Recently, Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention mainly for two reasons. First, it is one of the most commonly found childhood behavioral disorders. Around 5-10% of the children all over the world are diagnosed with ADHD. Second, the root cause of the problem is still unknown and therefore no biological measure exists to diagnose ADHD. Instead, doctors need to diagnose it based on the clinical symptoms, such as inattention, impulsivity and hyperactivity, which are all subjective. Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool to understand the functioning of the brain such as identifying the brain regions responsible for different cognitive tasks or analyzing the statistical differences of the brain functioning between the diseased and control subjects. ADHD is also being studied using the fMRI data. In this dissertation we aim to solve the problem of automatic diagnosis of the ADHD subjects using their resting state fMRI (rs-fMRI) data. As a core step of our approach, we model the functions of a brain as a connectivity network, which is expected to capture the information about how synchronous different brain regions are in terms of their functional activities. The network is constructed by representing different brain regions as the nodes where any two nodes of the network are connected by an edge if the correlation of the activity patterns of the two nodes is higher than some threshold. The brain regions, represented as the nodes of the network, can be selected at different granularities e.g. single voxels or cluster of functionally homogeneous voxels. The topological differences of the constructed networks of the ADHD and control group of subjects are then exploited in the classification approach. We have developed a simple method employing the Bag-of-Words (BoW) framework for the classification of the ADHD subjects. We represent each node in the network by a 4-D feature vector: node degree and 3-D location. The 4-D vectors of all the network nodes of the training data are then grouped in a number of clusters using K-means; where each such cluster is termed as a word. Finally, each subject is represented by a histogram (bag) of such words. The Support Vector Machine (SVM) classifier is used for the detection of the ADHD subjects using their histogram representation. The method is able to achieve 64% classification accuracy. The above simple approach has several shortcomings. First, there is a loss of spatial information while constructing the histogram because it only counts the occurrences of words ignoring the spatial positions. Second, features from the whole brain are used for classification, but some of the brain regions may not contain any useful information and may only increase the feature dimensions and noise of the system. Third, in our study we used only one network feature, the degree of a node which measures the connectivity of the node, while other complex network features may be useful for solving the proposed problem. In order to address the above shortcomings, we hypothesize that only a subset of the nodes of the network possesses important information for the classification of the ADHD subjects. To identify the important nodes of the network we have developed a novel algorithm. The algorithm generates different random subset of nodes each time extracting the features from a subset to compute the feature vector and perform classification. The subsets are then ranked based on the classification accuracy and the occurrences of each node in the top ranked subsets are measured. Our algorithm selects the highly occurring nodes for the final classification. Furthermore, along with the node degree, we employ three more node features: network cycles, the varying distance degree and the edge weight sum. We concatenate the features of the selected nodes in a fixed order to preserve the relative spatial information. Experimental validation suggests that the use of the features from the nodes selected using our algorithm indeed help to improve the classification accuracy. Also, our finding is in concordance with the existing literature as the brain regions identified by our algorithms are independently found by many other studies on the ADHD. We achieved a classification accuracy of 69.59% using this approach. However, since this method represents each voxel as a node of the network which makes the number of nodes of the network several thousands. As a result, the network construction step becomes computationally very expensive. Another limitation of the approach is that the network features, which are computed for each node of the network, captures only the local structures while ignore the global structure of the network. Next, in order to capture the global structure of the networks, we use the Multi-Dimensional Scaling (MDS) technique to project all the subjects from an unknown network-space to a low dimensional space based on their inter-network distance measures. For the purpose of computing distance between two networks, we represent each node by a set of attributes such as the node degree, the average power, the physical location, the neighbor node degrees, and the average powers of the neighbor nodes. The nodes of the two networks are then mapped in such a way that for all pair of nodes, the sum of the attribute distances, which is the inter-network distance, is minimized. To reduce the network computation cost, we enforce that the maximum relevant information is preserved with minimum redundancy. To achieve this, the nodes of the network are constructed with clusters of highly active voxels while the activity levels of the voxels are measured based on the average power of their corresponding fMRI time-series. Our method shows promise as we achieve impressive classification accuracies (73.55%) on the ADHD-200 data set. Our results also reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects. So far, we have only used the fMRI data for solving the ADHD diagnosis problem. Finally, we investigated the answers of the following questions. Do the structural brain images contain useful information related to the ADHD diagnosis problem? Can the classification accuracy of the automatic diagnosis system be improved combining the information of the structural and functional brain data? Towards that end, we developed a new method to combine the information of structural and functional brain images in a late fusion framework. For structural data we input the gray matter (GM) brain images to a Convolutional Neural Network (CNN). The output of the CNN is a feature vector per subject which is used to train the SVM classifier. For the functional data we compute the average power of each voxel based on its fMRI time series. The average power of the fMRI time series of a voxel measures the activity level of the voxel. We found significant differences in the voxel power distribution patterns of the ADHD and control groups of subjects. The Local binary pattern (LBP) texture feature is used on the voxel power map to capture these differences. We achieved 74.23% accuracy using GM features, 77.30% using LBP features and 79.14% using combined information. In summary this dissertation demonstrated that the structural and functional brain imaging data are useful for the automatic detection of the ADHD subjects as we achieve impressive classification accuracies on the ADHD-200 data set. Our study also helps to identify the brain regions which are useful for ADHD subject classification. These findings can help in understanding the pathophysiology of the problem. Finally, we expect that our approaches will contribute towards the development of a biological measure for the diagnosis of the ADHD subjects.

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Det här är en lexikal typologisk pilotstudie som undersöker begreppet LIV i elva olika språk. Huvudmaterialet består av texter från Bibeln, Nya testamentet. Studien utgår ifrån de koine-grekiska lexemen för liv, ζωή (zōḗ), ψυχή (psūkhḗ) och βῐ́ος (bíos). Uti- från huvudmaterial från parallelltexterna har probabilistiska semantiska kartor skapats genom multidimensionell skalering för varje språk och har analyserats med partitio- ning around medoids pam(). Resultatet visar på två intressanta fenomen. För det förs- ta har zōḗ översatts mest enhetligt. psūkhḗ och bíos översätts på ett mer varierande sätt i det flesta språken där fler olika lexem har använts och det här tyder på att deras be- tydelse är mer varierad. De koine-grekiska lexemen bildar en konceptuell struktur, vilket innebär att de olika koncepten för LIV inordnas efter en snävare och bredare be- tydelse. För det andra, visar resultatet även att språken anpassar sitt vokabulär för att efterliknar denna struktur. Det visar sig tydligast i språk med flera lexem för LIV samt hur de används för att översätta de tre koine-grekiska lexemen. / This is a lexical typological pilot study that examines the concept of LIFE in eleven different languages. The main material consists of texts retrieved from the Bible, the New Testament. The study is based on the Koine-Greek lexemes for life, ζωή (zōḗ), ψυχή (psūkhḗ) and βῐ́ος (bíos). Probabilistic semantic maps have been created through multidimensional scaling for each language and have been analyzed with partitioning around medoids pam(). The results show two interesting phenomena. Firstly, zōḗ has been translated most consistently. psūkhḗ and bíos have been translated with more va- riation in most languages where several different lexemes have been used, and this indicates that their meaning is more varied. The Koine-Greek lexemes form a concep- tual structure, which means that the various concepts of LIFE is arranged according to a narrower and broader meaning. Secondly the results also show that the languages adapt their vocabulary to mimic this structure, it is most evident in languages with several lexemes for LIFE and how they are used to translate the three Koine-Greek lexemes.

<p>The aim of this thesis is to interconnect human tactile perception with various physical properties of materials. Tactile perception necessitates contact and relative motion between the skin and the surfaces of interest. This implies that properties such as friction and surface roughness ought to be important physical properties for tactile sensing. In this work, a method to measure friction between human fingers and surfaces is presented. This method is believed to best represent friction in tactile perception.</p><p>This study is focused on the tactile perception of printing papers. However, the methodology of finger friction measurements, as well as the methodology to link physical properties with human perception data, can be applied to almost whichever material or surfaces.</p><p> </p><p>This thesis is based on three articles.</p><p> </p><p>In Article I, one participant performed finger friction measurements, using a piezoelectric force sensor, on 21 printing papers of different paper grades and grammage (weight of the papers). Friction coefficients were calculated as the ratio of the frictional force and the normal force, shown to have a linear relationship. The values were recorded while stroking the index finger over the surface. The results show that measurements with the device can be used to discriminate a set of similar surfaces in terms of finger friction. When comparing the friction coefficients, the papers group according to paper surface treatment and an emerging trend is that the rougher (uncoated) papers have a lower friction coefficient than the smoother (coated) papers. In the latter case, this is interpreted in terms of a larger contact between the finger and paper surface.</p><p> </p><p>In addition, a decrease in friction coefficient is noted for all papers on repeated stroking, where the coated papers display a larger decrease. XPS (X-ray Photoelectron Spectroscopy) reveals that skin lipids are transferred from the finger to the paper surface, acting as a lubricant and hence decrease friction. Nevertheless, there is evidence that mechanical changes of the surface cannot be completely ruled out.</p><p> </p><p>The reproducibility of the finger friction measurements is elaborated in Article II, by using many participants on a selection of eight printing papers out of the 21. The trends in friction are the same; once again, the coated papers display the highest friction. There are notably large variations in the exact value of the friction coefficient, which are tentatively attributed to different skin hydration and stroking modes.</p><p> </p><p>These same participants also took part in a tactile study of perceived paper coarseness (“strävhet” in Swedish). The results reveal that the participants can distinguish a set of printing papers in terms of perceived coarseness. Not unexpectedly, surface roughness appears to be an important property related to perceived coarseness, where group data display that perceived coarseness increases with increasing surface roughness. Interestingly, friction also appears to be a discriminatory property for some subjects. A few participants showed opposite trends, which is evidence for that what is considered coarse is subjective and that different participants “weigh” the importance of the properties differently. This is a good example of a challenge when measuring one-dimensional perceptions in psychophysics.</p><p> </p><p>In Article III, a multidimensional approach was used to explore the tactile perception of printing papers. To do this, the participants scaled similarity among all possible pairs of the papers, and this similarity data are best presented by a three-dimensional space solution. This means that there are three underlying dimensions or properties that the participants use to discriminate the surface feel. Also, there is a distinct perceptual difference between the rougher (uncoated) and smoother (coated) papers. The surface roughness appears to be the dominant physical property when discriminating between a real rough paper and a smooth paper, whereas friction, thermal conductivity and grammage are more important when discriminating among the smooth coated papers.</p>

Le mouvement de la marche est un processus essentiel de l'activité humaine et aussi le résultat de nombreuses interactions collaboratives entre les systèmes neurologiques, articulaires et musculo-squelettiques fonctionnant ensemble efficacement. Ceci explique pourquoi une analyse de la marche est aujourd'hui de plus en plus utilisée pour le diagnostic (et aussi la prévention) de différents types de maladies (neurologiques, musculaires, orthopédique, etc.). Ce rapport présente une nouvelle méthode pour visualiser rapidement les différentes parties du corps humain liées à une possible asymétrie (temporellement invariante par translation) existant dans la démarche d'un patient pour une possible utilisation clinique quotidienne. L'objectif est de fournir une méthode à la fois facile et peu dispendieuse permettant la mesure et l'affichage visuel, d'une manière intuitive et perceptive, des différentes parties asymétriques d'une démarche. La méthode proposée repose sur l'utilisation d'un capteur de profondeur peu dispendieux (la Kinect) qui est très bien adaptée pour un diagnostique rapide effectué dans de petites salles médicales car ce capteur est d'une part facile à installer et ne nécessitant aucun marqueur. L'algorithme que nous allons présenter est basé sur le fait que la marche saine possède des propriétés de symétrie (relativement à une invariance temporelle) dans le plan coronal. / The gait movement is an essential process of the human activity and also the result of coordinated effort between the neurological, articular and musculoskeletal systems. This motivates why gait analysis is important and also increasingly used nowadays for the (possible early) diagnosis of many different types (neurological, muscular, orthopedic, etc.) of diseases. This paper introduces a novel method to quickly visualize the different parts of the body related to an asymmetric movement in the human gait of a patient for daily clinical. The goal is to provide a cheap and easy-to-use method to measure the gait asymmetry and display results in a perceptually relevant manner. This method relies on an affordable consumer depth sensor, the Kinect. The Kinect was chosen because this device is amenable for use in small, confined area, like a living room. Also, since it is marker-less, it provides a fast non-invasive diagnostic. The algorithm we are going to introduce relies on the fact that a healthy walk has (temporally shift-invariant) symmetry properties in the coronal plane.

The aim of this thesis is to interconnect human tactile perception with various physical properties of materials. Tactile perception necessitates contact and relative motion between the skin and the surfaces of interest. This implies that properties such as friction and surface roughness ought to be important physical properties for tactile sensing. In this work, a method to measure friction between human fingers and surfaces is presented. This method is believed to best represent friction in tactile perception. This study is focused on the tactile perception of printing papers. However, the methodology of finger friction measurements, as well as the methodology to link physical properties with human perception data, can be applied to almost whichever material or surfaces.   This thesis is based on three articles.   In Article I, one participant performed finger friction measurements, using a piezoelectric force sensor, on 21 printing papers of different paper grades and grammage (weight of the papers). Friction coefficients were calculated as the ratio of the frictional force and the normal force, shown to have a linear relationship. The values were recorded while stroking the index finger over the surface. The results show that measurements with the device can be used to discriminate a set of similar surfaces in terms of finger friction. When comparing the friction coefficients, the papers group according to paper surface treatment and an emerging trend is that the rougher (uncoated) papers have a lower friction coefficient than the smoother (coated) papers. In the latter case, this is interpreted in terms of a larger contact between the finger and paper surface.   In addition, a decrease in friction coefficient is noted for all papers on repeated stroking, where the coated papers display a larger decrease. XPS (X-ray Photoelectron Spectroscopy) reveals that skin lipids are transferred from the finger to the paper surface, acting as a lubricant and hence decrease friction. Nevertheless, there is evidence that mechanical changes of the surface cannot be completely ruled out.   The reproducibility of the finger friction measurements is elaborated in Article II, by using many participants on a selection of eight printing papers out of the 21. The trends in friction are the same; once again, the coated papers display the highest friction. There are notably large variations in the exact value of the friction coefficient, which are tentatively attributed to different skin hydration and stroking modes.   These same participants also took part in a tactile study of perceived paper coarseness (“strävhet” in Swedish). The results reveal that the participants can distinguish a set of printing papers in terms of perceived coarseness. Not unexpectedly, surface roughness appears to be an important property related to perceived coarseness, where group data display that perceived coarseness increases with increasing surface roughness. Interestingly, friction also appears to be a discriminatory property for some subjects. A few participants showed opposite trends, which is evidence for that what is considered coarse is subjective and that different participants “weigh” the importance of the properties differently. This is a good example of a challenge when measuring one-dimensional perceptions in psychophysics.   In Article III, a multidimensional approach was used to explore the tactile perception of printing papers. To do this, the participants scaled similarity among all possible pairs of the papers, and this similarity data are best presented by a three-dimensional space solution. This means that there are three underlying dimensions or properties that the participants use to discriminate the surface feel. Also, there is a distinct perceptual difference between the rougher (uncoated) and smoother (coated) papers. The surface roughness appears to be the dominant physical property when discriminating between a real rough paper and a smooth paper, whereas friction, thermal conductivity and grammage are more important when discriminating among the smooth coated papers.

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Tactile perception is considered an important contributor to the overall consumer experience of a product. However, what physical properties that create the specifics of tactile perception, are still not completely understood. This thesis has researched how many dimensions that are required to differentiate the surfaces perceptually, and then tried to explain these dimensions in terms of physical properties, by interconnecting human perception measurements with various physical measurements. The tactile perception was assessed by multidimensional scaling or magnitude estimation, in which methods human participants assign numbers to how similar pairs of surfaces are perceived or to the relative quantity of a specified perceptual attribute, such as softness, smoothness, coarseness and coolness. The role of friction and surface texture in tactile perception was investigated in particular detail, because typically tactile exploration involves moving (at least) one finger over a textured surface. A tactile approach for measuring friction was developed by means of moving a finger over the surfaces, mounted on a force sensor. The contribution of finger friction to tactile perception was investigated for surfaces of printing papers and tissue papers, as well as for model surfaces with controlled topography. The overarching research goal of this thesis was to study, systematically, the role of texture in tactile perception of surfaces. The model surfaces displayed a sinusoidal texture with a characteristic wavelength and amplitude, fabricated by surface wrinkling and replica molding techniques. A library of surfaces was manufactured, ranging in wavelengths from 270 nm up to 100 µm and in amplitudes from 7 nm up to 6 µm. These surfaces were rigid and cleanable and could therefore be reused among the participants. To my knowledge, this is the first time in a psychophysical experiment, that the surface texture has been controlled over several orders of magnitude in length scale, without simultaneously changing other material properties of the stimuli. The finger friction coefficient was found to decrease with increasing aspect ratio (amplitude/wavelength) of the model surfaces and also with increasing average surface roughness of the printing papers. Analytical modeling of the finger’s interaction with the model surfaces shows how the friction coefficient increases with the real contact area, and that the friction mechanism is the same on both the nanoscale and microscale. The same interaction mechanism also explains the friction characteristics of tissue paper. Furthermore, it was found that the perceptions of smoothness, coarseness, coolness and dryness are satisfactorily related to the real contact area at the finger-surface interface.  It is shown that it is possible to discern perceptually among both printing papers and tissue papers, and this differentiation is based on either two or three underlying dimensions. Rough/smooth and thin/thick were the two main dimensions of surface feel found for the printing papers, whereas friction and wavelength were strongly related to the perceptual cues employed in scaling the model surfaces. These experimental results support the duplex theory of texture perception, which holds that both a “spatial sense”; used to discriminate the roughest textures from the others, and a “vibration sense”; used to discriminate among the smoother textures, are involved. The perception of what is considered rough and smooth depends on the experimental stimulus context. It is concluded that friction is important for human differentiation of surface textures below about 10 µm in surface roughness, and for larger surface textures, friction is less important or can even be neglected. The finger friction experiments also allowed the following conclusions to be drawn: (i) The interindividual variation in friction coefficients is too large to allow direct comparison; however, the trends in relative friction coefficients for a group of participants are the same. (ii) Lipids are transferred to the test surface of study, and this lowers the friction. (iii) Many of the studies point to a characteristic frequency during sliding of about 30 Hz, which is both characteristic of the resonance frequency of skin and the expected frequency associated with the fingerprints. (iv) The applied load in surface interrogation is in fact regulated in response to the friction force. The limits in tactile perception were indirectly researched by similarity scaling experiments on the model surfaces. Wrinkle wavelengths of 760 nm and 870 nm could be discriminated from untextured reference surfaces, whereas 270 nm could not. The amplitude of the wrinkles so discriminated was approximately 10 nm, suggesting that nanotechnology may well have a role to play in haptics and tactile perception. / Taktil perception bidrar starkt till den sammantagna upplevelsen av en produkt, men hur materials olika ytegenskaper påverkar och styr perceptionen är ännu inte helt klart. Den här avhandlingen undersöker hur många och vilka egenskaper som är viktiga när känslan mellan två ytor jämförs. Tillvägagångssättet är tvärvetenskapligt där fysikaliska mätningar kopplas ihop med perceptions mätningar där människor används som instrument. Två typer av perceptionsförsök har utförts, multidimensionell skalning där försökspersoner sätter siffror på hur lika två ytor känns, samt magnitud estimation där i stället intensiteten på specifika perceptuella storheter som t.ex. upplevt lenhet, upplevd mjukhet och upplevd strävhet bedömdes. Eftersom taktil perception innebär kontakt samt relativ rörelse mellan hud och ytor, har fokus i avhandlingen varit att undersöka hur friktion och ytans struktur (ytråhet) påverkar och bidrar till den taktila perceptionen. Förutom fysikaliska mätningar på friktion och ytstruktur har värmekonduktivitet, mjukhet samt olika standard mätningar inom pappersindustrin mätts. En metod för att mäta friktion mellan ett finger och olika ytor har utvecklats för att i möjligaste mån återspegla friktionskomponenten i upplevt taktil perception. Friktionskoefficienter beräknades och jämfördes mellan alla ytor. De stimuli som har studerats är tryckpapper och mjukpapper samt modellytor, gjorda för att systematiskt undersöka hur ytstruktur påverkar perceptionen. Tillverkningsmetoden för modellytorna valdes så att ytorna var tåliga och kunde tvättas och därmed återanvändas. Strukturen på ytorna bestod av ett vågformat mönster där våglängden varierade mellan 270 nm och 100 µm och amplituden mellan 7 nm och 6 µm. Enligt vår vetskap är det första gången som strukturer i de här skalorna har gjorts utan att samtidigt ändra andra material egenskaper. Friktionskoefficienten minskade med ökad kvot mellan amplituden och våglängden på modellytorna samt med ytråheten på tryckpappren. En analytisk modell tillämpades på kontakten mellan ett finger och ytorna som visade att friktionskoefficienten beror av den verkliga kontaktarean. För de mycket grövre mjukpappren uppmättes inga stora skillnader i friktion förmodligen för att kontakarean mellan de olika mjukpapprena var lika. Den faktiska kontakarean visade sig också vara viktig för perceptionen av lenhet, strävhet, torrhet och svalhet. Det visade sig vara en stor perceptuell skillnad mellan olika typer av tryckpapper och mjukpapper utifrån hur stimuli placerade sig på en taktil karta. För de tre materialen användes enbart två alternativt tre egenskaper hos materialet för att särskilja mellan alla olika par. För tryckpapper verkade en viktig dimension kunna beskrivas av alla de perceptuella och fysikaliska egenskaper som har med kontaktarean att göra, d.v.s. lenhet, svalhet, torrhet, ytråhet, värmekonduktivitet samt friktion. För att taktilt särskilja mellan olika ytor där bara strukturen är varierade, kunde friktion och våglängden relateras till spridningen i kartan. Båda studierna stödjer duplex theory of texture perception, där ett spatialt sinne används för att särskilja en av de grövre ytorna från en slät, och ett vibrationssinne för att särskilja mellan olika släta strukturer. Friktionen visade sig alltså vara en viktig fysikalisk egenskap för strukturer under åtminstone 10 µm i ytråhet. Från fingerfriktions mätningar kunde även följande slutsatser dras: (i) Stora skillnader i friktionskoefficient mellan olika personer uppmättes, men trenderna mellan olika individer var samma, vilket gör att relativa skillnader i friktion från en individ är representativa. (ii) Lipider (fingerfett) som överförs från fingret till ytan vid kontakt sänker friktionen. (iii) Frekvensinnehållet i friktionskraften varierar mellan olika ytor och den frekvenstopp som ses vid 30 Hz kan möjligtvis bero på fingrets struktur eller resonansfrekvensen på huden. (iv) Den pålagda kraften under en friktionsmätning visar sig omedvetet regleras av den friktionskraft som fingret möter under rörelse.  Hur små strukturer som kan diskrimineras har indirekt undersökts genom likhetsförsöket på modellytorna där försökspersoner skulle bedöma hur lika alla par av ytor kändes. Resultaten visade att ytorna med våglängder på 760 nm och 870 nm upplevdes olika jämfört med referens ytor utan något systematiskt mönster, medan ytan med 270 nm i våglängd inte kunde särskiljas. Amplituden på ytan som kunde diskrimineras var endast ca 10 nm, vilket indikerar att nanoteknologi mycket väl kan bidra inom haptiken och för att i framtiden kontrollera den taktila perceptionen. / <p>QC 20121026</p>