Brain region’s relative proximity as marker for Alzheimer’s disease based on structural MRI

Lillemark, Lene; Sørensen, Lauge; Pai, Akshay; Dam, Erik B.; Nielsen, Mads

doi:10.1186/1471-2342-14-21

Cited by 30 publications

(16 citation statements)

References 51 publications

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“…Multi-kernel learning, which are an extension of ordinary kernel-based classification algorithms, were also increasingly used in AD classification (Dyrba et al, 2015b; Liu et al, 2014; Zu et al, 2015). Other less common classification algorithms used in AD research were LDA (Lillemark et al, 2014; Tang et al, 2015), orthogonal partial least square regression (Westman et al, 2011), random forest (Moradi et al, 2015), regularization-based methods (Casanova et al, 2011), voting-based ensemble methods (Liu et al, 2015), kernel SVM decision-tree (Zhang et al, 2014), and LPBM (Hinrichs et al, 2009). While SVM could have the advantage of achieving high classification accuracy with small training sample size compared to other classification algorithms such as neural networks (Shao, Lunetta, 2012), they might have the disadvantage of the need for parameter tuning (Chapelle et al, 2002).…”

Section: Discussionmentioning

confidence: 99%

“…McEvoy et al also used average regional cortical thickness and volumetric measures for LDA-based AD classification and prediction of conversion from MCI to AD (McEvoy et al, 2009). Lillemark et al used the proximity between the center of mass and percentage surface connectivity of different brain regions as features for LDA-based AD and MCI classification (Lillemark et al, 2014). …”

Section: Classification Framework For Alzheimer’s Disease and Itsmentioning

confidence: 99%

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A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages

et al. 2017

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Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer’s disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD classification and mild cognitive impairment, selected after online database searches in Google Scholar and PubMed (January, 1985 to June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and address some future research directions.

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Section: Discussionmentioning

confidence: 99%

Section: Classification Framework For Alzheimer’s Disease and Itsmentioning

confidence: 99%

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages

et al. 2017

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“…Examples of other ROIs include the amygdala (Poulin et al, 2011), the ventricles (Tanabe et al, 1997), and the whole brain (Tanabe et al, 1997). Other types of biomarkers include cortical thickness measurements (Singh et al, 2006, Eskildsen et al, 2013), shape (Gerardin et al, 2009, Achterberg et al, 2014), texture (Chincarini et al, 2011, Sep, Sørensen et al, 2016), proximity of brain structures (Lillemark et al, 2014), whole brain dissimilarities computed from a deformation (Klein et al, 2010), and methods based on voxel-wise modulated intensities (Ashburner and Friston, 2000, Jun, Davatzikos et al, 2008, Klppel et al, 2008). …”

Section: Introductionmentioning

confidence: 99%

Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry

Sørensen

Igel

Pai

et al. 2017

NeuroImage: Clinical

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This paper presents a brain T1-weighted structural magnetic resonance imaging (MRI) biomarker that combines several individual MRI biomarkers (cortical thickness measurements, volumetric measurements, hippocampal shape, and hippocampal texture). The method was developed, trained, and evaluated using two publicly available reference datasets: a standardized dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the imaging arm of the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL). In addition, the method was evaluated by participation in the Computer-Aided Diagnosis of Dementia (CADDementia) challenge. Cross-validation using ADNI and AIBL data resulted in a multi-class classification accuracy of 62.7% for the discrimination of healthy normal controls (NC), subjects with mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD). This performance generalized to the CADDementia challenge where the method, trained using the ADNI and AIBL data, achieved a classification accuracy 63.0%. The obtained classification accuracy resulted in a first place in the challenge, and the method was significantly better (McNemar's test) than the bottom 24 methods out of the total of 29 methods contributed by 15 different teams in the challenge. The method was further investigated with learning curve and feature selection experiments using ADNI and AIBL data. The learning curve experiments suggested that neither more training data nor a more complex classifier would have improved the obtained results. The feature selection experiment showed that both common and uncommon individual MRI biomarkers contributed to the performance; hippocampal volume, ventricular volume, hippocampal texture, and parietal lobe thickness were the most important. This study highlights the need for both subtle, localized measurements and global measurements in order to discriminate NC, MCI, and AD simultaneously based on a single structural MRI scan. It is likely that additional non-structural MRI features are needed to further improve the obtained performance, especially to improve the discrimination between NC and MCI.

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“…This approach is grossly inefficient in large scale clinical trials where large volume of data are processed. Conflicting results can result from the variability and lack of reproducibility in the interpretation of data by radiologists [ 2 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study

et al. 2018

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BackgroundMulti-site neuroimaging offer several benefits and poses tough challenges in the drug development process. Although MRI protocol and clinical guidelines developed to address these challenges recommend the use of good quality images, reliable assessment of image quality is hampered by the several shortcomings of existing techniques.MethodsGiven a test image two feature images are extracted. They are grayscale and contrast feature images. Four binary images are generated by setting four different global thresholds on the feature images. Image quality is predicted by measuring the structural similarity between appropriate pairs of binary images. The lower and upper limits of the quality index are 0 and 1. Quality prediction is based on four quality attributes; luminance contrast, texture, texture contrast and lightness.ResultsPerformance evaluation on test data from three multi-site clinical trials show good objective quality evaluation across MRI sequences, levels of distortion and quality attributes. Correlation with subjective evaluation by human observers is ≥ 0.6.ConclusionThe results are promising for the evaluation of MRI protocols, specifically the standardization of quality index, designed to overcome the challenges encountered in multi-site clinical trials.

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Brain region’s relative proximity as marker for Alzheimer’s disease based on structural MRI

Cited by 30 publications

References 51 publications

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages

Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry

Standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study

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