Grupo: Signal Processing and Biomedical Applications (SIPBA) (TIC218)https://hdl.handle.net/10481/425362024-03-28T20:07:12Z2024-03-28T20:07:12ZEEG Connectivity Analysis Using Denoising Autoencoders for the Detection of DyslexiaMartinez-Murcia, Francisco JesúsGorriz Sáez, Juan ManuelOrtiz, AndrésRamírez Pérez De Inestrosa, JavierLopez-Abarejo, Pedro JavierLópez Zamora, MiguelLuque, Juan Luishttps://hdl.handle.net/10481/800262023-02-17T10:15:43ZEEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia
Martinez-Murcia, Francisco Jesús; Gorriz Sáez, Juan Manuel; Ortiz, Andrés; Ramírez Pérez De Inestrosa, Javier; Lopez-Abarejo, Pedro Javier; López Zamora, Miguel; Luque, Juan Luis
The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography (EEG) experiments on children listening to amplitude modulated (AM) noise with slow-rythmic prosodic (0.5–1Hz), syllabic (4–8Hz) or the phoneme (12–40Hz) rates, aimed at detecting differences in perception of oscillatory sampling that could be associated with dyslexia. The purpose of this work is to check whether these differences exist and how they are related to children’s performance in different language and cognitive tasks commonly used to detect dyslexia. To this purpose, temporal and spectral inter-channel EEG connectivity was estimated, and a denoising autoencoder (DAE) was trained to learn a low-dimensional representation of the connectivity matrices. This representation was studied via correlation and classification analysis, which revealed ability in detecting dyslexic subjects with an accuracy higher than 0.8, and balanced accuracy around 0.7. Some features of the DAE representation were significantly correlated (𝑝�<0.005
) with children’s performance in language and cognitive tasks of the phonological hypothesis category such as phonological awareness and rapid symbolic naming, as well as reading efficiency and reading comprehension. Finally, a deeper analysis of the adjacency matrix revealed a reduced bilateral connection between electrodes of the temporal lobe (roughly the primary auditory cortex) in DD subjects, as well as an increased connectivity of the F7 electrode, placed roughly on Broca’s area. These results pave the way for a complementary assessment of dyslexia using more objective methodologies such as EEG.
A Structural Parametrization of the Brain Using Hidden Markov Models Based Paths in Alzheimer's DiseaseMartínez Murcia, Francisco JesúsGorriz Sáez, Juan ManuelRamírez Pérez De Inestrosa, JavierOrtiz, Andréshttps://hdl.handle.net/10481/428082021-06-25T09:20:16ZA Structural Parametrization of the Brain Using Hidden Markov Models Based Paths in Alzheimer's Disease
Martínez Murcia, Francisco Jesús; Gorriz Sáez, Juan Manuel; Ramírez Pérez De Inestrosa, Javier; Ortiz, Andrés
The usage of biomedical imaging in the diagnosis of dementia is increasingly widespread. A number of works explore the possibilities of computational techniques and algorithms in what is called Computed Aided Diagnosis. Our work presents an automatic parametrization of the brain structure by means of a path generation algorithm based on Hidden Markov Models. The path is traced using information of intensity and spatial orientation in each node, adapting to the structural changes of the brain. Each path is itself a useful way to extract features from the MRI image, being the intensity levels at each node the most straightforward. However, a further processing consisting of a modification of the Gray Level Co-occurrence Matrix can be used to characterize the textural changes that occur throughout the path, yielding more meaningful values that could be associated to the structural changes in Alzheimer's Disease, as well as providing a significant feature reduction. This methodology achieves high performance, up to 80.3\% of accuracy using a single path in differential diagnosis involving Alzheimer-affected subjects versus controls belonging to the Alzheimer's Disease Neuroimaging Initiative (ADNI).
A Spherical Brain Mapping of MR Images for the Detection of Alzheimer's DiseaseMartínez Murcia, Francisco JesúsGorriz Sáez, Juan ManuelRamírez Pérez De Inestrosa, JavierOrtiz, Andréshttps://hdl.handle.net/10481/425432021-06-25T09:20:16ZA Spherical Brain Mapping of MR Images for the Detection of Alzheimer's Disease
Martínez Murcia, Francisco Jesús; Gorriz Sáez, Juan Manuel; Ramírez Pérez De Inestrosa, Javier; Ortiz, Andrés
Magnetic Resonance Imaging (MRI) is of fundamental importance in neuroscience, providing good contrast and resolution, as well as not being considered invasive. Despite the development of newer techniques involving radiopharmaceuticals, it is still a recommended tool in Alzheimer’s Disease (AD) neurological practice to assess neurodegeneration, and recent research suggests that it could reveal changes in the brain even before the symptomatology appears. In this paper we propose a method that performs a Spherical Brain Mapping, using different measures to project the three-dimensional MR brain images onto two-dimensional maps revealing statistical characteristics of the tissue. The resulting maps could be assessed visually, but also perform a significant feature reduction that will allow further supervised or unsupervised processing, reducing the computational load while maintaining a large amount of the original information. We have tested our methodology against a MRI database comprising 180 AD affected patients and 180 normal controls, where some of the mappings have revealed as an optimum strategy for the automatic processing and characterization of AD patterns, achieving up to a 90.9% of accuracy, as well as significantly reducing the computational load. Additionally, our maps allow the visual analysis and interpretation of the images, which can be of great help in the diagnosis of this and other types of dementia.