51
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Ali MB, Gu IYH, Berger MS, Pallud J, Southwell D, Widhalm G, Roux A, Vecchio TG, Jakola AS. Domain Mapping and Deep Learning from Multiple MRI Clinical Datasets for Prediction of Molecular Subtypes in Low Grade Gliomas. Brain Sci 2020; 10:E463. [PMID: 32708419 PMCID: PMC7408150 DOI: 10.3390/brainsci10070463] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 07/07/2020] [Accepted: 07/15/2020] [Indexed: 01/17/2023] Open
Abstract
Brain tumors, such as low grade gliomas (LGG), are molecularly classified which require the surgical collection of tissue samples. The pre-surgical or non-operative identification of LGG molecular type could improve patient counseling and treatment decisions. However, radiographic approaches to LGG molecular classification are currently lacking, as clinicians are unable to reliably predict LGG molecular type using magnetic resonance imaging (MRI) studies. Machine learning approaches may improve the prediction of LGG molecular classification through MRI, however, the development of these techniques requires large annotated data sets. Merging clinical data from different hospitals to increase case numbers is needed, but the use of different scanners and settings can affect the results and simply combining them into a large dataset often have a significant negative impact on performance. This calls for efficient domain adaption methods. Despite some previous studies on domain adaptations, mapping MR images from different datasets to a common domain without affecting subtitle molecular-biomarker information has not been reported yet. In this paper, we propose an effective domain adaptation method based on Cycle Generative Adversarial Network (CycleGAN). The dataset is further enlarged by augmenting more MRIs using another GAN approach. Further, to tackle the issue of brain tumor segmentation that requires time and anatomical expertise to put exact boundary around the tumor, we have used a tight bounding box as a strategy. Finally, an efficient deep feature learning method, multi-stream convolutional autoencoder (CAE) and feature fusion, is proposed for the prediction of molecular subtypes (1p/19q-codeletion and IDH mutation). The experiments were conducted on a total of 161 patients consisting of FLAIR and T1 weighted with contrast enhanced (T1ce) MRIs from two different institutions in the USA and France. The proposed scheme is shown to achieve the test accuracy of 74 . 81 % on 1p/19q codeletion and 81 . 19 % on IDH mutation, with marked improvement over the results obtained without domain mapping. This approach is also shown to have comparable performance to several state-of-the-art methods.
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Affiliation(s)
- Muhaddisa Barat Ali
- Department of Electrical Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden; (M.B.A.); (I.Y.-H.G.)
| | - Irene Yu-Hua Gu
- Department of Electrical Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden; (M.B.A.); (I.Y.-H.G.)
| | - Mitchel S. Berger
- Department of Neurological Surgery, University of California San Fransisco, San Francisco, CA 94143-0112, USA; (M.S.B.); (D.S.)
| | - Johan Pallud
- Department of Neurosurgery, GHU Paris—Sainte-Anne Hospital, University of Paris, F-75014 Paris, France; (J.P.); (A.R.)
| | - Derek Southwell
- Department of Neurological Surgery, University of California San Fransisco, San Francisco, CA 94143-0112, USA; (M.S.B.); (D.S.)
| | - Georg Widhalm
- Department of Neurosurgery, University Hospital of Vienna, 1090 Vienna, Austria;
| | - Alexandre Roux
- Department of Neurosurgery, GHU Paris—Sainte-Anne Hospital, University of Paris, F-75014 Paris, France; (J.P.); (A.R.)
| | - Tomás Gomez Vecchio
- Department of Clinical Neurosciences, Institution of Neuroscience and Physiology, Sahlgrenska Academy, 41345 Gothenburg, Sweden;
| | - Asgeir Store Jakola
- Department of Clinical Neurosciences, Institution of Neuroscience and Physiology, Sahlgrenska Academy, 41345 Gothenburg, Sweden;
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52
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López-García D, Sobrado A, Peñalver JMG, Górriz JM, Ruz M. Multivariate Pattern Analysis Techniques for Electroencephalography Data to Study Flanker Interference Effects. Int J Neural Syst 2020; 30:2050024. [PMID: 32496140 DOI: 10.1142/s0129065720500240] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A central challenge in cognitive neuroscience is to understand the neural mechanisms that underlie the capacity to control our behavior according to internal goals. Flanker tasks, which require responding to stimuli surrounded by distracters that trigger incompatible action tendencies, are frequently used to measure this conflict. Even though the interference generated in these situations has been broadly studied, multivariate analysis techniques can shed new light into the underlying neural mechanisms. The current study is an initial approximation to adapt an interference Flanker paradigm embedded in a Demand-Selection Task (DST) to a format that allows measuring concurrent high-density electroencephalography (EEG). We used multivariate pattern analysis (MVPA) to decode conflict-related electrophysiological markers associated with congruent or incongruent target events in a time-frequency resolved way. Our results replicate findings obtained with other analysis approaches and offer new information regarding the dynamics of the underlying mechanisms, which show signs of reinstantiation. Our findings, some of which could not have been obtained with classic analytical strategies, open novel avenues of research.
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Affiliation(s)
- David López-García
- Mind, Brain and Behavior Research Center, University of Granada, Granada, 18071 Spain
| | - Alberto Sobrado
- Mind, Brain and Behavior Research Center, University of Granada, Granada, 18071 Spain
| | - José M G Peñalver
- Mind, Brain and Behavior Research Center, University of Granada, Granada, 18071 Spain
| | - Juan Manuel Górriz
- Department of Signal Theory, Telematics and Communications, University of Granada, Granada, 18071 Spain
| | - María Ruz
- Mind, Brain and Behavior Research Center, Department of Experimental Psychology, University of Granada, Granada, 18071 Spain
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Martinez-Murcia FJ, Ortiz A, Gorriz JM, Ramirez J, Lopez-Abarejo PJ, Lopez-Zamora M, Luque JL. EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia. Int J Neural Syst 2020; 30:2050037. [PMID: 32466692 DOI: 10.1142/s0129065720500379] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
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 (0.5-1[Formula: see text]Hz), syllabic (4-8[Formula: see text]Hz) or the phoneme (12-40[Formula: see text]Hz) 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 ([Formula: see text]) 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.
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Affiliation(s)
- Francisco J Martinez-Murcia
- Department of Communications Engineering, University of Malaga, Malaga, Spain.,DaSCI Andalusian Institute of Data Science and Computational Intelligence, University of Granada, Granada, Spain
| | - Andres Ortiz
- Department of Communications Engineering, University of Malaga, Malaga, Spain.,DaSCI Andalusian Institute of Data Science and Computational Intelligence, University of Granada, Granada, Spain
| | - Juan Manuel Gorriz
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain.,DaSCI Andalusian Institute of Data Science and Computational Intelligence, University of Granada, Granada, Spain
| | - Javier Ramirez
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain.,DaSCI Andalusian Institute of Data Science and Computational Intelligence, University of Granada, Granada, Spain
| | | | - Miguel Lopez-Zamora
- Department of Evolutive Psychology and Education, University of Malaga, Malaga, Spain
| | - Juan Luis Luque
- Department of Evolutive Psychology and Education, University of Malaga, Malaga, Spain
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Kucharski D, Kleczek P, Jaworek-Korjakowska J, Dyduch G, Gorgon M. Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders. SENSORS 2020; 20:s20061546. [PMID: 32168748 PMCID: PMC7146382 DOI: 10.3390/s20061546] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Revised: 02/28/2020] [Accepted: 03/04/2020] [Indexed: 11/24/2022]
Abstract
In this research, we present a semi-supervised segmentation solution using convolutional autoencoders to solve the problem of segmentation tasks having a small number of ground-truth images. We evaluate the proposed deep network architecture for the detection of nests of nevus cells in histopathological images of skin specimens is an important step in dermatopathology. The diagnostic criteria based on the degree of uniformity and symmetry of border irregularities are particularly vital in dermatopathology, in order to distinguish between benign and malignant skin lesions. However, to the best of our knowledge, it is the first described method to segment the nests region. The novelty of our approach is not only the area of research, but, furthermore, we address a problem with a small ground-truth dataset. We propose an effective computer-vision based deep learning tool that can perform the nests segmentation based on an autoencoder architecture with two learning steps. Experimental results verified the effectiveness of the proposed approach and its ability to segment nests areas with Dice similarity coefficient 0.81, sensitivity 0.76, and specificity 0.94, which is a state-of-the-art result.
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Affiliation(s)
- Dariusz Kucharski
- Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland; (P.K.); (J.J.-K.); (M.G.)
- Correspondence:
| | - Pawel Kleczek
- Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland; (P.K.); (J.J.-K.); (M.G.)
| | - Joanna Jaworek-Korjakowska
- Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland; (P.K.); (J.J.-K.); (M.G.)
| | - Grzegorz Dyduch
- Chair of Pathomorphology, Jagiellonian University Medical College, ul. Grzegorzecka 16, 31-531 Krakow, Poland
| | - Marek Gorgon
- Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland; (P.K.); (J.J.-K.); (M.G.)
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Golriz Khatami S, Robinson C, Birkenbihl C, Domingo-Fernández D, Hoyt CT, Hofmann-Apitius M. Challenges of Integrative Disease Modeling in Alzheimer's Disease. Front Mol Biosci 2020; 6:158. [PMID: 31993440 PMCID: PMC6971060 DOI: 10.3389/fmolb.2019.00158] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 12/18/2019] [Indexed: 12/15/2022] Open
Abstract
Dementia-related diseases like Alzheimer's Disease (AD) have a tremendous social and economic cost. A deeper understanding of its underlying pathophysiologies may provide an opportunity for earlier detection and therapeutic intervention. Previous approaches for characterizing AD were targeted at single aspects of the disease. Yet, due to the complex nature of AD, the success of these approaches was limited. However, in recent years, advancements in integrative disease modeling, built on a wide range of AD biomarkers, have taken a global view on the disease, facilitating more comprehensive analysis and interpretation. Integrative AD models can be sorted in two primary types, namely hypothetical models and data-driven models. The latter group split into two subgroups: (i) Models that use traditional statistical methods such as linear models, (ii) Models that take advantage of more advanced artificial intelligence approaches such as machine learning. While many integrative AD models have been published over the last decade, their impact on clinical practice is limited. There exist major challenges in the course of integrative AD modeling, namely data missingness and censoring, imprecise human-involved priori knowledge, model reproducibility, dataset interoperability, dataset integration, and model interpretability. In this review, we highlight recent advancements and future possibilities of integrative modeling in the field of AD research, showcase and discuss the limitations and challenges involved, and finally, propose avenues to address several of these challenges.
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Affiliation(s)
- Sepehr Golriz Khatami
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Christine Robinson
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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