205 research outputs found

    Psychic embedding — vision and delusion

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    The paper introduces the idea that the human brain may apply complex mathematical modules in order to process and understand the world. We speculate that the substrate of what appears outwardly as intuition, or prophetic power, may be a mathematical apparatus such as time-delay embedding. In this context, predictive accuracy may be the reflection of an appropriate choice of the embedding parameters. We further put this in the perspective of mental illness, and search for the possible differences between good intuition and delusive ideation. We speculate that the task at which delusional schizophrenic patients falter is not necessarily of perception, but rather of model selection. Failure of the psychotic patient to correctly choose the embedding parameters may readily lead to misinterpretation of an accurate perception through an altered reconstructed of the object perceived

    Neurotechnology and Psychiatric Biomarkers

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    Body Perception: Intersensory Origins of Self and Other Perception in Newborns

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    SummarySelf-perception involves integrating changes in visual, tactile, and proprioceptive stimulation from self-motion and discriminating these changes from those of other objects. Recent evidence suggests even newborns discriminate synchronous from asynchronous visual-tactile stimulation to their own body, a foundation for self-perception

    Topological Detection of Alzheimer’s Disease Using Betti Curves

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    Spatial Memory in Intellectual Disability: Explanation with the Parallel Map Theory

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    The parallel map theory explains that the hippocampus encodes space with two mapping systems: The bearing map created from ―directional cues and stimulus gradients‖; The sketch map constructed from ―positional cues‖. The integrated map combines the two mapping systems. Such parallel functioning may explain paradoxes of spatial learning in intellectual disabilities. This people may be able to memorize their surroundings in a highly detailed way, thus ordering their sensory perceptions into a representation that includes the precise localization of static objects, they are not able to ―map‖ their own spatial relationship to those objects. The detection of moving objects by these same subjects contributes to a primary bearing map. The primary map is thus generated by relying on this kind of static map, but also by detecting moving objects. This process can be described as a spatial mode of processing separate objects within the structure of an absolute reference system

    Advanced Application of Diffusion Kurtosis Imaging

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    Diffusion tensor imaging (DTI) has become a standard procedure in clinical routine as well as research as it enables the reconstruction and visualization of fiber tracts in the human brain. Due to the simplified assumption the tensor model – a Gaussian distribution of the diffusion – it typically fails to provide neither accurate spatial mapping nor quantification of crossing or kissing fibers. A clinically feasible development might be diffusion kurtosis imaging (DKI), an extension of DTI also integrating non-Gaussian distribution diffusion processes and thereby shall overcome some of its limitations. The potential DKI will be evaluated in case of the detection of the interhemispheric asymmetry of the white matter in healthy volunteers (n = 20), as well as the analysis of tumor-related impairments of fiber tracts and their correlation with neurological deficits in patients (n = 13) diagnosed with glioma. In order to analyze interhemispheric asymmetry across the whole brain, especially of nine large fiber tracts, tract-based spatial statistics (TBSS) analysis was performed using DTI- and DKI-based parameters, a laterality index was calculated for asymmetries and DTI- and DKI-based results were compared. With regard to fractional anisotropy as marker of integrity, asymmetry was found for all nine fiber tracts based on DTI and seven tracts based on DKI. For mean diffusivity, asymmetries were found for three (DTI) and two (DKI) fiber tracts. Regarding mean kurtosis, asymmetry was found in one tract. The interhemispheric asymmetry thereby varied in anatomical location as well as in cluster size. Only small parts of the tracts were affected. A comparison of DTI and DKI showed significantly higher fractional anisotropy and mean diffusivity based on DKI compared to DTI. Gender and handedness did not seem to have any influence. For the assessment of tumor-related changes of fiber tracts in patients diagnosed with glioma, especially in relation to pre-existing and postoperative neurological deficits (hemiparesis, aphasia), templates for the corticospinal tract and the arcuate fasciculus were created based on DTI- and DKI-derived parameters, respectively. The corticospinal tract and the arcuate fasciculus were reconstructed for each patient and the associated parametric maps were projected onto the templates. Based on this, alterations along the tracts could be identified and quantified. Alterations were found on fiber tracts regardless of the spatial proximity to the lesion. There was a correlation between alterations based on fractional anisotropy, mean diffusivity and mean kurtosis. Increased mean diffusivity was associated with alteration in mean kurtosis, a decreased fractional anisotropy was found concurrent with a likewise decreased mean kurtosis. In the case of pre-existing neurological deficits (hemiparesis, aphasia) with regard to the changes along the fiber tracts (corticospinal tract, left arcuate fasciculus), most often increased mean diffusivity and altered mean kurtosis was found. Applying this pattern for prediction of corresponding postoperative neurological deficits a sensitivity of 75.0% and a specificity of 87.5% was achieved. DKI seems to more precisely estimated and depict the underlying microstructure in comparison to DTI. Thereby, in pathological cases especially the mean kurtosis seems to be of special interest. A combination of DTI- and DKI based parameters, particularly with regard to its clinical usability and value, offers great potential in clinical routine

    Biological motion processing in autism spectrum disorders: a behavioural and fMRI investigation

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    There has been much controversy as to whether people with Autism Spectrum Disorders (ASDs) have a specific impairment in processing biological motion, with some studies suggesting there is an impairment (Blake, et. al. 2003; Klin et. al. 2003, Klin & Jones, 2008, Klin et. al. 2009) and others finding that people with ASDs show intact abilities to detect biological motion and categorise actions, but are impaired in emotion categorisation (Moore et. al. 1997; Hubert et. al. 2007, Parron et. al. 2008). Recent studies have found that although behavioural measures of biological motion processing show no differences, adults with ASDs show different patterns of brain activation to controls in response to intact point-light displays (PLDs), with the STS, MT+ and ITG regions showing reduced activity in this population (Herrington et. al. 2007; Parron et. al. 2009). The current thesis aimed to clarify the nature of these difficulties and to try to elucidate the brain regions used to process configural information from PLDs using novel techniques and stimuli. The first set of experiments were designed to behaviourally test people with ASDs ability to detect biological motion in noise, to categorise actions and to categorise affect from PLDs. Despite finding differences in the two groups in detection of biological motion and affect categorisation in pilot experiments, there were no significant differences between the groups in the main experiments. However, the ASD group showed slightly poorer performance at detecting biological motion and significantly more variability in the action categorisation tasks, suggesting that there may have been an underlying difference between the two groups. Furthermore, an analysis of the pattern of errors tentatively suggested that the ASD group may be using different strategies to categorise affect than controls, particularly for negative affects. We then devised a novel technique for manipulating the amount of configural information available in a PLD without the need to add different degrees of background noise and used this technique to assess the contribution of configural cues in a direction discrimination task behaviourally and neurally. The results confirmed that in typically developed individuals configural cues significantly improved the participants’ ability to correctly determine the direction of locomotion of a point light walker. Furthermore, the fMRI task found that regions of the inferotemporal, parietal and frontal regions were sensitive to the amount of configural information present in the displays that corresponded to increases in individual participants’ behavioural performance. Lastly, we used the same technique, though with a more powerful fMRI design, to assess the behavioural and neural differences between people with ASDs and controls in response to displays containing different degrees of configural information. We found that both groups were comparable in their ability to discriminate the direction of locomotion from PLDs. However, the brain regions used to process this information were found to be substantially different. In displays in which the configural information enabled participants to accurately judge the direction of locomotion, the control group utilised a similar group of regions as found in the previous experiment. The ASD group showed a pattern of activation suggesting that they predominantly used regions in the temporal and occipital cortex, and more specifically a region in the fusiform gyrus. The results of Granger Causality Mapping analysis, which allows for the mapping of directional to and from seeded regions, confirmed that whereas the control group utilised a network of regions starting from the ITG and connecting to parietal and occipital regions, the ASD group seemed to utilise two separate networks, processing form information in the fusiform gyrus and motion information separately in middle-temporal regions. The results are discussed in terms of a potential dysfunction of the ITG region in early childhood and two different models of biological motion processing that have been proposed in the recent literature. In TD individuals the model of Giese & Poggio (2003) may be more applicable, in that it proposes the integration of static form cues with motion signals in areas such as the STS. However, a dysfunctional ITG or dysfunctional connections from the ITG to more dorsal regions would disrupt the integration of form and motion processing and force the brain to place additional processing demands on form processing regions in the fusiform gyrus. This would be more in line with the model proposed by Lange and Lappe (2006) in which information can be derived from biological motion in noise without recourse to the actual motion information, through a process of temporal analysis of static postures. Both systems though, may be intact in TD individuals and may share processing requirements depending on the task. Furthermore, it is hypothesised that a dysfunctional ITG may force the brain to place additional demands on regions in the fusiform gyrus and this neural rewiring may be the cause of the developmental delay seen in processing biological motion in people with ASDs (Annaz et. al. 2009). Future studies should examine the roles of the ITG and fusiform area in more detail, both in TD people and in people with ASDs, and determine the specific nature of these neural differences and there behavioural implications for both groups

    Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy

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    Aim: To detect pathological brain conditions early is a core procedure for patients so as to have enough time for treatment. Traditional manual detection is either cumbersome, or expensive, or time-consuming. We aim to offer a system that can automatically identify pathological brain images in this paper.Method: We propose a novel image feature, viz., Fractional Fourier Entropy (FRFE), which is based on the combination of Fractional Fourier Transform(FRFT) and Shannon entropy. Afterwards, the Welch’s t-test (WTT) and Mahalanobis distance (MD) were harnessed to select distinguishing features. Finally, we introduced an advanced classifier: twin support vector machine (TSVM). Results: A 10 x K-fold stratified cross validation test showed that this proposed “FRFE +WTT + TSVM” yielded an accuracy of 100.00%, 100.00%, and 99.57% on datasets that contained 66, 160, and 255 brain images, respectively. Conclusions: The proposed “FRFE +WTT + TSVM” method is superior to 20 state-of-the-art methods
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