Wavelet Features for Recognition of First Episode of Schizophrenia from MRI Brain Images

Machine learning methods are increasingly used in various fields of medicine, contributing to early diagnosis and better quality of care. These outputs are particularly desirable in case of neuropsychiatric disorders, such as schizophrenia, due to the inherent potential for creating a new gold standard in the diagnosis and differentiation of particular disorders. This paper presents a scheme for automated classification from magnetic resonance images based on multiresolution representation in the wavelet domain. Implementation of the proposed algorithm, utilizing support vector machines classifier, is introduced and tested on a dataset containing 104 patients with first episode schizophrenia and healthy volunteers. Optimal parameters of different phases of the algorithm are sought and the quality of classification is estimated by robust cross validation techniques. Values of accuracy, sensitivity and specificity over 71% are achieved

Frameworks to Investigate Robustness and Disease Characterization/Prediction Utility of Time-Varying Functional Connectivity State Profiles of the Human Brain at Rest

Neuroimaging technologies aim at delineating the highly complex structural and functional organization of the human brain. In recent years, several unimodal as well as multimodal analyses of structural MRI (sMRI) and functional MRI (fMRI) neuroimaging modalities, leveraging advanced signal processing and machine learning based feature extraction algorithms, have opened new avenues in diagnosis of complex brain syndromes and neurocognitive disorders. Generically regarding these neuroimaging modalities as filtered, complimentary insights of brain’s anatomical and functional organization, multimodal data fusion efforts could enable more comprehensive mapping of brain structure and function. Large scale functional organization of the brain is often studied by viewing the brain as a complex, integrative network composed of spatially distributed, but functionally interacting, sub-networks that continually share and process information. Such whole-brain functional interactions, also referred to as patterns of functional connectivity (FC), are typically examined as levels of synchronous co-activation in the different functional networks of the brain. More recently, there has been a major paradigm shift from measuring the whole-brain FC in an oversimplified, time-averaged manner to additional exploration of time-varying mechanisms to identify the recurring, transient brain configurations or brain states, referred to as time-varying FC state profiles in this dissertation. Notably, prior studies based on time-varying FC approaches have made use of these relatively lower dimensional fMRI features to characterize pathophysiology and have also been reported to relate to demographic characterization, consciousness levels and cognition. In this dissertation, we corroborate the efficacy of time-varying FC state profiles of the human brain at rest by implementing statistical frameworks to evaluate their robustness and statistical significance through an in-depth, novel evaluation on multiple, independent partitions of a very large rest-fMRI dataset, as well as extensive validation testing on surrogate rest-fMRI datasets. In the following, we present a novel data-driven, blind source separation based multimodal (sMRI-fMRI) data fusion framework that uses the time-varying FC state profiles as features from the fMRI modality to characterize diseased brain conditions and substantiate brain structure-function relationships. Finally, we present a novel data-driven, deep learning based multimodal (sMRI-fMRI) data fusion framework that examines the degree of diagnostic and prognostic performance improvement based on time-varying FC state profiles as features from the fMRI modality. The approaches developed and tested in this dissertation evince high levels of robustness and highlight the utility of time-varying FC state profiles as potential biomarkers to characterize, diagnose and predict diseased brain conditions. As such, the findings in this work argue in favor of the view of FC investigations of the brain that are centered on time-varying FC approaches, and also highlight the benefits of combining multiple neuroimaging data modalities via data fusion

Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: Evaluation in Alzheimer's disease

Background: Although convolutional neural networks (CNN) achieve high diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge. Methods: We trained a CNN for the detection of AD in N=663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including N=1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps. Results: Across three independent datasets, group separation showed high accuracy for AD dementia vs. controls (AUC$\geq$0.92) and moderate accuracy for MCI vs. controls (AUC$\approx$0.75). Relevance maps indicated that hippocampal atrophy was considered as the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson's r$\approx$-0.86, p<0.001). Conclusion: The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels.Comment: 24 pages, 9 figures/tables, supplementary material, source code available on GitHu

Machine Learning Methods for Depression Detection Using SMRI and RS-FMRI Images

Major Depression Disorder (MDD) is a common disease throughout the world that negatively influences people’s lives. Early diagnosis of MDD is beneficial, so detecting practical biomarkers would aid clinicians in the diagnosis of MDD. Having an automated method to find biomarkers for MDD is helpful even though it is difficult. The main aim of this research is to generate a method for detecting discriminative features for MDD diagnosis based on Magnetic Resonance Imaging (MRI) data. In this research, representational similarity analysis provides a framework to compare distributed patterns and obtain the similarity/dissimilarity of brain regions. Regions are obtained by either data-driven or model-driven methods such as cubes and atlases respectively. For structural MRI (sMRI) similarity of voxels of spatial cubes (data-driven) are explored. For resting-state fMRI (rs-fMRI) images, the similarity of the time series of both cubes (data-driven) and atlases (model-driven) are examined. Moreover, the similarity method of the inverse of Minimum Covariant Determinant is applied that excludes outliers from patterns and finds conditionally independent regions given the rest of regions. Next, a statistical test that is robust to outliers, identifies discriminative similarity features between two groups of MDDs and controls. Therefore, the key contribution is the way to get discriminative features that include obtaining similarity of voxel’s cubes/time series using the inverse of robust covariance along with the statistical test. The experimental results show that obtaining these features along with the Bernoulli Naïve Bayes classifier achieves superior performance compared with other methods. The performance of our method is verified by applying it to three imbalanced datasets. Moreover, the similarity-based methods are compared with deep learning and regional-based approaches for detecting MDD using either sMRI or rs-fMRI. Given that depression is famous to be a connectivity disorder problem, investigating the similarity of the brain’s regions is valuable to understand the behavior of the brain. The combinations of structural and functional brain similarities are explored to investigate the brain’s structural and functional properties together. Moreover, the combination of data-driven (cube) and model-driven (atlas) similarities of rs-fMRI are looked over to evaluate how they affect the performance of the classifier. Besides, discriminative similarities are visualized for both sMRI and rs-fMRI. Also, to measure the informativeness of a cube, the relationship of atlas regions with overlapping cubes and vise versa (cubes with overlapping regions) are explored and visualized. Furthermore, the relationship between brain structure and function has been probed through common similarities between structural and resting-state functional networks

Coronal slice segmentation using a watershed method for early identification of people with Alzheimer's

One physical sign of a person who has Alzheimer's is the diminution of the area of the hippocampus and ventricles. A good quality magnetic resonance imaging (MRI) will provide a high-quality image so that the doctor will quickly analyze the abnormalities of the hippocampus and ventricle area. However, for low-quality MRI, this is difficult to do. This condition will be a significant problem for some regions in developing countries including Indonesia, where many hospitals have only low-quality MRI, and many hospitals do not have them at all. The primary purpose of this research is to develop simple tools to analyze morphological characteristics in Alzheimer's patients. In this paper, we focus only on coronal slice analysis. We will use watershed method segmentation, because of this method able to segment the boundaries automatically, so that parts of the hippocampus and ventricles can be identified in an MRI image. Analysis of morphological characteristics is also classified by age and gender. Then by referring to the value of the clinical dementia rating (CDR), the process of identifying between images with Alzheimer's disease (AD) and healthy models is done based on the morphological analysis that has been done. The results show this method has a better performance compared to the previously work

Analytical fusion of multimodal magnetic resonance imaging to identify pathological states in genetically selected Marchigian Sardinian alcohol-preferring (msP) rats

[EN] Alcohol abuse is one of the most alarming issues for the health authorities. It is estimated that at least 23 million of European citizens are affected by alcoholism causing a cost around 270 million euros. Excessive alcohol consumption is related with physical harm and, although it damages the most of body organs, liver, pancreas, and brain are more severally affected. Not only physical harm is associated to alcohol-related disorders, but also other psychiatric disorders such as depression are often comorbiding. As well, alcohol is present in many of violent behaviors and traffic injures. Altogether reflects the high complexity of alcohol-related disorders suggesting the involvement of multiple brain systems. With the emergence of non-invasive diagnosis techniques such as neuroimaging or EEG, many neurobiological factors have been evidenced to be fundamental in the acquisition and maintenance of addictive behaviors, relapsing risk, and validity of available treatment alternatives. Alterations in brain structure and function reflected in non-invasive imaging studies have been repeatedly investigated. However, the extent to which imaging measures may precisely characterize and differentiate pathological stages of the disease often accompanied by other pathologies is not clear. The use of animal models has elucidated the role of neurobiological mechanisms paralleling alcohol misuses. Thus, combining animal research with non-invasive neuroimaging studies is a key tool in the advance of the disorder understanding. As the volume of data from very diverse nature available in clinical and research settings increases, an integration of data sets and methodologies is required to explore multidimensional aspects of psychiatric disorders. Complementing conventional mass-variate statistics, interests in predictive power of statistical machine learning to neuroimaging data is currently growing among scientific community. This doctoral thesis has covered most of the aspects mentioned above. Starting from a well-established animal model in alcohol research, Marchigian Sardinian rats, we have performed multimodal neuroimaging studies at several stages of alcohol-experimental design including the etiological mechanisms modulating high alcohol consumption (in comparison to Wistar control rats), alcohol consumption, and treatment with the opioid antagonist Naltrexone, a well-established drug in clinics but with heterogeneous response. Multimodal magnetic resonance imaging acquisition included Diffusion Tensor Imaging, structural imaging, and the calculation of magnetic-derived relaxometry maps. We have designed an analytical framework based on widely used algorithms in neuroimaging field, Random Forest and Support Vector Machine, combined in a wrapping fashion. Designed approach was applied on the same dataset with two different aims: exploring the validity of the approach to discriminate experimental stages running at subject-level and establishing predictive models at voxel-level to identify key anatomical regions modified during the experiment course. As expected, combination of multiple magnetic resonance imaging modalities resulted in an enhanced predictive power (between 3 and 16%) with heterogeneous modality contribution. Surprisingly, we have identified some inborn alterations correlating high alcohol preference and thalamic neuroadaptations related to Naltrexone efficacy. As well, reproducible contribution of DTI and relaxometry -related biomarkers has been repeatedly identified guiding further studies in alcohol research. In summary, along this research we demonstrate the feasibility of incorporating multimodal neuroimaging, machine learning algorithms, and animal research in the advance of the understanding alcohol-related disorders.[ES] El abuso de alcohol es una de las mayores preocupaciones de las autoridades sanitarias en la Unión Europea. El consumo de alcohol en exceso afecta en mayor o menor medida la totalidad del organismo siendo el páncreas e hígado los más severamente afectados. Además de estos, el sistema nervioso central sufre deterioros relacionados con el alcohol y con frecuencia se presenta en paralelo con otras patologías psiquiátricas como la depresión u otras adicciones como la ludopatía. La presencia de estas comorbidades demuestra la complejidad de la patología en la que multitud de sistemas neuronales interaccionan entre sí. El uso imágenes de resonancia magnética (RM) han ayudado en el estudio de enfermedades psiquiátricas facilitando el descubrimiento de mecanismos neurológicos fundamentales en el desarrollo y mantenimiento de la adicción al alcohol, recaídas y el efecto de los tratamientos disponibles. A pesar de los avances, todavía se necesita investigar más para identificar las bases biológicas que contribuyen a la enfermedad. En este sentido, los modelos animales sirven, por lo tanto, a discriminar aquellos factores únicamente relacionados con el alcohol controlando otros factores que facilitan el desarrollo del alcoholismo. Estudios de resonancia magnética en animales de laboratorio y su posterior evaluación en humanos juegan un papel fundamental en el entendimiento de las patologías psiquatricas como la addicción al alcohol. La imagen por resonancia magnética se ha integrado en entornos clínicos como prueba diagnósticas no invasivas. A medida que el volumen de datos se va incrementando, se necesitan herramientas y metodologías capaces de fusionar información de muy distinta naturaleza y así establecer criterios diagnósticos cada vez más exactos. El poder predictivo de herramientas derivadas de la inteligencia artificial como el aprendizaje automático sirven de complemento a tradicionales métodos estadísticos. En este trabajo se han abordado la mayoría de estos aspectos. Se han obtenido datos multimodales de resonancia magnética de un modelo validado en la investigación de patologías derivadas del consumo del alcohol, las ratas Marchigian-Sardinian desarrolladas en la Universidad de Camerino (Italia) y con consumos de alcohol comparables a los humanos. Para cada animal se han adquirido datos antes y después del consumo de alcohol y bajo dos condiciones de abstinencia (con y sin tratamiento de Naltrexona, una medicaciones anti-recaídas usada como farmacoterapia en el alcoholismo). Los datos de resonancia magnética multimodal consistentes en imágenes de difusión, de relaxometría y estructurales se han fusionado en un esquema analítico multivariable incorporando dos herramientas generalmente usadas en datos derivados de neuroimagen, Random Forest y Support Vector Machine. Nuestro esquema fue aplicado con dos objetivos diferenciados. Por un lado, determinar en qué fase experimental se encuentra el sujeto a partir de biomarcadores y por el otro, identificar sistemas cerebrales susceptibles de alterarse debido a una importante ingesta de alcohol y su evolución durante la abstinencia. Nuestros resultados demostraron que cuando biomarcadores derivados de múltiples modalidades de neuroimagen se fusionan en un único análisis producen diagnósticos más exactos que los derivados de una única modalidad (hasta un 16% de mejora). Biomarcadores derivados de imágenes de difusión y relaxometría discriminan estados experimentales. También se han identificado algunos aspectos innatos que están relacionados con posteriores comportamientos con el consumo de alcohol o la relación entre la respuesta al tratamiento y los datos de resonancia magnética. Resumiendo, a lo largo de esta tesis, se demuestra que el uso de datos de resonancia magnética multimodales en modelos animales combinados en esquemas analíticos multivariados es una herramienta válida en el entendimiento de patologías[CAT] L'abús de alcohol es una de les majors preocupacions per part de les autoritats sanitàries de la Unió Europea. Malgrat la dificultat de establir xifres exactes, se estima que uns 23 milions de europeus actualment sofreixen de malalties derivades del alcoholisme amb un cost que supera els 150.000 milions de euros per a la societat. Un consum de alcohol en excés afecta en major o menor mesura el cos humà sent el pàncreas i el fetge el més afectats. A més, el cervell sofreix de deterioraments produïts per l'alcohol i amb freqüència coexisteixen amb altres patologies com depressió o altres addiccions com la ludopatia. Tot aquest demostra la complexitat de la malaltia en la que múltiple sistemes neuronals interactuen entre si. Tècniques no invasives com el encefalograma (EEG) o imatges de ressonància magnètica (RM) han ajudat en l'estudi de malalties psiquiàtriques facilitant el descobriment de mecanismes neurològics fonamentals en el desenvolupament i manteniment de la addició, recaiguda i la efectivitat dels tractaments disponibles. Tot i els avanços, encara es necessiten més investigacions per identificar les bases biològiques que contribueixen a la malaltia. En aquesta direcció, el models animals serveixen per a identificar únicament dependents del abús del alcohol. Estudis de ressonància magnètica en animals de laboratori i posterior avaluació en humans jugarien un paper fonamental en l' enteniment de l'ús del alcohol. L'ús de probes diagnostiques no invasives en entorns clínics has sigut integrades. A mesura que el volum de dades es incrementa, eines i metodologies per a la fusió d' informació de molt distinta natura i per tant, establir criteris diagnòstics cada vegada més exactes. La predictibilitat de eines desenvolupades en el camp de la intel·ligència artificial com la aprenentatge automàtic serveixen de complement a mètodes estadístics tradicionals. En aquesta investigació se han abordat tots aquestes aspectes. Dades multimodals de ressonància magnètica se han obtingut de un model animal validat en l'estudi de patologies relacionades amb el consum d'alcohol, les rates Marchigian-Sardinian desenvolupades en la Universitat de Camerino (Italià) i amb consums d'alcohol comparables als humans. Per a cada animal es van adquirir dades previs i després al consum de alcohol i dos condicions diferents de abstinència (amb i sense tractament anti-recaiguda). Dades de ressonància magnètica multimodal constituides per imatges de difusió, de relaxometria magnètica i estructurals van ser fusionades en esquemes analítics multivariats incorporant dues metodologies validades en el camp de neuroimatge, Random Forest i Support Vector Machine. Nostre esquema ha sigut aplicat amb dos objectius diferenciats. El primer objectiu es determinar en quina fase experimental es troba el subjecte a partir de biomarcadors obtinguts per neuroimatge. Per l'altra banda, el segon objectiu es identificar el sistemes cerebrals susceptibles de ser alterats durant una important ingesta de alcohol i la seua evolució durant la fase del tractament. El nostres resultats demostraren que l'ús de biomarcadors derivats de varies modalitats de neuroimatge fusionades en un anàlisis multivariat produeixen diagnòstics més exactes que els derivats de una única modalitat (fins un 16% de millora). Biomarcadors derivats de imatges de difusió i relaxometria van contribuir de distints estats experimentals. També s'han identificat aspectes innats que estan relacionades amb posterior preferències d'alcohol o la relació entre la resposta al tractament anti-recaiguda i les dades de ressonància magnètica. En resum, al llarg de aquest treball, es demostra que l'ús de dades de ressonància magnètica multimodal en models animals combinats en esquemes analítics multivariats són una eina molt valida en l'enteniment i avanç de patologies psiquiàtriques com l'alcoholisme.Cosa Liñán, A. (2017). Analytical fusion of multimodal magnetic resonance imaging to identify pathological states in genetically selected Marchigian Sardinian alcohol-preferring (msP) rats [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90523TESI

Alzheimers Disease Diagnosis by Deep Learning Using MRI-Based Approaches

The most frequent kind of dementia of the nervous system, Alzheimer's disease, weakens several brain processes (such as memory) and eventually results in death. The clinical study uses magnetic resonance imaging to diagnose AD. Deep learning algorithms are capable of pattern recognition and feature extraction from the inputted raw data. As early diagnosis and stage detection are the most crucial elements in enhancing patient care and treatment outcomes, deep learning algorithms for MRI images have recently allowed for diagnosing a medical condition at the beginning stage and identifying particular symptoms of Alzheimer's disease. As a result, we aimed to analyze five specific studies focused on AD diagnosis using MRI-based deep learning algorithms between 2021 and 2023 in this study. To completely illustrate the differences between these techniques and comprehend how deep learning algorithms function, we attempted to explore selected approaches in depth