Examinando por Autor "Diez, Ibai"

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    Amygdala neurodegeneration: a key driver of visual dysfunction in Parkinson's disease
    (John Wiley and Sons Inc, 2025-02-17) Erramuzpe Aliaga, Asier; Murueta-Goyena Larrañaga, Ane; Jiménez Marín, Antonio; Acera Gil, María Ángeles; Teijeira Portas, Sara; Pino, Rocío del; Fernández Valle, Tamara; Diez, Ibai ; Sainz Lugarezaresti, Unai; Ibarretxe Bilbao, Naroa; Ayala Fernández, Unai; Barrenechea Carrasco, Maitane; Cabrera Zubizarreta, Alberto; Cortés, Jesús; Gómez Esteban, Juan Carlos; Gabilondo Cuellar, Iñigo
    Objective: Visual disability in Parkinson's disease (PD) is not fully explained by retinal neurodegeneration. We aimed to delineate the brain substrate of visual dysfunction in PD and its association with retinal thickness. Methods: Forty-two PD patients and 29 controls underwent 3-Tesla MRI, retinal spectral-domain optical coherence tomography, and visual testing across four domains. Voxel-level associations between gray matter volume and visual outcomes were used to define a visual impairment region (visualROI). Functional connectivity of the visualROI with brain networks was analyzed. Covariance analysis of brain regions associated with retinal thinning (retinalROI) was conducted using hierarchical clustering to develop a model of retinal and brain neurodegeneration linked to disease progression. Results: The amygdala was the primary component of the visualROI, comprising 32.3% and 14.6% of its left and right volumes. Functional connectivity analysis revealed significant disruptions between the visualROI and medial/lateral visual networks in PD. Covariance analysis identified three clusters within retinalROI: (1) the thalamic nucleus, (2) the amygdala and lateral/occipital visual regions, and (3) frontal regions, including the anterior cingulate cortex and frontal attention networks. Hierarchical clustering suggested a two-phase progression: early amygdala damage (Braak 1–3) disrupting visual network connections, followed by retinal and frontal atrophy (Braak 4–5) exacerbating visual dysfunction. Interpretation: Our findings support a novel, amygdala-centric two-phase model of visual dysfunction in PD. Early amygdala degeneration disrupts visual pathways, while advanced-stage disconnection between the amygdala and frontal regions and retinal neurodegeneration contributes to further visual disability.
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    Brain degeneration in synucleinopathies based on analysis of cognition and other nonmotor features: a multimodal imaging study
    (MDPI, 2023-02-15) Lucas Jiménez, Olaia; Ibarretxe Bilbao, Naroa; Diez, Ibai; Peña Lasa, Javier ; Tijero Merino, Beatriz; Galdos Iztueta, Marta; Murueta-Goyena Larrañaga, Ane; Pino, Rocío del; Acera Gil, María Ángeles ; Gómez Esteban, Juan Carlos; Gabilondo Cuellar, Iñigo; Ojeda del Pozo, Natalia
    Background: We aimed to characterize subtypes of synucleinopathies using a clustering approach based on cognitive and other nonmotor data and to explore structural and functional magnetic resonance imaging (MRI) brain differences between identified clusters. Methods: Sixty-two patients (n = 6 E46K-SNCA, n = 8 dementia with Lewy bodies (DLB) and n = 48 idiopathic Parkinson’s disease (PD)) and 37 normal controls underwent nonmotor evaluation with extensive cognitive assessment. Hierarchical cluster analysis (HCA) was performed on patients’ samples based on nonmotor variables. T1, diffusion-weighted, and resting-state functional MRI data were acquired. Whole-brain comparisons were performed. Results: HCA revealed two subtypes, the mild subtype (n = 29) and the severe subtype (n = 33). The mild subtype patients were slightly impaired in some nonmotor domains (fatigue, depression, olfaction, and orthostatic hypotension) with no detectable cognitive impairment; the severe subtype patients (PD patients, all DLB, and the symptomatic E46K-SNCA carriers) were severely impaired in motor and nonmotor domains with marked cognitive, visual and bradykinesia alterations. Multimodal MRI analyses suggested that the severe subtype exhibits widespread brain alterations in both structure and function, whereas the mild subtype shows relatively mild disruptions in occipital brain structure and function. Conclusions: These findings support the potential value of incorporating an extensive nonmotor evaluation to characterize specific clinical patterns and brain degeneration patterns of synucleinopathies.
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    Group-level progressive alterations in brain connectivity patterns revealed by diffusion-tensor brain networks across severity stages in Alzheimer's disease
    (Frontiers Media S.A., 2017-07-07) Rasero, Javier; Alonso Montes, Carmen ; Diez, Ibai; Olabarrieta Landa, Laiene ; Remaki, Lakhdar; Escudero Martínez, Iñaki ; Mateos Goñi, Beatriz ; Bonifazi, Paolo ; Fernández Martínez, Manuel; Arango Lasprilla, Juan Carlos; Stramaglia, Sebastiano; Cortes, Jesús M.
    Alzheimer's disease (AD) is a chronically progressive neurodegenerative disease highly correlated to aging. Whether AD originates by targeting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. Here, we aim to provide an answer to this question at the group-level by looking at differences in diffusion-tensor brain networks. In particular, making use of data from Alzheimer's Disease Neuroimaging Initiative (ADNI), four different groups were defined (all of them matched by age, sex and education level): G1 (N1 = 36, healthy control subjects, Control), G2 (N2 = 36, early mild cognitive impairment, EMCI), G3 (N3 = 36, late mild cognitive impairment, LMCI) and G4 (N4 = 36, AD). Diffusion-tensor brain networks were compared across three disease stages: stage I (Control vs. EMCI), stage II (Control vs. LMCI) and stage III (Control vs. AD). The group comparison was performed using the multivariate distance matrix regression analysis, a technique that was born in genomics and was recently proposed to handle brain functional networks, but here applied to diffusion-tensor data. The results were threefold: First, no significant differences were found in stage I. Second, significant differences were found in stage II in the connectivity pattern of a subnetwork strongly associated to memory function (including part of the hippocampus, amygdala, entorhinal cortex, fusiform gyrus, inferior and middle temporal gyrus, parahippocampal gyrus and temporal pole). Third, a widespread disconnection across the entire AD brain was found in stage III, affecting more strongly the same memory subnetwork appearing in stage II, plus the other new subnetworks, including the default mode network, medial visual network, frontoparietal regions and striatum. Our results are consistent with a scenario where progressive alterations of connectivity arise as the disease severity increases and provide the brain areas possibly involved in such a degenerative process. Further studies applying the same strategy to longitudinal data are needed to fully confirm this scenario.