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ABSTRACT: BACKGROUND AND PURPOSE: Previous imaging studies have described gray and white matter alterations in the cerebellum, the posterior aspects of the visual system and in the corpus callosum in patients with schizophrenia. Here, we investigated these regions in more detail using Tract-Based Spatial Statistics (TBSS). Additionally, we evaluated potential changes in lateralization of the optic radiation and the superior cerebellar peduncle. MATERIAL AND METHODS: We studied 12 patients with first-admission schizophrenia and a group of age-matched healthy controls. The diffusion tensor imaging data were preprocessed using tract-based spatial statistics and the obtained white matter skeleton was used to perform a regional analysis of fractional anisotropy in the corpus callosum, the optic radiation, and the superior and middle cerebellar peduncles. RESULTS: Using TBSS, a significant reduction of fractional anisotropy in the whole corpus callosum and the optic radiation but not in the middle and superior cerebellar peduncles was found. Furthermore, a significantly decreased lateralization of the optic radiation and the superior cerebellar peduncles in patients was observed. CONCLUSION: Our findings substantiate the concept that schizophrenia is a neurodevelopmental disorder and indicate that changes in lateralization may play a key role in the pathogenesis of this disease.
Neuroscience Letters 01/2013; · 2.11 Impact Factor
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ABSTRACT: Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that provides information on the fiber architecture of the brain by measuring water diffusion. Prior work has shown that neuronal degeneration in Alzheimer's disease (AD) and mild cognitive impairment (MCI) alters this architecture. Since the conversion rate to AD is much higher for MCI patients than for normal healthy people, it is important to identify biomarkers with a predictive value on this conversion. In this study, we applied tract-based spatial statistics (TBSS) on datasets of 15 healthy controls, 15 AD patients, and 17 MCI patients. Of these MCI patients eight remained stable, whereas nine developed AD within the first 12-18months of follow-up investigations. Analysis using TBSS combined with a maximum likelihood regression with random effects of the fornix, the corpus callosum, and the cingulum identified significant differences between these two types of MCI patients in fractional anisotropy (FA) and radial diffusivity (DR). Thus, DTI reveals Alzheimer-specific changes in those MCI subjects that later convert, although they were clinically identical to the other MCI-patients at the time the data were acquired. This finding could lead to early identification of AD and thereby aid early clinical intervention.
Psychiatry research. 09/2012; 203(2-3):184-93.
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ABSTRACT: PurposePrediction of progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) is challenging but essential for
early treatment. This study aims to investigate the use of hippocampal atrophy markers for the automatic detection of MCI
converters and to compare the predictive value to manually obtained hippocampal volume and temporal horn width.
MethodsA study was performed with 15 patients with Alzheimer and 18 patients with MCI (ten converted, eight remained stable in a
3-year follow-up) as well as 15 healthy subjects. MRI scans were obtained at baseline and evaluated with an automated system
for scoring of hippocampal atrophy. The predictive value of the automated system was compared with manual measurements of
hippocampal volume and temporal horn width in the same subjects.
ResultsThe conversion to AD was correctly predicted in 77.8% of the cases (sensitivity 70%, specificity 87.5%) in the MCI group using
automated morphometry and a plain linear classifier that was trained on the AD and healthy groups. Classification was improved
by limiting analysis to the left cerebral hemisphere (accuracy 83.3%, sensitivity 70%, specificity 100%). The manual linear
and volumetric approaches reached rates of 66.7% (40/100%) and 72.2% (60/87.5%), respectively.
ConclusionThe automatic approach fulfills many important preconditions for clinical application. Contrary to the manual approaches,
it is not observer-dependent and reduces human resource requirements. Automated assessment may be useful for individual patient
assessment and for predicting progression to dementia.
KeywordsBrain atrophy-Classification-Early detection-Converter-Baseline-Imaging biomarker
International Journal of Computer Assisted Radiology and Surgery 04/2012; 5(6):623-632. · 1.48 Impact Factor
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ABSTRACT: Background: Diffusion-MRI provides a unique window on brain anatomy and insights into aspects of tissue structure in living humans that could not be studied previously. There is a major effort in this rapidly evolving field of research to develop the algorithmic tools necessary to cope with the complexity of the datasets. Objectives: This work illustrates our strategy that encompasses the development of a modularized and open software tool for data processing, visualization and interactive exploration in diffusion imaging research and aims at reinforcing sustainable evaluation and progress in the field. Methods: In this paper, the usability and capabilities of a new application and toolkit component of the Medical Imaging and Interaction Toolkit (MITK, www.mitk.org), MITK-DI, are demonstrated using in-vivo datasets. Results: MITK-DI provides a comprehensive software framework for high-performance data processing, analysis and interactive data exploration, which is designed in a modular, extensible fashion (using CTK) and in adherence to widely accepted coding standards (e.g. ITK, VTK). MITK-DI is available both as an open source software development toolkit and as a ready-to-use installable application. Conclusions: The open source release of the modular MITK-DI tools will increase verifiability and comparability within the research community and will also be an important step towards bringing many of the current techniques towards clinical application.
Methods of Information in Medicine 01/2012; · 1.53 Impact Factor
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SPIE Medical ImagingSPIE Medical Imaging; 01/2011
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Bildverarbeitung für die Medizin 2009: Algorithmen - Systeme - Anwendungen, Proceedings des Workshops vom 20. bis 22. März 2011 in Lübeck; 01/2011
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ABSTRACT: Prediction of progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is challenging but essential for early treatment. This study aims to investigate the use of hippocampal atrophy markers for the automatic detection of MCI converters and to compare the predictive value to manually obtained hippocampal volume and temporal horn width.
A study was performed with 15 patients with Alzheimer and 18 patients with MCI (ten converted, eight remained stable in a 3-year follow-up) as well as 15 healthy subjects. MRI scans were obtained at baseline and evaluated with an automated system for scoring of hippocampal atrophy. The predictive value of the automated system was compared with manual measurements of hippocampal volume and temporal horn width in the same subjects.
The conversion to AD was correctly predicted in 77.8% of the cases (sensitivity 70%, specificity 87.5%) in the MCI group using automated morphometry and a plain linear classifier that was trained on the AD and healthy groups. Classification was improved by limiting analysis to the left cerebral hemisphere (accuracy 83.3%, sensitivity 70%, specificity 100%). The manual linear and volumetric approaches reached rates of 66.7% (40/100%) and 72.2% (60/87.5%), respectively.
The automatic approach fulfills many important preconditions for clinical application. Contrary to the manual approaches, it is not observer-dependent and reduces human resource requirements. Automated assessment may be useful for individual patient assessment and for predicting progression to dementia.
International Journal of Computer Assisted Radiology and Surgery 05/2010; 5(6):623-32. · 1.48 Impact Factor
