ArticleNeuroradiology2020
Automatic identification of atypical clinical fMRI results.
Article in Neuroradiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Large-scale fMRI dataset for the design of motor-based Brain-Computer Interfaces.Scientific data · 2025Article
- 7 T and beyond: toward a synergy between fMRI-based presurgical mapping at ultrahigh magnetic fields, AI, and robotic neurosurgery.European radiology experimental · 2024Review
- Methodological Recommendations for Studies on the Daily Life Implementation of Implantable Communication-Brain-Computer Interfaces for Individuals With Locked-in Syndrome.Neurorehabilitation and neural repair · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeFunctional MRI is not routinely used for neurosurgical planning despite potential important advantages, due to difficulty of determining quality. We introduce a novel method for objective evaluation of fMRI scan quality, based on activation maps. A template matching analysis (TMA) is presented and tested on data from two clinical fMRI protocols, performed by healthy controls in seven clinical centers. Preliminary clinical utility is tested with data from low-grade glioma patients.
methodsData were collected from 42 healthy subjects from seven centers, with standardized finger tapping (FT) and verb generation (VG) tasks. Copies of these "typical" data were deliberately analyzed incorrectly to assess feasibility of identifying them as "atypical." Analyses of the VG task administered to 32 tumor patients assessed sensitivity of the TMA method to anatomical abnormalities.
resultsTMA identified all atypical activity maps for both tasks, at the cost of incorrectly classifying 3.6 (VG)-6.5% (FT) of typical maps as atypical. For patients, the average TMA was significantly higher than atypical healthy scans, despite localized anatomical abnormalities caused by a tumor.
conclusionThis study supports feasibility of TMA for objective identification of atypical activation patterns for motor and verb generation fMRI protocols. TMA can facilitate the use and evaluation of clinical fMRI in hospital settings that have limited access to fMRI experts. In a clinical setting, this method could be applied to automatically flag fMRI scans showing atypical activation patterns for further investigation to determine whether atypicality is caused by poor scan data quality or abnormal functional topography.
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