Early-stage detection of cognitive impairment by hybrid quantum-classical algorithm using resting-state functional MRI time-series
<|reference_start|>Early-stage detection of cognitive impairment by hybrid quantum-classical algorithm using resting-state functional MRI time-series: Following the recent development of quantum machine learning techniques, the literature has reported several quantum machine learning algorithms for disease detection. This study explores the application of a hybrid quantum-classical algorithm for classifying region-of-interest time-series data obtained from resting-state functional magnetic resonance imaging in patients with early-stage cognitive impairment based on the importance of cognitive decline for dementia or aging. Classical one-dimensional convolutional layers are used together with quantum convolutional neural networks in our hybrid algorithm. In the classical simulation, the proposed hybrid algorithms showed higher balanced accuracies than classical convolutional neural networks under the similar training conditions. Moreover, a total of nine brain regions (left precentral gyrus, right superior temporal gyrus, left rolandic operculum, right rolandic operculum, left parahippocampus, right hippocampus, left medial frontal gyrus, right cerebellum crus, and cerebellar vermis) among 116 brain regions were found to be relatively effective brain regions for the classification based on the model performances. The associations of the selected nine regions with cognitive decline, as found in previous studies, were additionally validated through seed-based functional connectivity analysis. We confirmed both the improvement of model performance with the quantum convolutional neural network and neuroscientific validities of brain regions from our hybrid quantum-classical model.<|reference_end|>
@article{choi2024early-stage,
    title={Early-stage detection of cognitive impairment by hybrid
  quantum-classical algorithm using resting-state functional MRI time-series},
    author={Junggu Choi, Tak Hur, Daniel K. Park, Na-Young Shin, Seung-Koo Lee,
  Hakbae Lee, Sanghoon Han},
    journal={arXiv preprint arXiv:2405.01554},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01554},
    primaryClass={cs.LG cs.AI q-bio.NC}
}