Are Transversal MR Images Sufficient to Distinguish Persons with Mild Cognitive Impairment From Healthy Controls?

Mönninghoff, Christoph; Dlugaj, Martha; Kraff, Oliver; Geisel, Marie Henrike; Jöckel, Karl‐Heinz; Erbel, Raimund; Weimar, Christian; Wanke, Isabel

doi:10.1016/j.acra.2015.04.008

Cited by 4 publications

(5 citation statements)

References 41 publications

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“…Table 1 shows the distribution of the sociodemographic data variables age, gender, education, marital status and genetic data variable APOE- ε 4 genotype (defined as “Participant Data”) for aMCI and cognitively unimpaired controls on the applied sub sample. All participants were scanned with a single 1.5T MR scanner (Magneton Avanto, Siemens Healthcare, Erlangen) with 60cm bore diameter, 200T/m/s slew rate, 160cm length and 40/40/45 mT/m gradient strength [ 1 ].…”

Section: Methodsmentioning

confidence: 99%

“…This cognitive assessment was evaluated and validated in a sub-study [46]. The longitudinal sub-study comprises a more comprehensive neuropsychological assessment (see below), a neurological exam assessed by a certified neurologist and MRI volumetric data [1,46]. Participants with dementia (n = 7), severe depression (ADAS depression subscale score >4, n = 13), Parkinson disease (n = 5), mental retardation (n = 2), severe alcohol consumption (for women: >20 g/day; for men: >40 g/day, n = 2), known brain cancer (n = 1), severe problems with the German language (foreign persons, n = 9) and severe sensory impairment (n = 2) leading to invalid cognitive testing were excluded from the sub-study.…”

Section: Study Populationmentioning

confidence: 99%

“…Alzheimer's disease (AD) is a progressive neurodegenerative disease that causes behavioral changes and deterioration of memory and other cognitive domains [1]. Because there is no causal treatment for AD dementia, identifying early stages of the disease and preclinical markers will help to implement intervention and prevention strategies [2].…”

Section: Introductionmentioning

confidence: 99%

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Sociodemographic data and APOE-ε4 augmentation for MRI-based detection of amnestic mild cognitive impairment using deep learning systems

et al. 2020

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Detection and diagnosis of early and subclinical stages of Alzheimer's Disease (AD) play an essential role in the implementation of intervention and prevention strategies. Neuroimaging techniques predominantly provide insight into anatomic structure changes associated with AD. Deep learning methods have been extensively applied towards creating and evaluating models capable of differentiating between cognitively unimpaired, patients with Mild Cognitive Impairment (MCI) and AD dementia. Several published approaches apply information fusion techniques, providing ways of combining several input sources in the medical domain, which contributes to knowledge of broader and enriched quality. The aim of this paper is to fuse sociodemographic data such as age, marital status, education and gender, and genetic data (presence of an apolipoprotein E (APOE)-ε4 allele) with Magnetic Resonance Imaging (MRI) scans. This enables enriched multi-modal features, that adequately represent the MRI scan visually and is adopted for creating and modeling classification systems capable of detecting amnestic MCI (aMCI). To fully utilize the potential of deep convolutional neural networks, two extra color layers denoting contrast intensified and blurred image adaptations are virtually augmented to each MRI scan, completing the Red-Green-Blue (RGB) color channels. Deep convolutional activation features (DeCAF) are extracted from the average pooling layer of the deep learning system Inception_v3. These features from the fused MRI scans are used as visual representation for the Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) classification model. The proposed approach is evaluated on a sub-study containing 120 participants (aMCI = 61 and cognitively unimpaired = 59) of the Heinz Nixdorf Recall (HNR) Study with a baseline model

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Section: Methodsmentioning

confidence: 99%

Section: Study Populationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sociodemographic data and APOE-ε4 augmentation for MRI-based detection of amnestic mild cognitive impairment using deep learning systems

et al. 2020

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“…The measurement technique could be linear (like Evans' index), area, volumetric, geometric, morphometric, or qualitative; in terms of imaging processing, the segmentation could be done manually, automatically, semi-automatically, or stereological. 18,27,[54][55][56][57][58][59][60][61][62][63][64][65][66] Besides population and cross-sectional versus longitudinal studies, these design differences lead to heterogeneous results and should be kept in mind.…”

Section: Cognitive Impairmentmentioning

confidence: 99%

Brain ventricles, CSF and cognition: a narrative review

et al. 2022

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The brain ventricles are structures that have been related to cognition since antiquity. They are essential components in the development and maintenance of brain functions. The aging process runs with the enlargement of ventricles and is related to a less selective blood‐cerebrospinal fluid barrier and then a more toxic cerebrospinal fluid environment. The study of brain ventricles as a biological marker of aging is promissing because they are structures easily identified in neuroimaging studies, present good inter‐rater reliability, and measures of them can identify brain atrophy earlier than cortical structures. The ventricular system also plays roles in the development of dementia, since dysfunction in the clearance of beta‐amyloid protein is a key mechanism in sporadic Alzheimer's disease. The morphometric and volumetric studies of the brain ventricles can help to distinguish between healthy elderly and persons with mild cognitive impairment (MCI) and dementia. Brain ventricle data may contribute to the appropriate allocation of individuals in groups at higher risk for MCI‐dementia progression in clinical trials and to measuring therapeutic responses in these studies, as well as providing differential diagnosis, such as normal pressure hydrocephalus. Here, we reviewed the pathophysiology of healthy aging and cognitive decline, focusing on the role of the choroid plexus and brain ventricles in this process.

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“…MCI, in particular, has been a focus of intense study in recent years as it is widely believed to be the best stage for interventional treatments aimed at delaying or preventing progression to AD [Morris et al, ]. Discriminating MCI from controls, particularly with structural MRI, has proven to be more difficult however [Chu et al, ; Mönninghoff et al, ]. In addition to the problem of head size and shape differences between subjects that pertains to much of neuroimage‐based analysis, the study of early stage diseases and disease precursors is often confounded by demographic factors such as age, gender, and education level [Bakkour et al, ; Dukart et al, ].…”

Section: Introductionmentioning

confidence: 99%

Making use of longitudinal information in pattern recognition

Aksman

Lythgoe

Williams

et al. 2016

Human Brain Mapping

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Longitudinal designs are widely used in medical studies as a means of observing within‐subject changes over time in groups of subjects, thereby aiming to improve sensitivity for detecting disease effects. Paralleling an increased use of such studies in neuroimaging has been the adoption of pattern recognition algorithms for making individualized predictions of disease. However, at present few pattern recognition methods exist to make full use of neuroimaging data that have been collected longitudinally, with most methods relying instead on cross‐sectional style analysis. This article presents a principal component analysis‐based feature construction method that uses longitudinal high‐dimensional data to improve predictive performance of pattern recognition algorithms. The method can be applied to data from a wide range of longitudinal study designs and permits an arbitrary number of time‐points per subject. We apply the method to two longitudinal datasets, one containing subjects with mild cognitive impairment along with healthy controls, the other with early dementia subjects and healthy controls. Across both datasets, we show improvements in predictive accuracy relative to cross‐sectional classifiers for discriminating disease subjects from healthy controls on the basis of whole‐brain structural magnetic resonance image‐based voxels. In addition, we can transfer longitudinal information from one set of subjects to make disease predictions in another set of subjects. The proposed method is simple and, as a feature construction method, flexible with respect to the choice of classifier and image registration algorithm. Hum Brain Mapp 37:4385–4404, 2016. © 2016 Wiley Periodicals, Inc.

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Are Transversal MR Images Sufficient to Distinguish Persons with Mild Cognitive Impairment From Healthy Controls?

Cited by 4 publications

References 41 publications

Sociodemographic data and APOE-ε4 augmentation for MRI-based detection of amnestic mild cognitive impairment using deep learning systems

Sociodemographic data and APOE-ε4 augmentation for MRI-based detection of amnestic mild cognitive impairment using deep learning systems

Brain ventricles, CSF and cognition: a narrative review

Making use of longitudinal information in pattern recognition

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