Quantitative Machine Learning Analysis of Brain MRI Morphology throughout Aging

Shamir, Lior; Long, Joe

doi:10.2174/1874609809666160413113711

Cited by 4 publications

(5 citation statements)

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“…Before the implementation of the program none of the undergraduate students submitted papers to peer-reviewed journals or conferences. After the first year of the program two papers were published [47,42], three papers in 2013 [36,13,39], seven in 2014 [14,43,17,11,26,46,21], six in 2015 [8,24,35,22,15,27], and seven in 2016 [23,38,41,45,40,50,10].…”

Section: Resultsmentioning

confidence: 99%

“…Since different students have different topics of interest, supporting the interest of the students requires a broad range of research topics on which the students can perform their studies, and new topics and disciplines are added every year based on the student request. The first interdisciplinary program was bioinformatics and medical informatics [36,26,24,41], and based on the request of students was enhanced with astroinformatics [17,11,21,35,22,23]. Other programs that followed were Zoology [47,43], art [42], music [14,15,13], literature [39], sports [46,45,50], and human aspects of computing [8,38].…”

Section: Resultsmentioning

confidence: 99%

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Authentic computer science undergraduate research experience through computational science and research ownership

Shamir¹

2017

JOCSE

Self Cite

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Research experience has been identified as a high-impact intervention for increasing student engagement and retention in STEM. However, authentic undergraduate research leading to primary authorship peer-reviewed publications is a challenge due to the relatively short time the students work on their capstone projects, and the insufficient preparation of the students as researchers. The challenge is further magnified in the field of computer science, where the absence of "traditional" labs limits the opportunities of undergraduate students to participate in research. Here we present a novel approach to authentic computer science undergraduate research, based on interdisciplinary computational science and student ownership of their research projects. Instead of the traditional role of undergraduate research assistant, the students select their own research topic based on their personal interests, and with the assistance of a faculty complete all stages of their research project. The uniqueness of the approach is its ability to lead to scientific discoveries and peer-reviewed publications such that the primary author is the student, while allowing the student to experience the entire research process, from defining the research question through analysis of the experimental results. In three years the model led to a dramatic increase in the number of undergraduate students who publish primary-author peer-reviewed scientific papers. The intervention increased the number of peer-reviewed student-authored publications from none to a very high rate of about one third of the students, in many cases publishing in the top outlets in their field.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Authentic computer science undergraduate research experience through computational science and research ownership

Shamir¹

2017

JOCSE

Self Cite

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“…The concept of brain age was proposed with the knowledge that structures and functions of the brain change with the ageing process. Neural ageing is a significant part of human ageing, and neuroimaging, such as magnetic resonance imaging (MRI) and Positron Emission Tomography (PET), provides a unique window to identify neural ageing 182 . For instance, structural MRI showed various age‐related brain alterations including gray matter (GM) volume, GM concentration, connectivity of white matter, cortical thickness, cerebrospinal fluid increment, and so forth 183–185 …”

Section: Image‐based Ageing Biomarkersmentioning

confidence: 99%

“…Neural ageing is a significant part of human ageing, and neuroimaging, such as magnetic resonance imaging (MRI) and Positron Emission Tomography (PET), provides a unique window to identify neural ageing. 182 For instance, structural MRI showed various age-related brain alterations including gray matter (GM) volume, GM concentration, connectivity of white matter, cortical thickness, cerebrospinal fluid increment, and so forth. [183][184][185] Brain age was first built upon structural MRI scans where features such as the gray matter volume, cortical thickness were extracted to predict chronological age.…”

Section: Brain Agementioning

confidence: 99%

Biomarkers of ageing: Current state‐of‐art, challenges, and opportunities

Chen

Wang

Zhang

et al. 2023

MedComm – Future Medicine

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Given the unprecedented phenomenon of population ageing, studies have increasing captured the heterogeneity within the ageing process. In this context, the concept of "biological age" has been introduced as an integrated measure reflecting the individualized ageing pace. Identifying reliable and robust biomarkers of age is critical for the accurate risk stratification of individuals and exploration into antiageing interventions. Numerous potential biomarkers of ageing have been proposed, spanning from molecular changes and imaging characteristics to clinical phenotypes. In this review, we will start off with a discussion of the development of ageing biomarkers, then we will provide a comprehensive summary of currently identified ageing biomarkers in humans, discuss the rationale behind each biomarker and highlight their accuracy and clinical value with a contemporary perspective. Additionally, we MedComm -Future Medicine.

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“…Several attempts were previously made to identify the relation between chronological age and brain structure, using various feature extraction techniques, advanced preprocessing methods and a relatively limited sample size (Irimia, Torgerson, Goh, & Van Horn, 2015;Kandel, Wolk, Gee, & Avants, 2013;Shamir & Long, 2016). Here, we build upon recent progress in utilizing CNNs for predicting chronological age from raw structural brain imaging (Cole & Franke, 2017) and introduce substantial improvements using an ensemble of models.…”

Section: Estimating "Brain Age"mentioning

confidence: 99%

From a deep learning model back to the brain - inferring morphological markers and their relation to aging

Levakov

Rosenthal

Shelef

et al. 2019

Preprint

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Deep convolutional neural networks (CNN) enabled a major leap in image processing tasks including brain imaging analysis. In this work, we present a Deep Learning framework for the prediction of chronological age from structural MRI scans of healthy subjects. Previous findings associate an overestimation of brain age with neurodegenerative disease and higher mortality rates. However, the importance of brain age prediction and its discrepancy from the corresponding chronological age go beyond serving as biomarkers for neurological disorders. Specifically, utilizing CNN analysis to identify and locate brain regions and structures that contribute to the prediction can shed light on the complex multivariate process of brain aging. Previous work examined methods to attribute pixel/voxel-wise contribution to the prediction in a single image, resulting in 'explanation maps' (EM) that were found noisy and unreliable. To address this problem, we developed a novel inference framework for combining these maps across subjects, thus creating a population-based rather than subject-specific map. We apply this method to a CNN ensemble trained on predicting subjects' chronological age from raw anatomical T1 brain images of 10,176 healthy subjects, obtained from various open-source datasets. Evaluating the model on an untouched test set (n = 588) resulted in MAE of 3.07 years and a correlation between the chronological and predicted age of r=0.98. Using the inference method, we revealed that cavities containing CSF, previously found as general atrophy markers, had the highest contribution for age prediction in our model. These were followed by subcortical GM, WM, and finally cortical GM. Comparing these maps derived from different models within the ensemble allowed to assess differences and similarities in the brain regions utilized by the model. To validate our method, we showed that it substantially increases the replicability of the EM as a function of sample size. Moreover, benchmarking our results against a baseline of voxel-based morphometry (VBM) studies revealed a significant overlap. Finally, we demonstrate that the maps highlight brain regions whose volumetric variability contributed the most to the model prediction. Highlights:• CNNs ensemble is shown to estimate "brain age" from sMRI with an MAE of ~3.1 years • A novel framework enables to highlight brain regions contributing to the prediction • This framework results in explanation maps showing consistency with the literature • As sample size increases, these maps show higher inter-sample replicability • CSF cavities reflecting general atrophy were found as a prominent aging biomarker

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Quantitative Machine Learning Analysis of Brain MRI Morphology throughout Aging

Cited by 4 publications

References 0 publications

Authentic computer science undergraduate research experience through computational science and research ownership

Authentic computer science undergraduate research experience through computational science and research ownership

Biomarkers of ageing: Current state‐of‐art, challenges, and opportunities

From a deep learning model back to the brain - inferring morphological markers and their relation to aging

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