Age-dependent differences in human brain activity using a face- and location-matching task: An fMRI study

Purpose: To evaluate the differences of cortical activation patterns in young and elderly healthy subjects for object and spatial visual processing using a face- and location-matching task. Materials and Methods: We performed a face- and a location-matching task in 15 young (mean age: 28 +/- 9 years) and 19 elderly (mean age: 71 +/- 6 years) subjects. Each experiment consisted of 7 blocks alternating between activation and control condition. For face matching, the subjects had to indicate whether two displayed faces were identical or different. For location matching, the subjects had to press a button whenever two objects had an identical position. For control condition, we used a perception task with abstract images. Functional imaging was performed on a 1.5-tesla scanner using an EPI sequence. Results: In the face-matching task, the young subjects showed bilateral (right 1 left) activation in the occipito-temporal pathway (occipital gyrus, inferior and middle temporal gyrus). Predominantly right hemispheric activations were found in the fusiform gyrus, the right dorsolateral prefrontal cortex (inferior and middle frontal gyrus) and the superior parietal gyrus. In the elderly subjects, the activated areas in the right fronto-lateral cortex increased. An additional activated area could be found in the medial frontal gyrus (right > left). In the location-matching task, young subjects presented increased bilateral (right > left) activation in the superior parietal lobe and precuneus compared with face matching. The activations in the occipito-temporal pathway, in the right fronto-lateral cortex and the fusiform gyrus were similar to the activations found in the face-matching task. In the elderly subjects, we detected similar activation patterns compared to the young subjects with additional activations in the medial frontal gyrus. Conclusion: Activation patterns for object-based and spatial visual processing were established in the young and elderly healthy subjects. Differences between the elderly and young subjects could be evaluated, especially by using a face-matching task. Copyright (c) 2007 S. Karger AG, Basel

Association between cognitive performance and cortical glucose metabolism in patients with mild Alzheimer's disease

Background: Neuronal and synaptic function in Alzheimer's disease (AD) is measured in vivo by glucose metabolism using positron emission tomography (PET). Objective: We hypothesized that neuronal activation as measured by PET is a more sensitive index of neuronal dysfunction than activity during rest. We investigated if the correlations between dementia severity as measured with the Mini Mental State Examination (MMSE) and glucose metabolism are an artifact of brain atrophy. Method: Glucose metabolism was measured using {[}F-18]fluorodeoxyglucose PET during rest and activation due to audiovisual stimulation in 13 mild to moderate AD patients (MMSE score >= 17). PET data were corrected for brain atrophy. Results: In the rest condition, glucose metabolism was correlated with the MMSE score primarily within the posterior cingulate and parietal lobes. For the activation condition, additional correlations were within the primary and association audiovisual areas. Most local maxima remained significant after correcting for brain atrophy. Conclusion: PET activity measured during audiovisual stimulation was more sensitive to functional alterations in glucose metabolism in AD patients compared to the resting PET. The association between glucose metabolism and MMSE score was not dependent on brain atrophy. Copyright (C) 2005 S. Karger AG, Basel

Regional distribution of white matter hyperintensities in vascular dementia, Alzheimer's disease and healthy aging

Background: White matter hyperintensities (WMH) on MRI scans indicate lesions of the subcortical fiber system. The regional distribution of WMH may be related to their pathophysiology and clinical effect in vascular dementia (VaD), Alzheimer's disease (AD) and healthy aging. Methods: Regional WMH volumes were measured in MRI scans of 20 VaD patients, 25 AD patients and 22 healthy elderly subjects using FLAIR sequences and surface reconstructions from a three-dimensional MRI sequence. Results: The intraclass correlation coefficient for interrater reliability of WMH volume measurements ranged between 0.99 in the frontal and 0.72 in the occipital lobe. For each cerebral lobe, the WMH index, i.e. WMH volume divided by lobar volume, was highest in VaD and lowest in healthy controls. Within each group, the WMH index was higher in frontal and parietal lobes than in occipital and temporal lobes. Total WMH index and WMH indices in the frontal lobe correlated significantly with the MMSE score in VaD. Category fluency correlated with the frontal lobe WMH index in AD, while drawing performance correlated with parietal and temporal lobe WMH indices in VaD. Conclusions: A similar regional distribution of WMH between the three groups suggests a common (vascular) pathogenic factor leading to WMH in patients and controls. Our findings underscore the potential of regional WMH volumetry to determine correlations between subcortical pathology and cognitive impairment. Copyright (C) 2004 S. Karger AG, Basel

Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement

Purpose: To investigate methods developed for the characterisation of the morphology and enhancement kinetic features of both mass and non-mass lesions, and to determine their diagnostic performance to differentiate between malignant and benign lesions that present as mass versus non-mass types. Methods: Quantitative analysis of morphological features and enhancement kinetic parameters of breast lesions were used to differentiate among four groups of lesions: 88 malignant (43 mass, 45 non-mass) and 28 benign (19 mass, 9 non-mass). The enhancement kinetics was measured and analysed to obtain transfer constant (Ktrans) and rate constant (kep). For each mass eight shape/margin parameters and 10 enhancement texture features were obtained. For the lesions presenting as nonmass-like enhancement, only the texture parameters were obtained. An artificial neural network (ANN) was used to build the diagnostic model. Results: For lesions presenting as mass, the four selected morphological features could reach an area under the ROC curve (AUC) of 0.87 in differentiating between malignant and benign lesions. The kinetic parameter (kep) analysed from the hot spot of the tumour reached a comparable AUC of 0.88. The combined morphological and kinetic features improved the AUC to 0.93, with a sensitivity of 0.97 and a specificity of 0.80. For lesions presenting as non-mass-like enhancement, four texture features were selected by the ANN and achieved an AUC of 0.76. The kinetic parameter kepfrom the hot spot only achieved an AUC of 0.59, with a low added diagnostic value. Conclusion: The results suggest that the quantitative diagnostic features can be used for developing automated breast CAD (computer-aided diagnosis) for mass lesions to achieve a high diagnostic performance, but more advanced algorithms are needed for diagnosis of lesions presenting as non-mass-like enhancement. © The Author(s) 2009

Xenon-133 D-SPECT (Dynamische Single-Photon-Emissionscomputertomographie): Ergebnisse einer nicht-invasiven Methode zur regionalen Hirndurchblutungsmessung bei Patienten mit zerebrovaskulaerer Erkrankung

SIGLEAvailable from the library of Muenchen Univ. (DE) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Int. J. Comput. Vis.

In this paper, we present neural network clustering by deterministic annealing as a powerful strategy for self- organized segmentation of biomedical image time-series data identifying groups of pixels sharing common properties of local signal dynamics. After introducing the theoretical concept of minimal free energy vector quantization and related clustering techniques, we discuss its potential to serve as a multi- purpose computer vision strategy to image time-series analysis and visualization for many fields of medicine ranging from biomedical basic research to clinical assessment of patient data. In particular, we present applications to (i) functional MRI data analysis for human brain mapping, (ii) dynamic contrast-enhanced perfusion MRI for the diagnosis of cerebrovascular disease, and (iii) magnetic resonance mammography for the analysis of suspicious lesions in patients with breast cancer. This wide scope of completely different medical applications illustrates the flexibility and conceptual power of neural network vector quantization in this context. Although there are obvious methodological similarities, each application requires specific careful consideration w.r.t. data preprocessing, postprocessing and interpretation. This challenge can only be managed by close interdisciplinary cooperation of medical doctors, engineers, and computer scientists. Hence, this field of research can serve as an example for lively cross-fertilization between computer vision and related resear