Brain maturation: Predicting individual BrainAGE in children and adolescents using structural MRI

Franke, Katja; Lüders, Eileen; May, Arne; Wilke, Marko; Gaser, Christian

doi:10.1016/j.neuroimage.2012.08.001

Cited by 254 publications

(262 citation statements)

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“…In several recent studies, the authors used the PCA method to reduce the high‐dimensional input space into a low‐dimensional space (Franke, Hagemann, Schleussner, & Gaser, 2015; Franke et al., 2010, 2012, 2017). In the PCA method, the number of principal components has a major effect on the performance, and it is usually determined manually.…”

Section: Discussionmentioning

confidence: 99%

“…The use of the brain‐age technique has helped reveal the abnormal brain changes in many brain studies such as those of AD (Franke et al., 2010), the prediction of the conversion of mild cognitive impairment (MCI) to AD (Gaser, Franke, Klöppel, Koutsouleris, & Sauer, 2013), and investigations of the brains of children and adolescents (Franke, Luders, May, Wilke, & Gaser, 2012), long‐term meditation practitioners (Luders, Cherbuin, & Gaser, 2016), and schizophrenia patients (Koutsouleris et al., 2014). …”

Section: Introductionmentioning

confidence: 99%

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The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

2018

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IntroductionWe present the Brain‐Age Score (BAS) as a magnetic resonance imaging (MRI)‐based index for Alzheimer's disease (AD). We developed a fully automated framework for estimating the BAS in healthy controls (HCs) and individuals with mild cognitive impairment (MCI) or AD, using MRI scans.MethodsWe trained the proposed framework using 385 HCs from the IXI and OASIS datasets and evaluated 146 HCs, 102 stable‐MCI (sMCI), 112 progressive‐MCI (pMCI), and 147 AD patients from the J‐ADNI dataset. We used a correlation test to determine the association between the BAS and four traditional screening tools of AD: the Mini‐Mental State Examination (MMSE), Clinical Dementia Ratio (CDR), Alzheimer's Disease Assessment Score (ADAS), and Functional Assessment Questionnaire (FAQ). Furthermore, we assessed the association between BAS and anatomical MRI measurements: the normalized gray matter (nGM), normalized white matter (nWM), normalized cerebrospinal fluid (nCSF), mean cortical thickness as well as hippocampus volume.ResultsThe correlation results demonstrated that the BAS is in line with traditional screening tools of AD (i.e., the MMSE, CDR, ADAS, and FAQ scores) as well as anatomical MRI measurements (i.e., nGM, nCSF, mean cortical thickness, and hippocampus volume).DiscussionThe BAS may be useful for diagnosing the brain atrophy level and can be a reliable automated index for clinical applications and neuropsychological screening tools.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

2018

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“…Establishing such a relationship is a prerequisite for demonstrating the putative utility of the approach; as a subject's age is a ground truth that is easily ascertained, if brain imaging patterns could predict subject age but did not relate to measures of brain function, such a technique would be of academic interest only. In contrast, as previously noted (Bunge and Whitaker 2012; Franke et al 2012), if the degree to which a subject's estimated "brain age" diverged from their chronologic age was related to either precocity or delay of cognitive development, it would suggest that brain imaging may be a useful biomarker for the early detection of subtle developmental abnormalities.…”

Section: Introductionmentioning

confidence: 88%

“…In order to consider such complexity in an integrated fashion, recent studies (Dosenbach et al 2010;Franke et al 2010Franke et al , 2012Brown et al 2012) have used multivariate machine-learning techniques to derive an amalgamated index of brain development. Specifically, T 1 -weighted structural brain imaging can be used to predict an individual's chronologic age with a high degree of accuracy (subjects aged 19-86 years, r = 0.92, mean absolute error [MAE] = 5) (Franke et al 2010(Franke et al , 2012. Similarly, Dosenbach et al (2010) found that the complex patterns of functional connectivity can predict a subject's age during development, although with a somewhat lower degree of accuracy (subjects aged 7-30 years, r 2 = 0.553).…”

Section: Introductionmentioning

confidence: 99%

Imaging Patterns of Brain Development and their Relationship to Cognition

et al. 2014

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We present a brain development index (BDI) that concisely summarizes complex imaging patterns of structural brain maturation along a single dimension using a machine learning methodology. The brain was found to follow a remarkably consistent developmental trajectory in a sample of 621 subjects of ages 8-22 participating in the Philadelphia Neurodevelopmental Cohort, reflected by a cross-validated correlation coefficient between chronologic age and the BDI of r = 0.89. Critically, deviations from this trajectory related to cognitive performance. Specifically, subjects whose BDI was higher than their chronological age displayed significantly superior cognitive processing speed compared with subjects whose BDI was lower than their actual age. These results indicate that the multiparametric imaging patterns summarized by the BDI can accurately delineate trajectories of brain development and identify individuals with cognitive precocity or delay.

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“…These local features have been considered to reflect clinical conditions (Li et al, 2012), development (Franke et al, 2012), plasticity (Wei et al, 2013), and individual differences in various tasks (Kanai and Rees, 2011). However, inter-regional relations of local brain morphology (referred to as the morphological relations) in individuals have seldom been explored, although they could provide exclusive useful information about the inter-regional associations that are not evident from local morphological measures.…”

Section: Introductionmentioning

confidence: 99%

Measuring individual morphological relationship of cortical regions

Kong

Wang

Huang

et al. 2014

Journal of Neuroscience Methods

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h i g h l i g h t s• A new metric was proposed to quantify individual morphological relations of regions.• The new metric is indexed as the similarity of different morphological distributions.• We estimate the morphological distribution from individual MRI image.• The metric seemed to have potential application to individual differences studies. a r t i c l e i n f o b s t r a c tBackground: Although local features of brain morphology have been widely investigated in neuroscience, the inter-regional relations in brain morphology have rarely been investigated, especially not for individual participants. New method: In this paper, we proposed a novel framework for investigating this relation based on an individual's magnetic resonance imaging (MRI) data. The key idea was to estimate the probability density function (PDF) of local morphological features within a brain region to provide a global description of this region. Then, the inter-regional relations were quantified by calculating the similarity of the PDFs for pairs of regions based on the Kullback-Leibler (KL) divergence. Results: For illustration, we applied this approach to a pre-post intervention study to investigate the longitudinal changes in morphological relations after long-term sleep deprivation. The results suggest the potential application of this new method for studies on individual differences in brain structure.Comparison with existing methods: The current method can be employed to estimate individual morphological relations between regions, which have been largely ignored by previous studies. Conclusions: Our morphological relation metric, as a novel quantitative biomarker, can be used to investigate normal individual variability and even within-individual alterations/abnormalities in brain structure.

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Brain maturation: Predicting individual BrainAGE in children and adolescents using structural MRI

Cited by 254 publications

References 50 publications

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

Imaging Patterns of Brain Development and their Relationship to Cognition

Measuring individual morphological relationship of cortical regions

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