Kyoto University · Medicine
Professor Naoya Oishi's research lab specializes in neuroimaging and computational neuroscience, focusing on the early detection and prediction of neurodegenerative diseases such as Parkinson’s disease and Alzheimer’s disease. The lab employs advanced neuroimaging techniques—including MRI, diffusion tensor imaging, and perfusion imaging—combined with machine learning and deep learning algorithms to identify biomarkers for disease progression and clinical outcomes. Key research directions include understanding the neural basis of nonpsychotic visual hallucinations in Parkinson’s disease, predicting outcomes in acute ischemic stroke with large vessel occlusion, and developing imaging-based classifiers for glioma grading and mild cognitive impairment conversion to dementia.
Figures are computed from collected data and may differ slightly.
Nonpsychotic visual hallucinations in Parkinson disease (PD) may be associated with hypoperfusion in the right fusiform gyrus and hyperperfusion in the right superior and middle temporal gyri. These temporal regions are important for visual object recognition and these regional cerebral blood flow changes are associated with inappropriate visual processing and are responsible for nonpsychotic visual hallucinations in PD.
Background and Purpose- The clinical course of acute ischemic stroke with large vessel occlusion (LVO) is a multifactorial process with various prognostic factors. We aimed to model this process with machine learning and predict the long-term clinical outcome of LVO before endovascular treatment and to compare our method with previously developed pretreatment scoring methods. Methods- The derivation cohort included 387 LVO patients, and the external validation cohort included 115 LVO patients wi
Background and Purpose- For patients with large vessel occlusion, neuroimaging biomarkers that evaluate the changes in brain tissue are important for determining the indications for mechanical thrombectomy. In this study, we applied deep learning to derive imaging features from pretreatment diffusion-weighted image data and evaluated the ability of these features in predicting clinical outcomes for patients with large vessel occlusion. Methods- This multicenter retrospective study included patie
Gliomas are the most common intra-axial primary brain tumour; therefore, predicting glioma grade would influence therapeutic strategies. Although several methods based on single or multiple parameters from diagnostic images exist, a definitive method for pre-operatively determining glioma grade remains unknown. We aimed to develop an unsupervised method using multiple parameters from pre-operative diffusion tensor images for obtaining a clustered image that could enable visual grading of gliomas
Rapid eye movement sleep behavior disorder is parasomnia characterized by symptoms of dream enactment and loss of muscle atonia during rapid eye movement sleep. Mild motor impairment is present in some patients with rapid eye movement sleep behavior disorder and presumed to be a risk factor for conversion to synucleinopathies. The purpose of this study is to identify patients with mild motor impairment by evaluating finger tapping and to investigate its pathophysiology. Twenty-three patients wit
These results suggested that FA values in the right UF might be an effective predictor of conversion of aMCI to AD.
Resilience is a dynamic process that enables organisms to cope with demanding environments. Resting-state functional MRI (fMRI) studies have demonstrated a negative correlation between resilience and functional connectivities (FCs) within the default mode network (DMN). Considering the on-demand recruitment process of resilience, dynamic changes in FCs during cognitive load increases may reflect essential aspects of resilience. We compared DMN FC changes in resting and task states and their asso
These data demonstrate that activation of CART peptide signaling in GABAergic neurons of the anterior cingulate cortex is a common molecular mechanism across antidepressant responders and that this pathway also drives stress resilience.
Stratification with imaging biomarkers in amnestic MCI can be a good approach for improving predictive performance.
Habenula volume could be a pathophysiologically relevant factor and diagnostic and therapeutic marker for depression, particularly in women.
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