Journal articles on the topic 'Brain aging, MRI, machine learning'
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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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Younan, Diana, Andrew J. Petkus, Keith F. Widaman, Xinhui Wang, Ramon Casanova, Mark A. Espeland, Margaret Gatz, et al. "Particulate matter and episodic memory decline mediated by early neuroanatomic biomarkers of Alzheimer’s disease." Brain 143, no. 1 (November 20, 2019): 289–302. http://dx.doi.org/10.1093/brain/awz348.
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Abstract Evidence suggests exposure to particulate matter with aerodynamic diameter <2.5 μm (PM2.5) may increase the risk for Alzheimer’s disease and related dementias. Whether PM2.5 alters brain structure and accelerates the preclinical neuropsychological processes remains unknown. Early decline of episodic memory is detectable in preclinical Alzheimer’s disease. Therefore, we conducted a longitudinal study to examine whether PM2.5 affects the episodic memory decline, and also explored the potential mediating role of increased neuroanatomic risk of Alzheimer’s disease associated with exposure. Participants included older females (n = 998; aged 73–87) enrolled in both the Women’s Health Initiative Study of Cognitive Aging and the Women’s Health Initiative Memory Study of Magnetic Resonance Imaging, with annual (1999–2010) episodic memory assessment by the California Verbal Learning Test, including measures of immediate free recall/new learning (List A Trials 1–3; List B) and delayed free recall (short- and long-delay), and up to two brain scans (MRI-1: 2005–06; MRI-2: 2009–10). Subjects were assigned Alzheimer’s disease pattern similarity scores (a brain-MRI measured neuroanatomical risk for Alzheimer’s disease), developed by supervised machine learning and validated with data from the Alzheimer’s Disease Neuroimaging Initiative. Based on residential histories and environmental data on air monitoring and simulated atmospheric chemistry, we used a spatiotemporal model to estimate 3-year average PM2.5 exposure preceding MRI-1. In multilevel structural equation models, PM2.5 was associated with greater declines in immediate recall and new learning, but no association was found with decline in delayed-recall or composite scores. For each interquartile increment (2.81 μg/m3) of PM2.5, the annual decline rate was significantly accelerated by 19.3% [95% confidence interval (CI) = 1.9% to 36.2%] for Trials 1–3 and 14.8% (4.4% to 24.9%) for List B performance, adjusting for multiple potential confounders. Long-term PM2.5 exposure was associated with increased Alzheimer’s disease pattern similarity scores, which accounted for 22.6% (95% CI: 1% to 68.9%) and 10.7% (95% CI: 1.0% to 30.3%) of the total adverse PM2.5 effects on Trials 1–3 and List B, respectively. The observed associations remained after excluding incident cases of dementia and stroke during the follow-up, or further adjusting for small-vessel ischaemic disease volumes. Our findings illustrate the continuum of PM2.5 neurotoxicity that contributes to early decline of immediate free recall/new learning at the preclinical stage, which is mediated by progressive atrophy of grey matter indicative of increased Alzheimer’s disease risk, independent of cerebrovascular damage.
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Sridhar, Saraswati, and Vidya Manian. "EEG and Deep Learning Based Brain Cognitive Function Classification." Computers 9, no. 4 (December 21, 2020): 104. http://dx.doi.org/10.3390/computers9040104.
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Electroencephalogram signals are used to assess neurodegenerative diseases and develop sophisticated brain machine interfaces for rehabilitation and gaming. Most of the applications use only motor imagery or evoked potentials. Here, a deep learning network based on a sensory motor paradigm (auditory, olfactory, movement, and motor-imagery) that employs a subject-agnostic Bidirectional Long Short-Term Memory (BLSTM) Network is developed to assess cognitive functions and identify its relationship with brain signal features, which is hypothesized to consistently indicate cognitive decline. Testing occurred with healthy subjects of age 20–40, 40–60, and >60, and mildly cognitive impaired subjects. Auditory and olfactory stimuli were presented to the subjects and the subjects imagined and conducted movement of each arm during which Electroencephalogram (EEG)/Electromyogram (EMG) signals were recorded. A deep BLSTM Neural Network is trained with Principal Component features from evoked signals and assesses their corresponding pathways. Wavelet analysis is used to decompose evoked signals and calculate the band power of component frequency bands. This deep learning system performs better than conventional deep neural networks in detecting MCI. Most features studied peaked at the age range 40–60 and were lower for the MCI group than for any other group tested. Detection accuracy of left-hand motor imagery signals best indicated cognitive aging (p = 0.0012); here, the mean classification accuracy per age group declined from 91.93% to 81.64%, and is 69.53% for MCI subjects. Motor-imagery-evoked band power, particularly in gamma bands, best indicated (p = 0.007) cognitive aging. Although the classification accuracy of the potentials effectively distinguished cognitive aging from MCI (p < 0.05), followed by gamma-band power.
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Casanova, Ramon, Andrea Anderson, Ryan Barnard, Keenan Walker, Timothy Hughes, Stephen Kritchevsky, and Lynne Wagenknecht. "ACCELERATED BRAIN AGING IS ASSOCIATED WITH MORTALITY ACROSS RACE." Innovation in Aging 6, Supplement_1 (November 1, 2022): 784. http://dx.doi.org/10.1093/geroni/igac059.2834.
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Abstract There is an increasing interest in using machine learning and artificial intelligence to estimate chronological age using neuroimaging data. The gap between chronological age and estimated brain age (brain age gap, BAG) is used as a measure of accelerated/resilient brain aging. Accelerated brain aging has been associated with increased mortality risk. However, these reports are based on cohorts mostly composed by white individuals. Here we capitalized on the racially diverse nature of the Atherosclerosis Risk in Communities Study (ARIC) cohort to investigate associations of brain across race. We used brain MRI scans from 1172 cognitively normal ARIC participants that were collected at ARIC Visit 5. Of those 772 were White and 366 were African Americans. We used Cox regression models to investigate BAG values associations with mortality. There were 163 deaths (dw = 124 and daa = 39) over 8 years of follow-up. Participants were stratified by tertiles according to BAG values. We found that, compared to those individuals with BAG scores in the highest tertile (>=1.15), those who scored in the lowest tertile (<= -1.3 years) to be associated with significantly lower mortality among the White (HR=0.41, 95% CI, [0.26–0.66], p < 0.001) and Black (HR=0.43, 95% CI, [0.20–0.92], p = 0.03) participants after adjusting for age, race-center, sex, education, diabetes, smoking and hypertension. Our analyses show that our approach to estimate chronological age using high-dimensional elastic net regression, produces BAG values which are associated with mortality not only in White individuals but also in African Americans.
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Knopman, David S., Emily S. Lundt, Terry M. Therneau, Prashanthi Vemuri, Val J. Lowe, Kejal Kantarci, Jeffrey L. Gunter, et al. "Entorhinal cortex tau, amyloid-β, cortical thickness and memory performance in non-demented subjects." Brain 142, no. 4 (February 12, 2019): 1148–60. http://dx.doi.org/10.1093/brain/awz025.
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AbstractAs more biomarkers for Alzheimer’s disease and age-related brain conditions become available, more sophisticated analytic approaches are needed to take full advantage of the information they convey. Most work has been done using categorical approaches but the joint relationships of tau PET, amyloid PET and cortical thickness in their continuous distributions to cognition have been under-explored. We evaluated non-demented subjects over age 50 years in the Mayo Clinic Study of Aging, 2037 of whom had undergone 3 T MRI scan, 985 amyloid PET scan with 11C-Pittsburgh compound B (PIB) and MRI, and 577 PIB-PET, 18F-AV1451 flortaucipir PET and MRI. Participants received a nine-test cognitive battery. Three test scores (logical memory delayed recall, visual reproduction delayed recall and auditory verbal learning test delayed recall) were used to generate a memory composite z-score. We used Gradient Boosting Machine models to analyse the relationship between regional cortical thickness, flortaucipir PET signal, PIB-PET signal and memory z-scores. Age, education, sex and number of test exposures were included in the model as covariates. In this population-based study of non-demented subjects, most of the associations between biomarkers and memory z-scores accrued after 70 years of age. Entorhinal cortex exhibited the strongest associations between biomarkers and memory z-scores. Other temporal regions showed similar but attenuated associations, and non-temporal regions had negligible associations between memory z-scores and biomarkers. Entorhinal flortaucipir PET signal, PIB-PET signal and entorhinal cortical thickness were independently and additively associated with declining memory z-scores. In contrast to global PIB-PET signal where only very high amyloid-β levels were associated low memory z-scores, entorhinal flortaucipir PET signal just above background levels was associated with low memory z-scores. The lowest memory z-scores occurred with the confluence of elevated entorhinal flortaucipir PET signal and lower entorhinal cortical thickness.
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Dinesh, Deepika, Guan Yi, Jong Soo Lee, Amir Ebrahimzadeh, Bang-Bon Koo, Sherman Bigornia, Tammy Scott, Rafeeque Bhadelia, Katherine Tucker, and Natalia Palacios. "Bowel Health, Brain Age, Brain Volume and Cognitive Function in the Boston Puerto Rican Health Study." Current Developments in Nutrition 6, Supplement_1 (June 2022): 15. http://dx.doi.org/10.1093/cdn/nzac047.015.
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Abstract Objectives The gut-brain axis has been shown to play an important role in neurodegeneration. Bowel dysfunction, such as constipation, is a marker of gut dysbiosis, which has been associated with risk of dementia in prior studies. However, no work has been done in Puerto Ricans, who have unique dietary and lifestyle characteristics impacting both gut and brain health. We aimed to study the association between constipation and cognitive outcomes including MRI-derived brain age, brain volume and cognitive function in a cohort of Puerto Ricans. Methods This analysis was conducted within the Boston Puerto Rican Health Study (BPRHS), an ongoing longitudinal cohort that enrolled 1502 self-identified Puerto Rican adults residing in the Boston area, aged 45–75 y at baseline through wave-4 (mean 12.7 ± 1.2 y from baseline). Our study was comprised of 179 participants at wave-4 (12.7 y). Brain age was derived from magnetic resonance imaging (MRI) features that included cortical thickness, area, volume, cerebellar-subcortical and cortical summary, using a machine learning model. Brain age deviation score was used to represent the difference of the participants’ brain age from their biological age, with higher scores representing more advanced brain aging. Constipation was estimated from self-reported bowel health and defined as bowel frequency < 1/d. Global cognitive score (GCS) is a composite of executive function, memory and attention factors. Covariate-adjusted linear regression was used to examine the association between bowel health and brain age, brain volume and GCS at wave-4 (12.7 y). Results Among 179 participants with MRI, cognitive function (GCS), and bowel health measures at wave-4 (12.7 y), 45 (25.1%) self-reported constipation, defined as bowel frequency < 1/d (age 66.5 ± 7.8 y; female 75.6%). In covariate adjusted multivariable analyses, we observed that constipation was associated with higher brain age deviation (poorer brain health) (β = 0.377, P = 0.0451). We did not observe an association between constipation and brain volume (P = 0.175) or constipation and cognitive function (P = 0.573). Conclusions In this study of older, Boston–Area Puerto Ricans, we observed an association between constipation and brain age, but no association between constipation and brain volume or cognitive function. Funding Sources NIH
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Casanova, Ramon, Andrea Anderson, Jamie Justice, Gwen Windham, Rebecca Gottesman, Thomas Mosley, Lynne Wagenknecht, and Stephen Kritchevsky. "Can a Data-Driven Measure of Neuroanatomic Dementia Risk be Considered a Measure of Brain Aging?" Innovation in Aging 5, Supplement_1 (December 1, 2021): 962–63. http://dx.doi.org/10.1093/geroni/igab046.3470.
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Abstract There is an increasing interest in identifying aging-related factors which may be permissive of Alzheimer’s Disease (AD) emergence. We previously used machine learning to derive an index of neuroanatomic risk of dementia called AD pattern similarity (AD-PS) score using MRIs obtained in the Atherosclerosis Risk in Communities (ARIC) study. Here, we investigate the potential of the AD-PS scores as a brain-focused measure of biologic age. Among 1970 ARIC participants with MRI collected at ARIC Visit 5, we related AD-PS scores to three measures of aging: mortality (n=356) over 8 years of follow-up; an a priori panel of 32 proteins related to aging (N=1647); and a deficit accumulation index (DAI) based on 38 health-related measures. We found lower AD-PS scores associated with significantly lower mortality (HR=0.58, CI-95%, [0.45 - 0.75], p < 0.001) after adjusting for age, race, smoking and hypertension. Among the 32 proteins, nine were significantly associated to AD-PS scores (p < 0.05) with 4 remaining significant adjusting for multiple comparisons (Growth/differentiation factor 15, Tumor necrosis factor receptor superfamily member 1A and 1B and Collagen alpha-1(XVIII) chain). Finally, in a linear regression model after adjusting for age, race, sex, hypertension and smoking, AD-PS scores were associated with the DAI (p < 0.001). The consistent patterns of associations suggest that a data-driven measure of AD neuroanatomic risk may be capturing aspects of biologic age in older adults.
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Rossini, Paolo Maria, Francesca Miraglia, Francesca Alù, Maria Cotelli, Florinda Ferreri, Riccardo Di Iorio, Francesco Iodice, and Fabrizio Vecchio. "Neurophysiological Hallmarks of Neurodegenerative Cognitive Decline: The Study of Brain Connectivity as A Biomarker of Early Dementia." Journal of Personalized Medicine 10, no. 2 (April 30, 2020): 34. http://dx.doi.org/10.3390/jpm10020034.
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Neurodegenerative processes of various types of dementia start years before symptoms, but the presence of a “neural reserve”, which continuously feeds and supports neuroplastic mechanisms, helps the aging brain to preserve most of its functions within the “normality” frame. Mild cognitive impairment (MCI) is an intermediate stage between dementia and normal brain aging. About 50% of MCI subjects are already in a stage that is prodromal-to-dementia and during the following 3 to 5 years will develop clinically evident symptoms, while the other 50% remains at MCI or returns to normal. If the risk factors favoring degenerative mechanisms are modified during early stages (i.e., in the prodromal), the degenerative process and the loss of abilities in daily living activities will be delayed. It is therefore extremely important to have biomarkers able to identify—in association with neuropsychological tests—prodromal-to-dementia MCI subjects as early as possible. MCI is a large (i.e., several million in EU) and substantially healthy population; therefore, biomarkers should be financially affordable, largely available and non-invasive, but still accurate in their diagnostic prediction. Neurodegeneration initially affects synaptic transmission and brain connectivity; methods exploring them would represent a 1st line screening. Neurophysiological techniques able to evaluate mechanisms of synaptic function and brain connectivity are attracting general interest and are described here. Results are quite encouraging and suggest that by the application of artificial intelligence (i.e., learning-machine), neurophysiological techniques represent valid biomarkers for screening campaigns of the MCI population.
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Zhang, Fan, Melissa Petersen, Leigh Johnson, James Hall, and Sid E. O’Bryant. "Hyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer’s Disease Data." Applied Sciences 12, no. 13 (July 1, 2022): 6670. http://dx.doi.org/10.3390/app12136670.
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Accurate detection is still a challenge in machine learning (ML) for Alzheimer’s disease (AD). Class imbalance in imbalanced AD data is another big challenge for machine-learning algorithms working under the assumption that the data are evenly distributed within classes. Here, we present a hyperparameter tuning workflow with high-performance computing (HPC) for imbalanced data related to prevalent mild cognitive impairment (MCI) and AD in the Health and Aging Brain Study-Health Disparities (HABS-HD) project. We applied a single-node multicore parallel mode to hyperparameter tuning of gamma, cost, and class weight using a support vector machine (SVM) model with 10 times repeated fivefold cross-validation. We executed the hyperparameter tuning workflow with R’s bigmemory, foreach, and doParallel packages on Texas Advanced Computing Center (TACC)’s Lonestar6 system. The computational time was dramatically reduced by up to 98.2% for the high-performance SVM hyperparameter tuning model, and the performance of cross-validation was also improved (the positive predictive value and the negative predictive value at base rate 12% were, respectively, 16.42% and 92.72%). Our results show that a single-node multicore parallel structure and high-performance SVM hyperparameter tuning model can deliver efficient and fast computation and achieve outstanding agility, simplicity, and productivity for imbalanced data in AD applications.
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Zhao, Xuemei, John Kang, Vladimir Svetnik, Donald Warden, Gordon Wilcock, A. David Smith, Mary J. Savage, and Omar F. Laterza. "A Machine Learning Approach to Identify a Circulating MicroRNA Signature for Alzheimer Disease." Journal of Applied Laboratory Medicine 5, no. 1 (December 30, 2019): 15–28. http://dx.doi.org/10.1373/jalm.2019.029595.
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Abstract Background Accurate diagnosis of Alzheimer disease (AD) involving less invasive molecular procedures and at reasonable cost is an unmet medical need. We identified a serum miRNA signature for AD that is less invasive than a measure in cerebrospinal fluid. Methods From the Oxford Project to Investigate Memory and Aging (OPTIMA) study, 96 serum samples were profiled by a multiplex (>500 analytes) microRNA (miRNA) reverse transcription quantitative PCR analysis, including 51 controls, 32 samples from patients with AD, and 13 samples from patients with mild cognitive impairment (MCI). Clinical diagnosis of a subset of AD and the controls was confirmed by postmortem (PM) histologic examination of brain tissue. In a machine learning approach, the AD and control samples were split 70:30 as the training and test cohorts. A multivariate random forest statistical analysis was applied to construct and test a miRNA signature for AD identification. In addition, the MCI participants were included in the test cohort to assess whether the signature can identify early AD patients. Results A 12-miRNA signature for AD identification was constructed in the training cohort, demonstrating 76.0% accuracy in the independent test cohort with 90.0% sensitivity and 66.7% specificity. The signature, however, was not able to identify MCI participants. With a subset of AD and control participants with PM-confirmed diagnosis status, a separate 12-miRNA signature was constructed. Although sample size was limited, the PM-confirmed signature demonstrated improved accuracy of 85.7%, largely owing to improved specificity of 80.0% with comparable sensitivity of 88.9%. Conclusion Although additional and more diverse cohorts are needed for further clinical validation of the robustness, the miRNA signature appears to be a promising blood test to diagnose AD.
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Elahifasaee, Farzaneh, Fan Li, and Ming Yang. "A Classification Algorithm by Combination of Feature Decomposition and Kernel Discriminant Analysis (KDA) for Automatic MR Brain Image Classification and AD Diagnosis." Computational and Mathematical Methods in Medicine 2019 (December 30, 2019): 1–14. http://dx.doi.org/10.1155/2019/1437123.
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Magnetic resonance (MR) imaging is a widely used imaging modality for detection of brain anatomical variations caused by brain diseases such as Alzheimer's disease (AD) and mild cognitive impairment (MCI). AD considered as an irreversible neurodegenerative disorder with progressive memory impairment moreover cognitive functions, while MCI would be considered as a transitional phase amongst age-related cognitive weakening. Numerous machine learning approaches have been examined, aiming at AD computer-aided diagnosis through employing MR image analysis. Conversely, MR brain image changes could be caused by different effects such as aging and dementia. It is still a challenging difficulty to extract the relevant imaging features and classify the subjects of different groups. This paper would propose an automatic classification technique based on feature decomposition and kernel discriminant analysis (KDA) for classifications of progressive MCI (pMCI) vs. normal control (NC), AD vs. NC, and pMCI vs. stable MCI (sMCI). Feature decomposition would be based on dictionary learning, which is used for separation of class-specific components from the non-class-specific components in the features, while KDA would be applied for mapping original nonlinearly separable feature space to the separable features that are linear. The proposed technique would be evaluated by employing T1-weighted MR brain images from 830 subjects comprising 198 AD patients, 167 pMCI, 236 sMCI, and 229 NC from the Alzheimer’s disease neuroimaging initiative (ADNI) dataset. Experimental results demonstrate that classification accuracy (ACC) of 90.41%, 84.29%, and 65.94% can be achieved for classification of AD vs. NC, pMCI vs. NC, and pMCI vs. sMCI, respectively, indicating the promising performance of the proposed method.
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McCorkindale, Andrew N., Hamish D. Mundell, Boris Guennewig, and Greg T. Sutherland. "Vascular Dysfunction Is Central to Alzheimer’s Disease Pathogenesis in APOE e4 Carriers." International Journal of Molecular Sciences 23, no. 13 (June 26, 2022): 7106. http://dx.doi.org/10.3390/ijms23137106.
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Alzheimer’s disease (AD) is the most common form of dementia and the leading risk factor, after age, is possession of the apolipoprotein E epsilon 4 allele (APOE4). Approximately 50% of AD patients carry one or two copies of APOE4 but the mechanisms by which it confers risk are still unknown. APOE4 carriers are reported to demonstrate changes in brain structure, cognition, and neuropathology, but findings have been inconsistent across studies. In the present study, we used multi-modal data to characterise the effects of APOE4 on the brain, to investigate whether AD pathology manifests differently in APOE4 carriers, and to determine if AD pathomechanisms are different between carriers and non-carriers. Brain structural differences in APOE4 carriers were characterised by applying machine learning to over 2000 brain MRI measurements from 33,384 non-demented UK biobank study participants. APOE4 carriers showed brain changes consistent with vascular dysfunction, such as reduced white matter integrity in posterior brain regions. The relationship between APOE4 and AD pathology was explored among the 1260 individuals from the Religious Orders Study and Memory and Aging Project (ROSMAP). APOE4 status had a greater effect on amyloid than tau load, particularly amyloid in the posterior cortical regions. APOE status was also highly correlated with cerebral amyloid angiopathy (CAA). Bulk tissue brain transcriptomic data from ROSMAP and a similar dataset from the Mount Sinai Brain Bank showed that differentially expressed genes between the dementia and non-dementia groups were enriched for vascular-related processes (e.g., “angiogenesis”) in APOE4 carriers only. Immune-related transcripts were more strongly correlated with AD pathology in APOE4 carriers with some transcripts such as TREM2 and positively correlated with pathology severity in APOE4 carriers, but negatively in non-carriers. Overall, cumulative evidence from the largest neuroimaging, pathology, and transcriptomic studies available suggests that vascular dysfunction is key to the development of AD in APOE4 carriers. However, further studies are required to tease out non-APOE4-specific mechanisms.
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Lieslehto, Johannes, Erika Jääskeläinen, Jouko Miettunen, Matti Isohanni, Dominic Dwyer, and Nikolaos Koutsouleris. "T157. THE COURSE OF SCHIZOPHRENIA-RELATED NEURAL FINGERPRINTS OVER NINE YEARS - A LONGITUDINAL POPULATION-BASED MACHINE LEARNING STUDY." Schizophrenia Bulletin 46, Supplement_1 (April 2020): S290—S291. http://dx.doi.org/10.1093/schbul/sbaa029.717.
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Abstract Background Previous machine learning studies using structural MRI (sMRI) have been able to separate schizophrenia from controls with relatively high (about 80%) sensitivity and specificity (Kambeitz et al. Neuropsychopharmacology 2015). Interestingly, prediction accuracy in first-episode psychosis is lower compared to older and probably more chronic patients. One possibility is that the appearance of the neurodiagnostic fingerprints (NF) originated from the schizophrenia vs. controls classifier become more visible over time in schizophrenia due to the progressive nature of the disorder. Methods Using the Cobre sample (70 schizophrenia and 74 controls), we trained support vector machine (SVM) to differentiate schizophrenia from controls using sMRI. Next, we utilized the Northern Finland Birth Cohort 1966 (NFBC 1966) sample of 29 schizophrenia and 61 non-psychotic controls who participated in the nine-year follow-up. We applied the Cobre-trained SVM models at the baseline (participants 34 years old) and the follow-up (participants 43 years old) using out of sample cross-validation without any in-between retraining. Two independent schizophrenia datasets (the Neuromorphometry by Computer Algorithm Chicago [NMorphCH] and the Consortium for Neuropsychiatric Phenomics [CNP]) were utilized for replication analyses of the SVM generalizability. To address the possibility that the NF mainly capture some general psychopathology, we tested whether the NF generalize to depression using two independent MDD samples from Munich and Münster, Germany. Results Using the Cobre-trained SVM models for schizophrenia vs. controls differentiation in the NFBC 1966, we found balanced accuracy (i.e. mean of sensitivity and specificity, [BAC]) of 72.8% (sensitivity=58.6%, specificity=86.9%) at the baseline and BAC of 79.7% (sensitivity=75.9%, specificity=83.6%) at the follow-up. In the NFBC 1966 schizophrenia patients, we found that SVM decision scores varied as a function of timepoint into the direction of more schizophrenia-likeness at the follow-up (paired T-test, Cohen’s d=0.58, P=0.004). The same was not true in controls (Cohen’s d=0.09, P=0.49). The SVM decision score difference*timepoint interaction related to the decrease of hippocampus and medial prefrontal cortex. The SVM models’ performance was also validated at the two replication samples (BAC of 77.5% in the CNP and BAC of 69.1% in the NMorphCH). In the NFBC 1966 the strongest clinical variable correlating with the trajectory of SVM decision scores over the follow-up was poor performance in the California Verbal Learning Test. This finding was also replicated in the CNP dataset. Further, in the NFBC 1966, those schizophrenia patients with a low degree of SVM decision scores had a higher probability of being in remission, being able to work, and being without antipsychotic medication at the follow-up. The generalization of the SVM models to MDD was worse compared to schizophrenia classification (DeLong’s tests for the two ROC curves: P<0.001). Discussion The degree of schizophrenia-related neurodiagnostic fingerprints appear to magnify over time in schizophrenia. By contrast, the discernibility of these fingerprints in controls does not change over time. This indicates that the NF captures some schizophrenia-related progressive neural changes, and not, e.g., normal aging-related brain volume loss. The fingerprints were also generalizable to other schizophrenia samples. Further, the fingerprints seem to have some disorder specificity as the SVM models do not generalize to depression. Lastly, it appears that a low degree of schizophrenia-related NF in schizophrenia might possess some value in predicting patients’ future remission and recovery-related factors.
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Aneesh, Balla, Bijani Raghunandan, and Bollam Mithil. "BRAIN TUMOR DETECTION USING MACHINE LEARNING." International Journal of Computer Science and Mobile Computing 11, no. 1 (January 30, 2022): 146–52. http://dx.doi.org/10.47760/ijcsmc.2022.v11i01.018.
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The brain tumors, are the most widely recognized and forceful illness, prompting an exceptionally short future in their most elevated grade. Subsequently, treatment arranging is a vital stage to work on the personal satisfaction of patients. For the most part, different picture procedures like Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound picture are utilized to assess the growth in a cerebrum, lung, liver, bosom, prostate and so forth Particularly, in this work MRI pictures are utilized to analyze growth in the cerebrum. Anyway the immense measure of information produced by MRI examine frustrates manual characterization of growth versus non-cancer in a specific time. Yet, it having some impediment (i.e.) exact quantitative estimations is accommodated predetermined number of pictures. Thus trusted and programmed order plot are fundamental to forestall the passing pace of human. The programmed mind growth characterization is extremely difficult undertaking in huge spatial and primary inconstancy of encompassing area of cerebrum cancer. In this work, programmed cerebrum cancer recognition is proposed by utilizing Convolutional Neural Networks (CNN) arrangement. The further engineering configuration is performed by utilizing little parts. The heaviness of the neuron is given as little. Exploratory outcomes show that the CNN chronicles pace of 97.5% precision with low intricacy and contrasted and the any remaining condition of expressions strategies.
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Siddiqi, Muhammad Hameed, Mohammad Azad, and Yousef Alhwaiti. "An Enhanced Machine Learning Approach for Brain MRI Classification." Diagnostics 12, no. 11 (November 14, 2022): 2791. http://dx.doi.org/10.3390/diagnostics12112791.
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Magnetic Resonance Imaging (MRI) is a noninvasive technique used in medical imaging to diagnose a variety of disorders. The majority of previous systems performed well on MRI datasets with a small number of images, but their performance deteriorated when applied to large MRI datasets. Therefore, the objective is to develop a quick and trustworthy classification system that can sustain the best performance over a comprehensive MRI dataset. This paper presents a robust approach that has the ability to analyze and classify different types of brain diseases using MRI images. In this paper, global histogram equalization is utilized to remove unwanted details from the MRI images. After the picture has been enhanced, a symlet wavelet transform-based technique has been suggested that can extract the best features from the MRI images for feature extraction. On gray scale images, the suggested feature extraction approach is a compactly supported wavelet with the lowest asymmetry and the most vanishing moments for a given support width. Because the symlet wavelet can accommodate the orthogonal, biorthogonal, and reverse biorthogonal features of gray scale images, it delivers higher classification results. Following the extraction of the best feature, the linear discriminant analysis (LDA) is employed to minimize the feature space’s dimensions. The model was trained and evaluated using logistic regression, and it correctly classified several types of brain illnesses based on MRI pictures. To illustrate the importance of the proposed strategy, a standard dataset from Harvard Medical School and the Open Access Series of Imaging Studies (OASIS), which encompasses 24 different brain disorders (including normal), is used. The proposed technique achieved the best classification accuracy of 96.6% when measured against current cutting-edge systems.
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Malarvizhi, A. B., A. Mofika, M. Monapreetha, and A. M. Arunnagiri. "Brain tumour classification using machine learning algorithm." Journal of Physics: Conference Series 2318, no. 1 (August 1, 2022): 012042. http://dx.doi.org/10.1088/1742-6596/2318/1/012042.
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Abstract A Brain tumour is formed by a gradual addition of abnormal cells, and this is one of the major causes of death among other sorts of cancers. It is necessary to classify brain tumor using Magnetic Resonance Imaging (MRI) brain tumor image for treatment because MRI images assist as to detect the smallest defect of the body. This paper aimed to automatically classify brain tumours using a machine learning algorithm. In this work, the input image of the brain was pre-processed using median filter, segmented from the background using thresholding and K-means clustering algorithm and its features were extracted using GLCM. Using the SVM classifier, the brain tumour in the image was detected as either benign or malignant. This image classification process helps the doctors and research scientists to detect the tumour during its early stages, thereby controlling the spread of cancerous cells.
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Sowrirajan, Saran Raj, and Surendiran Balasubramanian. "Brain Tumor Classification Using Machine Learning and Deep Learning Algorithms." International Journal of Electrical and Electronics Research 10, no. 4 (December 30, 2022): 999–1004. http://dx.doi.org/10.37391/ijeer.100441.
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Early identification and diagnosis of brain tumors have been a difficult problem. Many approaches have been proposed using machine learning techniques and a recent study has explored deep learning techniques which are the subset of machine learning. In this analysis, Feature extraction techniques such as GLCM, Haralick, GLDM, and LBP are applied to the Brain tumor dataset to extract different features from MRI images. The features which have been extracted from the MRI brain tumor dataset are trained using classification algorithms such as SVM, Decision Tree, and Random Forest. Performances of traditional algorithms are analyzed using the accuracy metric and stated that LBP with SVM produces better classification accuracy of 84.95%. Brain tumor dataset is input to three-layer convolutional neural network and performance has been analyzed using accuracy which is of 93.10%. This study proves that CNN performs well over the machine learning algorithms considered in this work.
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Hassan, Mosaad W., Arabi Keshk, Amira Abd El-atey, and Elham Alfeky. "BRAIN STROKE DETECTION USING TENSOR FACTORIZATION AND MACHINE LEARNING MODELS." International Journal of Engineering Technologies and Management Research 8, no. 8 (August 16, 2021): 1–12. http://dx.doi.org/10.29121/ijetmr.v8.i8.2021.1006.
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Stroke is one of the foremost common disorders among the elderly. Early detection of stroke from Magnetic Resonance Imaging (MRI) is typically based on the representation method of these images. Representing MRI slices in two dimensional structures (matrices) implies ignoring the dependencies between these slices. Additionally, to combine all features exist in these slices requires more computations and time. However, this results in inexact diagnosis. In this paper, we propose a new tensor-based approach for stroke detection from MRI. The proposed methodology has two phases. In first phase, each patient’s MRI are represented as a tensor. Tensor representations are powerful because they capture the dependencies in high-dimensional data, MRI of patient, which gives more reliable and accurate results. Also, tensor factorization is used as a method for feature extraction and reduction, which improves the performance and accuracy of classifiers. In second phase, these extracted features are used to train support vector machine (SVM) and XGBoost classifiers to classify MRI images into normal and abnormal. The proposed method is assessed with MRI dataset, and the conducted experiments illustrate the efficiency of this approach. It achieves classification accuracy of 98%.
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Wang, Nicholas C., Douglas C. Noll, Ashok Srinivasan, Johann Gagnon-Bartsch, Michelle M. Kim, and Arvind Rao. "Simulated MRI Artifacts: Testing Machine Learning Failure Modes." BME Frontiers 2022 (November 1, 2022): 1–16. http://dx.doi.org/10.34133/2022/9807590.
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Objective. Seven types of MRI artifacts, including acquisition and preprocessing errors, were simulated to test a machine learning brain tumor segmentation model for potential failure modes. Introduction. Real-world medical deployments of machine learning algorithms are less common than the number of medical research papers using machine learning. Part of the gap between the performance of models in research and deployment comes from a lack of hard test cases in the data used to train a model. Methods. These failure modes were simulated for a pretrained brain tumor segmentation model that utilizes standard MRI and used to evaluate the performance of the model under duress. These simulated MRI artifacts consisted of motion, susceptibility induced signal loss, aliasing, field inhomogeneity, sequence mislabeling, sequence misalignment, and skull stripping failures. Results. The artifact with the largest effect was the simplest, sequence mislabeling, though motion, field inhomogeneity, and sequence misalignment also caused significant performance decreases. The model was most susceptible to artifacts affecting the FLAIR (fluid attenuation inversion recovery) sequence. Conclusion. Overall, these simulated artifacts could be used to test other brain MRI models, but this approach could be used across medical imaging applications.
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Kareem, Shahab Wahhab, Bikhtiyar Friyad Abdulrahman, Roojwan Sc Hawezi, Farah Sami Khoshaba, Shavan Askar, Karwan Muhammed Muheden, and Ibrahim Shamal Abdulkhaleq. "Comparative evaluation for detection of brain tumor using machine learning algorithms." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (March 1, 2023): 469. http://dx.doi.org/10.11591/ijai.v12.i1.pp469-477.
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<span lang="EN-US">Automated flaw identification has become more important in medical imaging. For patient preparation, unaided prediction of tumor (brain) detection in the magnetic resonance imaging process (MRI) is critical. Traditional ways of recognizing z are intended to make radiologists' jobs easier. The size and variety of molecular structures in brain tumors is one of the issues with MRI brain tumor diagnosis. Deep learning (DL) techniques (artificial neural network (ANN), naive Bayes (NB), multi-layer perceptron (MLP)) are used in this article to detect brain cancers in MRI data. The preprocessing techniques are used to eliminate textural features from the brain MRI images. These characteristics are then utilized to train a machine-learning system.</span>
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Alanazi, Muhannad Faleh, Muhammad Umair Ali, Shaik Javeed Hussain, Amad Zafar, Mohammed Mohatram, Muhammad Irfan, Raed AlRuwaili, Mubarak Alruwaili, Naif H. Ali, and Anas Mohammad Albarrak. "Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model." Sensors 22, no. 1 (January 4, 2022): 372. http://dx.doi.org/10.3390/s22010372.
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With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tumors into their subclasses, such as pituitary, meningioma, and glioma. First, various layers of isolated convolutional-neural-network (CNN) models are built from scratch to check their performances for brain MRI images. Then, the 22-layer, binary-classification (tumor or no tumor) isolated-CNN model is re-utilized to re-adjust the neurons’ weights for classifying brain MRI images into tumor subclasses using the transfer-learning concept. As a result, the developed transfer-learned model has a high accuracy of 95.75% for the MRI images of the same MRI machine. Furthermore, the developed transfer-learned model has also been tested using the brain MRI images of another machine to validate its adaptability, general capability, and reliability for real-time application in the future. The results showed that the proposed model has a high accuracy of 96.89% for an unseen brain MRI dataset. Thus, the proposed deep-learning framework can help doctors and radiologists diagnose brain tumors early.
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Almajmaie, Layth Kamil Adday, Ahmed Raad Raheem, Wisam Ali Mahmood, and Saad Albawi. "MRI image segmentation using machine learning networks and level set approaches." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (February 1, 2022): 793. http://dx.doi.org/10.11591/ijece.v12i1.pp793-801.
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<span>The segmented brain tissues from magnetic resonance images (MRI) always pose substantive challenges to the clinical researcher community, especially while making precise estimation of such tissues. In the recent years, advancements in deep learning techniques, more specifically in fully convolution neural networks (FCN) have yielded path breaking results in segmenting brain tumour tissues with pin-point accuracy and precision, much to the relief of clinical physicians and researchers alike. A new hybrid deep learning architecture combining SegNet and U-Net techniques to segment brain tissue is proposed here. Here, a skip connection of the concerned U-Net network was suitably explored. The results indicated optimal multi-scale information generated from the SegNet, which was further exploited to obtain precise tissue boundaries from the brain images. Further, in order to ensure that the segmentation method performed better in conjunction with precisely delineated contours, the output is incorporated as the level set layer in the deep learning network. The proposed method primarily focused on analysing brain tumor segmentation (BraTS) 2017 and BraTS 2018, dedicated datasets dealing with MRI brain tumour. The results clearly indicate better performance in segmenting brain tumours than existing ones.</span>
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Rezaei, Mansour, Ehsan Zereshki, Soodeh Shahsavari, Mohammad Gharib Salehi, and Hamid Sharini. "Prediction of Alzheimer’s Disease Using Machine Learning Classifiers." International Electronic Journal of Medicine 9, no. 3 (September 30, 2020): 116–20. http://dx.doi.org/10.34172/iejm.2020.21.
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Background: Alzheimer’s disease (AD) is the most common brain failure for which no cure has yet been found. The disease starts with a disturbance in the brain structure and then it manifests itself clinically. Therefore, by timely and correct diagnosis of changes in the structure of the brain, the occurrence of this disease or at least its progression can be prevented. Due to the fact that magnetic resonance imaging (MRI) can be used to obtain very useful information from the brain, and also because it is non-invasive, this method has been considered by researchers. Materials and Methods: The data were obtained from an MRI database (MIRIAD) of 69 subjects including 46 AD patients and 23 healthy controls (HC). Individuals were categorized based on two criteria including NINCDS-ADRAD and MMSE, as the gold standard. In this paper, we used the support vector machine (SVM) and Bayesian SVM classifiers. Results: Using the SVM classifier with Gaussian radial basis function (RBF) kernel, we distinguished AD and HC with an accuracy of 88.34%. The most important regions of interest (ROIs) in this study included right para hippocampal gyrus, left para hippocampal gyrus, right hippocampus, and left hippocampus. Conclusion: This study showed that the SVM model with Gaussian RBF kernel can distinguish AD from HC with high accuracy. These studies are of great importance in medical science. Based on the results of this study, MRI centers and neurologists can perform AD screening tests in people over the age of 50 years.
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Kang, Jaeyong, Zahid Ullah, and Jeonghwan Gwak. "MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers." Sensors 21, no. 6 (March 22, 2021): 2222. http://dx.doi.org/10.3390/s21062222.
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Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.
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Stadlbauer, Andreas, Franz Marhold, Stefan Oberndorfer, Gertraud Heinz, Michael Buchfelder, Thomas M. Kinfe, and Anke Meyer-Bäse. "Radiophysiomics: Brain Tumors Classification by Machine Learning and Physiological MRI Data." Cancers 14, no. 10 (May 10, 2022): 2363. http://dx.doi.org/10.3390/cancers14102363.
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The precise initial characterization of contrast-enhancing brain tumors has significant consequences for clinical outcomes. Various novel neuroimaging methods have been developed to increase the specificity of conventional magnetic resonance imaging (cMRI) but also the increased complexity of data analysis. Artificial intelligence offers new options to manage this challenge in clinical settings. Here, we investigated whether multiclass machine learning (ML) algorithms applied to a high-dimensional panel of radiomic features from advanced MRI (advMRI) and physiological MRI (phyMRI; thus, radiophysiomics) could reliably classify contrast-enhancing brain tumors. The recently developed phyMRI technique enables the quantitative assessment of microvascular architecture, neovascularization, oxygen metabolism, and tissue hypoxia. A training cohort of 167 patients suffering from one of the five most common brain tumor entities (glioblastoma, anaplastic glioma, meningioma, primary CNS lymphoma, or brain metastasis), combined with nine common ML algorithms, was used to develop overall 135 classifiers. Multiclass classification performance was investigated using tenfold cross-validation and an independent test cohort. Adaptive boosting and random forest in combination with advMRI and phyMRI data were superior to human reading in accuracy (0.875 vs. 0.850), precision (0.862 vs. 0.798), F-score (0.774 vs. 0.740), AUROC (0.886 vs. 0.813), and classification error (5 vs. 6). The radiologists, however, showed a higher sensitivity (0.767 vs. 0.750) and specificity (0.925 vs. 0.902). We demonstrated that ML-based radiophysiomics could be helpful in the clinical routine diagnosis of contrast-enhancing brain tumors; however, a high expenditure of time and work for data preprocessing requires the inclusion of deep neural networks.
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Fan, Zhao, Fanyu Xu, Xuedan Qi, Cai Li, and Lili Yao. "Classification of Alzheimer’s disease based on brain MRI and machine learning." Neural Computing and Applications 32, no. 7 (September 13, 2019): 1927–36. http://dx.doi.org/10.1007/s00521-019-04495-0.
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Zacharaki, Evangelia I., Vasileios G. Kanas, and Christos Davatzikos. "Investigating machine learning techniques for MRI-based classification of brain neoplasms." International Journal of Computer Assisted Radiology and Surgery 6, no. 6 (April 23, 2011): 821–28. http://dx.doi.org/10.1007/s11548-011-0559-3.
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Mhaske, Supriya A., and M. L. Dhore. "Brain Tumor Classification Using Machine Learning Mixed Approach." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (August 31, 2022): 1225–30. http://dx.doi.org/10.22214/ijraset.2022.45533.
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Abstract: In this paper, we propose an effective method using Machine learning for the classification of brain tumor tissues. For successful treatment correct and early detection of brain tumors is essential. Here proposed system is using Convolutional Neural Network for feature extraction and classification. In feature extraction, we reduce the number of features in the dataset by creating new features from the existing ones. Here we recognize the types of tissues using CNN. The pooling layer is used to reduce the spatial resolution of the feature maps. This layer brings down the number of parameters needed for image processing. This paper is focused on helping the radiologist and physician to have a second opinion on the diagnosis. These systems help specialists to perform tumor detection very easily. This study aims to diagnose brain tumors using MRI images.
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Bajaj, Aaishwarya Sanjay, and Usha Chouhan. "A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 8 (October 19, 2020): 937–45. http://dx.doi.org/10.2174/1573405615666190903144419.
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Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.
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Dong, Ningxin, Changyong Fu, Renren Li, Wei Zhang, Meng Liu, Weixin Xiao, Hugh M. Taylor, et al. "Machine Learning Decomposition of the Anatomy of Neuropsychological Deficit in Alzheimer’s Disease and Mild Cognitive Impairment." Frontiers in Aging Neuroscience 14 (May 3, 2022). http://dx.doi.org/10.3389/fnagi.2022.854733.
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ObjectiveAlzheimer’s Disease (AD) is a progressive condition characterized by cognitive decline. AD is often preceded by mild cognitive impairment (MCI), though the diagnosis of both conditions remains a challenge. Early diagnosis of AD, and prediction of MCI progression require data-driven approaches to improve patient selection for treatment. We used a machine learning tool to predict performance in neuropsychological tests in AD and MCI based on functional connectivity using a whole-brain connectome, in an attempt to identify network substrates of cognitive deficits in AD.MethodsNeuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI, and diffusion weighted imaging scans were obtained from 149 MCI, and 85 AD patients; and 140 cognitively unimpaired geriatric participants. A novel machine learning tool, Hollow Tree Super (HoTS) was utilized to extract feature importance from each machine learning model to identify brain regions that were associated with deficit and absence of deficit for 11 neuropsychological tests.Results11 models attained an area under the receiver operating curve (AUC-ROC) greater than 0.65, while five models had an AUC-ROC ≥ 0.7. 20 parcels of the Human Connectome Project Multimodal Parcelation Atlas matched to poor performance in at least two neuropsychological tests, while 14 parcels were associated with good performance in at least two tests. At a network level, most parcels predictive of both presence and absence of deficit were affiliated with the Central Executive Network, Default Mode Network, and the Sensorimotor Networks. Segregating predictors by the cognitive domain associated with each test revealed areas of coherent overlap between cognitive domains, with the parcels providing possible markers to screen for cognitive impairment.ConclusionApproaches such as ours which incorporate whole-brain functional connectivity and harness feature importance in machine learning models may aid in identifying diagnostic and therapeutic targets in AD.
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Hwang, Gyujoon, Ahmed Abdulkadir, Guray Erus, Mohamad Habes, Raymond Pomponio, Haochang Shou, Jimit Doshi, et al. "Disentangling Alzheimer’s disease neurodegeneration from typical brain aging using MRI and machine learning." Alzheimer's & Dementia 17, S4 (December 2021). http://dx.doi.org/10.1002/alz.051532.
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Shen, Ying, Qian Lu, Tianjiao Zhang, Hailang Yan, Negar Mansouri, Karol Osipowicz, Onur Tanglay, et al. "Use of machine learning to identify functional connectivity changes in a clinical cohort of patients at risk for dementia." Frontiers in Aging Neuroscience 14 (September 1, 2022). http://dx.doi.org/10.3389/fnagi.2022.962319.
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ObjectiveProgressive conditions characterized by cognitive decline, including mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are clinical conditions representing a major risk factor to develop dementia, however, the diagnosis of these pre-dementia conditions remains a challenge given the heterogeneity in clinical trajectories. Earlier diagnosis requires data-driven approaches for improved and targeted treatment modalities.MethodsNeuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 35 patients with SCD, 19 with MCI, and 36 age-matched healthy controls (HC). A recently developed machine learning technique, Hollow Tree Super (HoTS) was utilized to classify subjects into diagnostic categories based on their FC, and derive network and parcel-based FC features contributing to each model. The same approach was used to identify features associated with performance in a range of neuropsychological tests. We concluded our analysis by looking at changes in PageRank centrality (a measure of node hubness) between the diagnostic groups.ResultsSubjects were classified into diagnostic categories with a high area under the receiver operating characteristic curve (AUC-ROC), ranging from 0.73 to 0.84. The language networks were most notably associated with classification. Several central networks and sensory brain regions were predictors of poor performance in neuropsychological tests, suggesting maladaptive compensation. PageRank analysis highlighted that basal and limbic deep brain region, along with the frontal operculum demonstrated a reduction in centrality in both SCD and MCI patients compared to controls.ConclusionOur methods highlight the potential to explore the underlying neural networks contributing to the cognitive changes and neuroplastic responses in prodromal dementia.
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"451 - Estimating “Brain Age Gaps” in patients with brain injury: Applying machine learning to advanced neuroimaging techniques." International Psychogeriatrics 32, S1 (October 2020): 171. http://dx.doi.org/10.1017/s1041610220003038.
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Introduction:A single moderate or severe TBI is associated with accelerated brain aging and increased risk for dementia. Despite the high rate of falls that result in brain injury in older adults, numerous factors such as genetic predisposition to Alzheimer’s disease, sex, education, age are also known to affect multiple age-sensitive neuroimaging markers.METHODS:Here we use the “brain age” metric being tested by the global consortium, Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA), that employs machine learning to predict a person’s age from multiple age-sensitive imaging markers (e.g., hippocampal volume, regional cortical gray matter thickness, intracranial volume (ICV)), while also taking into account their sex and educational level. We will discuss results from brain injured patients ( n = 60; age range: 20-75 years) and healthy age-matched controls (n = 20 (20-75 years). We will compute the “brain age gap” – between a person’s actual chronological age and that predicted from their brain scan – and test relations between this measure and injury characteristics.RESULTS:In our pilot work, we predicted a person’s age from their MRI scan with a mean absolute error of about 5 years. ENIGMA’s current best model includes: (1) non-normalized brain volumetric measures as predictors including ICV, (2) separate models for males and females, (3) use of a large age range (12-80), and (4) Gaussian process regression (GPR).CONCLUSION:This “overall” marker of accelerated brain aging offers a metric that taps diverse sources of information, weighted by their relevance to brain aging, and is associated with decreased functionality in older adults.
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Billot, Benjamin, Colin Magdamo, You Cheng, Steven E. Arnold, Sudeshna Das, and Juan Eugenio Iglesias. "Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets." Proceedings of the National Academy of Sciences 120, no. 9 (February 21, 2023). http://dx.doi.org/10.1073/pnas.2216399120.
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Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset. Therefore, the ability to analyze such scans could transform neuroimaging research. Yet, their potential remains untapped since no automated algorithm is robust enough to cope with the high variability in clinical acquisitions (MR contrasts, resolutions, orientations, artifacts, and subject populations). Here, we present SynthSeg + , an AI segmentation suite that enables robust analysis of heterogeneous clinical datasets. In addition to whole-brain segmentation, SynthSeg + also performs cortical parcellation, intracranial volume estimation, and automated detection of faulty segmentations (mainly caused by scans of very low quality). We demonstrate SynthSeg + in seven experiments, including an aging study on 14,000 scans, where it accurately replicates atrophy patterns observed on data of much higher quality. SynthSeg + is publicly released as a ready-to-use tool to unlock the potential of quantitative morphometry.
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Christman, Seth, Camilo Bermudez, Lingyan Hao, Bennett A. Landman, Brian Boyd, Kimberly Albert, Neil Woodward, et al. "Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression." Translational Psychiatry 10, no. 1 (September 18, 2020). http://dx.doi.org/10.1038/s41398-020-01004-z.
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Abstract Depression is associated with markers of accelerated aging, but it is unclear how this relationship changes across the lifespan. We examined whether a brain-based measure of accelerated aging differed between depressed and never-depressed subjects across the adult lifespan and whether it was related to cognitive performance and disability. We applied a machine-learning approach that estimated brain age from structural MRI data in two depressed cohorts, respectively 170 midlife adults and 154 older adults enrolled in studies with common entry criteria. Both cohorts completed broad cognitive batteries and the older subgroup completed a disability assessment. The machine-learning model estimated brain age from MRI data, which was compared to chronological age to determine the brain–age gap (BAG; estimated age-chronological age). BAG did not differ between midlife depressed and nondepressed adults. Older depressed adults exhibited significantly higher BAG than nondepressed elders (Wald χ2 = 8.84, p = 0.0029), indicating a higher estimated brain age than chronological age. BAG was not associated with midlife cognitive performance. In the older cohort, higher BAG was associated with poorer episodic memory performance (Wald χ2 = 4.10, p = 0.0430) and, in the older depressed group alone, slower processing speed (Wald χ2 = 4.43, p = 0.0354). We also observed a statistical interaction where greater depressive symptom severity in context of higher BAG was associated with poorer executive function (Wald χ2 = 5.89, p = 0.0152) and working memory performance (Wald χ2 = 4.47, p = 0.0346). Increased BAG was associated with greater disability (Wald χ2 = 6.00, p = 0.0143). Unlike midlife depression, geriatric depression exhibits accelerated brain aging, which in turn is associated with cognitive and functional deficits.
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Ballester, Pedro L., Laura Tomaz da Silva, Matheus Marcon, Nathalia Bianchini Esper, Benicio N. Frey, Augusto Buchweitz, and Felipe Meneguzzi. "Predicting Brain Age at Slice Level: Convolutional Neural Networks and Consequences for Interpretability." Frontiers in Psychiatry 12 (February 25, 2021). http://dx.doi.org/10.3389/fpsyt.2021.598518.
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Problem: Chronological aging in later life is associated with brain degeneration processes and increased risk for disease such as stroke and dementia. With a worldwide tendency of aging populations and increased longevity, mental health, and psychiatric research have paid increasing attention to understanding brain-related changes of aging. Recent findings suggest there is a brain age gap (a difference between chronological age and brain age predicted by brain imaging indices); the magnitude of the gap may indicate early onset of brain aging processes and disease. Artificial intelligence has allowed for a narrowing of the gap in chronological and predicted brain age. However, the factors that drive model predictions of brain age are still unknown, and there is not much about these factors that can be gleaned from the black-box nature of machine learning models. The goal of the present study was to test a brain age regression approach that is more amenable to interpretation by researchers and clinicians.Methods: Using convolutional neural networks we trained multiple regressor models to predict brain age based on single slices of magnetic resonance imaging, which included gray matter- or white matter-segmented inputs. We evaluated the trained models in all brain image slices to generate a final prediction of brain age. Unlike whole-brain approaches to classification, the slice-level predictions allows for the identification of which brain slices and associated regions have the largest difference between chronological and neuroimaging-derived brain age. We also evaluated how model predictions were influenced by slice index and plane, participant age and sex, and MRI data collection site.Results: The results show, first, that the specific slice used for prediction affects prediction error (i.e., difference between chronological age and neuroimaging-derived brain age); second, the MRI site-stratified separation of training and test sets removed site effects and also minimized sex effects; third, the choice of MRI slice plane influences the overall error of the model.Conclusion: Compared to whole brain-based predictive models of neuroimaging-derived brain age, slice-based approach improves the interpretability and therefore the reliability of the prediction of brain age using MRI data.
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Ya, Yang, Lirong Ji, Yujing Jia, Nan Zou, Zhen Jiang, Hongkun Yin, Chengjie Mao, Weifeng Luo, Erlei Wang, and Guohua Fan. "Machine Learning Models for Diagnosis of Parkinson’s Disease Using Multiple Structural Magnetic Resonance Imaging Features." Frontiers in Aging Neuroscience 14 (April 13, 2022). http://dx.doi.org/10.3389/fnagi.2022.808520.
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PurposeThis study aimed to develop machine learning models for the diagnosis of Parkinson’s disease (PD) using multiple structural magnetic resonance imaging (MRI) features and validate their performance.MethodsBrain structural MRI scans of 60 patients with PD and 56 normal controls (NCs) were enrolled as development dataset and 69 patients with PD and 71 NCs from Parkinson’s Progression Markers Initiative (PPMI) dataset as independent test dataset. First, multiple structural MRI features were extracted from cerebellar, subcortical, and cortical regions of the brain. Then, the Pearson’s correlation test and least absolute shrinkage and selection operator (LASSO) regression were used to select the most discriminating features. Finally, using logistic regression (LR) classifier with the 5-fold cross-validation scheme in the development dataset, the cerebellar, subcortical, cortical, and a combined model based on all features were constructed separately. The diagnostic performance and clinical net benefit of each model were evaluated with the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA) in both datasets.ResultsAfter feature selection, 5 cerebellar (absolute value of left lobule crus II cortical thickness (CT) and right lobule IV volume, relative value of right lobule VIIIA CT and lobule VI/VIIIA gray matter volume), 3 subcortical (asymmetry index of caudate volume, relative value of left caudate volume, and absolute value of right lateral ventricle), and 4 cortical features (local gyrification index of right anterior circular insular sulcus and anterior agranular insula complex, local fractal dimension of right middle insular area, and CT of left supplementary and cingulate eye field) were selected as the most distinguishing features. The area under the curve (AUC) values of the cerebellar, subcortical, cortical, and combined models were 0.679, 0.555, 0.767, and 0.781, respectively, for the development dataset and 0.646, 0.632, 0.690, and 0.756, respectively, for the independent test dataset, respectively. The combined model showed higher performance than the other models (Delong’s test, all p-values < 0.05). All models showed good calibration, and the DCA demonstrated that the combined model has a higher net benefit than other models.ConclusionThe combined model showed favorable diagnostic performance and clinical net benefit and had the potential to be used as a non-invasive method for the diagnosis of PD.
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Statsenko, Yauhen, Sarah Meribout, Tetiana Habuza, Taleb M. Almansoori, Klaus Neidl-Van Gorkom, Juri G. Gelovani, and Milos Ljubisavljevic. "Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models." Frontiers in Aging Neuroscience 14 (February 23, 2023). http://dx.doi.org/10.3389/fnagi.2022.943566.
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BackgroundThe combined analysis of imaging and functional modalities is supposed to improve diagnostics of neurodegenerative diseases with advanced data science techniques.ObjectiveTo get an insight into normal and accelerated brain aging by developing the machine learning models that predict individual performance in neuropsychological and cognitive tests from brain MRI. With these models we endeavor to look for patterns of brain structure-function association (SFA) indicative of mild cognitive impairment (MCI) and Alzheimer's dementia.Materials and methodsWe explored the age-related variability of cognitive and neuropsychological test scores in normal and accelerated aging and constructed regression models predicting functional performance in cognitive tests from brain radiomics data. The models were trained on the three study cohorts from ADNI dataset—cognitively normal individuals, patients with MCI or dementia—separately. We also looked for significant correlations between cortical parcellation volumes and test scores in the cohorts to investigate neuroanatomical differences in relation to cognitive status. Finally, we worked out an approach for the classification of the examinees according to the pattern of structure-function associations into the cohorts of the cognitively normal elderly and patients with MCI or dementia.ResultsIn the healthy population, the global cognitive functioning slightly changes with age. It also remains stable across the disease course in the majority of cases. In healthy adults and patients with MCI or dementia, the trendlines of performance in digit symbol substitution test and trail making test converge at the approximated point of 100 years of age. According to the SFA pattern, we distinguish three cohorts: the cognitively normal elderly, patients with MCI, and dementia. The highest accuracy is achieved with the model trained to predict the mini-mental state examination score from voxel-based morphometry data. The application of the majority voting technique to models predicting results in cognitive tests improved the classification performance up to 91.95% true positive rate for healthy participants, 86.21%—for MCI and 80.18%—for dementia cases.ConclusionThe machine learning model, when trained on the cases of this of that group, describes a disease-specific SFA pattern. The pattern serves as a “stamp” of the disease reflected by the model.
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Jawinski, Philippe, Sebastian Markett, Johanna Drewelies, Sandra Düzel, Ilja Demuth, Elisabeth Steinhagen-Thiessen, Gert G. Wagner, et al. "Linking Brain Age Gap to Mental and Physical Health in the Berlin Aging Study II." Frontiers in Aging Neuroscience 14 (July 22, 2022). http://dx.doi.org/10.3389/fnagi.2022.791222.
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From a biological perspective, humans differ in the speed they age, and this may manifest in both mental and physical health disparities. The discrepancy between an individual’s biological and chronological age of the brain (“brain age gap”) can be assessed by applying machine learning techniques to Magnetic Resonance Imaging (MRI) data. Here, we examined the links between brain age gap and a broad range of cognitive, affective, socioeconomic, lifestyle, and physical health variables in up to 335 adults of the Berlin Aging Study II. Brain age gap was assessed using a validated prediction model that we previously trained on MRI scans of 32,634 UK Biobank individuals. Our statistical analyses revealed overall stronger evidence for a link between higher brain age gap and less favorable health characteristics than expected under the null hypothesis of no effect, with 80% of the tested associations showing hypothesis-consistent effect directions and 23% reaching nominal significance. The most compelling support was observed for a cluster covering both cognitive performance variables (episodic memory, working memory, fluid intelligence, digit symbol substitution test) and socioeconomic variables (years of education and household income). Furthermore, we observed higher brain age gap to be associated with heavy episodic drinking, higher blood pressure, and higher blood glucose. In sum, our results point toward multifaceted links between brain age gap and human health. Understanding differences in biological brain aging may therefore have broad implications for future informed interventions to preserve mental and physical health in old age.
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Syaifullah, Ali Haidar, Akihiko Shiino, Hitoshi Kitahara, Ryuta Ito, Manabu Ishida, and Kenji Tanigaki. "Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation." Frontiers in Neurology 11 (February 5, 2021). http://dx.doi.org/10.3389/fneur.2020.576029.
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Background: With the growing momentum for the adoption of machine learning (ML) in medical field, it is likely that reliance on ML for imaging will become routine over the next few years. We have developed a software named BAAD, which uses ML algorithms for the diagnosis of Alzheimer's disease (AD) and prediction of mild cognitive impairment (MCI) progression.Methods: We constructed an algorithm by combining a support vector machine (SVM) to classify and a voxel-based morphometry (VBM) to reduce concerned variables. We grouped progressive MCI and AD as an AD spectrum and trained SVM according to this classification. We randomly selected half from the total 1,314 subjects of AD neuroimaging Initiative (ADNI) from North America for SVM training, and the remaining half were used for validation to fine-tune the model hyperparameters. We created two types of SVMs, one based solely on the brain structure (SVMst), and the other based on both the brain structure and Mini-Mental State Examination score (SVMcog). We compared the model performance with two expert neuroradiologists, and further evaluated it in test datasets involving 519, 592, 69, and 128 subjects from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Aging (AIBL), Japanese ADNI, the Minimal Interval Resonance Imaging in AD (MIDIAD) and the Open Access Series of Imaging Studies (OASIS), respectively.Results: BAAD's SVMs outperformed radiologists for AD diagnosis in a structural magnetic resonance imaging review. The accuracy of the two radiologists was 57.5 and 70.0%, respectively, whereas, that of the SVMst was 90.5%. The diagnostic accuracy of the SVMst and SVMcog in the test datasets ranged from 88.0 to 97.1% and 92.5 to 100%, respectively. The prediction accuracy for MCI progression was 83.0% in SVMst and 85.0% in SVMcog. In the AD spectrum classified by SVMst, 87.1% of the subjects were Aβ positive according to an AV-45 positron emission tomography. Similarly, among MCI patients classified for the AD spectrum, 89.5% of the subjects progressed to AD.Conclusion: Our ML has shown high performance in AD diagnosis and prediction of MCI progression. It outperformed expert radiologists, and is expected to provide support in clinical practice.
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Varzandian, Ali, Miguel Angel Sanchez Razo, Michael Richard Sanders, Akhila Atmakuru, and Giuseppe Di Fatta. "Classification-Biased Apparent Brain Age for the Prediction of Alzheimer's Disease." Frontiers in Neuroscience 15 (May 28, 2021). http://dx.doi.org/10.3389/fnins.2021.673120.
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Machine Learning methods are often adopted to infer useful biomarkers for the early diagnosis of many neurodegenerative diseases and, in general, of neuroanatomical ageing. Some of these methods estimate the subject age from morphological brain data, which is then indicated as “brain age”. The difference between such a predicted brain age and the actual chronological age of a subject can be used as an indication of a pathological deviation from normal brain ageing. An important use of the brain age model as biomarker is the prediction of Alzheimer's disease (AD) from structural Magnetic Resonance Imaging (MRI). Many different machine learning approaches have been applied to this specific predictive task, some of which have achieved high accuracy at the expense of the descriptiveness of the model. This work investigates an appropriate combination of data science techniques and linear models to provide, at the same time, high accuracy and good descriptiveness. The proposed method is based on a data workflow that include typical data science methods, such as outliers detection, feature selection, linear regression, and logistic regression. In particular, a novel inductive bias is introduced in the regression model, which is aimed at improving the accuracy and the specificity of the classification task. The method is compared to other machine learning approaches for AD classification based on morphological brain data with and without the use of the brain age, including Support Vector Machines and Deep Neural Networks. This study adopts brain MRI scans of 1, 901 subjects which have been acquired from three repositories (ADNI, AIBL, and IXI). A predictive model based only on the proposed apparent brain age and the chronological age has an accuracy of 88% and 92%, respectively, for male and female subjects, in a repeated cross-validation analysis, thus achieving a comparable or superior performance than state of the art machine learning methods. The advantage of the proposed method is that it maintains the morphological semantics of the input space throughout the regression and classification tasks. The accurate predictive model is also highly descriptive and can be used to generate potentially useful insights on the predictions.