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[EN] Background: Retinal microvascular alterations have been previously described in Parkinson’s disease (PD) patients using optical coherence tomography angiography (OCT-A). However, an extensive description of retinal vascular morphological features, their association with PD-related clinical variables and their potential use as diagnostic biomarkers has not been explored. Methods: We performed a cross-sectional study including 49 PD patients (87 eyes) and 40 controls (73 eyes). Retinal microvasculature was evaluated with Spectralis OCT-A and cognitive status with Montreal Cognitive Assessment. Unified PD Rating Scale and disease duration were recorded in patients. We extracted microvascular parameters from superficial and deep vascular plexuses of the macula, including the area and circularity of foveal avascular zone (FAZ), skeleton density, perfusion density, vessel perimeter index, vessel mean diameter, fractal dimension (FD) and lacunarity using Python and MATLAB. We compared the microvascular parameters between groups and explored their association with thickness of macular layers and clinical outcomes. Data were analyzed with General Estimating Equations (GEE) and adjusted for age, sex, and hypertension. Logistic regression GEE models were fitted to predict diagnosis of PD versus controls from microvascular, demographic, and clinical data. The discrimination ability of models was tested with receiver operating characteristic curves. Results: FAZ area was significantly smaller in patients compared to controls in superficial and deep plexuses, whereas perfusion density, skeleton density, FD and lacunarity of capillaries were increased in the foveal zone of PD. In the parafovea, microvascular parameters of superficial plexus were associated with ganglion cellinner plexiform layer thickness, but this was mainly driven by PD with mild cognitive impairment. No such associations were observed in controls. FAZ area was negatively associated with cognition in PD (no

Disentangling the cellular anatomy that gives rise to human visual perception is one of the main challenges of ophthalmology. Of particular interest is the foveal pit, a concave depression located at the center of the retina that captures light from the gaze center. In recent years, there has been a growing interest in studying the morphology of the foveal pit by extracting geometrical features from optical coherence tomography (OCT) images. Despite this, research has devoted little attention to comparing existing approaches for two key methodological steps: the location of the foveal center and the mathematical modelling of the foveal pit. Building upon a dataset of 185 healthy subjects imaged twice, in the present paper the image alignment accuracy of four different foveal center location methods is studied in the first place. Secondly, state-of-the-art foveal pit mathematical models are compared in terms of fitting error, repeatability, and bias. The results indicate the importance of using a robust foveal center location method to align images. Moreover, we show that foveal pit models can improve the agreement between different acquisition protocols. Nevertheless, they can also introduce important biases in the parameter estimates that should be considered.

Background: Retinal microvascular alterations have been previously described in Parkinson’s disease (PD) patients using optical coherence tomography angiography (OCT-A). However, an extensive description of retinal vascular morphological features, their association with PD-related clinical variables and their potential use as diagnostic biomarkers has not been explored. Methods: We performed a cross-sectional study including 49 PD patients (87 eyes) and 40 controls (73 eyes). Retinal microvasculature was evaluated with Spectralis OCT-A and cognitive status with Montreal Cognitive Assessment. Unified PD Rating Scale and disease duration were recorded in patients. We extracted microvascular parameters from superficial and deep vascular plexuses of the macula, including the area and circularity of foveal avascular zone (FAZ), skeleton density, perfusion density, vessel perimeter index, vessel mean diameter, fractal dimension (FD) and lacunarity using Python and MATLAB. We compared the microvascular parameters between groups and explored their association with thickness of macular layers and clinical outcomes. Data were analyzed with General Estimating Equations (GEE) and adjusted for age, sex, and hypertension. Logistic regression GEE models were fitted to predict diagnosis of PD versus controls from microvascular, demographic, and clinical data. The discrimination ability of models was tested with receiver operating characteristic curves. Results: FAZ area was significantly smaller in patients compared to controls in superficial and deep plexuses, whereas perfusion density, skeleton density, FD and lacunarity of capillaries were increased in the foveal zone of PD. In the parafovea, microvascular parameters of superficial plexus were associated with ganglion cell-inner plexiform layer thickness, but this was mainly driven by PD with mild cognitive impairment. No such associations were observed in controls. FAZ area was negatively associated with cognition in PD (non-ad

Disentangling the cellular anatomy that gives rise to human visual perception is one of the main challenges of ophthalmology. Of particular interest is the foveal pit, a concave depression located at the center of the retina that captures light from the gaze center. In recent years, there has been a growing interest in studying the morphology of the foveal pit by extracting geometrical features from optical coherence tomography (OCT) images. Despite this, research has devoted little attention to comparing existing approaches for two key methodological steps: the location of the foveal center and the mathematical modelling of the foveal pit. Building upon a dataset of 185 healthy subjects imaged twice, in the present paper the image alignment accuracy of four different foveal center location methods is studied in the first place. Secondly, state-of-the-art foveal pit mathematical models are compared in terms of fitting error, repeatability, and bias. The results indicate the importance of using a robust foveal center location method to align images. Moreover, we show that foveal pit models can improve the agreement between different acquisition protocols. Nevertheless, they can also introduce important biases in the parameter estimates that should be considered.

Frente a los análisis convencionales de grosores de la retina, el análisis morfológico de la fóvea presenta una metodología alternativa de gran potencial pero poco explorada. En este estudio se implementa un pipeline completo de análisis morfológico basado en imágenes OCT (Optical Coherence Tomography), comparando los modelos matemáticos existentes para estudiar la fóvea y demostrando su capacidad de ajuste y mejora de la fiabilidad test-retest. Asimismo, se analiza la influencia de la edad y el sexo en parámetros morfológicos como la altura, anchura, pendiente o área de la fóvea en una cohorte de 272 sujetos sanos. Los resultados muestran un claro dimorfismo sexual en la fóvea junto con relevantes cambios experimentados durante el envejecimiento.

El cerebro humano, pese a su homogénea apariencia externa, presenta un intrincado patrón de conexiones internas que interconectan las diferentes regiones cerebrales. Estas conexiones, conocidas como tractos neuronales, están formadas por miles de neuronas agrupadas de forma ordenada y son la base de la correcta intercomunicación en un cerebro sano. Los recientes avances en técnicas de neuroimagen y análisis genético han dado pie a diversos estudios que apuntan a que la genética podría jugar un rol fundamental en la construcción y correcto funcionamiento de los tractos neuronales. El presente Trabajo Fin de Máster busca avanzar en la comprensión sobre cómo la expresión genética determina o condiciona la interconexión entre las distintas regiones cerebrales. Para ello, se ha llevado a cabo en primer lugar un preprocesado de datos de expresión genética que han permitido su adecuación a los análisis planteados en éste y futuros trabajos. Los análisis realizados parten de un enfoque basado en la ciencia de redes donde el cerebro es modelado como una red o grafo que es posteriormente analizado desde múltiples vertientes. En un primer análisis estadístico se ha estudiado si regiones genéticamente similares tienen mayor probabilidad de estar conectadas. Posteriormente, se ha observado en qué medida criterios basados en genética o conectividad agrupan las regiones cerebrales de forma distinta. Finalmente, se ha planteado el uso de técnicas de procesado de señal sobre grafos como una nueva herramienta de análisis. Este enfoque permite obtener una representación frecuencial de los patrones de expresión genética, y ha sido introducido para mejorar la comprensión de dichos patrones., The human brain, despite its external homogeneous appearance, hides a complex intern pattern of connections that interconnect the different brain regions. Underneath the brain surface, thousands of neurons group together in an organized way to form the so-called neural tracts. These connections are the basis of a correct intercommunication in a healthy brain. Recent progress in both neuroimaging and genetic analysis techniques has given rise to several studies that, all together, point to a fundamental role of genomics in the development and correct functioning of neural tracts. The aim of this Master’s Thesis is to improve the understanding of how genetic expression actually determines the interconnection between brain regions. In this sense, several steps of preprocessing were carried out to adapt genetic expression data for further analysis performed in this and future projects. Following a network science approach, the brain was modelled as a network or graph that was later studied using different analysis types. In a first statistical analysis, it was tested whether genetically similar regions are more likely to be connected or not. Later, the way brain regions group together based on different genetic and connectivity criteria was observed. Finally, a novel approach based on graph signal processing techniques was proposed. More specifically, a spectral representation of genetic data was used to better understand gene expression patterns along the brain., Giza garunak, nahiz eta kanpotik itxura homogeneoa eduki, garun eskualde ezberdinak konektatzen dituen konexio patroi korapilatsua aurkezten du barrutik. Traktu neuronalak deritzen konexio hauek era antolatuan elkartutako milaka neuronaz osatuta daude, eta garun osasuntsu batetan barne komunikazioaren oinarria dira. Neuroirudi eta analisi genetikoen arloetako azken aurrerapenak ikerketa lan ugari sustatu dituzte. Dirudienez, lortutako emaitzak traktu neuronalen garapen eta funtzionamendu egokietan genetikak garrantzi handia daukala antzematen dute. Master Amaierako Lan honen helburu nagusia adierazpen genetiko eta garun eskualdeen interkomunikazioaren arteko erlazioaren ulermenean aurrera egitea da. Horretarako, lehenik eta behin, adierazpen genetikoko datu-baseen tratamendu eta egokitzapen prozesu bat jarraitu da. Prozesu honen emaitzak lan honetan egindako analisiak eta baita etorkizuneko beste lan batzuetan egin daitezkeenak posible egin ditu. Egindako analisi guztiak sare-zientzian oinarritutako ikuspegitik garatu dira. Honen arabera, giza garuna sare edo grafo bat balitz bezala modelatu eta ikertu egin da. Lehen urrats batean, analisi estatistikoa erabili da genetikoki antzekoak diren garun eskualdeak konektatuta egoteko probabilitate handiagoa ote duten aztertzeko. Ondoren, garun eskualdeak genetika edo konektibitatea banaka kontuan hartuta nola elkartzen diren aztertu da. Azkenik, grafoen domeinurako egokitutako seinale tratamendu teknikak erabiliz, gene adierazpen balioen maiztasun irudikapenak kalkulatu dira. Balio hauek aztertuz, adierazpen patroiak hobeto ulertzea izan da helburua.