A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder

Working towards improved conceptualisation and identification of gaming disorder and co-occurring addictions in gamers

This doctoral research thesis investigated the neurophysiological underpinnings of gaming disorder (GD), and the way in which co-occurrence can influence and correlate with GD in a clinical and a multi-cultural context. The unique contribution of knowledge was (i) the assessment of the neurophysiological expression of gamers using a novel spiking neural network (SNN) methodology; (ii) exploring co-occurrence in gamers and substance abstinent gamers; and (iii) exploring co-occurrence in gamers across three different individualistic countries (i.e., Australia, New Zealand, and the United Kingdom). The conceptualisation of GD and related methodologies were explored using multiple systematic research methods. A number of methodologies were then employed, including the use of electroencephalographic (EEG) data, a machine learning (ML) approach which utilised a novel SNN architecture (i.e., the NeuCube), and the use of surveys to reach a clinical cohort and three cohorts spanning three different countries in an effort to investigate the way in which co-occurrence may influence gamers and at-risk gamers. The results of the empirical studies indicated that: (i) problematic gamers experience different neurophysiological expression than those who recreational game and that ML methodologies are an effective method of classifying recreational and problematic gamers when using EEG data; (ii) maladaptive coping strategies were significantly associated to gaming scores, and that gamers appeared to experience co-occurrence more so than their non-gamer counterparts; (iii) at-risk and high-risk gamers may utilise gaming as a maladaptive coping strategy and other accompanying potentially addictive behaviour, or substance use may be influenced as a result; (iv) the manifestation of maladaptive coping strategies and potentially addictive behaviours can be influenced by the country in which an individual resides. Taken together, the present doctoral project further clarified the conceptualisation of GD, utilising a neurophysiological underpinning, which is further supported with observed behaviour as suggested by the National Institute of Mental Health. In addition, it places an emphasis on the importance of understanding co-occurrence and specific at-risk factors (e.g., coping) which may contribute to the development and maintenance of problematic or disordered gaming in both a clinical sample and general population samples

Reading the brain’s personality: using machine learning to investigate the relationships between EEG and depressivity

Electroencephalography (EEG) measures electrical signals on the scalp and can give information about processes near the surface of the brain (cortex). The goal of our research was to create models that predict depressivity (mapping to personality in general, not just sickness) and to find potential biomarkers in EEG data. First, to provide our models with cleaner EEG data, we designed a novel single-channel physiology-based eye blink artefact removal method and a mains power noise removal method. Then, we assessed two main machine learning model types (classification- and regression-based) with a total of eighteen sub-types to predict the depressivity of participants. The models were generated by combining four signal processing techniques with a) three classification techniques, and b) three regression techniques. The experimental results showed that both types of models perform well in depressivity prediction and one regression-based model (Reg-FFT-LSBoost) showed a significant depressivity prediction performance, especially for female group. More importantly, we found that a specific EEG frequency band (the gamma band) made major contributions to depressivity prediction. Apart from that, the alpha and beta band may make modest contributions. Specific locations (T7, T8, and C3) made major contributions to depressivity prediction. Frontal locations may also have some influence. We also found that the combination of both eye states’ EEG data showed a better depressivity prediction ability. Compared to the eyes closed data, the EEG data obtained from the state of eyes open were more suitable for assessing depressivity. In brief, the outcomes of this research provided the possibilities for translating the EEG data for depressivity measure. Furthermore, there are possibilities to extend the research to apply to other mental disorders’ prediction, such as anxiety

Spectral and coherence estimates on electroencephalogram recordings during arithmetical tasks

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do Grau de Mestre em Engenharia Biomédic

Psychiatric Aspects of Migraine

Psychiatric aspects of migraine. Literature review. This section provides a survey of the literature on the multiple factors implicated in the aetiology of migraine and the links between migraine and psychiatric disorder. The relationship between epilepsy and migraine is reviewed. Olfactory auras in epilepsy are associated with depression. Cocaine abuse is suggested as a model for migraine hallucinations and the precipitation of psychiatric illness by stress. Endorphins are released along with stress hormones and are likely to play an, as yet, unspecified role in the production of stress-related psychiatric and physical illness. They are clearly involved in the experience of pain in migraine and possibly also in mood changes. Menstrual migraine is accepted as a subgroup of migraine although it can be defined objectively much less frequently than it is subjectively complained of. Finally cerebral asymmetry in physiology and cognition is discussed as it applies to migraine

Social and Affective Neuroscience of Everyday Human Interaction

This Open Access book presents the current state of the art knowledge on social and affective neuroscience based on empirical findings. This volume is divided into several sections first guiding the reader through important theoretical topics within affective neuroscience, social neuroscience and moral emotions, and clinical neuroscience. Each chapter addresses everyday social interactions and various aspects of social interactions from a different angle taking the reader on a diverse journey. The last section of the book is of methodological nature. Basic information is presented for the reader to learn about common methodologies used in neuroscience alongside advanced input to deepen the understanding and usability of these methods in social and affective neuroscience for more experienced readers

Assessment of a multi-measure functional connectivity approach

Efforts to find differences in brain activity patterns of subjects with neurological and psychiatric disorders that could help in their diagnosis and prognosis have been increasing in recent years and promise to revolutionise clinical practice and our understanding of such illnesses in the future. Resting-state functional magnetic resonance imaging (rsfMRI) data has been increasingly used to evaluate said activity and to characterize the connectivity between distinct brain regions, commonly organized in functional connectivity (FC) matrices. Here, machine learning methods were used to assess the extent to which multiple FC matrices, each determined with a different statistical method, could change classification performance relative to when only one matrix is used, as is common practice. Used statistical methods include correlation, coherence, mutual information, transfer entropy and non-linear correlation, as implemented in the MULAN toolbox. Classification was made using random forests and support vector machine (SVM) classifiers. Besides the previously mentioned objective, this study had three other goals: to individually investigate which of these statistical methods yielded better classification performances, to confirm the importance of the blood-oxygen-level-dependent (BOLD) signal in the frequency range 0.009-0.08 Hz for FC based classifications as well as to assess the impact of feature selection in SVM classifiers. Publicly available rs-fMRI data from the Addiction Connectome Preprocessed Initiative (ACPI) and the ADHD-200 databases was used to perform classification of controls vs subjects with Attention-Deficit/Hyperactivity Disorder (ADHD). Maximum accuracy and macro-averaged f-measure values of 0.744 and 0.677 were respectively achieved in the ACPI dataset and of 0.678 and 0.648 in the ADHD-200 dataset. Results show that combining matrices could significantly improve classification accuracy and macro-averaged f-measure if feature selection is made. Also, the results of this study suggest that mutual information methods might play an important role in FC based classifications, at least when classifying subjects with ADHD

Effects of exposure to colored light on cerebral and systemic physiology in humans

Humans in industrialized societies have become independent of the natural day and night cycle due to the invention and use of artificial light. Colored light is an element of everyday life, which affects various human functions. The main aim of this PhD thesis is to comprehensively investigate the effects of exposure to colored light on cerebral and human physiology. To achieve this goal, 201 healthy right-handed adults were recruited for 20 different colored light conditions. By using systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS) neuroimaging, each subject was measured 2-4 times on different days resulting in 676 single measurements. The SPA-fNIRS approach combines the measurement of brain activity and systemic physiological changes. fNIRS is a non-invasive neuroimaging technique employed to measure changes in cerebral hemodynamics and oxygenation. There is an interaction between these and changes in systemic physiology: consequently, the SPA-fNIRS generally enables us to identify and understand these interactions. We simultaneously assessed the effects of colored light exposure (CLE) in the visual cortex (VC), prefrontal cortex (PFC) and systemic physiology. Such a comprehensive study has not been carried out yet, and an integrative view of how the color of light affects the brain and systemic physiology is lacking. In general, CLE has relatively long-lasting effects on cerebral and systemic physiology in humans, and yellow light leads to higher brain activation in the PFC than the other colored lights. Yellow CLE is associated with more active and positive emotions, including happiness, joy, hope, and cheerfulness. We also show that long-term colored light exposures induce wavelength-dependent modulations of brain responses in the VC. Violet and blue lights elicit higher changes in cerebral parameters compared to the other colored lights during the CLE and recovery phase. Our results show that CLE affects individual humans differently. In particular, blue light leads to eight different hemodynamic response patterns, while the typical hemodynamic response pattern (increase in oxygenated ([O2Hb]) and decrease in deoxygenated ([HHb]) hemoglobin) is still observed and valid at the group-level analysis. The SPA-fNIRS approach is able to show that systemic and cerebral physiology interact. Experimental findings in most parts of this research display that inter-subject variability of hemodynamic responses is partially explained by systemic physiological changes. The finding of this research that blue light has an activating effect in the VC should be taken into consideration when assessing the impact of modern light sources such as screens and light-emitting diodes (LEDs) on the human body. Our findings that yellow light leads to higher PFC activation be tested as a potentially beneficial tool in chromotherapy, i.e., a complementary medicine method, to balance “energy” lacking in physical, emotional, and mental levels. Although yellow light, i.e., CLE in general term, influences humans in several positive ways, it should be noted that each individual reacts differently to the CLE, implying that colored light therapy has to be also adjusted to each individual. Therefore, further research should clarify which color in CLE benefits whom. In a civilization that is rapidly exposed to new and increasing lighting, the findings of this research are relevant for the scientific community, medical professionals, and society