AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project

An inhibitor of programmed death ligand 1 enhances natural killer cell-mediated immunity against malignant melanoma cells.

Since ionizing radiation has showed the dramatic effect to kill the cancer cells through direct DNA damage as well as triggering anti-cancer immune responses including induction of NKG2D ligands, it has used for long time to treat many cancer patients. However, it has been known that radiotherapy might promote the remnant cancer cells to escape immune system and metastasis. One of the suggested ways of immune evasion is induction of a ligand for programmed death-1 (PD-L1) in head and neck cancer, bladder cancer and lung cancer cells which engages the receptor, programmed death-1 (PD-1) in immune cells. PD-1/PD-L1 axis transduces the inhibitory signal and suppresses the adaptive immunity. However, their role in innate immunity remains poorly understood. Therefore, we investigated whether ionizing radiation could change the expression of PD-L1 in malignant melanoma cells and the receptor, programmed death-1 (PD-1), in NK-92 cells. Surface PD-L1 levels on melanoma cells were increased by ionizing radiation in a dose-independent manner but the level of PD-L1 was not changed significantly in NK-92 cells. Radiation-induced PD-L1 suppressed the activity of the NK-92 cells against melanoma cells despite of upregulation of NKG2D ligands. Furthermore, activated NK cells had high level of PD-1 and could not kill PD-L1+ melanoma cells effectively. When we used PD-L1 inhibitor or silenced PD-L1 gene, inhibited PD-1/PD-L1 axis reversed the activity of the suppressed NK cells. Through these results, we supposed that PD-1/PD-L1 blockade could enhance the immune responses of NK cells against melanoma cells after radiotherapy and might overcome the PD-L1 mediated radioresistance of cancer cells

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale.

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project