Clavero Ibarra, Pedro Luis
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Clavero Ibarra
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Pedro Luis
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Ciencias de la Salud
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Publication Open Access Nigrostriatal degeneration determines dynamics of glial inflammatory and phagocytic activity(BMC, 2024) Ayerra, Leyre; Abellanas, Miguel Ángel; Basurco, Leyre; Tamayo Uria, Ibon; Conde, Enrique; Tavira, Adriana; Trigo, Amaya; Vidaurre, Clara; Vilas, Amaia; San Martin-Uriz, Patxi; Luquin, Esther; Clavero Ibarra, Pedro Luis; Mengual, Elisa; Aymerich, María Soledad; Ciencias de la Salud; Osasun ZientziakGlial cells are key players in the initiation of innate immunity in neurodegeneration. Upon damage, they switch their basal activation state and acquire new functions in a context and time-dependent manner. Since modulation of neuroinflammation is becoming an interesting approach for the treatment of neurodegenerative diseases, it is crucial to understand the specific contribution of these cells to the inflammatory reaction and to select experimental models that recapitulate what occurs in the human disease. Previously, we have characterized a region-specific activation pattern of CD11b(+) cells and astrocytes in the alpha-synuclein overexpression mouse model of Parkinsons disease (PD). In this study we hypothesized that the time and the intensity of dopaminergic neuronal death would promote different glial activation states. Dopaminergic degeneration was induced with two administration regimens of the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), subacute (sMPTP) and chronic (cMPTP). Our results show that in the sMPTP mouse model, the pro-inflammatory phenotype of striatal CD11b(+) cells was counteracted by an anti-inflammatory astrocytic profile. In the midbrain the roles were inverted, CD11b(+) cells exhibited an anti-inflammatory profile and astrocytes were pro-inflammatory. The overall response generated resulted in decreased CD4 T cell infiltration in both regions. Chronic MPTP exposure resulted in a mild and prolonged neuronal degeneration that generated a pro-inflammatory response and increased CD4 T cell infiltration in both regions. At the onset of the neurodegenerative process, microglia and astrocytes cooperated in the removal of dopaminergic terminals. With time, only microglia maintained the phagocytic activity. In the ventral midbrain, astrocytes were the main phagocytic mediators at early stages of degeneration while microglia were the major phagocytic cells in the chronic state. In this scenario, we questioned which activation pattern recapitulates better the features of glial activation in PD. Glial activation in the cMPTP mouse model reflects many pathways of their corresponding counterparts in the human brain with advanced PD. Altogether, our results point toward a context-dependent cooperativity of microglia/myeloid cells and astrocytes in response to neuronal damage and the relevance of selecting the right experimental models for the study of neuroinflammation.Publication Open Access Subspace corrected relevance learning with application in neuroimaging(Elsevier, 2024) Veen, Rick van; Bari Tamboli, Neha Rajendra; Lövdal, Sofie; Meles, Sanne K.; Renken, Remco J.; Vries, Gert-Jan de; Arnaldi, Dario; Morbelli, Silvia; Clavero Ibarra, Pedro Luis; Obeso, José A.; Rodríguez Oroz, María Cruz; Leenders, Klaus L.; Villmann, Thomas; Biehl, Michael; Ciencias de la Salud; Osasun ZientziakIn machine learning, data often comes from different sources, but combining them can introduce extraneous variation that affects both generalization and interpretability. For example, we investigate the classification of neurodegenerative diseases using FDG-PET data collected from multiple neuroimaging centers. However, data collected at different centers introduces unwanted variation due to differences in scanners, scanning protocols, and processing methods. To address this issue, we propose a two-step approach to limit the influence of center-dependent variation on the classification of healthy controls and early vs. late-stage Parkinson’s disease patients. First, we train a Generalized Matrix Learning Vector Quantization (GMLVQ) model on healthy control data to identify a “relevance space” that distinguishes between centers. Second, we use this space to construct a correction matrix that restricts a second GMLVQ system’s training on the diagnostic problem. We evaluate the effectiveness of this approach on the real-world multi-center datasets and simulated artificial dataset. Our results demonstrate that the approach produces machine learning systems with reduced bias - being more specific due to eliminating information related to center differences during the training process - and more informative relevance profiles that can be interpreted by medical experts. This method can be adapted to similar problems outside the neuroimaging domain, as long as an appropriate “relevance space” can be identified to construct the correction matrix.