

Sci Rep

Self Cite

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show abstract

“…A customized analysis pipeline, optimized partial-coverage functional analysis pipeline (OPFAP; Yoo et al, 2018a), was used due to the partial-coverage 7T-fMRI data. We recommend referring to our previous work for specific technical information (Yoo et al, 2018a).…”

Section: Image Analysismentioning

confidence: 99%

Distinct Neural Correlates Underlie Inhibitory Mechanisms of Motor Inhibition and Motor Imagery Restraint

Yoo

Oxley

Hagan

et al. 2020

Front. Behav. Neurosci.

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State-of-the-Art Traditional to the Machine- and Deep-Learning-Based Skull Stripping Techniques, Models, and Algorithms

Fatima

Raza

et al. 2020

J Digit Imaging

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Optimized partial-coverage functional analysis pipeline (OPFAP): a semi-automated pipeline for skull stripping and co-registration of partial-coverage, ultra-high-field functional images

Abstract: These results suggest that the OPFAP should be used for processing partial-coverage UHF-fMRI data for detecting high-resolution macroscopic blood oxygenation level-dependent activations.

Cited by 3 publications

References 47 publications

Feasibility of identifying the ideal locations for motor intention decoding using unimodal and multimodal classification at 7T-fMRI

Feasibility of identifying the ideal locations for motor intention decoding using unimodal and multimodal classification at 7T-fMRI

Distinct Neural Correlates Underlie Inhibitory Mechanisms of Motor Inhibition and Motor Imagery Restraint

State-of-the-Art Traditional to the Machine- and Deep-Learning-Based Skull Stripping Techniques, Models, and Algorithms

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