scholarly journals Local Brain-Age: A U-Net Model

2021 ◽  
Vol 13 ◽  
Author(s):  
Sebastian G. Popescu ◽  
Ben Glocker ◽  
David J. Sharp ◽  
James H. Cole
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Spatial Information ◽  
Local Level ◽  
Brain Mri ◽  
Healthy People ◽  
Local Approach ◽  
Brain Ageing ◽  
Brain Age ◽  
Local Brain

We propose a new framework for estimating neuroimaging-derived “brain-age” at a local level within the brain, using deep learning. The local approach, contrary to existing global methods, provides spatial information on anatomical patterns of brain ageing. We trained a U-Net model using brain MRI scans from n = 3,463 healthy people (aged 18–90 years) to produce individualised 3D maps of brain-predicted age. When testing on n = 692 healthy people, we found a median (across participant) mean absolute error (within participant) of 9.5 years. Performance was more accurate (MAE around 7 years) in the prefrontal cortex and periventricular areas. We also introduce a new voxelwise method to reduce the age-bias when predicting local brain-age “gaps.” To validate local brain-age predictions, we tested the model in people with mild cognitive impairment or dementia using data from OASIS3 (n = 267). Different local brain-age patterns were evident between healthy controls and people with mild cognitive impairment or dementia, particularly in subcortical regions such as the accumbens, putamen, pallidum, hippocampus, and amygdala. Comparing groups based on mean local brain-age over regions-of-interest resulted in large effects sizes, with Cohen's d values >1.5, for example when comparing people with stable and progressive mild cognitive impairment. Our local brain-age framework has the potential to provide spatial information leading to a more mechanistic understanding of individual differences in patterns of brain ageing in health and disease.

scholarly journals A U-Net model of local brain-age

2021 ◽  
Author(s):  
Sebastian G. Popescu ◽  
Ben Glocker ◽  
David J. Sharp ◽  
James H. Cole
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Local Level ◽  
Brain Mri ◽  
Absolute Error ◽  
Healthy People ◽  
Local Approach ◽  
Brain Ageing ◽  
Brain Age ◽  
Local Brain

AbstractWe propose a new framework for estimating neuroimaging-derived “brain-age” at a local level within the brain, using deep learning. The local approach, contrary to existing global methods, provides spatial anatomical information on patterns of brain ageing. We trained a U-Net model on brain MRI scans from n=3463 healthy people to produce individualised 3D maps of brain-predicted age. Testing on n=692 healthy people resulted in a median (across subject) mean absolute error (within subject) of 9.0 years. Performance was more accurate (MAE around 7 years) in the prefrontal cortex and periventricular areas. We also introduce a new voxelwise method to reduce the age-bias when predicting local brain-age “gaps”. To validate local brain-age predictions, we tested the model in people with mild cognitive impairment or dementia using data from OASIS3 (n=267). Different local brain-age patterns were clearly evident between healthy controls and people with mild cognitive impairment or dementia, particularly in subcortical regions, with the accumbens, putamen, pallidum, hippocampus and amygdala. Comparing groups based on mean local brain-age over regions-of-interest resulted in large effects sizes, with Cohen’s d values >1.5, for example when comparing people with stable and progressive mild cognitive impairment.

scholarly journals 12-year prediction of mild cognitive impairment aided by Alzheimer’s brain signatures at mean age 56

2021 ◽  
Author(s):  
McKenna E Williams ◽  
Jeremy A Elman ◽  
Linda K McEvoy ◽  
Ole A Andreassen ◽  
Anders M Dale ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Mean Diffusivity ◽  
Area Under The Curve ◽  
Polygenic Risk Score ◽  
Age Difference ◽  
Cognitively Normal ◽  
Brain Age

Abstract Neuroimaging signatures based on composite scores of cortical thickness and hippocampal volume predict progression from mild cognitive impairment to Alzheimer’s disease. However, little is known about the ability of these signatures among cognitively normal adults to predict progression to mild cognitive impairment. Toward that end, a signature sensitive to microstructural changes that may predate macrostructural atrophy should be useful. We hypothesized that: 1) a validated MRI-derived Alzheimer’s disease signature based on cortical thickness and hippocampal volume in cognitively normal middle-aged adults would predict progression to mild cognitive impairment; and 2) a novel gray matter mean diffusivity signature would be a better predictor than the thickness/volume signature. This cohort study was part of the Vietnam Era Twin Study of Aging. Concurrent analyses compared cognitively normal and mild cognitive impairment groups at each of three study waves (ns = 246–367). Predictive analyses included 169 cognitively normal men at baseline (age = 56.1, range = 51–60). Our previously published thickness/volume signature derived from independent data, a novel mean diffusivity signature using the same regions and weights as the thickness/volume signature, age, and an Alzheimer’s disease polygenic risk score were used to predict incident mild cognitive impairment an average of 12 years after baseline (follow-up age = 67.2, range = 61–71). Additional analyses adjusted for predicted brain age difference scores (chronological age minus predicted brain age) to determine if signatures were Alzheimer-related and not simply aging-related. In concurrent analyses, individuals with mild cognitive impairment had higher (worse) mean diffusivity signature scores than cognitively normal participants, but thickness/volume signature scores did not differ between groups. In predictive analyses, age and polygenic risk score yielded an area under the curve of 0.74 (sensitivity = 80.00%; specificity = 65.10%). Prediction was significantly improved with addition of the mean diffusivity signature (area under the curve = 0.83; sensitivity = 85.00%; specificity = 77.85%; P=0.007), but not with addition of the thickness/volume signature. A model including both signatures did not improve prediction over a model with only the mean diffusivity signature. Results held up after adjusting for predicted brain age difference scores. The novel mean diffusivity signature was limited by being yoked to the thickness/volume signature weightings. An independently-derived mean diffusivity signature may thus provide even stronger prediction. The young age of the sample at baseline is particularly notable. Given that the brain signatures were examined when participants were only in their 50 s, our results suggest a promising step toward improving very early identification of Alzheimer’s disease risk and the potential value of mean diffusivity and/or multimodal brain signatures.

Manifestation of cognitive impairments after a prior coronavirus infection COVID-19 in a patient with ApoE ε4 genotype

2021 ◽  
Vol 26 (2) ◽  
pp. 25-29
Author(s):  
M. S. Novikova ◽  
V. V. Zakharov ◽  
N. V. Vakhnina
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Small Vessel Disease ◽  
Brain Mri ◽  
Periventricular White Matter ◽  
Everyday Activity ◽  
Apoe Ε4 ◽  
Coronavirus Infection ◽  
Novel Coronavirus

Nowadays, the novel coronavirus infection (COVID-19) pandemic is one of the most important global health problems. There is increasing evidence that COVID-19 affects central and peripheral nervous system as well. The paper presents a clinical case of a 47 old patient with the ApoE ε4 haplotype and family history of Alzheimer’s disease who developed cognitive impairment after acute COVID-19. Before the infection the patient has no cognitive complaints and preserved everyday activity. After novel coronavirus infection, which was observed in mild form, the patient had started to complain on constant excessive forgetfulness. Neuropsychological assessment confirmed the presence of pre-mild cognitive impairment of predominantly single domain amnestic type. However, brain MRI showed only subtle periventricular white matter changes usually attributed to small vessel disease. Memory complaints were observed for 3 months of follow up despite intensive cognitive training, optimization of lifestyle and therapy with choline alphoscerate. Probable links between coronavirus infectious and cognitive impairment manifestation are discussed. There is data that ApoE ε4 haplotype is associated with increase of microglia mediated neuro-inflammation and it can be significant for accelerating of progression of neurodegenerative diseases after COVID-19. Further follow up of the patient is necessary for determination of nosological diagnosis explaining manifested predominantly amnestic type pre-mild cognitive impairment.

scholarly journals A Deep Learning approach for Diagnosis of Mild Cognitive Impairment Based on MRI Images

2019 ◽  
Vol 9 (9) ◽  
pp. 217 ◽  
Author(s):  
Gorji ◽  
Kaabouch
Keyword(s):  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Diagnosis ◽  
Brain Structure ◽  
Neurodegenerative Disorder ◽  
Healthy People ◽  
Learning Approach ◽  
Brain Structure And Function ◽  
Percent Accuracy

Mild cognitive impairment (MCI) is an intermediary stage condition between healthy people and Alzheimer’s disease (AD) patients and other dementias. AD is a progressive and irreversible neurodegenerative disorder, which is a significant threat to people, age 65 and older. Although MCI does not always lead to AD, an early diagnosis at the stage of MCI can be very helpful in identifying people who are at risk of AD. Moreover, the early diagnosis of MCI can lead to more effective treatment, or at least, significantly delay the disease’s progress, and can lead to social and financial benefits. Magnetic resonance imaging (MRI), which has become a significant tool for the diagnosis of MCI and AD, can provide neuropsychological data for analyzing the variance in brain structure and function. MCI is divided into early and late MCI (EMCI and LMCI) and sadly, there is no clear differentiation between the brain structure of healthy people and MCI patients, especially in the EMCI stage. This paper aims to use a deep learning approach, which is one of the most powerful branches of machine learning, to discriminate between healthy people and the two types of MCI groups based on MRI results. The convolutional neural network (CNN) with an efficient architecture was used to extract high-quality features from MRIs to classify people into healthy, EMCI, or LMCI groups. The MRIs of 600 individuals used in this study included 200 control normal (CN) people, 200 EMCI patients, and 200 LMCI patients. This study randomly selected 70 percent of the data to train our model and 30 percent for the test set. The results showed the best overall classification between CN and LMCI groups in the sagittal view with an accuracy of 94.54 percent. In addition, 93.96 percent and 93.00 percent accuracy were reached for the pairs of EMCI/LMCI and CN/EMCI, respectively.

scholarly journals Optimising a Simple Fully Convolutional Network for Accurate Brain Age Prediction in the PAC 2019 Challenge

2021 ◽  
Vol 12 ◽  
Author(s):  
Weikang Gong ◽  
Christian F. Beckmann ◽  
Andrea Vedaldi ◽  
Stephen M. Smith ◽  
Han Peng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Prediction Models ◽  
Brain Mri ◽  
Predictive Analysis ◽  
Third Place ◽  
Brain Ageing ◽  
Brain Age ◽  
Age Prediction ◽  
Bias Removal

Brain age prediction from brain MRI scans not only helps improve brain ageing modelling generally, but also provides benchmarks for predictive analysis methods. Brain-age delta, which is the difference between a subject's predicted age and true age, has become a meaningful biomarker for the health of the brain. Here, we report the details of our brain age prediction models and results in the Predictive Analysis Challenge 2019. The aim of the challenge was to use T1-weighted brain MRIs to predict a subject's age in multicentre datasets. We apply a lightweight deep convolutional neural network architecture, Simple Fully Convolutional Neural Network (SFCN), and combined several techniques including data augmentation, transfer learning, model ensemble, and bias correction for brain age prediction. The model achieved first place in both of the two objectives in the PAC 2019 brain age prediction challenge: Mean absolute error (MAE) = 2.90 years without bias removal (Second Place = 3.09 yrs; Third Place = 3.33 yrs), and MAE = 2.95 years with bias removal, leading by a large margin (Second Place = 3.80 yrs; Third Place = 3.92 yrs).

scholarly journals Permutation Entropy and Statistical Complexity in Mild Cognitive Impairment and Alzheimer’s Disease: An Analysis Based on Frequency Bands

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 116 ◽  
Author(s):  
Ignacio Echegoyen ◽  
David López-Sanz ◽  
Johann H. Martínez ◽  
Fernando Maestú ◽  
Javier M. Buldú
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Frequency Band ◽  
Local Level ◽  
Permutation Entropy ◽  
Frequency Bands ◽  
Individual Level ◽  
Statistical Complexity

We present one of the first applications of Permutation Entropy (PE) and Statistical Complexity (SC) (measured as the product of PE and Jensen-Shanon Divergence) on Magnetoencephalography (MEG) recordings of 46 subjects suffering from Mild Cognitive Impairment (MCI), 17 individuals diagnosed with Alzheimer’s Disease (AD) and 48 healthy controls. We studied the differences in PE and SC in broadband signals and their decomposition into frequency bands ( δ , θ , α and β ), considering two modalities: (i) raw time series obtained from the magnetometers and (ii) a reconstruction into cortical sources or regions of interest (ROIs). We conducted our analyses at three levels: (i) at the group level we compared SC in each frequency band and modality between groups; (ii) at the individual level we compared how the [PE, SC] plane differs in each modality; and (iii) at the local level we explored differences in scalp and cortical space. We recovered classical results that considered only broadband signals and found a nontrivial pattern of alterations in each frequency band, showing that SC does not necessarily decrease in AD or MCI.

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