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Ogród w terapii schizofrenii

Myszka Izabela, Chotkowska Katarzyna, Zadrożna Aleksandra, Augustyniak Katarzyna

PUA Przestrzeń Urbanistyka Architektura

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2022

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Vol. 2

67--86

PL

Rozumienie ogrodu jako miejsca wzbudzającego poczucie dobrostanu zwraca uwagę na wykorzystanie hortiterapii. Celem opracowania jest określenie ogólnych zasad projektowania i urządzania ogrodu wspierającego terapię schizofrenii. Analizy literatury uszczegółowiono w badaniach projektów, które powstawały we współpracy architektów krajobrazu, terapeutów i chorych. Wyniki dotyczące form, funkcji i sesnoryki pozwoliły na opracowanie wytycznych do projektowania ogrodu dla osób w remisji schizofrenii.

EN

Understanding the garden as a place that evokes a sense of well-being draws attention to the use of hortitherapy. Analyses of literature have been performed in research projects created in cooperation with landscape architects, therapists and patients. The results of these analyses with regard to forms, functions and senses have enabled the development of guidelines for garden design for people in remission from schizophrenia.

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Diffuse and Localized Functional Dysconnectivity in Schizophrenia: a Bootstrapped Top-Down Approach

Coluzzi Davide, Basell Giuseppe

Fundamenta Informaticae

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2022

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Vol. 189, nr 2

171--198

EN

Schizophrenia is a brain disorder leading to detached mind's normally integrated processes. Hence, the exploration of the symptoms in relation to functional connectivity (FC) had great relevance in the field. Connectivity can be investigated on different levels, going from global features to single edges between pairs of regions, revealing diffuse and localized dysconnection patterns. In this context, schizophrenia is characterized by a different global integration with reduced connectivity in specific areas of the brain, part of the Default Mode Network (DMN). However, the assessment of FC presents various sources of uncertainty. This study proposes a multi-level approach for more robust group-comparison. FC data between 74 AAL brain areas of 15 healthy controls (HC) and 12 subjects with chronic schizophrenia (SZ) were used. Multi-level analyses were carried out by the previously published SPIDER-NET tool. Graph topological indexes were evaluated to assess global abnormalities. Robustness was augmented by bootstrapped (BOOT) data and the stability was evaluated by removing one (RST1) or two subjects (RST2). The DMN subgraph was extracted and specifically evaluated. Changes relevant to the overall local indexes were also analyzed. Finally, the connection weights were explored to enhance common strongest activations/deactivations. At a global level, expected trends of the indexes were found and the significance of modularity (p = 0.043) was not confirmed by BOOT (p = 0.133). The robustness assessment tests (both RST1 and RST2) highlighted more stable results for BOOT compared to the direct data testing. Conversely, significant results were found in the analysis at lower levels. The DMN highlighted reduced connectivity and strength as well as increased deactivation in the SZ group. At local level, 13 areas were found to be significantly different (p < 0.05) in the groups, highlighting a greater divergence in the frontal lobe. These results were confirmed analyzing the single negative edges, suggesting inverted connectivity between prefronto-temporal areas. In conclusion, multi-level analysis supported by BOOT is highly recommended when analyzing FC, especially when diffuse and localized dysconnections must be investigated in limited samples.

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Altered modular organization in schizophrenia patients and analysis using supervised association rule mining

GeethaRamani R., Sivaselvi K.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 2

397--412

EN

Complex neuro-degenerative disorders affect the intrinsic topological architecture of brain connectivity. There are very few studies concentrating on the occurrence of modular changes in the structural and functional connectome of people diagnosed with Schizophrenia. In this study, group averaged analysis on modular organization of 15 healthy and 12 Schizophrenic subjects were performed to understand the topological alterations occurring in brain networks of diseased against normal. The major contributing regions for changes in optimal brain architecture were also identified. It also involves the investigation of individual subject's functional connectivity and the attempts were made to extract the modular specific roles of brain regions through supervised association rule mining. On comparison with group average measurements, it was found to produce similar results and it was understood that inter and intra-module connections evidently varied in Schizophrenia because of alterations in extremely organized modular architecture. This is believed to provide new insights in understanding the complex neuro-degenerative disorder through analysis on modular organization of functional brain networks. Highly influential regions were also determined. These regions were found to be potential biomarkers for Schizophrenia diagnosis.

4

A texture-based method for classification of schizophrenia using fMRI data

Pouyan A. A., Shahamat H.

Biocybernetics and Biomedical Engineering

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2015

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Vol. 35, no. 1

45--53

EN

This paper presents a texture-based method for classification of individuals into schizophrenia patient and healthy control groups based on their resting state functional magnetic resonance imaging (R-fMRI) data. In this research a combination of three different classifiers is proposed for classification of subjects into predefined groups. For all fMRI scans, the number of time points is reduced using principal component analysis (PCA) method, which projects data onto a new space. Then, independent component analysis (ICA) algorithm is used for estimation of the independent components (ICs). ICs are sorted based on their variance. For feature extraction a texture based operator called volume local binary patterns (VLBP) is applied on the estimated ICs. In order to obtain a set of features with large discrimination power, a two-sample t-test method is used. Finally, a test subject is classified into patient or control group using a combination of three different classifiers based on a majority vote method. The performance of the proposed method is evaluated using a leave-one-out cross validation method. Experimental results reveal that the proposed method has a very high accuracy.

5

Discriminant analysis and graphical aid of medical diagnosis on the example of schizophrenia recognition

Ćwiklińska-Jurkowska M., Jurkowski P., Dróżdż W.

Biocybernetics and Biomedical Engineering

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2001

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Vol. 21, no. 2

67-81

EN

On the basis of stepwise selection of two variabies derived from spectral EEG parameters that optimally differentiated schizopbrenic patients from controi subjects, the Bayesian discriminant methods were applied. Parametric methods of discrimination (linear and quadratic) and kernel methods with normal kemel functions were employed. Because one can directly illustrate on a piane as well two as tbree-dimensional classification problems. the tbree-dimensional discrimination on the basis of the most discriminating original variabies was also considered. Third variable did not improve classification significantly. Two-dimensional graphs of classification regions and probabilities a posteriori, which may support diagnosis toward schizophrenia, are presented.

PL

Na podstawie krokowego wyboru 2 zmiennych otrzymanych z parametrów widmowych EEG, które najlepiej różnicowały pacjentów ze schizofrenią i kontrolną grupę osób zdrowych zastosowano bayesowskie metody dyskryminacji: metody parametryczne (liniową, kwadratową) i metody jądrowe z normalną funkcją jądrową. Ponieważ na płaszczyźnie możemy zilustrować zarówno dwu, jak i trójwymiarowe problemy klasyfikacji, rozważaliśmy także trójwymiarową dyskryminację na podstawie najbardziej różnicujących zmiennych oryginalnych. Trzecia zmienna nie poprawia istotnie klasyfikacji. Przedstawiono dwuwymiarowe wykresy obszarów klasyfikacji i prawdopodobieństw a posteriori, które mogą wspomagać diagnozę schizofrenii.