Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

[1]  Kai Wang,et al.  Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI , 2018, EBioMedicine.

[2]  Yanqing Zhang,et al.  GAN-based synthetic brain PET image generation , 2020, Brain Informatics.

[3]  Christian N. L. Olivers,et al.  High-pass filtering artifacts in multivariate classification of neural time series data , 2018 .

[4]  Pietro Liò,et al.  A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease , 2018, NeuroImage.

[5]  Daniel B. Mark,et al.  TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS , 1996 .

[6]  Shalini Jha,et al.  The diagnosis of Parkinson's disease. , 2003, Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology.

[7]  Shantenu Jha,et al.  Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks , 2017, ArXiv.

[8]  Timothy O. Laumann,et al.  Methods to detect, characterize, and remove motion artifact in resting state fMRI , 2014, NeuroImage.

[9]  Mufti Mahmud,et al.  Processing and Analysis of Multichannel Extracellular Neuronal Signals: State-of-the-Art and Challenges , 2016, Front. Neurosci..

[10]  Jun Qi,et al.  Deep multi-view representation learning for multi-modal features of the schizophrenia and schizo-affective disorder , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  Mufti Mahmud,et al.  Deep Learning in Mining Biological Data , 2020, Cognitive Computation.

[12]  Mufti Mahmud,et al.  TeKET: a Tree-Based Unsupervised Keyphrase Extraction Technique , 2020, Cognitive Computation.

[13]  Massimo Filippi,et al.  Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks , 2018, NeuroImage: Clinical.

[14]  Christos Davatzikos,et al.  Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. , 2020, Brain : a journal of neurology.

[15]  Cynthia Rudin,et al.  Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.

[16]  Karl J. Friston,et al.  Slice-timing effects and their correction in functional MRI , 2011, NeuroImage.

[17]  Jyoti Islam,et al.  A Novel Deep Learning Based Multi-class Classification Method for Alzheimer's Disease Detection Using Brain MRI Data , 2017, BI.

[18]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[19]  Martin Weygandt,et al.  Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification , 2019, Front. Aging Neurosci..

[20]  Manohar Latha,et al.  Detection of Schizophrenia in brain MR images based on segmented ventricle region and deep belief networks , 2019, Neural Computing and Applications.

[21]  Mufti Mahmud,et al.  Performance Comparison of Machine Learning Techniques in Identifying Dementia from Open Access Clinical Datasets , 2020, Advances on Smart and Soft Computing.

[22]  András Kocsor,et al.  ROC analysis: applications to the classification of biological sequences and 3D structures , 2008, Briefings Bioinform..

[23]  Mufti Mahmud,et al.  Early hippocampal hyperexcitability in PS2APP mice: role of mutant PS2 and APP , 2017, Neurobiology of Aging.

[24]  Jitender Saini,et al.  Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI , 2019, NeuroImage: Clinical.

[25]  P. R. Anurenjan,et al.  Early Diagnosis of Alzheimer's Disease from MRI Images Using PNN , 2018, 2018 International CET Conference on Control, Communication, and Computing (IC4).

[26]  Saad Rehman,et al.  A deep CNN based multi-class classification of Alzheimer's disease using MRI , 2017, 2017 IEEE International Conference on Imaging Systems and Techniques (IST).

[27]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[28]  Mufti Mahmud,et al.  A Multi-recurrent Network for Crude Oil Price Prediction , 2019, 2019 IEEE Symposium Series on Computational Intelligence (SSCI).

[29]  Mufti Mahmud,et al.  Open-Source Tools for Processing and Analysis of In Vitro Extracellular Neuronal Signals. , 2019, Advances in neurobiology.

[30]  Vince D. Calhoun,et al.  Improving Classification Rate of Schizophrenia Using a Multimodal Multi-Layer Perceptron Model with Structural and Functional MR , 2018, ArXiv.

[31]  Richard Berg,et al.  Sensitivity and specificity. , 2005, Clinical medicine & research.

[32]  C. M. Sujatha,et al.  Deep learning based diagnosis of Parkinson’s disease using convolutional neural network , 2019, Multimedia Tools and Applications.

[33]  Yulia Dodonova,et al.  Residual and plain convolutional neural networks for 3D brain MRI classification , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[34]  Karsten Klein,et al.  A review and outlook on visual analytics for uncertainties in functional magnetic resonance imaging , 2018, Brain Informatics.

[35]  Sébastien Ourselin,et al.  MIRIAD—Public release of a multiple time point Alzheimer's MR imaging dataset , 2013, NeuroImage.

[36]  Mufti Mahmud,et al.  Application of Convolutional Neural Network in Segmenting Brain Regions from MRI Data , 2019, BI.

[37]  F. Harrell,et al.  Prognostic/Clinical Prediction Models: Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors , 2005 .

[38]  Ehsan Adeli,et al.  End-to-End Parkinson Disease Diagnosis using Brain MR-Images by 3D-CNN , 2018, ArXiv.

[39]  Vince D. Calhoun,et al.  Synthetic structural magnetic resonance image generator improves deep learning prediction of schizophrenia , 2015, 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP).

[40]  Adrian B. R. Shatte,et al.  Machine learning in mental health: a scoping review of methods and applications , 2019, Psychological Medicine.

[41]  Amir Hussain,et al.  Applications of Deep Learning and Reinforcement Learning to Biological Data , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[42]  Peter A. Flach.  ROC Analysis , 2010, Encyclopedia of Machine Learning and Data Mining.

[43]  Yong Fan,et al.  Early Prediction Of Alzheimer’s Disease Dementia Based On Baseline Hippocampal MRI and 1-Year Follow-Up Cognitive Measures Using Deep Recurrent Neural Networks , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[44]  Mohamad Habes,et al.  A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data , 2019, Alzheimer's & Dementia.

[45]  Muhammad Naveed Iqbal Qureshi,et al.  3D-CNN based discrimination of schizophrenia using resting-state fMRI , 2019, Artif. Intell. Medicine.

[46]  J. Sato,et al.  Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia , 2016, Scientific Reports.

[47]  Ghassem Tofighi,et al.  DeepAD: Alzheimer’s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI , 2016, bioRxiv.

[48]  Andreas Stafylopatis,et al.  Predicting Parkinson’s Disease using Latent Information extracted from Deep Neural Networks , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

[49]  K. K. Sahu,et al.  Normalization: A Preprocessing Stage , 2015, ArXiv.

[50]  Nicola Amoroso,et al.  Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge , 2017, Journal of Neuroscience Methods.

[51]  Sidong Liu,et al.  Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders , 2015, Brain Informatics.

[52]  A. Cerasa,et al.  Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy , 2014, Journal of Neuroscience Methods.

[53]  Cihan H. Dagli,et al.  Analysis of Parkinson’s Disease Data , 2018 .

[54]  Russell A. Poldrack,et al.  Computational and informatics advances for reproducible data analysis in neuroimaging , 2018, ArXiv.

[55]  Mufti Mahmud,et al.  Dampened Slow Oscillation Connectivity Anticipates Amyloid Deposition in the PS2APP Mouse Model of Alzheimer’s Disease , 2019, Cells.

[56]  Suhuai Luo,et al.  Automatic Alzheimer’s Disease Recognition from MRI Data Using Deep Learning Method , 2017 .

[57]  Giovanni Montana,et al.  Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks , 2015, ICPRAM 2015.

[58]  Marcia Hon,et al.  Towards Alzheimer's disease classification through transfer learning , 2017, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[59]  Mufti Mahmud,et al.  Detecting Neurodegenerative Disease from MRI: A Brief Review on a Deep Learning Perspective , 2019, BI.

[60]  Helen Skouteris,et al.  Health Outcomes and Healthcare Efficiencies Associated with the Use of Electronic Health Records in Hospital Emergency Departments: a Systematic Review , 2020, Journal of Medical Systems.

[61]  Michele T. Diaz,et al.  The Function Biomedical Informatics Research Network Data Repository , 2016, NeuroImage.

[62]  Martijn J. Schuemie,et al.  Improving sensitivity of machine learning methods for automated case identification from free-text electronic medical records , 2013, BMC Medical Informatics and Decision Making.

[63]  Mufti Mahmud,et al.  Neural Network-based Artifact Detection in Local Field Potentials Recorded from Chronically Implanted Neural Probes , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

[64]  Xin Li,et al.  Linear Registration of Brain MRI Using Knowledge-Based Multiple Intermediator Libraries , 2019, Front. Neurosci..

[65]  Jyoti Islam,et al.  Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks , 2018, Brain Informatics.

[66]  Mufti Mahmud,et al.  SENSE: a Student Performance Quantifier using Sentiment Analysis , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

[67]  Khan M. Iftekharuddin,et al.  Deep learning of texture and structural features for multiclass Alzheimer's disease classification , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[68]  Sharma V. Thankachan,et al.  A deep learning approach for diagnosing schizophrenic patients , 2019, J. Exp. Theor. Artif. Intell..

[69]  Vince D. Calhoun,et al.  Classification of Schizophrenia Patients and Healthy Controls Using ICA of Complex-Valued fMRI Data and Convolutional Neural Networks , 2019, ISNN.

[70]  Z. Işik,et al.  Applying deep learning models to structural MRI for stage prediction of Alzheimer's disease , 2020, Turkish J. Electr. Eng. Comput. Sci..

[71]  C. Jack,et al.  Alzheimer's Disease Neuroimaging Initiative , 2008 .

[72]  Panagiotis K. Artemiadis,et al.  Relating postural synergies to low-D muscular activations: Towards bio-inspired control of robotic hands , 2012, 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE).

[73]  Priya Aggarwal,et al.  Classification of Schizophrenia versus normal subjects using deep learning , 2016, ICVGIP '16.

[74]  Ahmad Lotfi,et al.  A Consensus Novelty Detection Ensemble Approach for Anomaly Detection in Activities of Daily Living , 2019, Appl. Soft Comput..

[75]  Moh. Faturrahman,et al.  Structural MRI classification for Alzheimer's disease detection using deep belief network , 2017, 2017 11th International Conference on Information & Communication Technology and System (ICTS).

[76]  Thomas Kailath,et al.  On spatial smoothing for direction-of-arrival estimation of coherent signals , 1985, IEEE Trans. Acoust. Speech Signal Process..

[77]  Irene Y. H. Gu,et al.  An efficient 3D deep convolutional network for Alzheimer's disease diagnosis using MR images , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[78]  W. A. Sandham,et al.  Preprocessing and segmentation of brain magnetic resonance images , 2003, 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003..

[79]  Tanzila Saba,et al.  A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks , 2019, Journal of Medical Systems.

[80]  Samuel Kadoury,et al.  Deep Spectral-Based Shape Features for Alzheimer's Disease Classification , 2016, SeSAMI@MICCAI.

[81]  Vince D. Calhoun,et al.  Discriminating schizophrenia from normal controls using resting state functional network connectivity: A deep neural network and layer-wise relevance propagation method , 2017, 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP).

[82]  H. Nasrallah,et al.  Neurological disorders in schizophrenia. , 2009, The Psychiatric clinics of North America.

[83]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[84]  Hugo Alexandre Ferreira,et al.  Combination of Medical Imaging and Demographic Data for Parkinson's Disease Diagnosis , 2020, DoCEIS.

[85]  Mufti Mahmud,et al.  Advances in Crowd Analysis for Urban Applications Through Urban Event Detection , 2018, IEEE Transactions on Intelligent Transportation Systems.

[86]  Vince D. Calhoun,et al.  Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia , 2016, NeuroImage.

[87]  Feng Zhou,et al.  Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis , 2020, Medical Image Anal..

[88]  Takashi Matsubara,et al.  Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis , 2017, IEEE Transactions on Biomedical Engineering.

[89]  Andreas Stafylopatis,et al.  Deep neural architectures for prediction in healthcare , 2017, Complex & Intelligent Systems.

[90]  Richard A. Harshman,et al.  Noise Reduction in BOLD-Based fMRI Using Component Analysis , 2002, NeuroImage.

[91]  Huafu Chen,et al.  Recognition of early-onset schizophrenia using deep-learning method , 2017, Applied Informatics.

[92]  Charles E. Dean,et al.  Neural circuitry and precision medicines for mental disorders: are they compatible? , 2018, Psychological Medicine.

[93]  Ghassan Hamarneh,et al.  Machine Learning on Human Connectome Data from MRI , 2016, ArXiv.