Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer's Disease

Model-based investigations of transneuronal spreading mechanisms in neurodegenerative diseases relate the pattern of pathology severity to the brain’s connectivity matrix, which reveals information about how pathology propagates through the connectivity network. Such network models typically use networks based on functional or structural connectivity in young and healthy individuals, and only end-stage patterns of pathology, thereby ignoring/excluding the effects of normal aging and disease progression. Here, we examine the sequence of changes in the elderly brain’s anatomical connectivity over the course of a neurodegenerative disease. We do this in a data-driven manner that is not dependent upon clinical disease stage, by using event-based disease progression modeling. Using data from the Alzheimer’s Disease Neuroimaging Initiative dataset, we sequence the progressive decline of anatomical connectivity, as quantified by graph-theory metrics, in the Alzheimer’s disease brain. Ours is the first single model to contribute to understanding all three of the nature, the location, and the sequence of changes to anatomical connectivity in the human brain due to Alzheimer’s disease. Our experimental results reveal new insights into Alzheimer’s disease: that degeneration of anatomical connectivity in the brain may be a viable, even early, biomarker and should be considered when studying such neurodegenerative diseases

Phonatory and articulatory representations of speech production in cortical and subcortical fMRI responses

Speaking involves coordination of multiple neuromotor systems, including respiration, phonation and articulation. Developing non-invasive imaging methods to study how the brain controls these systems is critical for understanding the neurobiology of speech production. Recent models and animal research suggest that regions beyond the primary motor cortex (M1) help orchestrate the neuromotor control needed for speaking, including cortical and sub-cortical regions. Using contrasts between speech conditions with controlled respiratory behavior, this fMRI study investigates articulatory gestures involving the tongue, lips and velum (i.e., alveolars versus bilabials, and nasals versus orals), and phonatory gestures (i.e., voiced versus whispered speech). Multivariate pattern analysis (MVPA) was used to decode articulatory gestures in M1, cerebellum and basal ganglia. Furthermore, apart from confirming the role of a mid-M1 region for phonation, we found that a dorsal M1 region, linked to respiratory control, showed significant differences for voiced compared to whispered speech despite matched lung volume observations. This region was also functionally connected to tongue and lip M1 seed regions, underlying its importance in the coordination of speech. Our study confirms and extends current knowledge regarding the neural mechanisms underlying neuromotor speech control, which hold promise to study neural dysfunctions involved in motor-speech disorders non-invasively.Tis work was supported by the Spanish Ministry of Economy and Competitiveness through the Juan de la Cierva Fellowship (FJCI-2015-26814), and the Ramon y Cajal Fellowship (RYC-2017- 21845), the Spanish State Research Agency through the BCBL “Severo Ochoa” excellence accreditation (SEV-2015-490), the Basque Government (BERC 2018- 2021) and the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant (No 799554).info:eu-repo/semantics/publishedVersio

A cross-sectional study of explainable machine learning in Alzheimer’s disease: diagnostic classification using MR radiomic features

IntroductionAlzheimer’s disease (AD) even nowadays remains a complex neurodegenerative disease and its diagnosis relies mainly on cognitive tests which have many limitations. On the other hand, qualitative imaging will not provide an early diagnosis because the radiologist will perceive brain atrophy on a late disease stage. Therefore, the main objective of this study is to investigate the necessity of quantitative imaging in the assessment of AD by using machine learning (ML) methods. Nowadays, ML methods are used to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers in the assessment of AD.MethodsIn this study radiomic features from both entorhinal cortex and hippocampus were extracted from 194 normal controls (NC), 284 mild cognitive impairment (MCI) and 130 AD subjects. Texture analysis evaluates statistical properties of the image intensities which might represent changes in MRI image pixel intensity due to the pathophysiology of a disease. Therefore, this quantitative method could detect smaller-scale changes of neurodegeneration. Then the radiomics signatures extracted by texture analysis and baseline neuropsychological scales, were used to build an XGBoost integrated model which has been trained and integrated.ResultsThe model was explained by using the Shapley values produced by the SHAP (SHapley Additive exPlanations) method. XGBoost produced a f1-score of 0.949, 0.818, and 0.810 between NC vs. AD, MC vs. MCI, and MCI vs. AD, respectively.DiscussionThese directions have the potential to help to the earlier diagnosis and to a better manage of the disease progression and therefore, develop novel treatment strategies. This study clearly showed the importance of explainable ML approach in the assessment of AD

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

Techniques for Analysis and Motion Correction of Arterial Spin Labelling (ASL) Data from Dementia Group Studies

This investigation examines how Arterial Spin Labelling (ASL) Magnetic Resonance Imaging can be optimised to assist in the early diagnosis of diseases which cause dementia, by considering group study analysis and control of motion artefacts. ASL can produce quantitative cerebral blood flow maps noninvasively - without a radioactive or paramagnetic contrast agent being injected. ASL studies have already shown perfusion changes which correlate with the metabolic changes measured by Positron Emission Tomography in the early stages of dementia, before structural changes are evident. But the clinical use of ASL for dementia diagnosis is not yet widespread, due to a combination of a lack of protocol consistency, lack of accepted biomarkers, and sensitivity to motion artefacts. Applying ASL to improve early diagnosis of dementia may allow emerging treatments to be administered earlier, thus with greater effect. In this project, ASL data acquired from two separate patient cohorts ( (i) Young Onset Alzheimer’s Disease (YOAD) study, acquired at Queen Square; and (ii) Incidence and RISk of dementia (IRIS) study, acquired in Rotterdam) were analysed using a pipeline optimised for each acquisition protocol, with several statistical approaches considered including support-vector machine learning. Machine learning was also applied to improve the compatibility of the two studies, and to demonstrate a novel method to disentangle perfusion changes measured by ASL from grey matter atrophy. Also in this project, retrospective motion correction techniques for specific ASL sequences were developed, based on autofocusing and exploiting parallel imaging algorithms. These were tested using a specially developed simulation of the 3D GRASE ASL protocol, which is capable of modelling motion. The parallel imaging based approach was verified by performing a specifically designed MRI experiment involving deliberate motion, then applying the algorithm to demonstrably reduce motion artefacts retrospectively

Structural gray matter features and behavioral preliterate skills predict future literacy – A machine learning approach

When children learn to read, their neural system undergoes major changes to become responsive to print. There seem to be nuanced interindividual differences in the neurostructural anatomy of regions that later become integral parts of the reading network. These differences might affect literacy acquisition and, in some cases, might result in developmental disorders like dyslexia. Consequently, the main objective of this longitudinal study was to investigate those interindividual differences in gray matter morphology that might facilitate or hamper future reading acquisition. We used a machine learning approach to examine to what extent gray matter macrostructural features and cognitive-linguistic skills measured before formal literacy teaching could predict literacy 2 years later. Forty-two native German-speaking children underwent T1-weighted magnetic resonance imaging and psychometric testing at the end of kindergarten. They were tested again 2 years later to assess their literacy skills. A leave-one-out cross-validated machine-learning regression approach was applied to identify the best predictors of future literacy based on cognitive-linguistic preliterate behavioral skills and cortical measures in a priori selected areas of the future reading network. With surprisingly high accuracy, future literacy was predicted, predominantly based on gray matter volume in the left occipito-temporal cortex and local gyrification in the left insular, inferior frontal, and supramarginal gyri. Furthermore, phonological awareness significantly predicted future literacy. In sum, the results indicate that the brain morphology of the large-scale reading network at a preliterate age can predict how well children learn to read

Imaging biomarkers extraction and classification for Prion disease

Prion diseases are a group of rare neurodegenerative conditions characterised by a high rate of progression and highly heterogeneous phenotypes. Whilst the most common form of prion disease occurs sporadically (sporadic Creutzfeldt-Jakob disease, sCJD), other forms are caused by inheritance of prion protein gene mutations or exposure to prions. To date, there are no accurate imaging biomarkers that can be used to predict the future diagnosis of a subject or to quantify the progression of symptoms over time. Besides, CJD is commonly mistaken for other forms of dementia. Due to the large heterogeneity of phenotypes of prion disease and the lack of a consistent spatial pattern of disease progression, the approaches used to study other types of neurodegenerative diseases are not satisfactory to capture the progression of the human form of prion disease. Using a tailored framework, I extracted quantitative imaging biomarkers for characterisation of patients with Prion diseases. Following the extraction of patient-specific imaging biomarkers from multiple images, I implemented a Gaussian Process approach to correlated symptoms with disease types and stages. The model was used on three different tasks: diagnosis, differential diagnosis and stratification, addressing an unmet need to automatically identify patients with or at risk of developing Prion disease. The work presented in this thesis has been extensively validated in a unique Prion disease cohort, comprising both the inherited and sporadic forms of the disease. The model has shown to be effective in the prediction of this illness. Furthermore, this approach may have used in other disorders with heterogeneous imaging features, being an added value for the understanding of neurodegenerative diseases. Lastly, given the rarity of this disease, I also addressed the issue of missing data and the limitations raised by it. Overall, this work presents progress towards modelling of Prion diseases and which computational methodologies are potentially suitable for its characterisation

ApoE ε4 Allele Related Alterations in Hippocampal Connectivity in Early Alzheimer’s Disease Support Memory Performance

Background: Whether the presence of the Apolipoprotein E ε4 allele modulates hippocampal connectivity networks in abnormal ageing has yet to be fully clarified. Objective: Allele-dependent differences in this pattern of functional connectivity were investigated in patients with very mild neurodegeneration of the Alzheimer’s type, carriers and non-carriers of the ε4 allele. Method: A seed-based connectivity approach was used. The two groups were similar in demographics, volumetric measures of brain structure, and cognitive profiles. Results: ε4-carriers had increased connectivity between the seed area in the left hippocampus and 1) a left insular/lateral prefrontal region and 2) the contralateral right parietal cortex. Moreover, hippocampus- to-parietal connectivity in the group of ε4 carriers was positively associated with memory performance, indicating that the between-group difference reflects compensatory processes. Retrospective analyses of functional connectivity based on patients from the ADNI initiative confirmed this pattern. Conclusion: We suggest that increased connectivity with areas external to the Default Mode Network (DMN) reflects both compensatory recruitment of additional areas, and pathological interwining between the DMN and the salience network as part of a global ε4-dependent circuital disruption. These differences indicate that the ε4 allele is associated with a more profound degree of DMN network breakdown even in the prodromal stage of neurodegeneration

Lesion identification and the effect of lesion on motor mapping after stroke

Stroke is the most common cause of long-term severe disability and the motor system that is most commonly affected in stroke. One of the mechanisms that underlies recovery of motor deficits is reorganization or remapping of functional representations around the motor cortex. This mechanism has been shown in monkeys, but results in human subjects have been variable. In this thesis, I used a database that includes longitudinal behavioral and multimodal imaging data in both stroke patients and healthy controls for two research projects. Firstly, I improved an automatic lesion segmentation method to aid in the identification of the location and extent of the stroke in structural magnetic resonance imaging (MRI) images. I developed a point and click interface that allows for the automatic segmentation as well as selecting lesions generated at different thresholds based on the contrast of the T1 images. Second, I investigated the effect of subcortical strokes on motor representations by measuring changes in the topography of inter-hemispheric resting state functional connectivity (FC) MRI to track changes of the hand representation in the damaged hemisphere shows a higher variation across the medial-lateral axis, suggesting a shift in neighboring body representations along the motor strip. During recovery, however, there is a shift in an anterior-posterior direction suggesting a shift into sensory and premotor regions. Obtaining lesion profile and understanding its effect on the functional connectivity can provide us with useful information on the effects of stroke on brain structure and function, which in turn will help in the prognosis and rehabilitation of stroke patients

Understanding space by moving through it: neural networks of motion- and space processing in humans

Humans explore the world by moving in it, whether moving their whole body as during walking or driving a car, or moving their arm to explore the immediate environment. During movement, self-motion cues arise from the sensorimotor system comprising vestibular, proprioceptive, visual and motor cues, which provide information about direction and speed of the movement. Such cues allow the body to keep track of its location while it moves through space. Sensorimotor signals providing self-motion information can therefore serve as a source for spatial processing in the brain. This thesis is an inquiry into human brain systems of movement and motion processing in a number of different sensory and motor modalities using functional magnetic resonance imaging (fMRI). By characterizing connections between these systems and the spatial representation system in the brain, this thesis investigated how humans understand space by moving through it. In the first study of this thesis, the recollection networks of whole-body movement were explored. Brain activation was measured during the retrieval of active and passive self-motion and retrieval of observing another person performing these tasks. Primary sensorimotor areas dominated the recollection network of active movement, while higher association areas in parietal and mid-occipital cortex were recruited during the recollection of passive transport. Common to both self-motion conditions were bilateral activations in the posterior medial temporal lobe (MTL). No MTL activations were observed during recollection of movement observation. Considering that on a behavioral level, both active and passive self-motion provide sufficient information for spatial estimations, the common activation in MTL might represent the common physiological substrate for such estimations. The second study investigated processing in the 'parahippocampal place area' (PPA), a region in the posterior MTL, during haptic exploration of spatial layout. The PPA in known to respond strongly to visuo-spatial layout. The study explored if this region is processing visuo-spatial layout specifically or spatial layout in general, independent from the encoding sensory modality. In both a cohort of sighted and blind participants, activation patterns in PPA were measured while participants haptically explored the spatial layout of model scenes or the shape of information-matched objects. Both in sighted and blind individuals, PPA activity was greater during layout exploration than during object-shape exploration. While PPA activity in the sighted could also be caused by a transformation of haptic information into a mental visual image of the layout, two points speak against this: Firstly, no increase in connectivity between the visual cortex and the PPA were observed, which would be expected if visual imagery took place. Secondly, blind participates, who cannot resort to visual imagery, showed the same pattern of PPA activity. Together, these results suggest that the PPA processes spatial layout information independent from the encoding modality. The third and last study addressed error accumulation in motion processing on different levels of the visual system. Using novel analysis methods of fMRI data, possible links between physiological properties in hMT+ and V1 and inter-individual differences in perceptual performance were explored. A correlation between noise characteristics and performance score was found in hMT+ but not V1. Better performance correlated with greater signal variability in hMT+. Though neurophysiological variability is traditionally seen as detrimental for behavioral accuracy, the results of this thesis contribute to the increasing evidence which suggests the opposite: that more efficient processing under certain circumstances can be related to more noise in neurophysiological signals. In summary, the results of this doctoral thesis contribute to our current understanding of motion and movement processing in the brain and its interface with spatial processing networks. The posterior MTL appears to be a key region for both self-motion and spatial processing. The results further indicate that physiological characteristics on the level of category-specific processing but not primary encoding reflect behavioral judgments on motion. This thesis also makes methodological contributions to the field of neuroimaging: it was found that the analysis of signal variability is a good gauge for analysing inter-individual physiological differences, while superior head-movement correction techniques have to be developed before pattern classification can be used to this end