Attention Deficit Hyperactivity Disorder (ADHD) affects 5-10% of children worldwide. Its effects are mainly behavioral, manifesting in symptoms such as inattention, hyperactivity, and impulsivity. If not monitored and treated, ADHD may adversely affect a child's health, education, and social life. Furthermore, the neurological disorder is currently diagnosed through interviews and opinions of teachers, parents, and physicians. Because this is a subjective method of identifying ADHD, it is easily prone to error and misdiagnosis. Therefore, there is a clear need to develop an objective diagnostic method for ADHD. The focus of this study is to explore the use of machine language classifiers on information from the brain MRI and fMRI of both ADHD and non-ADHD subjects. The imaging data are preprocessed to remove any intra-subject and inter-subject variation. For both MRI and fMRI, similar preprocessing stages are performed, including normalization, skull stripping, realignment, smoothing, and co-registration. The next step is to extract features from the data. For MRI, anatomical features such as cortical thickness, surface area, volume, and intensity are obtained. For fMRI, region of interest (ROI) correlation coefficients between 116 cortical structures are determined. A large number of image features are collected, yet many of them may include redundant and useless information. Therefore, the features used for training and testing the classifiers are selected in two separate ways, feature ranking and stability selection, and their results are compared. Once the best features from MRI and fMRI are determined, the following classifiers are trained and tested through leave-one-out cross validation, experimenting with varying feature numbers, for each imaging modality and feature selection method: support vector machine, support vector regression, random forest, and elastic net. Thus, there are four experiments (MRI-rank, MRI-stability, fMRI-rank, fMRI-stability) with four classifiers in each for a total of 16 classifiers trained per each feature count attempted. The results of each classifier are the decisions of each subject, ADHD or non-ADHD. Finally, a classifier decision ensemble is created through the combination of the outputs of the best classifiers in a majority voting method that includes results of both the MRI and fMRI classifiers and keeps both feature selection results independent. The results suggest that ADHD is more easily identified through fMRI because the classification accuracies are a lot higher using fMRI data rather than MRI data. Furthermore, significant activity correlation differences exist between the brain's frontal lobe and cerebellum and also the left and right hemispheres among ADHD and non-ADHD subjects. When including MRI decisions with fMRI in the classifier ensemble, performance is boosted to a high ADHD detection accuracy of 96.2%, suggesting that MRI information assists in validating fMRI classification decisions. This study is an important step towards the development of an automatic and objective method for ADHD diagnosis. While more work is needed to externally validate and improve the classification accuracy, new applications of current methods with promising results are introduced here.

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Inledning: Rörelseartefakter är patientrelaterade artefakter som uppstår på grund av patientens medvetna eller omedvetna rörelser vid magnetresonansundersökningar och anses vara den vanligaste typen av artefakter som försvårar diagnostiken och påverkar därmed patientsäkerheten negativt. Syfte: Syftet med studien var att kartlägga olika åtgärder för att minimera risken för uppkomst av rörelseartefakter i magnetresonans-bilder. Metod: Studien genomfördes som en allmän litteraturöversikt där tolv kvantitativa artiklar som söktes i databaserna PubMed och CINAHL ingick i analysen. Resultat: Resultatet visade att utökad muntlig information är ett effektivt och lättillgängligt alternativ som skulle kunna bidra till minskning av rörelseartefakter. Spasmolytiska och sederande läkemedel anses vara effektiva åtgärder. Dock krävs det enhetliga rutiner kring användandet av dessa läkemedel. Tekniska interventioner som taktila återkopplingar ses som en lovande metod med ett brett användningsområde då interventionen kan användas ihop med musik och/eller VR. Slutsats: Studien visade att utökad information samt medicinska interventioner ledde till minskning av rörelseartefakter i MR-bilder. Tekniska interventioner anses vara lovande metoder som skulle kunna minska risken för uppkomst av rörelseartefakter. Dock kan installation av dessa utrustningar vara svårgenomförd på grund av tekniska och ekonomiska skäl. En slutsats kunde dras att den ångest och oro som patienten upplever i samband med MR-undersökning anses vara en övergripande faktor för uppkomst av rörelseartefakter.

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