references.bib

@article{sullivan_diffusion_2006,
	series = {Methodological and {Conceptual} {Advances} in the {Study} of {Brain}-{Behavior} {Dynamics}: {A} {Multivariate} {Lifespan} {Perspective}},
	title = {Diffusion tensor imaging and aging},
	volume = {30},
	issn = {0149-7634},
	url = {http://www.sciencedirect.com/science/article/pii/S0149763406000467},
	doi = {10.1016/j.neubiorev.2006.06.002},
	abstract = {Magnetic resonance diffusion tensor imaging (DTI) is a non-invasive in vivo method for characterizing the integrity of anatomical connections and white matter circuitry and provides a quantitative assessment of the brain's white matter microstructure. DTI studies reveal age-related declines in white matter fractional ansiotropy (FA) in normal healthy adults in whom volume declines are not necessarily detectable. The decline is equivalent in men and women, is linear from about age 20 years onwards, and has a frontal distribution. Studies combining regional DTI metrics and tests of specific cognitive and motor functions have shown that age-related declines in white matter integrity are associated with similar declines in interhemispheric transfer, especially dependent on frontal systems. Emerging from recent DTI findings and conceptualizations of neural causes of cognitive decline in aging, we propose three white matter-mediated neural system hypotheses of aging brain structure and function: (1) the anteroposterior gradient, (2) bilateral recruitment of brain systems via the corpus callosum for frontally based task execution, and (3) frontocerebellar synergism. These hypotheses are not mutually exclusive but establish a basis for posing testable questions about brain systems recruited when those used in youth are altered by aging.},
	number = {6},
	journal = {Neuroscience \& Biobehavioral Reviews},
	author = {Sullivan, Edith V. and Pfefferbaum, Adolf},
	month = jan,
	year = {2006},
	keywords = {age, Aging, Microstructure, MRI, White matter},
	pages = {749--761},
	file = {ScienceDirect Full Text PDF:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/Q6HKKXIF/Sullivan and Pfefferbaum - 2006 - Diffusion tensor imaging and aging.pdf:application/pdf;ScienceDirect Snapshot:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/UCV9C7MU/S0149763406000467.html:text/html}
}

@inproceedings{ciresan_convolutional_2011,
	title = {Convolutional {Neural} {Network} {Committees} for {Handwritten} {Character} {Classification}},
	doi = {10.1109/ICDAR.2011.229},
	abstract = {In 2010, after many years of stagnation, the MNIST handwriting recognition benchmark record dropped from 0.40\% error rate to 0.35\%. Here we report 0.27\% for a committee of seven deep CNNs trained on graphics cards, narrowing the gap to human performance. We also apply the same architecture to NIST SD 19, a more challenging dataset including lower and upper case letters. A committee of seven CNNs obtains the best results published so far for both NIST digits and NIST letters. The robustness of our method is verified by analyzing 78125 different 7-net committees.},
	booktitle = {2011 {International} {Conference} on {Document} {Analysis} and {Recognition}},
	author = {Ciresan, D. C. and Meier, U. and Gambardella, L. M. and Schmidhuber, J.},
	month = sep,
	year = {2011},
	keywords = {Character recognition, Committee, Computer architecture, convolutional neural network committees, convolutional neural networks, Error analysis, graphics cards, Graphics Processing Unit, Handwriting recognition, handwritten character classification, handwritten character recognition, image classification, learning (artificial intelligence), MNIST handwriting recognition benchmark, neural nets, neural networks, NIST, NIST digits, NIST letters, NIST SD 19, trained CNN, Training},
	pages = {1135--1139},
	file = {IEEE Xplore Abstract Record:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/SDBVMCHR/6065487.html:text/html;IEEE Xplore Full Text PDF:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/DN8KZCI5/Ciresan et al. - 2011 - Convolutional Neural Network Committees for Handwr.pdf:application/pdf}
}

@incollection{krizhevsky_imagenet_2012,
	title = {{ImageNet} {Classification} with {Deep} {Convolutional} {Neural} {Networks}},
	url = {http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 25},
	publisher = {Curran Associates, Inc.},
	author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
	editor = {Pereira, F. and Burges, C. J. C. and Bottou, L. and Weinberger, K. Q.},
	year = {2012},
	pages = {1097--1105},
	file = {NIPS Snapshot:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/P4H2XZM8/4824-imagenet-classification-with-deep-convolutional-neural-networ.html:text/html}
}

@article{pereira_brain_2016,
	title = {Brain {Tumor} {Segmentation} {Using} {Convolutional} {Neural} {Networks} in {MRI} {Images}},
	volume = {35},
	issn = {0278-0062},
	doi = {10.1109/TMI.2016.2538465},
	abstract = {Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.},
	number = {5},
	journal = {IEEE Transactions on Medical Imaging},
	author = {Pereira, S. and Pinto, A. and Alves, V. and Silva, C. A.},
	month = may,
	year = {2016},
	keywords = {automatic segmentation, automatic segmentation methods, biomedical MRI, Brain, Brain modeling, Brain Neoplasms, Brain tumor, brain tumor segmentation, cancer, clinical practice, CNN-based segmentation methods, Context, convolutional neural networks, data augmentation, Deep learning, Dice similarity coefficient metrics, glioma, gliomas, Humans, Image Interpretation, Computer-Assisted, Image segmentation, imaging technique, intensity normalization, Kernel, kernels, Machine learning, magnetic resonance imaging, manual segmentation, medical image processing, MRI images, Neural Networks (Computer), neurophysiology, oncological patients, online evaluation platform, on-site BRATS 2015 Challenge, precise quantitative measurements, preprocessing step, quality-of-life, reliable segmentation methods, spatial variability, structural variability, Training, Tumors, tumours},
	pages = {1240--1251},
	file = {IEEE Xplore Abstract Record:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/NMDQGSXE/7426413.html:text/html;IEEE Xplore Full Text PDF:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/XFZVI2KH/Pereira et al. - 2016 - Brain Tumor Segmentation Using Convolutional Neura.pdf:application/pdf}
}

@article{zou_3d_2017,
	title = {3D {CNN} {Based} {Automatic} {Diagnosis} of {Attention} {Deficit} {Hyperactivity} {Disorder} {Using} {Functional} and {Structural} {MRI}},
	volume = {5},
	issn = {2169-3536},
	doi = {10.1109/ACCESS.2017.2762703},
	abstract = {Attention deficit hyperactivity disorder (ADHD) is one of the most common mental health disorders. As a neuro development disorder, neuroimaging technologies, such as magnetic resonance imaging (MRI), coupled with machine learning algorithms, are being increasingly explored as biomarkers in ADHD. Among various machine learning methods, deep learning has demonstrated excellent performance on many imaging tasks. With the availability of publically-available, large neuroimaging data sets for training purposes, deep learning-based automatic diagnosis of psychiatric disorders can become feasible. In this paper, we develop a deep learning-based ADHD classification method via 3-D convolutional neural networks (CNNs) applied to MRI scans. Since deep neural networks may utilize millions of parameters, even the large number of MRI samples in pooled data sets is still relatively limited if one is to learn discriminative features from the raw data. Instead, here we propose to first extract meaningful 3-D low-level features from functional MRI (fMRI) and structural MRI (sMRI) data. Furthermore, inspired by radiologists' typical approach for examining brain images, we design a 3-D CNN model to investigate the local spatial patterns of MRI features. Finally, we discover that brain functional and structural information are complementary, and design a multi-modality CNN architecture to combine fMRI and sMRI features. Evaluations on the hold-out testing data of the ADHD-200 global competition shows that the proposed multi-modality 3-D CNN approach achieves the state-of-the-art accuracy of 69.15\% and outperforms reported classifiers in the literature, even with fewer training samples. We suggest that multi-modality classification will be a promising direction to find potential neuroimaging biomarkers of neuro development disorders.},
	journal = {IEEE Access},
	author = {Zou, L. and Zheng, J. and Miao, C. and Mckeown, M. J. and Wang, Z. J.},
	year = {2017},
	keywords = {3D CNN, 3D CNN based automatic diagnosis, 3D CNN model, 3D convolutional neural networks, 3D low-level features, ADHD-200 global competition, Attention deficit hyperactive disorder, Biological neural networks, biomedical MRI, Brain, brain functional, brain images, common mental health disorders, deep learning-based ADHD classification method, deep neural networks, diseases, feature extraction, fMRI features, Functional MRI, hyperactivity disorder, image classification, learning (artificial intelligence), machine learning algorithms, magnetic resonance imaging, medical disorders, medical image processing, MRI features, MRI samples, multimodality 3D CNN approach, multi-modality analysis, multimodality CNN architecture, neural nets, neuro development disorder, neuroimaging, neuroimaging biomarkers, neuroimaging data sets, neuroimaging technologies, neurophysiology, pooled data sets, psychiatric disorders, sMRI features, structural MRI, Testing, Three-dimensional displays, Training},
	pages = {23626--23636},
	file = {IEEE Xplore Abstract Record:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/X2VEV22Z/8067637.html:text/html;IEEE Xplore Full Text PDF:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/7R7BGHAD/Zou et al. - 2017 - 3D CNN Based Automatic Diagnosis of Attention Defi.pdf:application/pdf}
}

@article{payan_predicting_2015,
	title = {Predicting {Alzheimer}'s disease: a neuroimaging study with 3D convolutional neural networks},
	shorttitle = {Predicting {Alzheimer}'s disease},
	url = {http://arxiv.org/abs/1502.02506},
	abstract = {Pattern recognition methods using neuroimaging data for the diagnosis of Alzheimer's disease have been the subject of extensive research in recent years. In this paper, we use deep learning methods, and in particular sparse autoencoders and 3D convolutional neural networks, to build an algorithm that can predict the disease status of a patient, based on an MRI scan of the brain. We report on experiments using the ADNI data set involving 2,265 historical scans. We demonstrate that 3D convolutional neural networks outperform several other classifiers reported in the literature and produce state-of-art results.},
	journal = {arXiv:1502.02506 [cs, stat]},
	author = {Payan, Adrien and Montana, Giovanni},
	month = feb,
	year = {2015},
	note = {arXiv: 1502.02506},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Applications, Statistics - Machine Learning},
	file = {arXiv\:1502.02506 PDF:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/KSKMPV42/Payan and Montana - 2015 - Predicting Alzheimer's disease a neuroimaging stu.pdf:application/pdf;arXiv.org Snapshot:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/UB7KTAZQ/1502.html:text/html}
}

@article{kalaria_alzheimers_2008,
	title = {Alzheimer's disease and vascular dementia in developing countries: prevalence, management, and risk factors},
	volume = {7},
	issn = {1474-4422},
	shorttitle = {Alzheimer's disease and vascular dementia in developing countries},
	url = {http://www.sciencedirect.com/science/article/pii/S1474442208701698},
	doi = {10.1016/S1474-4422(08)70169-8},
	abstract = {Summary
Despite mortality due to communicable diseases, poverty, and human conflicts, dementia incidence is destined to increase in the developing world in tandem with the ageing population. Current data from developing countries suggest that age-adjusted dementia prevalence estimates in 65 year olds are high (≥5\%) in certain Asian and Latin American countries, but consistently low (1–3\%) in India and sub-Saharan Africa; Alzheimer's disease accounts for 60\% whereas vascular dementia accounts for ∼30\% of the prevalence. Early-onset familial forms of dementia with single-gene defects occur in Latin America, Asia, and Africa. Illiteracy remains a risk factor for dementia. The APOE ɛ4 allele does not influence dementia progression in sub-Saharan Africans. Vascular factors, such as hypertension and type 2 diabetes, are likely to increase the burden of dementia. Use of traditional diets and medicinal plant extracts might aid prevention and treatment. Dementia costs in developing countries are estimated to be US\$73 billion yearly, but care demands social protection, which seems scarce in these regions.},
	number = {9},
	journal = {The Lancet Neurology},
	author = {Kalaria, Raj N and Maestre, Gladys E and Arizaga, Raul and Friedland, Robert P and Galasko, Doug and Hall, Kathleen and Luchsinger, José A and Ogunniyi, Adesola and Perry, Elaine K and Potocnik, Felix and Prince, Martin and Stewart, Robert and Wimo, Anders and Zhang, Zhen-Xin and Antuono, Piero},
	month = sep,
	year = {2008},
	pages = {812--826},
	file = {ScienceDirect Full Text PDF:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/TZ5MFZGB/Kalaria et al. - 2008 - Alzheimer's disease and vascular dementia in devel.pdf:application/pdf;ScienceDirect Snapshot:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/C6GS2IJV/S1474442208701698.html:text/html}
}

@article{mckhann_diagnosis_2011,
	title = {The diagnosis of dementia due to {Alzheimer}’s disease: {Recommendations} from the {National} {Institute} on {Aging}-{Alzheimer}’s {Association} workgroups on diagnostic guidelines for {Alzheimer}'s disease},
	volume = {7},
	issn = {1552-5260},
	shorttitle = {The diagnosis of dementia due to {Alzheimer}’s disease},
	url = {http://www.sciencedirect.com/science/article/pii/S1552526011001014},
	doi = {10.1016/j.jalz.2011.03.005},
	abstract = {The National Institute on Aging and the Alzheimer’s Association charged a workgroup with the task of revising the 1984 criteria for Alzheimer’s disease (AD) dementia. The workgroup sought to ensure that the revised criteria would be flexible enough to be used by both general healthcare providers without access to neuropsychological testing, advanced imaging, and cerebrospinal fluid measures, and specialized investigators involved in research or in clinical trial studies who would have these tools available. We present criteria for all-cause dementia and for AD dementia. We retained the general framework of probable AD dementia from the 1984 criteria. On the basis of the past 27 years of experience, we made several changes in the clinical criteria for the diagnosis. We also retained the term possible AD dementia, but redefined it in a manner more focused than before. Biomarker evidence was also integrated into the diagnostic formulations for probable and possible AD dementia for use in research settings. The core clinical criteria for AD dementia will continue to be the cornerstone of the diagnosis in clinical practice, but biomarker evidence is expected to enhance the pathophysiological specificity of the diagnosis of AD dementia. Much work lies ahead for validating the biomarker diagnosis of AD dementia.},
	number = {3},
	journal = {Alzheimer's \& Dementia},
	author = {McKhann, Guy M. and Knopman, David S. and Chertkow, Howard and Hyman, Bradley T. and Jack, Clifford R. and Kawas, Claudia H. and Klunk, William E. and Koroshetz, Walter J. and Manly, Jennifer J. and Mayeux, Richard and Mohs, Richard C. and Morris, John C. and Rossor, Martin N. and Scheltens, Philip and Carrillo, Maria C. and Thies, Bill and Weintraub, Sandra and Phelps, Creighton H.},
	month = may,
	year = {2011},
	keywords = {Alzheimer's disease, Cerebrospinal fluid, Dementia, Diagnosis, Magnetic resonance brain imaging, Positron emission tomography},
	pages = {263--269},
	file = {ScienceDirect Full Text PDF:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/K92FG37S/McKhann et al. - 2011 - The diagnosis of dementia due to Alzheimer’s disea.pdf:application/pdf;ScienceDirect Snapshot:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/7F4K98QN/S1552526011001014.html:text/html}
}

@article{sudlow_uk_2015,
	title = {{UK} {Biobank}: {An} {Open} {Access} {Resource} for {Identifying} the {Causes} of a {Wide} {Range} of {Complex} {Diseases} of {Middle} and {Old} {Age}},
	volume = {12},
	issn = {1549-1676},
	shorttitle = {{UK} {Biobank}},
	url = {https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001779},
	doi = {10.1371/journal.pmed.1001779},
	abstract = {Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.},
	language = {en},
	number = {3},
	urldate = {2019-08-10},
	journal = {PLOS Medicine},
	author = {Sudlow, Cathie and Gallacher, John and Allen, Naomi and Beral, Valerie and Burton, Paul and Danesh, John and Downey, Paul and Elliott, Paul and Green, Jane and Landray, Martin and Liu, Bette and Matthews, Paul and Ong, Giok and Pell, Jill and Silman, Alan and Young, Alan and Sprosen, Tim and Peakman, Tim and Collins, Rory},
	month = mar,
	year = {2015},
	keywords = {Cohort studies, Global health, Health services research, Intelligence tests, magnetic resonance imaging, Open access publishing, Prospective studies, Questionnaires},
	pages = {e1001779},
	file = {Full Text PDF:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/ZIMRG24T/Sudlow et al. - 2015 - UK Biobank An Open Access Resource for Identifyin.pdf:application/pdf;Snapshot:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/I3TFQ6JN/article.html:text/html}
}

@inproceedings{li_brain_2018,
	title = {Brain age prediction based on resting-state functional connectivity patterns using convolutional neural networks},
	doi = {10.1109/ISBI.2018.8363532},
	abstract = {Brain age prediction based on neuroimaging data could help characterize both the typical brain development and neuropsychiatric disorders. Pattern recognition models built upon functional connectivity (FC) measures derived from resting state fMRI (rsfMRI) data have been successfully used to predict the brain age. However, most existing studies focus on coarse-grained FC measures between brain regions or intrinsic connectivity networks (ICNs), which may sacrifice fine-grained FC information of the rsfMRI data. Whole brain voxel-wise FC measures could provide fine-grained FC information of the brain and may improve the prediction performance. In this study, we develop a deep learning method to use convolutional neural networks (CNNs) to learn informative features from the fine-grained whole brain FC measures for the brain age prediction. Experimental results on a large dataset of resting-state fMRI demonstrate that the deep learning model with fine-grained FC measures could better predict the brain age.},
	booktitle = {2018 {IEEE} 15th {International} {Symposium} on {Biomedical} {Imaging} ({ISBI} 2018)},
	author = {Li, H. and Satterthwaite, T. D. and Fan, Y.},
	month = apr,
	year = {2018},
	keywords = {age, Atmospheric measurements, Biomedical measurement, biomedical MRI, Brain, brain age, Brain modeling, Brain regions, brain voxel-wise FC measures, coarse-grained FC measures, convolutional neural networks, fine-grained FC information, fine-grained FC measures, functional connectivity patterns, Functional magnetic resonance imaging, intrinsic connectivity networks, learning (artificial intelligence), Machine learning, medical image processing, neural nets, neurophysiology, Particle measurements, Pattern recognition, pattern recognition models, Predictive models, resting-state fMRI, resting-state functional connectivity patterns, state fMRI data},
	pages = {101--104},
	file = {IEEE Xplore Abstract Record:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/CUZH9IWJ/8363532.html:text/html;IEEE Xplore Full Text PDF:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/GFQESRTT/Li et al. - 2018 - Brain age prediction based on resting-state functi.pdf:application/pdf}
}

@article{wang_mri-based_2011,
	title = {{MRI}-based age prediction using hidden {Markov} models},
	volume = {199},
	issn = {0165-0270},
	url = {http://www.sciencedirect.com/science/article/pii/S0165027011002263},
	doi = {10.1016/j.jneumeth.2011.04.022},
	abstract = {Cortical thinning and intracortical gray matter volume losses are widely observed in normal ageing, while the decreasing rate of the volume loss in subjects with neurodegenerative disorders such as Alzheimer's disease is reported to be faster than the average speed. Therefore, neurodegenerative disease is considered as accelerated ageing. Accurate detection of accelerated ageing based on the magnetic resonance imaging (MRI) of the brain is a relatively new direction of research in computational neuroscience as it has the potential to offer positive clinical outcome through early intervention. In order to capture the faster structural alterations in the brain with ageing, we propose in this paper a computational approach for modelling the MRI-based structure of the brain using the framework of hidden Markov models, which can be utilized for age prediction. Experiments were carried out on healthy subjects to validate its accuracy and its robustness. The results have shown its ability of predicting the brain age with an average normalized age-gap error of two to three years, which is superior to several recently developed methods for brain age prediction.},
	number = {1},
	journal = {Journal of Neuroscience Methods},
	author = {Wang, Bing and Pham, Tuan D.},
	month = jul,
	year = {2011},
	keywords = {age prediction, Hidden Markov models, Kullback–Leibler divergence, MRI, Vector quantization, Wavelet transforms},
	pages = {140--145},
	file = {ScienceDirect Full Text PDF:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/2NMF63R2/Wang and Pham - 2011 - MRI-based age prediction using hidden Markov model.pdf:application/pdf;ScienceDirect Snapshot:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/UJUBII2X/S0165027011002263.html:text/html}
}

@article{valizadeh_age_2017,
	title = {Age prediction on the basis of brain anatomical measures},
	volume = {38},
	copyright = {© 2016 Wiley Periodicals, Inc.},
	issn = {1097-0193},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.23434},
	doi = {10.1002/hbm.23434},
	abstract = {In this study, we examined whether age can be predicted on the basis of different anatomical features obtained from a large sample of healthy subjects (n = 3,144). From this sample we obtained different anatomical feature sets: (1) 11 larger brain regions (including cortical volume, thickness, area, subcortical volume, cerebellar volume, etc.), (2) 148 cortical compartmental thickness measures, (3) 148 cortical compartmental area measures, (4) 148 cortical compartmental volume measures, and (5) a combination of the above-mentioned measures. With these anatomical feature sets, we predicted age using 6 statistical techniques (multiple linear regression, ridge regression, neural network, k-nearest neighbourhood, support vector machine, and random forest). We obtained very good age prediction accuracies, with the highest accuracy being R2 = 0.84 (prediction on the basis of a neural network and support vector machine approaches for the entire data set) and the lowest being R2 = 0.40 (prediction on the basis of a k-nearest neighborhood for cortical surface measures). Interestingly, the easy-to-calculate multiple linear regression approach with the 11 large brain compartments resulted in a very good prediction accuracy (R2 = 0.73), whereas the application of the neural network approach for this data set revealed very good age prediction accuracy (R2 = 0.83). Taken together, these results demonstrate that age can be predicted well on the basis of anatomical measures. The neural network approach turned out to be the approach with the best results. In addition, it was evident that good prediction accuracies can be achieved using a small but nevertheless age-representative dataset of brain features. Hum Brain Mapp 38:997–1008, 2017. © 2016 Wiley Periodicals, Inc.},
	language = {en},
	number = {2},
	journal = {Human Brain Mapping},
	author = {Valizadeh, S. A. and Hänggi, J. and Mérillat, S. and Jäncke, L.},
	year = {2017},
	keywords = {age, age prediction, Brain, brain anatomy, Classification, FreeSurfer, neural networks},
	pages = {997--1008},
	file = {Full Text PDF:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/DGXTUIN9/Valizadeh et al. - 2017 - Age prediction on the basis of brain anatomical me.pdf:application/pdf;Snapshot:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/QDJ88D4H/hbm.html:text/html}
}

@article{aycheh_biological_2018,
	title = {Biological {Brain} {Age} {Prediction} {Using} {Cortical} {Thickness} {Data}: {A} {Large} {Scale} {Cohort} {Study}},
	volume = {10},
	issn = {1663-4365},
	shorttitle = {Biological {Brain} {Age} {Prediction} {Using} {Cortical} {Thickness} {Data}},
	url = {https://www.frontiersin.org/articles/10.3389/fnagi.2018.00252/full},
	doi = {10.3389/fnagi.2018.00252},
	abstract = {Brain age estimation from anatomical features has been attracting more attention in recent years. This interest in brain age estimation is motivated by the importance of biological age prediction in health informatics, with an application to early prediction of neurocognitive disorders. It is well known that normal brain aging follows a specific pattern, which enables researchers and practitioners to predict the age of a human’s brain from its degeneration. In this paper, we model brain age predicted by cortical thickness data gathered from large cohort brain images. We collected 2,911 cognitively normal subjects (age 45-91 years) at a single medical center and acquired their brain magnetic resonance (MR) images. All images were acquired using the same scanner with the same protocol. We propose to first apply Sparse Group Lasso (SGL) for feature selection by utilizing the brain’s anatomical grouping. Once the features are selected, a non-parametric non-linear regression using the Gaussian Process Regression (GPR) algorithm is applied to fit the final age prediction model. Experimental results demonstrate that the proposed method achieves the mean absolute error of 4.05 years, which is comparable with or superior to several recent methods. Our method can also be a critical tool for clinicians to differentiate patients with neurodegenerative brain disease by extracting a cortical thinning pattern associated with normal aging.},
	language = {English},
	urldate = {2019-08-10},
	journal = {Frontiers in Aging Neuroscience},
	author = {Aycheh, Habtamu M. and Seong, Joon-Kyung and Shin, Jeong-Hyeon and Na, Duk L. and Kang, Byungkon and Seo, Sang W. and Sohn, Kyung-Ah},
	year = {2018},
	keywords = {Aging, cortical lobe, cortical thickness, gaussian processes, Regression Analysis, ROI, Sparse group LASSO},
	file = {Full Text PDF:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/IC7WH9G4/Aycheh et al. - 2018 - Biological Brain Age Prediction Using Cortical Thi.pdf:application/pdf}
}

@article{smyser_prediction_2016,
	title = {Prediction of brain maturity in infants using machine-learning algorithms},
	volume = {136},
	issn = {1053-8119},
	url = {http://www.sciencedirect.com/science/article/pii/S1053811916301483},
	doi = {10.1016/j.neuroimage.2016.05.029},
	abstract = {Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prematurely-born infants. Application of new analytical approaches can help translate the improved understanding of early functional connectivity provided through these studies into predictive models of neurodevelopmental outcome. One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data. It has previously been adapted to predict brain maturity in children and adolescents using structural and resting state-functional MRI data. In this study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23–29weeks of gestation and without moderate–severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84\% accuracy (p{\textless}0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brain's functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants.},
	journal = {NeuroImage},
	author = {Smyser, Christopher D. and Dosenbach, Nico U. F. and Smyser, Tara A. and Snyder, Abraham Z. and Rogers, Cynthia E. and Inder, Terrie E. and Schlaggar, Bradley L. and Neil, Jeffrey J.},
	month = aug,
	year = {2016},
	keywords = {Developmental neuroimaging, Functional MRI, Infant, Multivariate pattern analysis, Prematurity},
	pages = {1--9},
	file = {ScienceDirect Full Text PDF:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/F4RPPSBJ/Smyser et al. - 2016 - Prediction of brain maturity in infants using mach.pdf:application/pdf;ScienceDirect Snapshot:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/7H9H9N3H/S1053811916301483.html:text/html}
}

@article{cole_prediction_2015,
	title = {Prediction of brain age suggests accelerated atrophy after traumatic brain injury},
	volume = {77},
	copyright = {© 2015 The Authors Annals of Neurology published by Wiley Periodicals, Inc. on behalf of American Neurological Association.},
	issn = {1531-8249},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ana.24367},
	doi = {10.1002/ana.24367},
	abstract = {Objective The long-term effects of traumatic brain injury (TBI) can resemble observed in normal ageing, suggesting that TBI may accelerate the ageing process. We investigate this using a neuroimaging model that predicts brain age in healthy individuals and then apply it to TBI patients. We define individuals' differences in chronological and predicted structural 'brain age,' and test whether TBI produces progressive atrophy and how this relates to cognitive function. Methods A predictive model of normal ageing was defined using machine learning in 1,537 healthy individuals, based on magnetic resonance imaging–derived estimates of gray matter (GM) and white matter (WM). This ageing model was then applied to test 99 TBI patients and 113 healthy controls to estimate brain age. Results The initial model accurately predicted age in healthy individuals (r = 0.92). TBI brains were estimated to be 'older,' with a mean predicted age difference (PAD) between chronological and estimated brain age of 4.66 years (±10.8) for GM and 5.97 years (±11.22) for WM. This PAD predicted cognitive impairment and correlated strongly with the time since TBI, indicating that brain tissue loss increases throughout the chronic postinjury phase. Interpretation TBI patients' brains were estimated to be older than their chronological age. This discrepancy increases with time since injury, suggesting that TBI accelerates the rate of brain atrophy. This may be an important factor in the increased susceptibility in TBI patients for dementia and other age-associated conditions, motivating further research into the age-like effects of brain injury and other neurological diseases. Ann Neurol 2015;77:571–581},
	language = {en},
	number = {4},
	journal = {Annals of Neurology},
	author = {Cole, James H. and Leech, Robert and Sharp, David J.},
	year = {2015},
	pages = {571--581},
	file = {Full Text PDF:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/7TNX75HI/Cole et al. - 2015 - Prediction of brain age suggests accelerated atrop.pdf:application/pdf;Snapshot:/home/forrest/.zotero/zotero/b3mz49ex.default/zotero/storage/6DV98CGG/ana.html:text/html}
}