Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org

Image-Based Learning Using Gradient Class Activation Maps for Enhanced Physiological Interpretability of Motor Imagery Skills

Brain activity stimulated by the motor imagery paradigm (MI) is measured by Electroencephalography (EEG), which has several advantages to be implemented with the widely used Brain–Computer Interfaces (BCIs) technology. However, the substantial inter/intra variability of recorded data significantly influences individual skills on the achieved performance. This study explores the ability to distinguish between MI tasks and the interpretability of the brain’s ability to produce elicited mental responses with improved accuracy. We develop a Deep and Wide Convolutional Neuronal Network fed by a set of topoplots extracted from the multichannel EEG data. Further, we perform a visualization technique based on gradient-based class activation maps (namely, GradCam++) at different intervals along the MI paradigm timeline to account for intra-subject variability in neural responses over time. We also cluster the dynamic spatial representation of the extracted maps across the subject set to come to a deeper understanding of MI-BCI coordination skills. According to the results obtained from the evaluated GigaScience Database of motor-evoked potentials, the developed approach enhances the physiological explanation of motor imagery in aspects such as neural synchronization between rhythms, brain lateralization, and the ability to predict the MI onset responses and their evolution during training sessions

Image-Based Learning Using Gradient Class Activation Maps for Enhanced Physiological Interpretability of Motor Imagery Skills

Brain activity stimulated by the motor imagery paradigm (MI) is measured by Electroencephalography (EEG), which has several advantages to be implemented with the widely used Brain–Computer Interfaces (BCIs) technology. However, the substantial inter/intra variability of recorded data significantly influences individual skills on the achieved performance. This study explores the ability to distinguish between MI tasks and the interpretability of the brain’s ability to produce elicited mental responses with improved accuracy. We develop a Deep and Wide Convolutional Neuronal Network fed by a set of topoplots extracted from the multichannel EEG data. Further, we perform a visualization technique based on gradient-based class activation maps (namely, GradCam++) at different intervals along the MI paradigm timeline to account for intra-subject variability in neural responses over time. We also cluster the dynamic spatial representation of the extracted maps across the subject set to come to a deeper understanding of MI-BCI coordination skills. According to the results obtained from the evaluated GigaScience Database of motor-evoked potentials, the developed approach enhances the physiological explanation of motor imagery in aspects such as neural synchronization between rhythms, brain lateralization, and the ability to predict the MI onset responses and their evolution during training sessions

Tdnn-Based Engine In-Cylinder Pressure Estimation from Shaft Velocity Spectral Representation

Pressure is one of the essential variables to give information about engine condition and monitoring. Direct recording of this signal is complex and invasive, while angular velocity can be measured. Nonetheless, the challenge is to predict the cylinder pressure using the shaft kinematics accurately. In this paper, a time-delay neural network (TDNN), interpreted as a finite pulse response (FIR) filter, is proposed to estimate the in-cylinder pressure of a single-cylinder internal combustion engine (ICE) from fluctuations in shaft angular velocity. The experiments are conducted over data obtained from an ICE operating in 12 different states by changing the angular velocity and load. The TDNN’s delay is adjusted to get the highest possible correlation-based score. Our methodology can predict pressure with an R2 >0.9, avoiding complicated pre-processing steps

Temporal overlap in the activity of Lynx rufus and Canis latrans and their potential prey in the Pico de Orizaba National Park, Mexico

Superposición temporal de la actividad de Lynx rufus y Canis latrans y sus presas potenciales en el Parque Nacional Pico de Orizaba, en México Se cree que el uso diferencial de los recursos, en especial del espacio, la comida y el tiempo, permite la coexistencia de especies del mismo gremio trófico. El tiempo entendido como el patrón de actividad es altamente dinámico. En el Parque Nacional Pico de Orizaba se instalaron 14 cámarastrampa que estuvieron activas durante 12 meses. Se analizaron los patrones de actividad (PA) de las especies mediante histogramas de frecuencia y se calculó el índice de solapamiento (Δ) para determinar la superposición temporal entre dos depredadores, Lynx rufus y Canis latrans y entre los depredadores y sus presas potenciales. Con un esfuerzo de muestreo de 5.110 noches/trampa se obtuvieron 217 registros independientes de L. rufus (45), C. latrans (27) y de ocho especies de presas potenciales (145). Los depredadores fueron catamerales y cuatro presas, nocturnas, principalmente lagomorfos y roedores. La superposición temporal entre ambos depredadores fue Δ = 0,80 y entre estos y sus presas, los valores más altos se encontraron entre C. latrans y los roedores (Δ = 0,80) y entre L. rufus y los lagomorfos (Δ = 0,58), con variaciones entre la estación seca y la de lluvias. Al ser de hábitos catamerales, los depredadores tienen más posibilidades de cazar más presas, en especial las que tienen patrones de actividad variables. Los PA validan la información sobre la variedad de la alimentación y la utilización diferencial de los recursos y las diferencias temporales como estrategias de coexistencia de los depredadores, que se adaptan constantemente a un entorno muy dinámico y cambiante.Superposición temporal de la actividad de Lynx rufus y Canis latrans y sus presas potenciales en el Parque Nacional Pico de Orizaba, en México Se cree que el uso diferencial de los recursos, en especial del espacio, la comida y el tiempo, permite la coexistencia de especies del mismo gremio trófico. El tiempo entendido como el patrón de actividad es altamente dinámico. En el Parque Nacional Pico de Orizaba se instalaron 14 cámarastrampa que estuvieron activas durante 12 meses. Se analizaron los patrones de actividad (PA) de las especies mediante histogramas de frecuencia y se calculó el índice de solapamiento (Δ) para determinar la superposición temporal entre dos depredadores, Lynx rufus y Canis latrans y entre los depredadores y sus presas potenciales. Con un esfuerzo de muestreo de 5.110 noches/trampa se obtuvieron 217 registros independientes de L. rufus (45), C. latrans (27) y de ocho especies de presas potenciales (145). Los depredadores fueron catamerales y cuatro presas, nocturnas, principalmente lagomorfos y roedores. La superposición temporal entre ambos depredadores fue Δ = 0,80 y entre estos y sus presas, los valores más altos se encontraron entre C. latrans y los roedores (Δ = 0,80) y entre L. rufus y los lagomorfos (Δ = 0,58), con variaciones entre la estación seca y la de lluvias. Al ser de hábitos catamerales, los depredadores tienen más posibilidades de cazar más presas, en especial las que tienen patrones de actividad variables. Los PA validan la información sobre la variedad de la alimentación y la utilización diferencial de los recursos y las diferencias temporales como estrategias de coexistencia de los depredadores, que se adaptan constantemente a un entorno muy dinámico y cambiante.Species of the same trophic guild are thought to coexist through their differential use of resources, including food, space and time. Time understood as the pattern of activity is highly dynamic. Fourteen camera–traps were set up in the Pico de Orizaba National Park and active for 12 months. Frequency histograms were used to analyze their activity patterns (AP) and a coefficient of overlap (Δ) was used to determine the temporal overlap between two predators, Lynx rufus and Canis latrans, and the predators and their potential prey. A sampling effort of 5,110 traps/night obtained 217 independent records of L. rufus (45), . latrans (27) and eight potential prey species (145). The predators were cathemeral and four potential prey mainly lagomorphs and rodents were nocturnal. The temporal overlap between the predators Δ = 0.80, and the highest overlap between predators and prey were for C. latrans and rodents (Δ = 0.80), and L. rufus and lagomorphs (Δ = 0.58), with differences between the degree of overlap in dry and rainy seasons. The cathemeral habits of the predators likely increase their likelihood of hunting success, particularly for prey with variable activity patterns. The APs support information on dietary breadth and the differential use of resources and temporal differences as strategies for coexisting predators, continually adapting to a highly dynamic and changing environment