Detecting Neurodegenerative Disease from MRI: A Brief Review on a Deep Learning Perspective

Rapid development of high speed computing devices and infrastructure along with improved understanding of deep machine learning techniques during the last decade have opened up possibilities for advanced analysis of neuroimaging data. Using those computing tools Neuroscientists now can identify Neurodegenerative diseases from neuroimaging data. Due to the similarities in disease phenotypes, accurate detection of such disorders from neuroimaging data is very challenging. In this article, we have reviewed the methodological research papers proposing to detect neurodegenerative diseases using deep machine learning techniques only from MRI data. The results show that deep learning based techniques can detect the level of disorder with relatively high accuracy. Towards the end, current challenges are reviewed and some possible future research directions are provided.

[1]  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).

[2]  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).

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

[4]  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.

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

[6]  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..

[7]  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).

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

[9]  Dip Nandi,et al.  Alzheimer's Disease and Dementia Detection from 3D Brain MRI Data Using Deep Convolutional Neural Networks , 2018, 2018 3rd International Conference for Convergence in Technology (I2CT).

[10]  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).

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

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

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

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

[15]  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).

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

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

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

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

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

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

[22]  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.

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

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

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

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

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

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

[29]  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).

[30]  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).

[31]  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.

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

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

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

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

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

[37]  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.

[38]  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).

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

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

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

[42]  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).

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