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Добірка наукової літератури з теми "Brain aging, MRI, machine learning"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Brain aging, MRI, machine learning"

Shamir, Lior, and Joe Long. "Quantitative Machine Learning Analysis of Brain MRI Morphology throughout Aging." Current Aging Science 9, no. 4 (October 14, 2016): 310–17. http://dx.doi.org/10.2174/1874609809666160413113711.

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Varanasi, Sravani, Roopan Tuli, Fei Han, Rong Chen, and Fow-Sen Choa. "Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques." Sensors 23, no. 3 (February 1, 2023): 1603. http://dx.doi.org/10.3390/s23031603.

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The study of brain connectivity plays an important role in understanding the functional organizations of the brain. It also helps to identify connectivity signatures that can be used for evaluating neural disorders and monitoring treatment efficacy. In this work, age-related changes in brain connectivity are studied to obtain aging signatures based on various modeling techniques. These include an energy-based machine learning technique to identify brain network interaction differences between two age groups with a large (30 years) age gap between them. Disconnectivity graphs and activation maps of the seven prominent resting-state networks (RSN) were obtained from functional MRI data of old and young adult subjects. Two-sample t-tests were performed on the local minimums with Bonferroni correction to control the family-wise error rate. These local minimums are connectivity states showing not only which brain regions but also how strong they are working together. They work as aging signatures that can be used to differentiate young and old groups. We found that the attention network’s connectivity signature is a state with all the regions working together and young subjects have a stronger average connectivity among these regions. We have also found a common pattern between young and old subjects where the left and right brain regions of the frontal network are sometimes working separately instead of together. In summary, in this work, we combined machine learning and statistical approaches to extract connectivity signatures, which can be utilized to distinguish aging brains and monitor possible treatment efficacy.

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Lee, Won Hee. "The Choice of Machine Learning Algorithms Impacts the Association between Brain-Predicted Age Difference and Cognitive Function." Mathematics 11, no. 5 (March 2, 2023): 1229. http://dx.doi.org/10.3390/math11051229.

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Machine learning has been increasingly applied to neuroimaging data to compute personalized estimates of the biological age of an individual’s brain (brain age). The difference between an individual’s brain-predicted age and their chronological age (brainPAD) is used as a biomarker of brain aging and disease, but the potential contribution of different machine learning algorithms used for brain age prediction to the association between brainPAD and cognitive function has not been investigated yet. Here, we applied seven commonly used algorithms to the same multimodal brain imaging data (structural and diffusion MRI) from 601 healthy participants aged 18–88 years in the Cambridge Centre for Ageing and Neuroscience to assess variations in brain-predicted age. The inter-algorithm similarity in brain-predicted age and brain regional regression weights was examined using the Pearson’s correlation analyses and hierarchical clustering. We then assessed to what extent machine learning algorithms impact the association between brainPAD and seven cognitive variables. The regression models achieved mean absolute errors of 5.46–7.72 years and Pearson’s correlation coefficients of 0.86–0.92 between predicted brain age and chronological age. Furthermore, we identified a substantial difference in linking brainPAD to cognitive measures, indicating that the choice of algorithm could be an important source of variability that confounds the relationship between brainPAD and cognition.

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Gómez-Ramírez, Jaime, Miguel A. Fernández-Blázquez, and Javier J. González-Rosa. "Prediction of Chronological Age in Healthy Elderly Subjects with Machine Learning from MRI Brain Segmentation and Cortical Parcellation." Brain Sciences 12, no. 5 (April 29, 2022): 579. http://dx.doi.org/10.3390/brainsci12050579.

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Normal aging is associated with changes in volumetric indices of brain atrophy. A quantitative understanding of age-related brain changes can shed light on successful aging. To investigate the effect of age on global and regional brain volumes and cortical thickness, 3514 magnetic resonance imaging scans were analyzed using automated brain segmentation and parcellation methods in elderly healthy individuals (69–88 years of age). The machine learning algorithm extreme gradient boosting (XGBoost) achieved a mean absolute error of 2 years in predicting the age of new subjects. Feature importance analysis showed that the brain-to-intracranial-volume ratio is the most important feature in predicting age, followed by the hippocampi volumes. The cortical thickness in temporal and parietal lobes showed a superior predictive value than frontal and occipital lobes. Insights from this approach that integrate model prediction and interpretation may help to shorten the current explanatory gap between chronological age and biological brain age.

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Knight, S., R. Boyle, L. Newman, J. Davis, R. Rizzo, E. Duggan, C. De Looze, R. Whelan, R. A. Kenny, and R. Romero-Ortuno. "78 HIGHER NEUROVASCULAR SIGNAL ENTROPY IS ASSOCIATED WITH ACCELERATED BRAIN AGEING." Age and Ageing 50, Supplement_3 (November 2021): ii9—ii41. http://dx.doi.org/10.1093/ageing/afab219.78.

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Abstract Background Often chronological age is not the most accurate marker of an individual’s health status since ageing is a heterogeneous process across individuals. Machine learning can be used to quantify the relationship between structural brain MRI data and chronological age, to estimate an individual’s ‘brain age’, which, when subtracted from chronological age, provides a brain predicted-age difference score (BrainPAD) [1]. BrainPAD reflects the biological ageing of the brain. Increased complexity in neurovascular signals has been shown to be associated with poorer cognitive performance and physical frailty [2]. The aim of this study was to investigate associations between the complexity of frontal-lobe oxygenation (tissue saturation index (TSI)) data and BrainPAD in a cohort of older community-dwelling adults. Methods To calculate BrainPAD, machine learning was applied to 1,359 T1-weighted MRI brain scans from various open-access repositories, and this model was subsequently applied to MRI data acquired from the study cohort. TSI was non-invasively measured in the left frontal lobe using near-infrared spectroscopy. TSI data were acquired continuously during five minutes of supine rest and the last minute was utilized in this analysis. The complexity of TSI signals was quantified using sample entropy (SampEn). Multivariable linear regression was employed, controlling for age, sex, education, antihypertensive medications, diabetes, cardiovascular conditions, smoking, alcohol, depression, BMI, physical activity, and blood pressure. Results Complete data were available for 397 individuals (age: 67.9 ± 7.7 years; 53.7% female). An increase in TSI SampEn of 0.1 was associated with an increase in BrainPAD of 0.9 years (P = 0.007, 95%CIs: 0.3 to 1.6). Similar results were found with and without the inclusion of chronological age in the models. Conclusion This study reports significant associations between higher complexity in peripherally measured frontal lobe oxygenation concentration and accelerated brain ageing. References 1. Boyle R. et al. Brain Imaging and Behavior. 15,327–345 (2021) https://doi.org/10.1007/s11682-020-00260-3. 2. Knight S. et al. Entropy. 23(1):4 (2021) https://doi.org/10.3390/e23010004.

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Madole, James, James W. Madole, Simon R. Cox, Colin R. Buchanan, Stuart J. Ritchie, Mark E. Bastin, Ian J. Deary, and Elliot M. Tucker-Drob. "PREDICTING TRANSDIAGNOSTIC PSYCHOPATHOLOGY FROM INDICES OF AGING IN THE HUMAN STRUCTURAL CONNECTOME." Innovation in Aging 3, Supplement_1 (November 2019): S348. http://dx.doi.org/10.1093/geroni/igz038.1261.

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Abstract Imaging-derived indices of brain structure and white-matter connectivity evince steep declines with adult age and are robustly linked to neurological disease and a wide range of psychopathologies. Risk for psychopathology may be related to rapid structural brain aging, but the specific patterns of relations are not well documented. Using structural and diffusion MRI data from UK Biobank, we estimated a structural connectome for each participant (N = 3155), and used empirically-driven machine-learning algorithms to identify features of the connectome most susceptible to brain aging. In an age-homogenous hold-out sample of older adults, we score participants’ “connectome age” using the coefficients saved from the training sample. We examine associations between connectome age and both psychiatric symptom counts and polygenic risk scores for a range of psychiatric disease traits. This will be amongst the first and most comprehensive investigation of the extent to which psychopathology relates to signatures of structural connectome aging.

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Guo, Yingying, Xi Yang, Zilong Yuan, Jianfeng Qiu, and Weizhao Lu. "A comparison between diffusion tensor imaging and generalized q-sampling imaging in the age prediction of healthy adults via machine learning approaches." Journal of Neural Engineering 19, no. 1 (February 1, 2022): 016013. http://dx.doi.org/10.1088/1741-2552/ac4bfe.

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Abstract Objective. Brain age, which is predicted using neuroimaging data, has become an important biomarker in aging research. This study applied diffusion tensor imaging (DTI) and generalized q-sampling imaging (GQI) model to predict age respectively, with the purpose of evaluating which diffusion model is more accurate in estimating age and revealing age-related changes in the brain. Approach. Diffusion MRI data of 125 subjects from two sites were collected. Fractional anisotropy (FA) and quantitative anisotropy (QA) from the two diffusion models were calculated and were used as features of machine learning models. Sequential backward elimination algorithm was used for feature selection. Six machine learning approaches including linear regression, ridge regression, support vector regression (SVR) with linear kernel, quadratic kernel and radial basis function (RBF) kernel and feedforward neural network were used to predict age using FA and QA features respectively. Main results. Age predictions using FA features were more accurate than predictions using QA features for all the six machine learning algorithms. Post-hoc analysis revealed that FA was more sensitive to age-related white matter alterations in the brain. In addition, SVR with RBF kernel based on FA features achieved better performances than the competing algorithms with mean absolute error ranging from 7.74 to 10.54, mean square error (MSE) ranging from 87.79 to 150.86, and normalized MSE ranging from 0.05 to 0.14. Significance. FA from DTI model was more suitable than QA from GQI model in age prediction. FA metric was more sensitive to age-related white matter changes in the brain and FA of several brain regions could be used as white matter biomarkers in aging.

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Massetti, Noemi, Mirella Russo, Raffaella Franciotti, Davide Nardini, Giorgio Maria Mandolini, Alberto Granzotto, Manuela Bomba, et al. "A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer’s Disease Spectrum." Journal of Alzheimer's Disease 85, no. 4 (February 15, 2022): 1639–55. http://dx.doi.org/10.3233/jad-210573.

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Background: Alzheimer’s disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD. Objective: To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Alzheimer’s Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion. Methods: We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables. Results: The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects. Conclusion: Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.

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Cole, James H., Jonathan Underwood, Matthan W. A. Caan, Davide De Francesco, Rosan A. van Zoest, Robert Leech, Ferdinand W. N. M. Wit, et al. "Increased brain-predicted aging in treated HIV disease." Neurology 88, no. 14 (March 3, 2017): 1349–57. http://dx.doi.org/10.1212/wnl.0000000000003790.

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Objective:To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent brain age using neuroimaging and exploring whether these estimates related to HIV status, age, cognitive performance, and HIV-related clinical parameters.Methods:A large sample of virologically suppressed HIV-positive adults (n = 162, age 45–82 years) and highly comparable HIV-negative controls (n = 105) were recruited as part of the Comorbidity in Relation to AIDS (COBRA) collaboration. Using T1-weighted MRI scans, a machine-learning model of healthy brain aging was defined in an independent cohort (n = 2,001, aged 18–90 years). Neuroimaging data from HIV-positive and HIV-negative individuals were then used to estimate brain-predicted age; then brain-predicted age difference (brain-PAD = brain-predicted brain age − chronological age) scores were calculated. Neuropsychological and clinical assessments were also carried out.Results:HIV-positive individuals had greater brain-PAD score (mean ± SD 2.15 ± 7.79 years) compared to HIV-negative individuals (−0.87 ± 8.40 years; b = 3.48, p < 0.01). Increased brain-PAD score was associated with decreased performance in multiple cognitive domains (information processing speed, executive function, memory) and general cognitive performance across all participants. Brain-PAD score was not associated with age, duration of HIV infection, or other HIV-related measures.Conclusion:Increased apparent brain aging, predicted using neuroimaging, was observed in HIV-positive adults, despite effective viral suppression. Furthermore, the magnitude of increased apparent brain aging related to cognitive deficits. However, predicted brain age difference did not correlate with chronological age or duration of HIV infection, suggesting that HIV disease may accentuate rather than accelerate brain aging.

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Bashyam, Vishnu M., Guray Erus, Jimit Doshi, Mohamad Habes, Ilya M. Nasrallah, Monica Truelove-Hill, Dhivya Srinivasan, et al. "MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide." Brain 143, no. 7 (June 27, 2020): 2312–24. http://dx.doi.org/10.1093/brain/awaa160.

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Abstract 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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Більше джерел

Дисертації з теми "Brain aging, MRI, machine learning"

Mahbod, Amirreza. "Structural Brain MRI Segmentation Using Machine Learning Technique." Thesis, KTH, Skolan för teknik och hälsa (STH), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189985.

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Segmenting brain MR scans could be highly benecial for diagnosing, treating and evaluating the progress of specic diseases. Up to this point, manual segmentation,performed by experts, is the conventional method in hospitals and clinical environments. Although manual segmentation is accurate, it is time consuming, expensive and might not be reliable. Many non-automatic and semi automatic methods have been proposed in the literature in order to segment MR brain images, but the levelof accuracy is not comparable with manual segmentation. The aim of this project is to implement and make a preliminary evaluation of a method based on machine learning technique for segmenting gray matter (GM),white matter (WM) and cerebrospinal uid (CSF) of brain MR scans using images available within the open MICCAI grand challenge (MRBrainS13).The proposed method employs supervised articial neural network based autocontext algorithm, exploiting intensity-based, spatial-based and shape model-basedlevel set segmentation results as features of the network. The obtained average results based on Dice similarity index were 97.73%, 95.37%, 82.76%, 88.47% and 84.78% for intracranial volume, brain (WM + GM), CSF, WM and GM respectively. This method achieved competitive results with considerably shorter required training time in MRBrainsS13 challenge.

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Zarogianni, Eleni. "Machine learning and brain imaging in psychosis." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/22814.

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Over the past years early detection and intervention in schizophrenia have become a major objective in psychiatry. Early intervention strategies are intended to identify and treat psychosis prior to fulfilling diagnostic criteria for the disorder. To this aim, reliable early diagnostic biomarkers are needed in order to identify a high-risk state for psychosis and also predict transition to frank psychosis in those high-risk individuals destined to develop the disorder. Recently, machine learning methods have been successfully applied in the diagnostic classification of schizophrenia and in predicting transition to psychosis at an individual level based on magnetic resonance imaging (MRI) data and also neurocognitive variables. This work investigates the application of machine learning methods for the early identification of schizophrenia in subjects at high risk for developing the disorder. The dataset used in this work involves data from the Edinburgh High Risk Study (EHRS), which examined individuals at a heightened risk for developing schizophrenia for familial reasons, and the FePsy (Fruherkennung von Psychosen) study that was conducted in Basel and involves subjects at a clinical high-risk state for psychosis. The overriding aim of this thesis was to use machine learning, and specifically Support Vector Machine (SVM), in order to identify predictors of transition to psychosis in high-risk individuals, using baseline structural MRI data. There are three aims pertaining to this main one. (i) Firstly, our aim was to examine the feasibility of distinguishing at baseline those individuals who later developed schizophrenia from those who did not, yet had psychotic symptoms using SVM and baseline data from the EHRS study. (ii) Secondly, we intended to examine if our classification approach could generalize to clinical high-risk cohorts, using neuroanatomical data from the FePsy study. (iii) In a more exploratory context, we have also examined the diagnostic performance of our classifier by pooling the two datasets together. With regards to the first aim, our findings suggest that the early prediction of schizophrenia is feasible using a MRI-based linear SVM classifier operating at the single-subject level. Additionally, we have shown that the combination of baseline neuroanatomical data with measures of neurocognitive functioning and schizotypal cognition can improve predictive performance. The application of our pattern classification approach to baseline structural MRI data from the FePsy study highly replicated our previous findings. Our classification method identified spatially distributed networks that discriminate at baseline between subjects that later developed schizophrenia and other related psychoses and those that did not. Finally, a preliminary classification analysis using pooled datasets from the EHRS and the FePsy study supports the existence of a neuroanatomical pattern that differentiates between groups of high-risk subjects that develop psychosis against those who do not across research sites and despite any between-sites differences. Taken together, our findings suggest that machine learning is capable of distinguishing between cohorts of high risk subjects that later convert to psychosis and those that do not based on patterns of structural abnormalities that are present before disease onset. Our findings have some clinical implications in that machine learning-based approaches could advise or complement clinical decision-making in early intervention strategies in schizophrenia and related psychoses. Future work will be, however, required to tackle issues of reproducibility of early diagnostic biomarkers across research sites, where different assessment criteria and imaging equipment and protocols are used. In addition, future projects may also examine the diagnostic and prognostic value of multimodal neuroimaging data, possibly combined with other clinical, neurocognitive, genetic information.

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Abdulkadir, Ahmed [Verfasser], and Thomas [Akademischer Betreuer] Brox. "Brain MRI analysis and machine learning for diagnosis of neurodegeneration." Freiburg : Universität, 2018. http://d-nb.info/117696805X/34.

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Kim, Eun Young. "Machine-learning based automated segmentation tool development for large-scale multicenter MRI data analysis." Diss., University of Iowa, 2013. https://ir.uiowa.edu/etd/4998.

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Background: Volumetric analysis of brain structures from structural Mag- netic Resonance (MR) images advances the understanding of the brain by providing means to study brain morphometric changes quantitatively along aging, development, and disease status. Due to the recent increased emphasis on large-scale multicenter brain MR study design, the demand for an automated brain MRI processing tool has increased as well. This dissertation describes an automatic segmentation framework for subcortical structures of brain MRI that is robust for a wide variety of MR data. Method: The proposed segmentation framework, BRAINSCut, is an inte- gration of robust data standardization techniques and machine-learning approaches. First, a robust multi-modal pre-processing tool for automated registration, bias cor- rection, and tissue classification, has been implemented for large-scale heterogeneous multi-site longitudinal MR data analysis. The segmentation framework was then constructed to achieve robustness for large-scale data via the following comparative experiments: 1) Find the best machine-learning algorithm among several available approaches in the field. 2) Find an efficient intensity normalization technique for the proposed region-specific localized normalization with a choice of robust statistics. 3) Find high quality features that best characterize the MR brain subcortical structures. Our tool is built upon 32 handpicked multi-modal muticenter MR images with man- ual traces of six subcortical structures (nucleus accumben, caudate nucleus, globus pallidum, putamen, thalamus, and hippocampus) from three experts. A fundamental task associated with brain MR image segmentation for re- search and clinical trials is the validation of segmentation accuracy. This dissertation evaluated the proposed segmentation framework in terms of validity and reliability. Three groups of data were employed for the various evaluation aspects: 1) traveling human phantom data for the multicenter reliability, 2) a set of repeated scans for the measurement stability across various disease statuses, and 3) a large-scale data from Huntington's disease (HD) study for software robustness as well as segmentation accuracy. Result: Segmentation accuracy of six subcortical structures was improved with 1) the bias-corrected inputs, 2) the two region-specific intensity normalization strategies and 3) the random forest machine-learning algorithm with the selected feature-enhanced image. The analysis of traveling human phantom data showed no center-specific bias in volume measurements from BRAINSCut. The repeated mea- sure reliability of the most of structures also displayed no specific association to disease progression except for caudate nucleus from the group of high risk for HD. The constructed segmentation framework was successfully applied on multicenter MR data from PREDICT-HD [133] study ( < 10% failure rate over 3000 scan sessions pro- cessed). Conclusion: Random-forest based segmentation method is effective and robust to large-scale multicenter data variation, especially with a proper choice of the intensity normalization techniques. Benefits of proper normalization approaches are more apparent compared to the custom set of feature-enhanced images for the ccuracy and robustness of the segmentation tool. BRAINSCut effectively produced subcortical volumetric measurements that are robust to center and disease status with validity confirmed by human experts and low failure rate from large-scale multicenter MR data. Sample size estimation, which is crutial for designing efficient clinical and research trials, is provided based on our experiments for six subcortical structures.

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O'Leary, Brian. "A Vertex-Based Approach to the Statistical and Machine Learning Analyses of Brain Structure." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1576254162111087.

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Ginsburger, Kévin. "Modeling and simulation of the diffusion MRI signal from human brain white matter to decode its microstructure and produce an anatomic atlas at high fields (3T)." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS158/document.

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L'imagerie par résonance magnétique du processus de diffusion (IRMd) de l'eau dans le cerveau a connu un succès fulgurant au cours de la décennie passée pour cartographier les connexions cérébrales. C'est toujours aujourd'hui la seule technique d'investigation de la connectivité anatomique du cerveau humain in vivo. Mais depuis quelques années, il a été démontré que l'IRMd est également un outil unique de biopsie virtuelle in vivo en permettant de sonder la composition du parenchyme cérébral également in vivo. Toutefois, les modèles développés à l'heure actuelle (AxCaliber, ActiveAx, CHARMED) reposent uniquement sur la modélisation des membranes axonales à l'aide de géométries cylindriques, et restent trop simplistes pour rendre compte précisément de l'ultrastructure de la substance blanche et du processus de diffusion dans l’espace extra-axonal. Dans un premier temps, un modèle analytique plus réaliste de la substance blanche cérébrale tenant compte notamment de la dépendance temporelle du processus de diffusion dans le milieu extra-axonal a été développé. Un outil de décodage complexe permettant de résoudre le problème inverse visant à estimer les divers paramètres de la cytoarchitecture de la substance blanche à partir du signal IRMd a été mis en place en choisissant un schéma d'optimisation robuste pour l'estimation des paramètres. Dans un second temps, une approche Big Data a été conduite pour améliorer le décodage de la microstructure cérébrale. Un outil de création de tissus synthétiques réalistes de la matière blanche a été développé, permettant de générer très rapidement un grand nombre de voxels virtuels. Un outil de simulation ultra-rapide du processus de diffusion des particules d'eau dans ces voxels virtuels a ensuite été mis en place, permettant la génération de signaux IRMd synthétiques associés à chaque voxel du dictionnaire. Un dictionnaire de voxels virtuels contenant un grand nombre de configurations géométriques rencontrées dans la matière blanche cérébrale a ainsi été construit, faisant en particulier varier le degré de gonflement de la membrane axonale qui peut survenir comme conséquence de pathologies neurologiques telles que l’accident vasculaire cérébral. L'ensemble des signaux simulés associés aux configurations géométriques des voxels virtuels dont ils sont issus a ensuite été utilisé comme un jeu de données permettant l'entraînement d'un algorithme de machine learning pour décoder la microstructure de la matière blanche cérébrale à partir du signal IRMd et estimer le degré de gonflement axonal. Ce décodeur a montré des résultats de régression encourageants sur des données simulées inconnues, montrant le potentiel de l’approche computationnelle présentée pour cartographier la microstructure de tissus cérébraux sains et pathologiques in vivo. Les outils de simulation développés durant cette thèse permettront, en utilisant un algorithme de recalage difféomorphe de propagateurs de diffusion d’ensemble également développé dans le cadre de cette thèse, de construire un atlas probabiliste des paramètres microstructuraux des faisceaux de matière blanche
Diffusion Magnetic Resonance Imaging of water in the brain has proven very useful to establish a cartography of brain connections. It is the only in vivo modality to study anatomical connectivity. A few years ago, it has been shown that diffusion MRI is also a unique tool to perform virtual biopsy of cerebral tissues. However, most of current analytical models (AxCaliber, ActiveAx, CHARMED) employed for the estimation of white matter microstructure rely upon a basic modeling of white matter, with axons represented by simple cylinders and extra-axonal diffusion assumed to be Gaussian. First, a more physically plausible analytical model of the human brain white matter accounting for the time-dependence of the diffusion process in the extra-axonal space was developed for Oscillating Gradient Spin Echo (OGSE) sequence signals. A decoding tool enabling to solve the inverse problem of estimating the parameters of the white matter microstructure from the OGSE-weighted diffusion MRI signal was designed using a robust optimization scheme for parameter estimation. Second, a Big Data approach was designed to further improve the brain microstructure decoding. All the simulation tools necessary to construct computational models of brain tissues were developed in the frame of this thesis. An algorithm creating realistic white matter tissue numerical phantoms based on a spherical meshing of cell shapes was designed, enabling to generate a massive amount of virtual voxels in a computationally efficient way thanks to a GPU-based implementation. An ultra-fast simulation tool of the water molecules diffusion process in those virtual voxels was designed, enabling to generate synthetic diffusion MRI signal for each virtual voxel. A dictionary of virtual voxels containing a huge set of geometrical configurations present in white matter was built. This dictionary contained virtual voxels with varying degrees of axonal beading, a swelling of the axonal membrane which occurs after strokes and other pathologies. The set of synthetic signals and associated geometrical configurations of the corresponding voxels was used as a training data set for a machine learning algorithm designed to decode white matter microstructure from the diffusion MRI signal and estimate the degree of axonal beading. This decoder showed encouraging regression results on unknown simulated data, showing the potential of the presented approach to characterize the microstructure of healthy and injured brain tissues in vivo. The microstructure decoding tools developed during this thesis will in particular be used to characterize white matter tissue microstructural parameters (axonal density, mean axonal diameter, glial density, mean glial cells diameter, microvascular density ) in short and long bundles. The simulation tools developed in the frame of this thesis will enable the construction of a probabilistic atlas of the white matter bundles microstructural parameters, using a mean propagator based diffeomorphic registration tool also designed in the frame of this thesis to register each individual

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SALVATORE, CHRISTIAN. "Development and validation of a Decision Support System for the automatic diagnosis of medical images from brain MRI studies." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/94834.

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Decision Support Systems (DSS) for assisted medical diagnosis are computer-based systems designed to assist clinicians with decision-making tasks by automatically determining diagnosis or improving diagnostic confidence. This could allow to perform early and differential diagnosis of neurological diseases, such as Alzheimer’s Disease (AD) and Parkinson’s Disease (PD), for which definite diagnosis still remains a crucial issue. Multivariate Machine Learning (ML) methods are gaining popularity within the neuroimaging community. Among these, supervised ML methods are able to automatically extract multiple information from image sets without requiring prior knowledge of where information may be coded. These methods have been proposed as a revolutionary approach for identifying sensitive biomarkers allowing for automatic classification of individual subjects. The aim of this thesis was to implement, optimize and validate a ML method able to perform automatic diagnosis of medical images by structural Magnetic Resonance Imaging data (sMRI). This method consists of 3 phases: 1) image preprocessing, mainly devoted to the co-registration of data from different patients to a common reference system; 2) feature extraction and selection, performed through Principal Components Analysis and Fisher’s Discriminant Ratio, with the aim of extracting and selecting the most discriminative features; 3) classification, performed by Support Vector Machine, with the aim of computing a predictive model for the diagnosis of new subjects. Moreover, I implemented a method for the generation of pattern distribution maps of brain structural differences, reflecting the importance of each voxel for classification. These maps could allow to identify new MR-related biomarkers for the diagnosis of neurological diseases. In order to test the feasibility of the implemented method, I applied it to the diagnosis of 3 pathologies: AD, PD and Eating Disorders (ED). Regarding PD, we acquired T1-weighted brain sMRI of 28 PD, 28 PSP (Progressive Supranuclear Palsy) and 28 healthy controls (CN). Classification performance in terms of accuracy (specificity/sensitivity) (%) was 94(91/97) for PD vs CN, 92(93/92) for PSP vs CN, 92(91/94) for PSP vs PD. Voxels influencing differential diagnosis of PD were localized in midbrain, pons, corpus callosum and thalamus, four critical regions involved in the pathophysiological mechanisms of PD. Regarding AD, I enrolled 509 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, obtaining T1-weighted brain sMRI of 137 AD, 76 Mild Cognitive Impairment (MCI) patients who converted to AD (MCIc), 134 MCI who did not convert to AD (MCInc) within 18 months, and 162 CN. Classification performance (%) was 76±11 for AD vs CN, 72±12 for MCIc vs CN, 66±16 for MCIc vs MCInc. Voxels influencing the classification of AD vs CN were localized in the temporal pole, hippocampus, entorhinal cortex, amygdala, thalamus, putamen, caudate, insula, gyrus rectus, frontal and orbitofrontal cortices, anterior cingulate cortex, precuneus, posterior cerebellar lobule. Voxels influencing the classification of MCIc vs CN and MCIc vs MCInc were similar to those found for AD. Regarding ED, we acquired T1-weighted brain sMRI of 17 ED and 17 CN. The classifier allowed ED vs CN diagnosis with accuracy (specificity/sensitivity) of 85(73/93)%. Pattern distribution maps showed that voxels influencing ED vs CN discrimination were localized in the occipital cortex, posterior cerebellar lobule, precuneus, sensorimotor and premotor cortices, anterior cingulate and orbitofrontal cortices, all brain regions involved in the regulation of appetite and emotional processing. Results of this work were published in 7 ISI international papers, 3 indexed international papers, 1 international book chapter, 5 international conference proceedings and 1 national conference proceedings.

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Magrì, Salvatore. "Characterization of cerebral cortex folding in humans through MRI: quality control and dementia prediction." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21245/.

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Magnetic resonance imaging (MRI) is a valuable tool for non-invasively investigating human brain anatomy and functions. The features extracted from MRI data can be used as biomarkers for neurodegenerative diseases, like Dementia. To deeply understand the mechanisms driving the brain changes it is crucial to extract reliable measures from the brain MRI scans and to increase the statistical power by harmonizing different datasets, such as in the ENIGMA studies. Here we applied the ENIGMA-SULCI pipeline to estimate the reliability of the sulcal descriptors extracted across the whole brain and to investigate their correlation with CDR (Clinical Dementia Rating) in the open access dataset OASIS. The OASIS dataset includes T1-weighted acquired from 416 right-handed subjects, for 227 of whose we know CDR. The measurement reliability has been estimated through technical replicates of a subgroup of patients MRI scans. The correlation of each sulcal shape descriptor with the degree of Dementia has been tested through linear regressions between each feature and the CDR series. We have trained linear (regression) and nonlinear (Neural Networks) Machine Learning models in order to classify the subjects in two classes (Dementia and healthy subjects). We got models able to correctly classify more than the 70% of the dataset, starting from sulcal measures.

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Olešová, Kristína. "Klasifikace stupně gliomů v MR datech mozku." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413113.

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This thesis deals with a classification of glioma grade in high and low aggressive tumours and overall survival prediction based on magnetic resonance imaging. Data used in this work is from BRATS challenge 2019 and each set contains information from 4 weighting sequences of MRI. Thesis is implemented in PYTHON programming language and Jupyter Notebooks environment. Software PyRadiomics is used for calculation of image features. Goal of this work is to determine best tumour region and weighting sequence for calculation of image features and consequently select set of features that are the best ones for classification of tumour grade and survival prediction. Part of thesis is dedicated to survival prediction using set of statistical tests, specifically Cox regression

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YAMIN, MUHAMMAD ABUBAKAR. "Investigating Brain Functional Networks in a Riemannian Framework." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1040663.

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The brain is a complex system of several interconnected components which can be categorized at different Spatio-temporal levels, evaluate the physical connections and the corresponding functionalities. To study brain connectivity at the macroscale, Magnetic Resonance Imaging (MRI) technique in all the different modalities has been exemplified to be an important tool. In particular, functional MRI (fMRI) enables to record the brain activity either at rest or in different conditions of cognitive task and assist in mapping the functional connectivity of the brain. The information of brain functional connectivity extracted from fMRI images can be defined using a graph representation, i.e. a mathematical object consisting of nodes, the brain regions, and edges, the link between regions. With this representation, novel insights have emerged about understanding brain connectivity and providing evidence that the brain networks are not randomly linked. Indeed, the brain network represents a small-world structure, with several different properties of segregation and integration that are accountable for specific functions and mental conditions. Moreover, network analysis enables to recognize and analyze patterns of brain functional connectivity characterizing a group of subjects. In recent decades, many developments have been made to understand the functioning of the human brain and many issues, related to the biological and the methodological perspective, are still need to be addressed. For example, sub-modular brain organization is still under debate, since it is necessary to understand how the brain is functionally organized. At the same time a comprehensive organization of functional connectivity is mostly unknown and also the dynamical reorganization of functional connectivity is appearing as a new frontier for analyzing brain dynamics. Moreover, the recognition of functional connectivity patterns in patients affected by mental disorders is still a challenging task, making plausible the development of new tools to solve them. Indeed, in this dissertation, we proposed novel methodological approaches to answer some of these biological and neuroscientific questions. We have investigated methods for analyzing and detecting heritability in twin's task-induced functional connectivity profiles. in this approach we are proposing a geodesic metric-based method for the estimation of similarity between functional connectivity, taking into account the manifold related properties of symmetric and positive definite matrices. Moreover, we also proposed a computational framework for classification and discrimination of brain connectivity graphs between healthy and pathological subjects affected by mental disorder, using geodesic metric-based clustering of brain graphs on manifold space. Within the same framework, we also propose an approach based on the dictionary learning method to encode the high dimensional connectivity data into a vectorial representation which is useful for classification and determining regions of brain graphs responsible for this segregation. We also propose an effective way to analyze the dynamical functional connectivity, building a similarity representation of fMRI dynamic functional connectivity states, exploiting modular properties of graph laplacians, geodesic clustering, and manifold learning.

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Частини книг з теми "Brain aging, MRI, machine learning"

Kodner, Boris, Shiri Gordon, Jacob Goldberger, and Tammy Riklin Raviv. "Atlas of Classifiers for Brain MRI Segmentation." In Machine Learning in Medical Imaging, 36–44. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67389-9_5.

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Yu, Renping, Minghui Deng, Pew-Thian Yap, Zhihui Wei, Li Wang, and Dinggang Shen. "Learning-Based 3T Brain MRI Segmentation with Guidance from 7T MRI Labeling." In Machine Learning in Medical Imaging, 213–20. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47157-0_26.

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Chen, Xu, Chunfeng Lian, Li Wang, Pew-Thian Yap, James J. Xia, and Dinggang Shen. "Segmenting Bones from Brain MRI via Generative Adversarial Learning." In Machine Learning in Dentistry, 27–40. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71881-7_3.

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Dong, Pei, Xiaohuan Cao, Jun Zhang, Minjeong Kim, Guorong Wu, and Dinggang Shen. "Efficient Groupwise Registration for Brain MRI by Fast Initialization." In Machine Learning in Medical Imaging, 150–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67389-9_18.

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Valverde, Juan Miguel, Artem Shatillo, Riccardo De Feo, Olli Gröhn, Alejandra Sierra, and Jussi Tohka. "Automatic Rodent Brain MRI Lesion Segmentation with Fully Convolutional Networks." In Machine Learning in Medical Imaging, 195–202. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32692-0_23.

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Bao, Dongxing, Xiaoming Li, and Jin Li. "Lorentzian Norm Based Super-Resolution Reconstruction of Brain MRI Image." In Machine Learning and Intelligent Communications, 326–32. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73447-7_36.

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Tahir, Muhammad. "Brain MRI Classification Using Gradient Boosting." In Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology, 294–301. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66843-3_29.

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Roy, Snehashis, Aaron Carass, Jerry L. Prince, and Dzung L. Pham. "Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation." In Machine Learning in Medical Imaging, 248–55. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10581-9_31.

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Habib, Md Rawshan, Ahmed Yousuf Suhan, Abhishek Vadher, Md Ashiqur Rahman Swapno, Md Rashedul Arefin, Saiful Islam, Khan Anik Rahman, and Md Shahnewaz Tanvir. "Clustering of MRI in Brain Images Using Fuzzy C Means Algorithm." In Machine Learning and Autonomous Systems, 437–48. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7996-4_31.

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Guan, Hao, Erkun Yang, Li Wang, Pew-Thian Yap, Mingxia Liu, and Dinggang Shen. "Linking Adolescent Brain MRI to Obesity via Deep Multi-cue Regression Network." In Machine Learning in Medical Imaging, 111–19. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59861-7_12.

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Тези доповідей конференцій з теми "Brain aging, MRI, machine learning"

Chaphekarande, Prachi, and Deepa Deshpande. "Machine Learning Based Brain MRI Estimation Method." In 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE, 2019. http://dx.doi.org/10.1109/icicict46008.2019.8993363.

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Dasanayaka, Sasmitha, Sanju Silva, Vimuth Shantha, Dulani Meedeniya, and Thanuja Ambegoda. "Interpretable Machine Learning for Brain Tumor Analysis Using MRI." In 2022 2nd International Conference on Advanced Research in Computing (ICARC). IEEE, 2022. http://dx.doi.org/10.1109/icarc54489.2022.9754131.

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Qu, Yili, Chufu Deng, Wanqi Su, Ying Wang, Yutong Lu, and Zhiguang Chen. "Multimodal Brain MRI Translation Focused on Lesions." In ICMLC 2020: 2020 12th International Conference on Machine Learning and Computing. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3383972.3384024.

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Yong Yang, Ni-Ni Rao, and Shu-Ying Huang. "A novel fuzzy approach for segmentation of brain MRI." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620871.

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B, Preetika, M. Latha, M. Senthilmurugan, and R. Chinnaiyan. "MRI Image based Brain Tumour Segmentation using Machine Learning Classifiers." In 2021 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2021. http://dx.doi.org/10.1109/iccci50826.2021.9402508.

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Afshar, Leila Keshavarz, and Hedieh Sajedi. "Age Prediction based on Brain MRI Images using Extreme Learning Machine." In 2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS). IEEE, 2019. http://dx.doi.org/10.1109/cfis.2019.8692156.

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Parveen, Afiya, and Prabha Selvaraj. "Machine Learning Techniques for analysis of AD Detection using brain MRI." In 2022 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2022. http://dx.doi.org/10.1109/iccci54379.2022.9740739.

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Jayasuriya, Surani Anuradha, and Alan Wee-Chung Liew. "Fractal dimension as a symmetry measure in 3D brain MRI analysis." In 2012 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2012. http://dx.doi.org/10.1109/icmlc.2012.6359511.

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Donnelly Kehoe, P., G. Pascariello, M. Quaglino, J. Nagel, and J. C. Gómez. "The changing brain in healthy aging: a multi-MRI machine and multicenter surface-based morphometry study." In 12th International Symposium on Medical Information Processing and Analysis, edited by Eduardo Romero, Natasha Lepore, Jorge Brieva, and Ignacio Larrabide. SPIE, 2017. http://dx.doi.org/10.1117/12.2256894.

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Asodekar, Bhagyashri H., Sonal A. Gore, and A. D. Thakare. "Brain Tumor analysis Based on Shape Features of MRI using Machine Learning." In 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA). IEEE, 2019. http://dx.doi.org/10.1109/iccubea47591.2019.9129512.

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