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We examined associations of an Alzheimer's disease (AD) Genetic Risk Score (AD-GRS) and midlife cognitive and neuroimaging outcomes in 1,252 middle-aged participants (311 with brain MRI). A higher AD-GRS based on 25 previously identified loci (excluding apolipoprotein E [APOE]) was associated with worse Montreal Cognitive Assessment (-0.14 standard deviation [SD] [95% confidence interval {CI}: -0.26, -0.02]), older machine learning predicted brain age (2.35 years[95%CI: 0.01, 4.69]), and white matter hyperintensity volume (0.35 SD [95% CI: 0.00, 0.71]), but not with a composite cognitive outcome, total brain, or hippocampal volume. APOE ε4 allele was not associated with any outcomes. AD risk genes beyond APOE may contribute to subclinical differences in cognition and brain health in midlife. ANN NEUROL 2023;93:629-634.
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Enfermedad de Alzheimer , Persona de Mediana Edad , Humanos , Preescolar , Enfermedad de Alzheimer/genética , Encéfalo , Envejecimiento/genética , Cognición , Apolipoproteínas E , Apolipoproteína E4/genéticaRESUMEN
INTRODUCTION: Heart rate (HR) fragmentation indices quantify breakdown of HR regulation and are associated with atrial fibrillation and cognitive impairment. Their association with brain magnetic resonance imaging (MRI) markers of small vessel disease is unexplored. METHODS: In 606 stroke-free participants of the Multi-Ethnic Study of Atherosclerosis (mean age 67), HR fragmentation indices including percentage of inflection points (PIP) were derived from sleep study recordings. We examined PIP in relation to white matter hyperintensity (WMH) volume, total white matter fractional anisotropy (FA), and microbleeds from 3-Tesla brain MRI completed 7 years later. RESULTS: In adjusted analyses, higher PIP was associated with greater WMH volume (14% per standard deviation [SD], 95% confidence interval [CI]: 2, 27%, P = 0.02) and lower WM FA (-0.09 SD per SD, 95% CI: -0.16, -0.01, P = 0.03). DISCUSSION: HR fragmentation was associated with small vessel disease. HR fragmentation can be measured automatically from ambulatory electrocardiogram devices and may be useful as a biomarker of vascular brain injury.
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Enfermedades de los Pequeños Vasos Cerebrales , Accidente Cerebrovascular , Sustancia Blanca , Humanos , Anciano , Frecuencia Cardíaca , Encéfalo/diagnóstico por imagen , Encéfalo/patología , Imagen por Resonancia Magnética/métodos , Accidente Cerebrovascular/patología , Sustancia Blanca/diagnóstico por imagen , Sustancia Blanca/patología , Enfermedades de los Pequeños Vasos Cerebrales/diagnóstico por imagen , Enfermedades de los Pequeños Vasos Cerebrales/patologíaRESUMEN
INTRODUCTION: We investigated the associations of leptin markers with cognitive function and magnetic resonance imaging (MRI) measures of brain atrophy and vascular injury in healthy middle-aged adults. METHODS: We included 2262 cognitively healthy participants from the Framingham Heart Study with neuropsychological evaluation; of these, 2028 also had available brain MRI. Concentrations of leptin, soluble leptin receptor (sOB-R), and their ratio (free leptin index [FLI]), indicating leptin bioavailability, were measured using enzyme-linked immunosorbent assays. Cognitive and MRI measures were derived using standardized protocols. RESULTS: Higher sOB-R was associated with lower fractional anisotropy (FA, ß = -0.114 ± 0.02, p < 0.001), and higher free water (FW, ß = 0.091 ± 0.022, p < 0.001) and peak-width skeletonized mean diffusivity (PSMD, ß = 0.078 ± 0.021, p < 0.001). Correspondingly, higher FLI was associated with higher FA (ß = 0.115 ± 0.027, p < 0.001) and lower FW (ß = -0.096 ± 0.029, p = 0.001) and PSMD (ß = -0.085 ± 0.028, p = 0.002). DISCUSSION: Higher leptin bioavailability was associated with better white matter (WM) integrity in healthy middle-aged adults, supporting the putative neuroprotective role of leptin in late-life dementia risk. HIGHLIGHTS: Higher leptin bioavailability was related to better preservation of white matter microstructure. Higher leptin bioavailability during midlife might confer protection against dementia. Potential benefits might be even stronger for individuals with visceral obesity. DTI measures might be sensitive surrogate markers of subclinical neuropathology.
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Deep neural networks currently provide the most advanced and accurate machine learning models to distinguish between structural MRI scans of subjects with Alzheimer's disease and healthy controls. Unfortunately, the subtle brain alterations captured by these models are difficult to interpret because of the complexity of these multi-layer and non-linear models. Several heatmap methods have been proposed to address this issue and analyze the imaging patterns extracted from the deep neural networks, but no quantitative comparison between these methods has been carried out so far. In this work, we explore these questions by deriving heatmaps from Convolutional Neural Networks (CNN) trained using T1 MRI scans of the ADNI data set and by comparing these heatmaps with brain maps corresponding to Support Vector Machine (SVM) activation patterns. Three prominent heatmap methods are studied: Layer-wise Relevance Propagation (LRP), Integrated Gradients (IG), and Guided Grad-CAM (GGC). Contrary to prior studies where the quality of heatmaps was visually or qualitatively assessed, we obtained precise quantitative measures by computing overlap with a ground-truth map from a large meta-analysis that combined 77 voxel-based morphometry (VBM) studies independently from ADNI. Our results indicate that all three heatmap methods were able to capture brain regions covering the meta-analysis map and achieved better results than SVM activation patterns. Among them, IG produced the heatmaps with the best overlap with the independent meta-analysis.
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Enfermedad de Alzheimer , Humanos , Neuroimagen/métodos , Redes Neurales de la Computación , Imagen por Resonancia Magnética/métodos , Encéfalo/fisiologíaRESUMEN
BACKGROUND: Numerous upper airway anatomy characteristics are risk factors for sleep apnea, which affects 26% of older Americans, and more severe sleep apnea is associated with cognitive impairment. This study explores the pathophysiology and links between upper airway anatomy, sleep, and cognition. METHODS: Participants in the Multi-Ethnic Study of Atherosclerosis underwent an upper airway MRI, polysomnography to assess sleep measures including the apnea-hypopnea index (AHI) and completed the Cognitive Abilities Screening Instrument (CASI). Two model selection techniques selected from among 67 upper airway measures those that are most strongly associated with CASI score. The associations of selected upper airway measures with AHI, AHI with CASI score, and selected upper airway anatomy measures with CASI score, both alone and after adjustment for AHI, were assessed using linear regression. RESULTS: Soft palate volume, maxillary divergence, and upper facial height were significantly positively associated with higher CASI score, indicating better cognition. The coefficients were small, with a 1 standard deviation (SD) increase in these variables being associated with a 0.83, 0.75, and 0.70 point higher CASI score, respectively. Additional adjustment for AHI very slightly attenuated these associations. Larger soft palate volume was significantly associated with higher AHI (15% higher AHI (95% CI 2%,28%) per SD). Higher AHI was marginally associated with higher CASI score (0.43 (95% CI 0.01,0.85) per AHI doubling). CONCLUSIONS: Three upper airway measures were weakly but significantly associated with higher global cognitive test performance. Sleep apnea did not appear to be the mechanism through which these upper airway and cognition associations were acting. Further research on the selected upper airway measures is recommended.
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Aterosclerosis , Síndromes de la Apnea del Sueño , Apnea Obstructiva del Sueño , Humanos , Anciano , Síndromes de la Apnea del Sueño/complicaciones , Polisomnografía/efectos adversos , Factores de Riesgo , Aterosclerosis/complicacionesRESUMEN
INTRODUCTION: Supplementation with spermidine may support healthy aging, but elevated spermidine tissue levels were shown to be an indicator of Alzheimer's disease (AD). METHODS: Data from 659 participants (age range: 21-81 years) of the population-based Study of Health in Pomerania TREND were included. We investigated the association between spermidine plasma levels and markers of brain aging (hippocampal volume, AD score, global cortical thickness [CT], and white matter hyperintensities [WMH]). RESULTS: Higher spermidine levels were significantly associated with lower hippocampal volume (ß = -0.076; 95% confidence interval [CI]: -0.13 to -0.02; q = 0.026), higher AD score (ß = 0.118; 95% CI: 0.05 to 0.19; q = 0.006), lower global CT (ß = -0.104; 95% CI: -0.17 to -0.04; q = 0.014), but not WMH volume. Sensitivity analysis revealed no substantial changes after excluding participants with cancer, depression, or hemolysis. DISCUSSION: Elevated spermidine plasma levels are associated with advanced brain aging and might serve as potential early biomarker for AD and vascular brain pathology.
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Enfermedad de Alzheimer , Sustancia Blanca , Humanos , Adulto Joven , Adulto , Persona de Mediana Edad , Anciano , Anciano de 80 o más Años , Espermidina , Sustancia Blanca/patología , Imagen por Resonancia Magnética , Encéfalo/diagnóstico por imagen , Encéfalo/patología , Envejecimiento/patología , Enfermedad de Alzheimer/patologíaRESUMEN
INTRODUCTION: Little is known about the epidemiology of brain microbleeds in racially/ethnically diverse populations. METHODS: In the Multi-Ethnic Study of Atherosclerosis, brain microbleeds were identified from 3T magnetic resonance imaging susceptibility-weighted imaging sequences using deep learning models followed by radiologist review. RESULTS: Among 1016 participants without prior stroke (25% Black, 15% Chinese, 19% Hispanic, 41% White, mean age 72), microbleed prevalence was 20% at age 60 to 64.9 and 45% at ≥85 years. Deep microbleeds were associated with older age, hypertension, higher body mass index, and atrial fibrillation, and lobar microbleeds with male sex and atrial fibrillation. Overall, microbleeds were associated with greater white matter hyperintensity volume and lower total white matter fractional anisotropy. DISCUSSION: Results suggest differing associations for lobar versus deep locations. Sensitive microbleed quantification will facilitate future longitudinal studies of their potential role as an early indicator of vascular pathology.
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Fibrilación Atrial , Hemorragia Cerebral , Humanos , Masculino , Anciano , Persona de Mediana Edad , Hemorragia Cerebral/diagnóstico por imagen , Hemorragia Cerebral/epidemiología , Encéfalo/diagnóstico por imagen , Encéfalo/patología , Imagen por Resonancia Magnética/métodos , Factores de Riesgo , CogniciónRESUMEN
BACKGROUND: Hippocampal formation atrophy is a well-established imaging biomarker of several neurological diseases, including Alzheimer's disease, temporal lobe epilepsy, and schizophrenia. The hippocampus is divided into subfields that have different functions and vary in sensitivity to different diseases. This study investigates the potential interaction between COVID-19 and the various hippocampus subfields, which may shed light on the long-term neurological consequences of the virus. METHOD: We obtained high-resolution T1-weighted (T1w) and T2-weighted (T2w) MRI images using 7T scanners located at three sites in two countries: Pittsburgh (n = 14) and Texas (San Antonio and Houston) (n = 40) in the USA, and Nottingham, UK (n = 33). We evaluated the hippocampus subfields using the ASHS package [1-3]. Imaging sets of 51 subjects with minimal or no manual segmentation corrections (Figures 1 and 2) were included in the analysis. We conducted T-tests with Bonferroni correction, adjusting for age and intracranial volume to identify the differences in hippocampus subfield volumes across groups. RESULT: Participants who needed admission into the ICU due to Covid-19 showed a significantly lower (p-value = 0.0034) left CA1 volume compared to participants who did not require ICU (Figure 3). In addition, several other non-significant trends were observed. CONCLUSION: Our preliminary findings suggest that Covid-19 may impact the hippocampus, particularly in patients who required intensive care. However, the study - as of to date - has a small sample size and lacks a comparison group with patients who were admitted into ICU for acute illnesses other than Covid-19. Additionally, longitudinal data is needed to track the long-term effects of the disease on the hippocampal subfields. FUNDING: NIH R56AG074467, R01MH111265 and R01AG063525 References: 1. Berron et al. Neuroimage 2017 2. Yushkevich et al. Human Brain Mapping 2015 3. Santini et al. Neuroimage: clinical 2021.
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COVID-19 , Hipocampo , Imagen por Resonancia Magnética , Humanos , Hipocampo/diagnóstico por imagen , Hipocampo/patología , Masculino , Femenino , Persona de Mediana Edad , Atrofia/patología , SARS-CoV-2 , AncianoRESUMEN
Connectomes derived from resting-state functional MRI scans have significantly benefited from the development of dedicated fMRI motion correction and denoising algorithms. But they are based on empirical correlations that can produce unreliable results in high dimension low sample size settings. A family of statistical estimators, the covariance shrinkage methods, could mitigate this issue. Unfortunately, these methods have rarely been used to correct functional connectomes and no extensive experiment has been conducted so far to compare the shrinkage methods available for this task. In this work, we propose to fix this issue by processing a benchmark dataset made of a thousand high-resolution resting-state fMRI scans provided by the Human Connectome Project to compare the ability of five prominent covariance shrinkage methods to produce reliable functional connectomes at different spatial resolutions and scans duration: the pioneer linear covariance shrinkage method introduced by Ledoit and Wolf, the Oracle Approximating Shrinkage, the QuEST method, the NERCOME method, and a recent analytical approximation of the QuEST approach. Our experiments establish that all covariance shrinkage methods significantly improve functional connectomes derived from short fMRI scans. The Oracle Approximating Shrinkage and the QuEST method produced the best results. Lastly, we present shrinkage intensity charts that can be used for designing and analyzing fMRI studies. These charts indicate that sparse connectomes are difficult to estimate from short fMRI scans, and they describe a range of settings where dynamic functional connectivity should not be computed.
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Conectoma , Algoritmos , Encéfalo/diagnóstico por imagen , Conectoma/métodos , Humanos , Imagen por Resonancia Magnética/métodos , Movimiento (Física)RESUMEN
BACKGROUND: In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between estimated brain age and biological age is an established biomarker of brain health and such models may benefit from increased cross-site generalizability. PURPOSE: To develop and evaluate a deep learning-based image harmonization method to improve cross-site generalizability of deep learning age prediction. STUDY TYPE: Retrospective. POPULATION: Eight thousand eight hundred and seventy-six subjects from six sites. Harmonization models were trained using all subjects. Age prediction models were trained using 2739 subjects from a single site and tested using the remaining 6137 subjects from various other sites. FIELD STRENGTH/SEQUENCE: Brain imaging with magnetization prepared rapid acquisition with gradient echo or spoiled gradient echo sequences at 1.5 T and 3 T. ASSESSMENT: StarGAN v2, was used to perform a canonical mapping from diverse datasets to a reference domain to reduce site-based variation while preserving semantic information. Generalization performance of deep learning age prediction was evaluated using harmonized, histogram matched, and unharmonized data. STATISTICAL TESTS: Mean absolute error (MAE) and Pearson correlation between estimated age and biological age quantified the performance of the age prediction model. RESULTS: Our results indicated a substantial improvement in age prediction in out-of-sample data, with the overall MAE improving from 15.81 (±0.21) years to 11.86 (±0.11) with histogram matching to 7.21 (±0.22) years with generative adversarial network (GAN)-based harmonization. In the multisite case, across the 5 out-of-sample sites, MAE improved from 9.78 (±6.69) years to 7.74 (±3.03) years with histogram normalization to 5.32 (±4.07) years with GAN-based harmonization. DATA CONCLUSION: While further research is needed, GAN-based medical image harmonization appears to be a promising tool for improving cross-site deep learning generalization. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 1.
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Aprendizaje Profundo , Adolescente , Encéfalo/diagnóstico por imagen , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Proyectos de Investigación , Estudios RetrospectivosRESUMEN
BACKGROUND: High blood pressure (BP) is a risk factor for late-life brain health; however, the association of elevated BP with brain health in mid-life is unclear. METHODS: We identified 661 participants from the Coronary Artery Risk Development in Young Adults Study (age 18-30 at baseline) with 30 years of follow-up and brain magnetic resonance imaging at year 30. Cumulative exposure of BP was estimated by time-weighted averages (TWA). Ideal cardiovascular health was defined as systolic BP < 120 mm Hg, diastolic BP < 80 mm Hg. Brain age was calculated using previously validated high dimensional machine learning pattern analyses. RESULTS: Every 5 mmHg increment in TWA systolic BP was associated with approximately 1-year greater brain age (95% confidence interval [CI]: 0.50-1.36) Participants with TWA systolic or diastolic BP over the recommended guidelines for ideal cardiovascular health, had on average 3-year greater brain age (95% CI: 1.00-4.67; 95% CI: 1.45-5.13, respectively). CONCLUSION: Elevated BP from early to mid adulthood, even below clinical cut-offs, is associated with advanced brain aging in mid-life.
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Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer's disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.
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Envejecimiento , Encefalopatías/diagnóstico por imagen , Encéfalo/diagnóstico por imagen , Aprendizaje Profundo , Neuroimagen/métodos , Femenino , Humanos , Procesamiento de Imagen Asistido por Computador , Longevidad , Imagen por Resonancia Magnética , MasculinoRESUMEN
INTRODUCTION: Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects). METHODS: Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD. RESULTS: WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aß) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD. DISCUSSION: A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.
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Envejecimiento/fisiología , Péptidos beta-Amiloides/metabolismo , Encéfalo/crecimiento & desarrollo , Aprendizaje Automático , Imagen por Resonancia Magnética/métodos , Sustancia Blanca/crecimiento & desarrollo , Adulto , Anciano , Anciano de 80 o más Años , Atrofia , Biomarcadores , Enfermedades de los Pequeños Vasos Cerebrales/metabolismo , Enfermedades de los Pequeños Vasos Cerebrales/psicología , Disfunción Cognitiva , Progresión de la Enfermedad , Femenino , Humanos , Procesamiento de Imagen Asistido por Computador , Masculino , Persona de Mediana Edad , Pruebas Neuropsicológicas , Sustancia Blanca/patología , Adulto JovenRESUMEN
Automatic segmentation of brain anatomy has been a key processing step in quantitative neuroimaging analyses. An extensive body of literature has relied on Freesurfer segmentations. Yet, in recent years, the multi-atlas segmentation framework has consistently obtained results with superior accuracy in various evaluations. We compared brain anatomy segmentations from Freesurfer, which uses a single probabilistic atlas strategy, against segmentations from Multi-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters and locally optimal atlas selection (MUSE), one of the leading ensemble-based methods that calculates a consensus segmentation through fusion of anatomical labels from multiple atlases and registrations. The focus of our evaluation was twofold. First, using manual ground-truth hippocampus segmentations, we found that Freesurfer segmentations showed a bias towards over-segmentation of larger hippocampi, and under-segmentation in older age. This bias was more pronounced in Freesurfer-v5.3, which has been used in multiple previous studies of aging, while the effect was mitigated in more recent Freesurfer-v6.0, albeit still present. Second, we evaluated inter-scanner segmentation stability using same day scan pairs from ADNI acquired on 1.5T and 3T scanners. We also found that MUSE obtains more consistent segmentations across scanners compared to Freesurfer, particularly in the deep structures.
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Encéfalo/anatomía & histología , Encéfalo/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador/métodos , Imagen por Resonancia Magnética , Programas Informáticos , Adulto , Anciano , Algoritmos , Femenino , Hipocampo/anatomía & histología , Hipocampo/diagnóstico por imagen , Humanos , Masculino , Tamaño de los Órganos , Reproducibilidad de los Resultados , Adulto JovenRESUMEN
As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.
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Encéfalo/anatomía & histología , Encéfalo/diagnóstico por imagen , Conjuntos de Datos como Asunto , Procesamiento de Imagen Asistido por Computador/métodos , Imagen por Resonancia Magnética/métodos , Estudios Multicéntricos como Asunto , Neuroimagen/métodos , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Atlas como Asunto , Niño , Preescolar , Femenino , Humanos , Procesamiento de Imagen Asistido por Computador/normas , Imagen por Resonancia Magnética/normas , Masculino , Persona de Mediana Edad , Neuroimagen/normas , Reproducibilidad de los Resultados , Adulto JovenRESUMEN
INTRODUCTION: It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia. METHODS: A deep learning method is developed and validated based on magnetic resonance imaging scans of 2146 subjects (803 for training and 1343 for validation) to predict MCI subjects' progression to AD dementia in a time-to-event analysis setting. RESULTS: The deep-learning time-to-event model predicted individual subjects' progression to AD dementia with a concordance index of 0.762 on 439 Alzheimer's Disease Neuroimaging Initiative testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a concordance index of 0.781 on 40 Australian Imaging Biomarkers and Lifestyle Study of Aging testing MCI subjects with follow-up duration from 18 to 54 months (quartiles: [18, 36, 54]). The predicted progression risk also clustered individual subjects into subgroups with significant differences in their progression time to AD dementia (P < .0002). Improved performance for predicting progression to AD dementia (concordance index = 0.864) was obtained when the deep learning-based progression risk was combined with baseline clinical measures. DISCUSSION: Our method provides a cost effective and accurate means for prognosis and potentially to facilitate enrollment in clinical trials with individuals likely to progress within a specific temporal period.
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Enfermedad de Alzheimer/diagnóstico por imagen , Aprendizaje Profundo , Demencia/diagnóstico por imagen , Hipocampo/diagnóstico por imagen , Interpretación de Imagen Asistida por Computador/métodos , Modelos Neurológicos , Progresión de la Enfermedad , Humanos , Imagen por Resonancia Magnética/métodos , Neuroimagen/métodosRESUMEN
Background and Purpose- White matter hyperintensities (WMH) on brain magnetic resonance imaging are typical signs of cerebral small vessel disease and may indicate various preclinical, age-related neurological disorders, such as stroke. Though WMH are highly heritable, known common variants explain a small proportion of the WMH variance. The contribution of low-frequency/rare coding variants to WMH burden has not been explored. Methods- In the discovery sample we recruited 20 719 stroke/dementia-free adults from 13 population-based cohort studies within the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, among which 17 790 were of European ancestry and 2929 of African ancestry. We genotyped these participants at ≈250 000 mostly exonic variants with Illumina HumanExome BeadChip arrays. We performed ethnicity-specific linear regression on rank-normalized WMH in each study separately, which were then combined in meta-analyses to test for association with single variants and genes aggregating the effects of putatively functional low-frequency/rare variants. We then sought replication of the top findings in 1192 adults (European ancestry) with whole exome/genome sequencing data from 2 independent studies. Results- At 17q25, we confirmed the association of multiple common variants in TRIM65, FBF1, and ACOX1 ( P<6×10-7). We also identified a novel association with 2 low-frequency nonsynonymous variants in MRPL38 (lead, rs34136221; PEA=4.5×10-8) partially independent of known common signal ( PEA(conditional)=1.4×10-3). We further identified a locus at 2q33 containing common variants in NBEAL1, CARF, and WDR12 (lead, rs2351524; Pall=1.9×10-10). Although our novel findings were not replicated because of limited power and possible differences in study design, meta-analysis of the discovery and replication samples yielded stronger association for the 2 low-frequency MRPL38 variants ( Prs34136221=2.8×10-8). Conclusions- Both common and low-frequency/rare functional variants influence WMH. Larger replication and experimental follow-up are essential to confirm our findings and uncover the biological causal mechanisms of age-related WMH.
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Encéfalo/diagnóstico por imagen , Exoma/genética , Variación Genética/genética , Imagen por Resonancia Magnética/métodos , Proteínas Mitocondriales/genética , Sustancia Blanca/diagnóstico por imagen , Estudios de Cohortes , HumanosRESUMEN
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 and mild cognitive impairment classification, selected after online database searches in Google Scholar and PubMed (January, 1985-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 (FDG-PET) (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 addressed some future research directions.
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Enfermedad de Alzheimer/diagnóstico por imagen , Disfunción Cognitiva/diagnóstico , Aprendizaje Automático , Neuroimagen , Síntomas Prodrómicos , Enfermedad de Alzheimer/clasificación , Disfunción Cognitiva/clasificación , HumanosRESUMEN
White matter hyperintensities are associated with increased risk of dementia and cognitive decline. The current study investigates the relationship between white matter hyperintensities burden and patterns of brain atrophy associated with brain ageing and Alzheimer's disease in a large populatison-based sample (n = 2367) encompassing a wide age range (20-90 years), from the Study of Health in Pomerania. We quantified white matter hyperintensities using automated segmentation and summarized atrophy patterns using machine learning methods resulting in two indices: the SPARE-BA index (capturing age-related brain atrophy), and the SPARE-AD index (previously developed to capture patterns of atrophy found in patients with Alzheimer's disease). A characteristic pattern of age-related accumulation of white matter hyperintensities in both periventricular and deep white matter areas was found. Individuals with high white matter hyperintensities burden showed significantly (P < 0.0001) lower SPARE-BA and higher SPARE-AD values compared to those with low white matter hyperintensities burden, indicating that the former had more patterns of atrophy in brain regions typically affected by ageing and Alzheimer's disease dementia. To investigate a possibly causal role of white matter hyperintensities, structural equation modelling was used to quantify the effect of Framingham cardiovascular disease risk score and white matter hyperintensities burden on SPARE-BA, revealing a statistically significant (P < 0.0001) causal relationship between them. Structural equation modelling showed that the age effect on SPARE-BA was mediated by white matter hyperintensities and cardiovascular risk score each explaining 10.4% and 21.6% of the variance, respectively. The direct age effect explained 70.2% of the SPARE-BA variance. Only white matter hyperintensities significantly mediated the age effect on SPARE-AD explaining 32.8% of the variance. The direct age effect explained 66.0% of the SPARE-AD variance. Multivariable regression showed significant relationship between white matter hyperintensities volume and hypertension (P = 0.001), diabetes mellitus (P = 0.023), smoking (P = 0.002) and education level (P = 0.003). The only significant association with cognitive tests was with the immediate recall of the California verbal and learning memory test. No significant association was present with the APOE genotype. These results support the hypothesis that white matter hyperintensities contribute to patterns of brain atrophy found in beyond-normal brain ageing in the general population. White matter hyperintensities also contribute to brain atrophy patterns in regions related to Alzheimer's disease dementia, in agreement with their known additive role to the likelihood of dementia. Preventive strategies reducing the odds to develop cardiovascular disease and white matter hyperintensities could decrease the incidence or delay the onset of dementia.
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Envejecimiento/patología , Encéfalo/patología , Vigilancia de la Población , Sustancia Blanca/patología , Adulto , Anciano , Anciano de 80 o más Años , Enfermedad de Alzheimer/diagnóstico , Enfermedad de Alzheimer/epidemiología , Trastornos del Conocimiento/diagnóstico , Trastornos del Conocimiento/epidemiología , Estudios de Cohortes , Demencia/diagnóstico , Demencia/epidemiología , Femenino , Alemania/epidemiología , Humanos , Imagen por Resonancia Magnética/tendencias , Masculino , Persona de Mediana Edad , Polonia/epidemiología , Vigilancia de la Población/métodos , Factores de Riesgo , Adulto JovenRESUMEN
OBJECTIVES: Evidence on possible associations between facial morphology, attachment loss and gingival recession is lacking. We analysed whether the facial type, which can be described by the ratio of facial width and length (facial index), is related to periodontal loss of attachment, hypothesizing that a broad face might be associated with less gingival recession (GR) and less clinical attachment loss (CAL) than a long face. MATERIALS AND METHODS: Data from the 11-year follow-up of the population-based Study of Health in Pomerania were used. Periodontal loss of attachment was assessed by GR and CAL. Linear regression models, adjusted for age and gender, were used to assess associations between specific landmark based distances extracted from magnetic resonance imaging head scans and clinically assessed GR or CAL (N = 556). RESULTS: Analysing all teeth, a higher maximum cranial width was associated with a lower mean GR (B = -0.016, 95% CI: -0.030; -0.003, p = 0.02) and a lower mean CAL (B = -0.023, 95% CI: -0.040; -0.005, p = 0.01). Moreover, a long narrow face was significantly associated with increased mean GR and CAL (facial index, P for trend = 0.02 and p = 0.01, respectively). Observed associations were more pronounced for incisors and canines than for premolars and molars. CONCLUSION: This study revealed craniofacial morphology, specifically the cranial width and the facial index, as a putative risk factor for periodontal loss of attachment.