Complex network modeling of EEG band coupling in dyslexia: An exploratory analysis of auditory processing and diagnosis

Complex network analysis has an increasing relevance in the study of neurological disorders, enhancing the knowledge of brain’s structural and functional organization. Network structure and efficiency reveal different brain states along with different ways of processing the information. This work is structured around the exploratory analysis of the brain processes involved in low-level auditory processing. A complex network analysis was performed on the basis of brain coupling obtained from electroencephalography (EEG) data, while different auditory stimuli were presented to the subjects. This coupling is inferred from the Phase-Amplitude coupling (PAC) from different EEG electrodes to explore differences between control and dyslexic subjects. Coupling data allows the construction of a graph, and then, graph theory is used to study the characteristics of the complex networks throughout time for control and dyslexic subjects. This results in a set of metrics including clustering coefficient, path length and small-worldness. From this, different characteristics linked to the temporal evolution of networks and coupling are pointed out for dyslexics. Our study revealed patterns related to Dyslexia as losing the small-world topology. Finally, these graph-based features are used to classify between control and dyslexic subjects by means of a Support Vector Machine (SVM).Spanish Government PGC2018-098813-B-C32Junta de Andalucia UMA20-FEDERJA-086European CommissionNVIDIA CorporationMinistry of Science and Innovation, Spain (MICINN) Spanish GovernmentEuropean CommissionUniversidad de Malaga/CBU

Frequency and Longitudinal Course of Motor Signs in Genetic Frontotemporal Dementia

Background and Objectives Frontotemporal dementia (FTD) is a highly heritable disorder. The majority of genetic cases are caused by autosomal dominant pathogenic variants in the chromosome 9 open reading frame 72 (c9orf72), progranulin (GRN), and microtubule-associated protein tau (MAPT) gene. As motor disorders are increasingly recognized as part of the clinical spectrum, the current study aimed to describe motor phenotypes caused by genetic FTD, quantify their temporal association, and investigate their regional association with brain atrophy. Methods We analyzed baseline visit data of known carriers of a pathogenic variant in the c9orf72, GRN, or MAPT gene from the Genetic Frontotemporal Dementia Initiative cohort study. Principal component analysis with varimax rotation was performed to identify motor sign clusters that were compared with respect to frequency and severity between groups. Associations with cross-sectional atrophy patterns were determined using voxel-wise regression. We applied linear mixed effects models to assess whether groups differed in the association between motor signs and estimated time to symptom onset. Results A total of 322 pathogenic variant carriers were included in the analysis: 122 c9orf72 (79 presymptomatic), 143 GRN (112 presymptomatic), and 57 MAPT (43 presymptomatic) pathogenic variant carriers. Principal component analysis revealed 5 motor clusters, which we call progressive supranuclear palsy (PSP)-like, bulbar amyotrophic lateral sclerosis (ALS)-like, mixed/ALS-like, Parkinson disease (PD) like, and corticobasal syndrome–like motor phenotypes. There was no significant group difference in the frequency of signs of different motor phenotypes. However, mixed/ALS-like motor signs were most frequent, followed by PD-like motor signs. Although the PSP-like phenotype was associated with mesencephalic atrophy, the mixed/ALS-like phenotype was associated with motor cortex and corticospinal tract atrophy. The PD-like phenotype was associated with widespread cortical and subcortical atrophy. Estimated time to onset, genetic group and their interaction influenced motor signs. In c9orf72 pathogenic variant carriers, motor signs could be detected up to 25 years before expected symptom onset. Discussion These results indicate the presence of multiple natural clusters of motor signs in genetic FTD, each correlated with specific atrophy patterns. Their motor severity depends on time and the affected gene. These clinicogenetic associations can guide diagnostic evaluations and the design of clinical trials for new disease-modifying and preventive treatments.UK Research & Innovation (UKRI) Medical Research Council UK (MRC) MR/M023664/1German Research Foundation (DFG) EXC 2145 390857198Ministry of Health, ItalyCanadian Institutes of Health Research (CIHR)JPND grant GENFIproxEuropean Reference Network for Rare Neurological Diseases (ERN-RND) European Commission European Commission Joint Research Centre ERNRND: 3HP 767231Ministerio de Ciencia e Innovacion (Espana)/FEDER RTI2018-098913-B100Junta de AndaluciaEuropean Commission CV20-45250 A-TIC080-UGR18Canadian Institutes of Health Research (CIHR) MOP 327387Weston Brain InstituteUK Research & Innovation (UKRI)Medical Research Council UK (MRC)European Commission SUAG/051 G101400National Institute for Health Research (NIHR) BRC-1215-2001

A Spherical Brain Mapping of MR Images for the Detection of Alzheimer's Disease

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.This work was partly supported by the MICINN under the TEC2008-02113 and TEC2012-34306 projects and the Consejerıa de Econom´ıa, Innovacion, Ciencia y Empleo (Junta de Andalucıa, Spain) under the Excellence Projects P09-TIC-4530 and P11-TIC-7103, as well as the “Programa de fortalecimiento de las capacidades de I+D+I en las Universidades 2014-2015”, cofunded by the European Regional Development Fund (ERDF) under Project FC14-SAF-3

Deep Residual Transfer Learning for Automatic Diabetic Retinopathy Grading.

Evaluation and diagnosis of retina pathology is usually made via the analysis of different image modalities that allow to explore its structure. The most popular retina image method is retinography, a technique that displays the fundus of the eye, including the retina and other structures. Retinography is the most common imaging method to diagnose retina diseases such as Diabetic Retinopathy (DB) or Macular Edema (ME). However, retinography evaluation to score the image according to the disease grade presents difficulties due to differences in contrast, brightness and the presence of artifacts. Therefore, it is mainly done via manual analysis; a time consuming task that requires a trained clinician to examine and evaluate the images. In this paper, we present a computer aided diagnosis tool that takes advantage of the performance provided by deep learning architectures for image analysis. Our proposal is based on a deep residual convolutional neural network for extracting discriminatory features with no prior complex image transformations to enhance the image quality or to highlight specific structures. Moreover, we used the transfer learning paradigm to reuse layers from deep neural networks previously trained on the ImageNet dataset, under the hypothesis that first layers capture abstract features than can be reused for different problems. Experiments using different convolutional architectures have been carried out and their performance has been evaluated on the MESSIDOR database using cross-validation. Best results were found using a ResNet50-based architecture, showing an AUC of 0.93 for grades 0 + 1, AUC of 0.81 for grade 2 and AUC of 0.92 for grade 3 labelling, as well as AUCs higher than 0.97 when considering a binary classification problem (grades 0 vs 3).This work was partly supported by the MINECO/FEDER under TEC2015-64718-R, RTI2018-098913-B-I00, PSI2015-65848-R and PGC2018-098813-B-C32 projects. We gratefully acknowledge the support of NVIDIA Cor poration with the donation of one of the GPUs used for this research. Work by F.J.M.M. was supported by the MICINN “Juan de la Cierva - Formacion” Fellowship

Temporal Phase Synchrony Disruption in Dyslexia: Anomaly Patterns in Auditory Processing

The search for a dyslexia diagnosis based on exclusively objective methods is currently a challenging task. Usually, this disorder is analyzed by means of behavioral tests prone to errors due to their subjective nature; e.g. the subject’s mood while doing the test can affect the results. Understanding the brain processes involved is key to proportionate a correct analysis and avoid these types of problems. It is in this task, biomarkers like electroencephalograms can help to obtain an objective measurement of the brain behavior that can be used to perform several analyses and ultimately making a diagnosis, keeping the human interaction at minimum. In this work, we used recorded electroencephalograms of children with and without dyslexia while a sound stimulus is played. We aim to detect whether there are significant differences in adaptation when the same stimulus is applied at different times. Our results show that following this process, a machine learning pipeline can be built with AUC values up to 0.73.Spanish Government PGC2018-098813-BC32 PGC2018-098813-B-C31Junta de Andalucia UMA20-FEDERJA-086 P18-RT-1624European CommissionBioSiP research group TIC-251MCIN/AEI by "ESF Investing in your future" PRE2019-087350 MICINN "Juan de la Cierva -Incorporacion" FellowshipLeeduca research groupJunta de Andalucia Spanish Governmen

Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling.

Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.This work was partly supported by the MINECO/ FEDER under TEC2015-64718-R and PSI2015- 65848-R projects and the Consejer´ıa de Innovaci´on, Ciencia y Empresa (Junta de Andaluc´ıa, Spain) under the Excellence Project P11-TIC-7103 as well as the Salvador deMadariaga Mobility Grants 2017. Data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Depart ment of Defense award number W81XWH-12-2- 0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contribu tions from the following: AbbVie, Alzheimer’s Asso ciation; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol Myer Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Ho mann-La Roche Ltd and its ali ated company Genentech, Inc.; Fujirebio; GE Health care; IXICO Ltd.; Janssen Alzheimer Immunother apy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; P zer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clin ical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coor dinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern Cali fornia. PPMI a public-private partnership is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including [list the full names of all of the PPMI funding partners found at www.ppmi-info.org/fundingpartners]

Label Aided Deep Ranking for the Automatic Diagnosis of Parkinsonian Syndromes.

Parkinsonism is the second most common neurodegenerative disease in the world. Its diagnosis usually relies on visual analysis of Emission Computed Tomography (SPECT) images acquired using 123I − io f lupane radiotracer. This aims to detect a deficit of dopamine transporters at the striatum. The use of Computer Aided tools for diagnosis based on statistical data processing and machine learning methods have significantly improved the diagnosis accuracy. In this paper we propose a classification method based on Deep Ranking which learns an embedding function that projects the source images into a new space in which samples belonging to the same class are closer to each other, while samples from different classes are moved apart. Moreover, the proposed approach introduces a new cost-sensitive loss function to avoid overfitting due to class imbalance (an usual issue in practical biomedical applications), along with label information to produce sparser embedding spaces. The experiments carried out in this work demonstrate the superiority of the proposed method, improving the diagnosis accuracy achieved by previous methodologies and validate our approach as an efficient way to construct linear classifiers.This work was partly supported by the MINECO/FEDER under TEC2015-64718- R and PSI2015-65848-R projects. We gratefully acknowledge the support of NVIDIA Corporation with the donation of one of the GPUs used for this research. PPMI - a pub435 lic - private partnership - is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc

Complex network modelling of EEG band coupling in dyslexia: An exploratory analysis of auditory processing and diagnosis

Complex network analysis has an increasing relevance in the study of neurological disorders, enhancing the knowledge of brain’s structural and functional organization. Network structure and efficiency reveal different brain states along with different ways of processing the informa- tion. This work is structured around the exploratory analysis of the brain processes involved in low-level auditory processing. A complex network analysis was performed on the basis of brain coupling obtained from electroencephalography (EEG) data, while different auditory stim- uli were presented to the subjects. This coupling is inferred from the Phase-Amplitude coupling (PAC) from different EEG electrodes to explore differences between control and dyslexic sub- jects. Coupling data allows the construction of a graph, and then, graph theory is used to study the characteristics of the complex networks throughout time for control and dyslexic subjects. This results in a set of metrics including clustering coefficient, path length and small-worldness. From this, different characteristics linked to the temporal evolution of networks and coupling are pointed out for dyslexics. Our study revealed patterns related to Dyslexia as losing the small- world topology. Finally, these graph-based features are used to classify between control and dyslexic subjects by means of a Support Vector Machine (SVM).This work was supported by projects PGC2018-098813-B-C32 (Spanish “Ministerio de Cien- cia, Innovación y Universidades”), UMA20-FEDERJA-086 (Consejería de econnomía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF). We gratefully ac- knowledge the support of NVIDIA Corporation with the donation of one of the GPUs used for this research. Work by F.J.M.M. was supported by the MICINN “Juan de la Cierva - Incorpo- ración” Fellowship. We also thank the Leeduca research group and Junta de Andalucía for the data supplied and the support. Funding for open access charge: Universidad de Málaga / CBU

A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms

This work was partly supported by the MINECO Under the TEC2015-64718-R Project, the Salvador de Madariaga Mobility Grants 2017 and the Consejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía, Spain) under the Excellence Project P11-TIC-7103. The study was conducted in association with the National Institute for Health Research Collaborations for Leadership in Applied Health Research and Care (NIHR CLAHRC) East of England (EoE). The Project was supported by the UK Medical Research Council (Grant No. GO 400061) and European Autism Interventions — a Multicentre Study for Developing New Medications (EU-AIMS); EU-AIMS has received support from the Innovative Medicines Initiative Joint Undertaking Under Grant Agreement No. 115300, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in-kind contribution. During the period of this work, M-CL was supported by the OBrien Scholars Program in the Child and Youth Mental Health Collaborative at the Centre for Addiction and Mental Health (CAMH) and The Hospital for Sick Children, Toronto, the Academic Scholar Award from the Department of Psychiatry, University of Toronto, the Slaight Family Child and Youth Mental Health Innovation Fund, CAMH Foundation, and the Ontario Brain Institute via the Province of Ontario Neurodevelopmental Disorders (POND) Network; MVL was supported by the British Academy, Jesus College Cambridge, Wellcome Trust, and an ERC Starting Grant (ERC-2017-STG; 755816); SB-C was supported by the Autism Research Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health, UK.Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autism remains enigmatic. One of the difficulties in investigating differences between the sexes in autism is the small sample sizes of available imaging datasets with mixed sex. Thus, the majority of the investigations have involved male samples, with females somewhat overlooked. This paper deploys machine learning on partial least squares feature extraction to reveal differences in regional brain structure between individuals with autism and typically developing participants. A four-class classification problem (sex and condition) is specified, with theoretical restrictions based on the evaluation of a novel upper bound in the resubstitution estimate. These conditions were imposed on the classifier complexity and feature space dimension to assure generalizable results from the training set to test samples. Accuracies above 80% on gray and white matter tissues estimated from voxel-based morphometry (VBM) features are obtained in a sample of equal-sized high-functioning male and female adults with and without autism (N=120, n=30/group). The proposed learning machine revealed how autism is modulated by biological sex using a low-dimensional feature space extracted from VBM. In addition, a spatial overlap analysis on reference maps partially corroborated predictions of the “extreme male brain” theory of autism, in sexual dimorphic areas.This work was partly supported by the MINECO Under the TEC2015-64718-R Project, the Salvador de Madariaga Mobility Grants 2017 and the Consejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía, Spain) under the Excellence Project P11-TIC-7103The Project was supported by the UK Medical Research Council (Grant No. GO 400061) and European Autism Interventions — a Multicentre Study for Developing New Medications (EU-AIMS)EU-AIMS has received support from the Innovative Medicines Initiative Joint Undertaking Under Grant Agreement No. 115300MVL was supported by the British Academy, Jesus College Cambridge, Wellcome Trust, and an ERC Starting Grant (ERC-2017-STG; 755816

Optimized One vs One approach in multiclass classification for early Alzheimer’s Disease and Mild Cognitive Impairment diagnosis

The detection of Alzheimer’s Disease in its early stages is crucial for patient care and drugs development. Motivated by this fact, the neuroimaging community has extensively applied machine learning techniques to the early diagnosis problem with promising results. The organization of challenges has helped the community to address different raised problems and to standardize the approaches to the problem. In this work we use the data from international challenge for automated prediction of MCI from MRI data to address the multiclass classification problem. We propose a novel multiclass classification approach that addresses the outlier detection problem, uses pairwise t-test feature selection, project the selected features onto a Partial-Least-Squares multiclass subspace, and applies one-versus-one error correction output codes classification. The proposed method yields to an accuracy of 67 % in the multiclass classification, outperforming all the proposals of the competition.Ministerio de Innovacion y Ciencia Project DEEP-NEUROMAPS RTI2018-098913-B100Consejeria de Economia, Innovacion, Ciencia, y Empleo of the Junta de Andalucia A-TIC-080-UGR18 TIC FRONTERAGerman Research Foundation (DFG) FPU 18/04902United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Neurological Disorders & Stroke (NINDS) U01 AG024904DOD ADNI Department of Defense W81XWH-12-2-001