Modern Views of Machine Learning for Precision Psychiatry

In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research

An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed the temporal and anterior lobes of hippocampus regions of brain get affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. The magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to obtain accurate diagnosis of SZ. This paper presents a comprehensive overview of studies conducted on automated diagnosis of SZ using MRI modalities. Main findings, various challenges, and future works in developing the automated SZ detection are described in this paper

An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. Magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder, owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to accurately diagnose SZ. This paper presents a comprehensive overview of studies conducted on the automated diagnosis of SZ using MRI modalities. First, an AI-based computer aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections are presented. Then, this section introduces the most important conventional machine learning (ML) and deep learning (DL) techniques in the diagnosis of diagnosing SZ. A comprehensive comparison is also made between ML and DL studies in the discussion section. In the following, the most important challenges in diagnosing SZ are addressed. Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section. Results, conclusion, and research findings are also presented at the end.Ministerio de Ciencia e Innovación (España)/ FEDER under the RTI2018-098913-B100 projectConsejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía) and FEDER under CV20-45250 and A-TIC-080-UGR18 project

Phenotyping functional brain dynamics:A deep learning prespective on psychiatry

This thesis explores the potential of deep learning (DL) techniques combined with multi-site functional magnetic resonance imaging (fMRI) to enable automated diagnosis and biomarker discovery for psychiatric disorders. This marks a shift from the convention in the field of applying standard machine learning techniques on hand-crafted features from a single cohort.To enable this, we have focused on three main strategies: utilizing minimally pre-processed data to maintain spatio-temporal dynamics, developing sample-efficient DL models, and applying emerging DL training techniques like self-supervised and transfer learning to leverage large population-based datasets.Our empirical results suggest that DL models can sometimes outperform existing machine learning methods in diagnosing Autism Spectrum Disorder (ASD) and Major Depressive Disorder (MDD) from resting-state fMRI data, despite the smaller datasets and the high data dimensionality. Nonetheless, the generalization performance of these models is currently insufficient for clinical use, raising questions about the feasibility of applying supervised DL for diagnosis or biomarker discovery due to the highly heterogeneous nature of the disorders. Our findings suggest that normative modeling on functional brain dynamics provides a promising alternative to the current paradigm

Spatiotemporal precision of neuroimaging in psychiatry

Aberrant patterns of cognition, perception, and behaviour seen in psychiatric disorders are thought to be driven by a complex interplay of neural processes that evolve at a rapid temporal scale. Understanding these dynamic processes in vivo in humans has been hampered by a trade-off between the spatial and temporal resolution inherent to current neuroimaging technology. A recent trend in psychiatric research has been the use of high temporal resolution imaging, particularly magnetoencephalography (MEG), often in conjunction with sophisticated machine learning decoding techniques. Developments here promise novel insights into the spatiotemporal dynamics of cognitive phenomena, including domains relevant to psychiatric illness such as reward and avoidance learning, memory, and planning. This review considers recent advances afforded by exploiting this increased spatiotemporal precision, with specific reference to applications the seek to drive a mechanistic understanding of psychopathology and the realisation of preclinical translation

Normative model for the diagnosis of neuropsychiatric disorders using deep learning methods

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2021The diagnosis of neuropsychiatric disorders (NPDs) is still exclusively dependent on the analysis of the signs and symptoms of the patients since there are no biomarkers useful for clinical practice. Considering that several signs and symptoms are shared among different NPDs, the diagnosis is sometimes incorrect. Therefore, therapeutic approaches do not always succeed, which has an impact on the quality of life of neuropsychiatric patients. Furthermore, NPDs have a global economic and demographic impact. For this reason, technological solutions, such as DL, have been researched for the optimization of diagnosis, in the non-technological field of neuropsychiatry. However, the most promising studies on the diagnosis of NPDs with deep learning (DL) are based on binary classification, which may not be the most adequate approach to deal with the continuous spectrum of NPDs. Here, a DL-based normative model was developed to investigate functional connectivity abnormalities, that may contribute to the development of a novel diagnostic procedure. This method is here used to evaluate how patients deviate from a normal pattern learned by a group of healthy people. To create and evaluate the normative model, resting-state functional magnetic resonance imaging (rs-fMRI) data from three different databases were used. In order to maximise the balance between the amount and the quality of the data, conditions were defined to restrict the variability of the scan parameters. Subsequently, rs-fMRI data were trimmed to the lowest number of time points presented in the sample (150). Then, standard preprocessing steps were performed, including removal of the first 4 volumes of functional data, motion correction, spatial smoothing, and high pass filtering. Single-session independent component analysis (ICA) was run, and the FSL-FIX tool was used to clean noise and artefacts. The functional images were then registered to the T1-weighted brain extracted structural images, and finally to the Montreal Neurosciences Institute 152 standard space. Dual regression was applied using fourteen resting-state functional brain networks (FBN) previously identified in the literature. The Pearson’s correlation coefficient between the extracted blood oxygen level-dependent (BOLD) time series of each FBN was calculated, and a 14x14 network connectivity matrix was generated for each subject. The second part of the project consisted of the creation and optimization of a normative model. The normative model consisted of an autoencoder (AE) with three hidden layers. The AE was trained only in healthy subjects and was tested in both healthy subjects and neuropsychiatric patients, including schizophrenia (SCZ), bipolar disorder (BD), and attention deficit hyperactivity disorder (ADHD) patients. The hypothesis was that the model would “fail” on reconstructing data from neuropsychiatric patients. To evaluate the model performance, graph theory metrics were applied. Besides, the mean squared error was calculated for each feature (correlation between pairs of FBN) to evaluate which regions were worse reconstructed for each group of subjects. The pipeline for NPDs was tested for a SCZ case study, with the addition of a clustering algorithm. The results of this dissertation revealed that the proposed pipeline was able to identify patterns of functional connectivity abnormality that characterize different NPDs. Moreover, the results found for the two SCZ groups of patients were similar, which demonstrated that the normative model here presented was also generalizable. However, the group-level differences did not withstand individual-level analysis, implying that NPDs are highly heterogeneous. These findings support the idea that a precision-based medical approach, focusing on the specific functional network changes of individual patients, may be more beneficial than the traditional group-based diagnostic classification. A personalised diagnosis would allow for personalised therapy, improving the quality of life of neuropsychiatric patients

Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy