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Diagnostic Potential of Pulsed Arterial Spin Labeling in Alzheimer's Disease.

Trebeschi S, Riederer I, Preibisch C, Bohn KP, Förster S, Alexopoulos P, Zimmer C, Kirschke JS, Valentinitsch A - Front Neurosci (2016)

Bottom Line: The discriminant analysis is carried out to maximize the accuracy of the classification.The algorithm has been trained on a dataset of 81 subjects and achieved a sensitivity of 0.750 and a specificity of 0.875.Moreover, in accordance with the current pathological knowledge, the parietal lobe, and limbic system are shown to be the main discriminant factors.

View Article: PubMed Central - PubMed

Affiliation: Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München Munich, Germany.

ABSTRACT
Alzheimers disease (AD) is the most common cause of dementia. Although the underlying pathology is still not completely understood, several diagnostic methods are available. Frequently, the most accurate methods are also the most invasive. The present work investigates the diagnostic potential of Pulsed Arterial Spin Labeling (PASL) for AD: a non-invasive, MRI-based technique for the quantification of regional cerebral blood flow (rCBF). In particular, we propose a pilot computer aided diagnostic (CAD) procedure able to discriminate between healthy and diseased subjects, and at the same time, providing visual informative results. This method encompasses the creation of a healthy model, the computation of a voxel-wise likelihood function as comparison between the healthy model and the subject under examination, and the correction of the likelihood function via prior distributions. The discriminant analysis is carried out to maximize the accuracy of the classification. The algorithm has been trained on a dataset of 81 subjects and achieved a sensitivity of 0.750 and a specificity of 0.875. Moreover, in accordance with the current pathological knowledge, the parietal lobe, and limbic system are shown to be the main discriminant factors.

No MeSH data available.


Related in: MedlinePlus

Sensitivity, specificity, and misclassification rate functions. Discrete representation over varying threshold values. The x-axis (ranging from 0 to 5000) is associated with the Between Subject Threshold. The y-axes (ranging from 1 to 0.5) is associated with the Within Subject Threshold.
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Figure 4: Sensitivity, specificity, and misclassification rate functions. Discrete representation over varying threshold values. The x-axis (ranging from 0 to 5000) is associated with the Between Subject Threshold. The y-axes (ranging from 1 to 0.5) is associated with the Within Subject Threshold.

Mentions: For the discriminant analysis we determined the following thresholds: the within subject threshold tw, that is the probability for which a voxel shall be considered diseased, equals to 0.86; and the between subject threshold tb, which is the number of voxels for which a subject shall be considered diseased, equals to 200 (actually most of the runs in the testing phase produced tb = 200 and tw = 0.86). In Figure 4 specificity, sensitivity and misclassification functions for varying thresholds values are plotted.


Diagnostic Potential of Pulsed Arterial Spin Labeling in Alzheimer's Disease.

Trebeschi S, Riederer I, Preibisch C, Bohn KP, Förster S, Alexopoulos P, Zimmer C, Kirschke JS, Valentinitsch A - Front Neurosci (2016)

Sensitivity, specificity, and misclassification rate functions. Discrete representation over varying threshold values. The x-axis (ranging from 0 to 5000) is associated with the Between Subject Threshold. The y-axes (ranging from 1 to 0.5) is associated with the Within Subject Threshold.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835490&req=5

Figure 4: Sensitivity, specificity, and misclassification rate functions. Discrete representation over varying threshold values. The x-axis (ranging from 0 to 5000) is associated with the Between Subject Threshold. The y-axes (ranging from 1 to 0.5) is associated with the Within Subject Threshold.
Mentions: For the discriminant analysis we determined the following thresholds: the within subject threshold tw, that is the probability for which a voxel shall be considered diseased, equals to 0.86; and the between subject threshold tb, which is the number of voxels for which a subject shall be considered diseased, equals to 200 (actually most of the runs in the testing phase produced tb = 200 and tw = 0.86). In Figure 4 specificity, sensitivity and misclassification functions for varying thresholds values are plotted.

Bottom Line: The discriminant analysis is carried out to maximize the accuracy of the classification.The algorithm has been trained on a dataset of 81 subjects and achieved a sensitivity of 0.750 and a specificity of 0.875.Moreover, in accordance with the current pathological knowledge, the parietal lobe, and limbic system are shown to be the main discriminant factors.

View Article: PubMed Central - PubMed

Affiliation: Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München Munich, Germany.

ABSTRACT
Alzheimers disease (AD) is the most common cause of dementia. Although the underlying pathology is still not completely understood, several diagnostic methods are available. Frequently, the most accurate methods are also the most invasive. The present work investigates the diagnostic potential of Pulsed Arterial Spin Labeling (PASL) for AD: a non-invasive, MRI-based technique for the quantification of regional cerebral blood flow (rCBF). In particular, we propose a pilot computer aided diagnostic (CAD) procedure able to discriminate between healthy and diseased subjects, and at the same time, providing visual informative results. This method encompasses the creation of a healthy model, the computation of a voxel-wise likelihood function as comparison between the healthy model and the subject under examination, and the correction of the likelihood function via prior distributions. The discriminant analysis is carried out to maximize the accuracy of the classification. The algorithm has been trained on a dataset of 81 subjects and achieved a sensitivity of 0.750 and a specificity of 0.875. Moreover, in accordance with the current pathological knowledge, the parietal lobe, and limbic system are shown to be the main discriminant factors.

No MeSH data available.


Related in: MedlinePlus