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

Functional network correlates of language and semiology in epilepsy

Epilepsy surgery is appropriate for 2-3% of all epilepsy diagnoses. The goal of the presurgical workup is to delineate the seizure network and to identify the risks associated with surgery. While interpretation of functional MRI and results in EEG-fMRI studies have largely focused on anatomical parameters, the focus of this thesis was to investigate canonical intrinsic connectivity networks in language function and seizure semiology. Epilepsy surgery aims to remove brain areas that generate seizures. Language dysfunction is frequently observed after anterior temporal lobe resection (ATLR), and the presurgical workup seeks to identify the risks associated with surgical outcome. The principal aim of experimental studies was to elaborate understanding of language function as expressed in the recruitment of relevant connectivity networks and to evaluate whether it has value in the prediction of language decline after anterior temporal lobe resection. Using cognitive fMRI, we assessed brain areas defined by parameters of anatomy and canonical intrinsic connectivity networks (ICN) that are involved in language function, specifically word retrieval as expressed in naming and fluency. fMRI data was quantified by lateralisation indices and by ICN_atlas metrics in a priori defined ICN and anatomical regions of interest. Reliability of language ICN recruitment was studied in 59 patients and 30 healthy controls who were included in our language experiments. New and established language fMRI paradigms were employed on a three Tesla scanner, while intellectual ability, language performance and emotional status were established for all subjects with standard psychometric assessment. Patients who had surgery were reinvestigated at an early postoperative stage of four months after anterior temporal lobe resection. A major part of the work sought to elucidate the association between fMRI patterns and disease characteristics including features of anxiety and depression, and prediction of postoperative language outcome. We studied the efficiency of reorganisation of language function associated with disease features prior to and following surgery. A further aim of experimental work was to use EEG-fMRI data to investigate the relationship between canonical intrinsic connectivity networks and seizure semiology, potentially providing an avenue for characterising the seizure network in the presurgical workup. The association of clinical signs with the EEG-fMRI informed activation patterns were studied using the data from eighteen patients’ whose seizures and simultaneous EEG-fMRI activations were reported in a previous study. The accuracy of ICN_atlas was validated and the ICN construct upheld in the language maps of TLE patients. The ICN construct was not evident in ictal fMRI maps and simulated ICN_atlas data. Intrinsic connectivity network recruitment was stable between sessions in controls. Amodal linguistic processing and the relevance of temporal intrinsic connectivity networks for naming and that of frontal intrinsic connectivity networks for word retrieval in the context of fluency was evident in intrinsic connectivity networks regions. The relevance of intrinsic connectivity networks in the study of language was further reiterated by significant association between some disease features and language performance, and disease features and activation in intrinsic connectivity networks. However, the anterior temporal lobe (ATL) showed significantly greater activation compared to intrinsic connectivity networks – a result which indicated that ATL functional language networks are better studied in the context of the anatomically demarked ATL, rather than its functionally connected intrinsic connectivity networks. Activation in temporal lobe networks served as a predictor for naming and fluency impairment after ATLR and an increasing likelihood of significant decline with greater magnitude of left lateralisation. Impairment of awareness served as a significant classifying feature of clinical expression and was significantly associated with the inhibition of normal brain functions. Canonical intrinsic connectivity networks including the default mode network were recruited along an anterior-posterior anatomical axis and were not significantly associated with clinical signs

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DLbased CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper

Development and Application of Functional Magnetic Resonance Imaging in Paediatric Focal Epilepsy

There are two major applications of fMRI in paediatric focal epilepsy. The first is mapping of eloquent cortex. The second is the use of simultaneous EEG-fMRI to map the epileptogenic zone. The main methodological issues faced by these fMRI applications are: motion, physiological noise, quality assurance, and statistical analysis. To address the issues of subject motion and physiological noise we constructed a simple analytical biophysical model of Blood Oxygenation Level Dependent (BOLD) signal capable of identifying and correcting these artefacts (named FIACH). This model was validated in a sample of children performing a language task with high motion levels. FIACH outperformed 6 other competitive methods of noise control. In the second study, we characterized how metrics of quality assurance could predict the clinical utility of EEG-fMRI. We also quantified the impact of a natural stimulus (a cartoon) on reducing subject motion. During this analysis it was noted that the corrections for multiple comparisons employed using Random Field Theory (RFT) at an individual level were overly conservative. This led to an exploration of RFT sensitivity and its relationship to image smoothing and degrees of freedom. By reviewing over 150 papers published in 2016 it was possible to estimate that 80% of studies suffer from a similar loss in sensitivity. Simulations are provided to help identify and prevent this loss in sensitivity. In the final study we sought to use EEG-fMRI to characterize the relationship between the brain’s functional organization and Interictal Epileptiform Discharges (IEDs) in paediatric focal epilepsy. Interestingly, we identified increasing connectivity of the piriform cortex and caudate to the default mode network as a function of IEDs. This suggested a mechanism by which IEDs may propagate through functional networks in the brain

Haemodynamic correlates of interictal and ictal epileptic discharges and ictal semiology using simultaneous scalp video-EEG-fMRI and intracranial EEG-fMRI

Interictal and ictal epileptic discharges are produced by focal and widespread dysfunctional neuronal networks. Identification and characterization of epileptic discharges underlie the diagnosis and the choice of treatment for epilepsy patients. A better knowledge of the generation, propagation and localisation of epileptic discharges, and their interaction with the physiological and pathological brain networks can be very helpful in planning epilepsy surgery and minimizing the risk of damaging the physiological brain networks. This work describes a number of methodological developments and novel applications investigating the epileptic networks in humans using EEG-fMRI. First, I implemented synchronized video recording inside the MRI-scanner during simultaneous EEG-fMRI studies, which did not deteriorate the imaging and EEG data quality. Secondly, I used video recordings to identify physiological activities to be modelled as confounds in the functional imaging data analysis for interictal activity, thus increasing the sensitivity of video-EEG-fMRI. Thirdly, I applied this modelling approach to investigate seizure related functional networks in patients with focal epilepsy. Video recordings allowed partitioning seizures into phases separating the ictal onset related functional networks from propagation related networks. Localisation of the ictal onset related networks may be useful in the planning for epilepsy surgery in a selected group of patients, as demonstrated by their comparison with intracranial-EEG recordings. Further, I investigated haemodynamic changes during preictal period which suggested recruitment of an inhibitory followed by an excitatory network prior to the ictal onset on scalp EEG. In the next step, I used simultaneous intracranial-EEG-fMRI in patients undergoing invasive evaluation, demonstrating that local and remote networks associated with very focal interictal discharges recorded on intracranial-EEG may predict the surgical outcome. Finally, I investigated the interaction of epileptic discharges with the working memory, using scalp video-EEG-fMRI, showing that the presence of epileptic activity may alter the working memory related networks. Methodological constraints, clinical applications and future perspectives are discussed

Statistical approaches for resting state fMRI data analysis

This doctoral dissertation investigates the methodology to explore brain dynamics from resting state fMRI data. A standard resting state fMRI study gives rise to massive amounts of noisy data with a complicated spatio-temporal correlation structure. There are two main objectives in the analysis of these noisy data: establishing the link between neural activity and the measured signal, and determining distributed brain networks that correspond to brain function. These measures can then be used as indicators of psychological, cognitive or pathological states. Two main issues will be addressed: retrieving and interpreting the hemodynamic response function (HRF) at rest, and dealing with the redundancy inherent to fMRI data. Novel approaches are introduced, discussed and validated on simulated data and on real datasets, in health and disease, in order to track modulation of brain dynamics and HRF across different pathophysiological conditions

Fluctuations of attention network activation during the resting state reflect fluctuations in subjective attentional state.

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