With the aging population, prevalence of neurodegenerative diseases is increasing, thus placing a growing burden on individuals and the whole society. However, individual rates of aging are shaped by ...a great variety of and the interactions between environmental, genetic, and epigenetic factors. Establishing biomarkers of the neuroanatomical aging processes exemplifies a new trend in neuroscience in order to provide risk-assessments and predictions for age-associated neurodegenerative and neuropsychiatric diseases at a single-subject level. The "
method constitutes the first and actually most widely applied concept for predicting and evaluating individual brain age based on structural MRI. This review summarizes all studies published within the last 10 years that have established and utilized the
method to evaluate the effects of interaction of genes, environment, life burden, diseases, or life time on individual neuroanatomical aging. In future,
and other brain age prediction approaches based on structural or functional markers may improve the assessment of individual risks for neurological, neuropsychiatric and neurodegenerative diseases as well as aid in developing personalized neuroprotective treatments and interventions.
Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be important to identify MCI ...patients at high risk for conversion to AD. In this study, we present a novel magnetic resonance imaging (MRI)-based method for predicting the MCI-to-AD conversion from one to three years before the clinical diagnosis. First, we developed a novel MRI biomarker of MCI-to-AD conversion using semi-supervised learning and then integrated it with age and cognitive measures about the subjects using a supervised learning algorithm resulting in what we call the aggregate biomarker. The novel characteristics of the methods for learning the biomarkers are as follows: 1) We used a semi-supervised learning method (low density separation) for the construction of MRI biomarker as opposed to more typical supervised methods; 2) We performed a feature selection on MRI data from AD subjects and normal controls without using data from MCI subjects via regularized logistic regression; 3) We removed the aging effects from the MRI data before the classifier training to prevent possible confounding between AD and age related atrophies; and 4) We constructed the aggregate biomarker by first learning a separate MRI biomarker and then combining it with age and cognitive measures about the MCI subjects at the baseline by applying a random forest classifier. We experimentally demonstrated the added value of these novel characteristics in predicting the MCI-to-AD conversion on data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. With the ADNI data, the MRI biomarker achieved a 10-fold cross-validated area under the receiver operating characteristic curve (AUC) of 0.7661 in discriminating progressive MCI patients (pMCI) from stable MCI patients (sMCI). Our aggregate biomarker based on MRI data together with baseline cognitive measurements and age achieved a 10-fold cross-validated AUC score of 0.9020 in discriminating pMCI from sMCI. The results presented in this study demonstrate the potential of the suggested approach for early AD diagnosis and an important role of MRI in the MCI-to-AD conversion prediction. However, it is evident based on our results that combining MRI data with cognitive test results improved the accuracy of the MCI-to-AD conversion prediction.
•Multi-step procedure combining several ideas for early AD-to-MCI conversion prediction•MRI biomarker using low density separation and auxiliary data from AD and NC subjects•Aggregate biomarker for combining MRI and cognitive test data•Cross-validated AUC 0.9020 for conversion prediction up to 3years before diagnosis
Several properties of the human brain cortex, e.g., cortical thickness and gyrification, have been found to correlate with the progress of neuropsychiatric disorders. The relationship between brain ...structure and function harbors a broad range of potential uses, particularly in clinical contexts, provided that robust methods for the extraction of suitable representations of the brain cortex from neuroimaging data are available. One such representation is the computationally defined central surface (CS) of the brain cortex. Previous approaches to semi-automated reconstruction of this surface relied on image segmentation procedures that required manual interaction, thereby rendering them error-prone and complicating the analysis of brains that were not from healthy human adults. Validation of these approaches and thickness measures is often done only for simple artificial phantoms that cover just a few standard cases. Here, we present a new fully automated method that allows for measurement of cortical thickness and reconstructions of the CS in one step. It uses a tissue segmentation to estimate the WM distance, then projects the local maxima (which is equal to the cortical thickness) to other GM voxels by using a neighbor relationship described by the WM distance. This projection-based thickness (PBT) allows the handling of partial volume information, sulcal blurring, and sulcal asymmetries without explicit sulcus reconstruction via skeleton or thinning methods. Furthermore, we introduce a validation framework using spherical and brain phantoms that confirms accurate CS construction and cortical thickness measurement under a wide set of parameters for several thickness levels. The results indicate that both the quality and computational cost of our method are comparable, and may be superior in certain respects, to existing approaches.
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► Cortical thickness estimation and central surface reconstruction ► Voxel-based projection scheme based on tissue segmentation ► Phantom creation scheme for thickness and surface validation ► Third party phantoms and real data for comparison with other software (Freesurfer)
Normal aging is known to be accompanied by loss of brain substance. The present study was designed to examine whether the practice of meditation is associated with a reduced brain age. Specific focus ...was directed at age fifty and beyond, as mid-life is a time when aging processes are known to become more prominent. We applied a recently developed machine learning algorithm trained to identify anatomical correlates of age in the brain translating those into one single score: the BrainAGE index (in years). Using this validated approach based on high-dimensional pattern recognition, we re-analyzed a large sample of 50 long-term meditators and 50 control subjects estimating and comparing their brain ages. We observed that, at age fifty, brains of meditators were estimated to be 7.5years younger than those of controls. In addition, we examined if the brain age estimates change with increasing age. While brain age estimates varied only little in controls, significant changes were detected in meditators: for every additional year over fifty, meditators' brains were estimated to be an additional 1month and 22days younger than their chronological age. Altogether, these findings seem to suggest that meditation is beneficial for brain preservation, effectively protecting against age-related atrophy with a consistently slower rate of brain aging throughout life.
•Normal aging is known to be accompanied by loss of brain substance.•Machine learning was used to estimate brain ages in meditators and controls.•At age 50, brains of meditators were estimated to be 7.5years younger than those of controls.•These findings suggest that meditation may be beneficial for brain preservation.
The early identification of brain anatomy deviating from the normal pattern of growth and atrophy, such as in Alzheimer's disease (AD), has the potential to improve clinical outcomes through early ...intervention. Recently, Davatzikos et al. (2009) supported the hypothesis that pathologic atrophy in AD is an accelerated aging process, implying accelerated brain atrophy. In order to recognize faster brain atrophy, a model of healthy brain aging is needed first. Here, we introduce a framework for automatically and efficiently estimating the age of healthy subjects from their T1-weighted MRI scans using a kernel method for regression. This method was tested on over 650 healthy subjects, aged 19–86 years, and collected from four different scanners. Furthermore, the influence of various parameters on estimation accuracy was analyzed. Our age estimation framework included automatic preprocessing of the T1-weighted images, dimension reduction via principal component analysis, training of a relevance vector machine (RVM; Tipping, 2000) for regression, and finally estimating the age of the subjects from the test samples. The framework proved to be a reliable, scanner-independent, and efficient method for age estimation in healthy subjects, yielding a correlation of r=0.92 between the estimated and the real age in the test samples and a mean absolute error of 5 years. The results indicated favorable performance of the RVM and identified the number of training samples as the critical factor for prediction accuracy. Applying the framework to people with mild AD resulted in a mean brain age gap estimate (BrainAGE) score of +10 years.
Alzheimer's disease (AD), the most common form of dementia, shares many aspects of abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based biomarker that predicts the ...individual progression of mild cognitive impairment (MCI) to AD on the basis of pathological brain aging patterns. By employing kernel regression methods, the expression of normal brain-aging patterns forms the basis to estimate the brain age of a given new subject. If the estimated age is higher than the chronological age, a positive brain age gap estimation (BrainAGE) score indicates accelerated atrophy and is considered a risk factor for conversion to AD. Here, the BrainAGE framework was applied to predict the individual brain ages of 195 subjects with MCI at baseline, of which a total of 133 developed AD during 36 months of follow-up (corresponding to a pre-test probability of 68%). The ability of the BrainAGE framework to correctly identify MCI-converters was compared with the performance of commonly used cognitive scales, hippocampus volume, and state-of-the-art biomarkers derived from cerebrospinal fluid (CSF). With accuracy rates of up to 81%, BrainAGE outperformed all cognitive scales and CSF biomarkers in predicting conversion of MCI to AD within 3 years of follow-up. Each additional year in the BrainAGE score was associated with a 10% greater risk of developing AD (hazard rate: 1.10 CI: 1.07-1.13). Furthermore, the post-test probability was increased to 90% when using baseline BrainAGE scores to predict conversion to AD. The presented framework allows an accurate prediction even with multicenter data. Its fast and fully automated nature facilitates the integration into the clinical workflow. It can be exploited as a tool for screening as well as for monitoring treatment options.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Schizophrenia is modelled as a neurodevelopmental disease with high heritability. However, established markers like cortical thickness and grey matter volume are heavily influenced by post-onset ...changes and thus provide limited possibility of accessing early pathologies. Gyrification on the other side is assumed to be more specifically determined by genetic and early developmental factors. Here, we compare T1 weighted 3 Tesla MRI scans of 51 schizophrenia patients and 102 healthy controls (matched for age and gender) using a unified processing pipeline with the CAT12 toolbox. Our study provides a direct comparison between 3D gyrification, cortical thickness, and grey matter volume. We demonstrate that significant (p < 0.05, FWE corrected) results only partially overlap between modalities. Gyrification is altered in bilateral insula, temporal pole and left orbitofrontal cortex, while cortical thickness is additionally reduced in the prefrontal cortex, precuneus, and occipital cortex. Grey matter volume (VBM) was reduced in bilateral medial temporal lobes including the amygdala as well as medial and dorsolateral prefrontal cortices and cerebellum. Our results lend further support for altered gyrification as a marker of early neurodevelopmental disturbance in schizophrenia and show its relation to other morphological markers.
Human psychopathology is the result of complex and subtle neurobiological alterations. Categorial DSM or ICD diagnoses do not allow a biologically founded and differentiated description of these ...diverse processes across a spectrum or continuum, emphasising the need for a scientific and clinical paradigm shift towards a dimensional psychiatric nosology. The subclinical part of the spectrum is, however, of special interest for early detection of mental disorders. We review the current evidence of brain structural correlates (grey matter volume, cortical thickness, and gyrification) in non-clinical (psychiatrically healthy) subjects with minor depressive and anxiety symptoms. We identified 16 studies in the depressive spectrum and 20 studies in the anxiety spectrum. These studies show effects associated with subclinical symptoms in the hippocampus, anterior cingulate cortex, and anterior insula similar to major depression and changes in amygdala similar to anxiety disorders. Precuneus and temporal areas as parts of the default mode network were affected specifically in the subclinical studies. We derive several methodical considerations crucial to investigations of brain structural correlates of minor psycho(patho)logical symptoms in healthy participants. And we discuss neurobiological overlaps with findings in patients as well as distinct findings, e.g. in areas involved in the default mode network. These results might lead to more insight into the early pathogenesis of clinical significant depression or anxiety and need to be enhanced by multi-centre and longitudinal studies.
From an early age, musicians learn complex motor and auditory skills (e.g., the translation of visually perceived musical symbols into motor commands with simultaneous auditory monitoring of output), ...which they practice extensively from childhood throughout their entire careers. Using a voxel-by-voxel morphometric technique, we found gray matter volume differences in motor, auditory, and visual-spatial brain regions when comparing professional musicians (keyboard players) with a matched group of amateur musicians and non-musicians. Although some of these multiregional differences could be attributable to innate predisposition, we believe they may represent structural adaptations in response to long-term skill acquisition and the repetitive rehearsal of those skills. This hypothesis is supported by the strong association we found between structural differences, musician status, and practice intensity, as well as the wealth of supporting animal data showing structural changes in response to long-term motor training. However, only future experiments can determine the relative contribution of predisposition and practice.
Neural development during human childhood and adolescence involves highly coordinated and sequenced events, characterized by both progressive and regressive processes. Despite a multitude of results ...demonstrating the age-dependent development of gray matter, white matter, and total brain volume, a reference curve allowing prediction of structural brain maturation is still lacking but would be clinically valuable. For the first time, the present study provides a validated reference curve for structural brain maturation during childhood and adolescence, based on structural MRI data.
By employing kernel regression methods, a novel but well-validated BrainAGE framework uses the complex multidimensional maturation pattern across the whole brain to estimate an individual's brain age. The BrainAGE framework was applied to a large human sample (n=394) of healthy children and adolescents, whose image data had been acquired during the NIH MRI study of normal brain development. Using this approach, we were able to predict individual brain maturation with a clinically meaningful accuracy: the correlation between predicted brain age and chronological age resulted in r=0.93. The mean absolute error was only 1.1years. Moreover, the predicted brain age reliably differentiated between all age groups (i.e., preschool childhood, late childhood, early adolescence, middle adolescence, late adolescence). Applying the framework to preterm-born adolescents resulted in a significantly lower estimated brain age than chronological age in subjects who were born before the end of the 27th week of gestation, demonstrating the successful clinical application and future potential of this method.
Consequently, in the future this novel BrainAGE approach may prove clinically valuable in detecting both normal and abnormal brain maturation, providing important prognostic information.
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► A sensitive reference curve for structural brain maturation in humans is provided. ► The BrainAGE framework uses structural MRI data and is fully automatic. ► The multidimensional maturation pattern is aggregated to the individual brain age. ► Preterm-born subjects showed a delay in brain maturation of 1.5years in adolescence. ► The novel BrainAGE method proved to detect both normal and abnormal brain maturation.
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