Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data

Early detection of Alzheimer’s disease (AD) progression is crucial for proper disease management. Most studies concentrate on neuroimaging data analysis of baseline visits only. They ignore the fact that AD is a chronic disease and patient’s data are naturally longitudinal. In addition, there are no studies that examine the effect of dementia medicines on the behavior of the disease. In this paper, we propose a machine learning-based architecture for early progression detection of AD based on multimodal data of AD drugs and cognitive scores data. We compare the performance of five popular machine learning techniques including support vector machine, random forest, logistic regression, decision tree, and K-nearest neighbor to predict AD progression after 2.5 years. Extensive experiments are performed using an ADNI dataset of 1036 subjects. The cross-validation performance of most algorithms has been improved by fusing the drugs and cognitive scores data. The results indicate the important role of patient’s taken drugs on the progression of AD disease.

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Feeding the machine: Challenges to reproducible predictive modeling in resting-state connectomics

Network Neuroscience ◽

10.1162/netn_a_00212 ◽

2021 ◽

pp. 1-44

Author(s):

Andrew Cwiek ◽

Sarah M. Rajtmajer ◽

Bradley Wyble ◽

Vasant Honavar ◽

Emily Grossner ◽

...

Keyword(s):

Machine Learning ◽

Patient Outcomes ◽

Predictive Models ◽

Resting State ◽

Cross Validation ◽

Functional Neuroimaging ◽

Imaging Biomarkers ◽

Brain Disorders ◽

Neuroimaging Data ◽

Key Steps

Abstract In this critical review, we examine the application of predictive models, e.g. classifiers, trained using Machine Learning (ML) to assist in interpretation of functional neuroimaging data. Our primary goal is to summarize how ML is being applied and critically assess common practices. Our review covers 250 studies published using ML and resting-state functional MRI (fMRI) to infer various dimensions of the human functional connectome. Results for hold-out (“lockbox”) performance was, on average, ~13% less accurate than performance measured through cross-validation alone, highlighting the importance of lockbox data which was included in only 16% of the studies. There was also a concerning lack of transparency across the key steps in training and evaluating predictive models. The summary of this literature underscores the importance of the use of a lockbox and highlights several methodological pitfalls that can be addressed by the imaging community. We argue that, ideally, studies are motivated both by the reproducibility and generalizability of findings as well as the potential clinical significance of the insights. We offer recommendations for principled integration of machine learning into the clinical neurosciences with the goal of advancing imaging biomarkers of brain disorders, understanding causative determinants for health risks, and parsing heterogeneous patient outcomes.

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Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis

Translational Psychiatry ◽

10.1038/s41398-020-00965-5 ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Walid Yassin ◽

Hironori Nakatani ◽

Yinghan Zhu ◽

Masaki Kojima ◽

Keiho Owada ◽

...

Keyword(s):

Machine Learning ◽

High Risk ◽

First Episode Psychosis ◽

First Episode ◽

Machine Learning Classification ◽

Episode Psychosis ◽

Neuroimaging Data ◽

Ultra High Risk

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Deep-Learning-Based Multivariate Pattern Analysis (dMVPA): A Tutorial and a Toolbox

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2021.638052 ◽

2021 ◽

Vol 15 ◽

Author(s):

Karl M. Kuntzelman ◽

Jacob M. Williams ◽

Phui Cheng Lim ◽

Ashok Samal ◽

Prahalada K. Rao ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Pattern Analysis ◽

Time Frame ◽

Multivariate Pattern Analysis ◽

Similar Time ◽

Learning Techniques ◽

Neuroimaging Data ◽

Pros And Cons ◽

Multivariate Pattern

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox (the “Deep Learning In Neuroimaging: Exploration, Analysis, Tools, and Education” package, DeLINEATE for short) intended to facilitate dMVPA for neuroscientists (and indeed, scientists more broadly) everywhere.

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A machine learning tool for interpreting differences in cognition using brain features

10.1101/558403 ◽

2019 ◽

Author(s):

Tiago Azevedo ◽

Luca Passamonti ◽

Pietro Lió ◽

Nicola Toschi

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Machine Learning Algorithms ◽

Public Repository ◽

Cognitive Differences ◽

Health Measures ◽

Human Connectome Project ◽

Neuroimaging Data ◽

Recent Developments ◽

Surface Based Morphometry

AbstractPredicting variability in cognition traits is an attractive and challenging area of research, where different approaches and datasets have been implemented with mixed results. Some powerful Machine Learning algorithms employed before are difficult to interpret, while other algorithms are easy to interpret but might not be as powerful. To improve understanding of individual cognitive differences in humans, we make use of the most recent developments in Machine Learning in which powerful prediction models can be interpreted with confidence. We used neuroimaging data and a variety of behavioural, cognitive, affective and health measures from 905 people obtained from the Human Connectome Project (HCP). As a main contribution of this paper, we show how one could interpret the neuroanatomical basis of cognition, with recent methods which we believe are not yet fully explored in the field. By reducing neuroimages to a well characterised set of features generated from surface-based morphometry and cortical myelin estimates, we make the interpretation of such models easier as each feature is self-explanatory. The code used in this tool is available in a public repository: https://github.com/tjiagoM/interpreting-cognition-paper-2019

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Theoretical Approaches of Machine Learning to Schizophrenia

Engineering International ◽

10.18034/ei.v6i2.568 ◽

2018 ◽

Vol 6 (2) ◽

pp. 155-168 ◽

Cited By ~ 2

Author(s):

Naresh Babu Bynagari ◽

Takudzwa Fadziso

Keyword(s):

Machine Learning ◽

Disease Diagnosis ◽

Machine Learning Techniques ◽

Support Vector ◽

Healthy Controls ◽

Theoretical Approaches ◽

Automatic Categorization ◽

Learning Techniques ◽

Key Characteristics ◽

Neuroimaging Data

Machine learning techniques have been successfully used to analyze neuroimaging data in the context of disease diagnosis in recent years. In this study, we present an overview of contemporary support vector machine-based methods developed and used in psychiatric neuroimaging for schizophrenia research. We focus in particular on our group's algorithms, which have been used to categorize schizophrenia patients and healthy controls, and compare their accuracy findings to those of other recently published studies. First, we'll go over some basic pattern recognition and machine learning terms. Then, for each study, we describe and discuss it independently, emphasizing the key characteristics that distinguish each approach. Finally, conclusions are reached as a result of comparing the data obtained using the various methodologies presented to determine how beneficial automatic categorization systems are in understanding the molecular underpinnings of schizophrenia. The primary implications of applying these approaches in clinical practice are then discussed.

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Machine Learning Approaches: From Theory to Application in Schizophrenia

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/867924 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 37

Author(s):

Elisa Veronese ◽

Umberto Castellani ◽

Denis Peruzzo ◽

Marcella Bellani ◽

Paolo Brambilla

Keyword(s):

Machine Learning ◽

Pattern Recognition ◽

Support Vector Machine ◽

Clinical Practice ◽

Disease Diagnosis ◽

Support Vector ◽

Healthy Controls ◽

Learning Approaches ◽

Neuroimaging Data ◽

Comparison Of The Results

In recent years, machine learning approaches have been successfully applied for analysis of neuroimaging data, to help in the context of disease diagnosis. We provide, in this paper, an overview of recent support vector machine-based methods developed and applied in psychiatric neuroimaging for the investigation of schizophrenia. In particular, we focus on the algorithms implemented by our group, which have been applied to classify subjects affected by schizophrenia and healthy controls, comparing them in terms of accuracy results with other recently published studies. First we give a description of the basic terminology used in pattern recognition and machine learning. Then we separately summarize and explain each study, highlighting the main features that characterize each method. Finally, as an outcome of the comparison of the results obtained applying the described different techniques, conclusions are drawn in order to understand how much automatic classification approaches can be considered a useful tool in understanding the biological underpinnings of schizophrenia. We then conclude by discussing the main implications achievable by the application of these methods into clinical practice.

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