Deep Learning Methods to Process fMRI Data and Their Application in the Diagnosis of Cognitive Impairment: A Brief Overview and Our Opinion

Dong, Wen; Wei, Zhenhao; Zhou, Yanhong; Li, Guolin; Zhang, Xu; Han, Wei

doi:10.3389/fninf.2018.00023

Cited by 73 publications

(55 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overfitting means that the algorithm finds a solution that perfectly parameterises the given dataset but may fail to classify new data correctly. One reason for overfitting is the use of too-small sample sizes as training data (96). But the field of machine learning has been hit by the replication crisis, as well (97).…”

Section: Is Machine Learning the Solution?mentioning

confidence: 99%

Current Challenges in Translational and Clinical fMRI and Future Directions

Specht

2020

Front. Psychiatry

View full text Add to dashboard Cite

Translational neuroscience is an important field that brings together clinical praxis with neuroscience methods. In this review article, the focus will be on functional neuroimaging (fMRI) and its applicability in clinical fMRI studies. In the light of the "replication crisis," three aspects will be critically discussed: First, the fMRI signal itself, second, current fMRI praxis, and, third, the next generation of analysis strategies. Current attempts such as restingstate fMRI, meta-analyses, and machine learning will be discussed with their advantages and potential pitfalls and disadvantages. One major concern is that the fMRI signal shows substantial within-and between-subject variability, which affects the reliability of both taskrelated, but in particularly resting-state fMRI studies. Furthermore, the lack of standardized acquisition and analysis methods hinders the further development of clinical relevant approaches. However, meta-analyses and machine-learning approaches may help to overcome current shortcomings in the methods by identifying new, and yet hidden relationships, and may help to build new models on disorder mechanisms. Furthermore, better control of parameters that may have an influence on the fMRI signal and that can easily be controlled for, like blood pressure, heart rate, diet, time of day, might improve reliability substantially.

show abstract

Section: Is Machine Learning the Solution?mentioning

confidence: 99%

Current Challenges in Translational and Clinical fMRI and Future Directions

Specht

2020

Front. Psychiatry

View full text Add to dashboard Cite

show abstract

“…Unsupervised methods like independent component analysis (ICA) and principal component analysis (PCA) decomposition are also used to find latent variable models in fMRI data. Deep learning methods such as convolutional neural networks and feedforward neural networks were used to successfully discriminate the fMRI data of AD and schizophrenia patients from those of healthy control patients with 96.85 % and 85.8 % accuracy, respectively (Wen et al, 2018). These machine learning methods can also be applied for electrophysiological data to find disease-specific oscillation patterns in EEG or intracranial LFP data from humans and experimental animals (Reardon, 2017).…”

Section: Machine Learning-mediated Approaches For Analysismentioning

confidence: 99%

Oscillotherapeutics – Time-targeted interventions in epilepsy and beyond

Takeuchi

Berényi

2020

Neuroscience Research

View full text Add to dashboard Cite

Oscillatory brain activities support many physiological functions from motor control to cognition. Disruptions of the normal oscillatory brain activities are commonly observed in neurological and psychiatric disorders including epilepsy, Parkinson's disease, Alzheimer's disease, schizophrenia, anxiety/traumarelated disorders, major depressive disorders, and drug addiction. Therefore, these disorders can be considered as common oscillation defects despite having distinct behavioral manifestations and genetic causes. Recent technical advances of neuronal activity recording and analysis have allowed us to study the pathological oscillations of each disorder as a possible biomarker of symptoms. Furthermore, recent advances in brain stimulation technologies enable time-and space-targeted interventions of the pathological oscillations of both neurological disorders and psychiatric disorders as possible targets for regulating their symptoms.

show abstract

“…Another study showed a deep learning approach based on convolutional neural networks to accurately predict MCI-to-AD using structural MRI data with an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-oneout cross validations [50]. Mazrina et al [51], Wen et al [52] and Srinivasan et al [53] developed similar methodologies to predict MCI and AD brains. Nicola et al developed a new method for early prediction of Alzheimer's' disease that involves extracting random forest features from the data of an international challenge and classifying them via deep neural networks.…”

Section: Related Workmentioning

confidence: 99%

MCADNNet: Recognizing Stages of Cognitive Impairment Through Efficient Convolutional fMRI and MRI Neural Network Topology Models

et al. 2019

View full text Add to dashboard Cite

Mild cognitive impairment (MCI) represents the intermediate stage between normal cerebral aging and dementia associated with Alzheimer's disease (AD). Early diagnosis of MCI and AD through artificial intelligence has captured considerable scholarly interest; researchers hope to develop therapies capable of slowing or halting these processes. We developed a state-of-the-art deep learning algorithm based on an optimized convolutional neural network (CNN) topology called MCADNNet that simultaneously recognizes MCI, AD, and normally aging brains in adults over the age of 75 years, using structural and functional magnetic resonance imaging (fMRI) data. Following highly detailed preprocessing, fourdimensional (4D) fMRI and 3D MRI were decomposed to create 2D images using a lossless transformation, which enables maximum preservation of data details. The samples were shuffled and subject-level training and testing datasets were completely independent. The optimized MCADNNet was trained and extracted invariant and hierarchical features through convolutional layers followed by multi-classification in the last layer using a softmax layer. A decision-making algorithm was also designed to stabilize the outcome of the trained models. To measure the performance of classification, the accuracy rates for various pipelines were calculated before and after applying the decision-making algorithm. Accuracy rates of 99.77% ± 0.36% and 97.5% ± 1.16% were achieved for MRI and fMRI pipelines, respectively, after applying the decisionmaking algorithm. In conclusion, a cutting-edge and optimized topology called MCADNNet was designed and preceded a preprocessing pipeline; this was followed by a decision-making step that yielded the highest performance achieved for simultaneous classification of the three cohorts examined. INDEX TERMS Deep learning, classification, structural and functional magnetic resonance imaging, brain, Alzheimer's disease, MCI. I. INTRODUCTION A. COGNITIVE IMPAIRMENT Cognitive impairment is a general term referring to impairments in cognition among the domains of memory, learning, The associate editor coordinating the review of this manuscript and approving it for publication was Mohan Venkateshkumar .

show abstract

Deep Learning Methods to Process fMRI Data and Their Application in the Diagnosis of Cognitive Impairment: A Brief Overview and Our Opinion

Cited by 73 publications

References 38 publications

Current Challenges in Translational and Clinical fMRI and Future Directions

Current Challenges in Translational and Clinical fMRI and Future Directions

Oscillotherapeutics – Time-targeted interventions in epilepsy and beyond

MCADNNet: Recognizing Stages of Cognitive Impairment Through Efficient Convolutional fMRI and MRI Neural Network Topology Models

Contact Info

Product

Resources

About