Author name cluster

Iman Beheshti

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5 papers

1 author row

JBHI Journal 2023 Journal Article

Brain Age Prediction With Improved Least Squares Twin SVR

M. A. Ganaie
M. Tanveer
Iman Beheshti

Alzheimer’s disease (AD) is the prevalent form of dementia and shares many aspects with the aging pattern of the abnormal brain. Machine learning models like support vector regression (SVR) based models have been successfully employed in the estimation of brain age. However, SVR is computationally inefficient than twin support vector machine based models. Hence, different twin support vector machine based models like twin SVR (TSVR), $\varepsilon$ -TSVR, and Lagrangian TSVR (LTSVR) models have been used for the regression problems. $\varepsilon$ -TSVR and LTSVR models seek a pair of $\varepsilon$ -insensitive proximal planes for generation of end regressor. However, SVR and TSVR based models have several drawbacks- i) SVR model is computationally inefficient compared to the TSVR based models. ii) Twin SVM based models involve the computation of matrix inverse which is intractable in real world scenario’s. iii) Both TSVR and LTSVR models are based on empirical risk minimization principle and hence may be prone to overfitting. iv) TSVR and LTSVR assume that the matrices appearing in their formulation are positive definite which may not be satisfied in real world scenario’s. To overcome these issues, we formulate improved least squares twin support vector regression (ILSTSVR). The proposed ILSTSVR modifies the TSVR by replacing the inequality constraints with the equality constraints and minimizes the slack variables using squares of $L_2$ norm instead of $L_1$. Also, we introduce a different Lagrangian function to avoid the computation of matrix inverses. We evaluated the proposed ILSTSVR model on the subjects including cognitively healthy, mild cognitive impairment and Alzheimer’s disease for brain-age estimation. Experimental evaluation and statistical tests demonstrate the efficiency of the proposed ILSTSVR model for brain-age prediction.

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JBHI Journal 2023 Journal Article

Diagnosis of Schizophrenia: A Comprehensive Evaluation

M. Tanveer
Jatin Jangir
M. A. Ganaie
Iman Beheshti
M. Tabish
Nikunj Chhabra

Machine learning models have been successfully employed in the diagnosis of Schizophrenia disease. The impact of classification models and the feature selection techniques on the diagnosis of Schizophrenia have not been evaluated. Here, we sought to access the performance of classification models along with different feature selection approaches on the structural magnetic resonance imaging data. The data consist of 72 subjects with Schizophrenia and 74 healthy control subjects. We evaluated different classification algorithms based on support vector machine (SVM), random forest, kernel ridge regression and randomized neural networks. Moreover, we evaluated T-Test, Receiver Operator Characteristics (ROC), Wilcoxon, entropy, Bhattacharyya, Minimum Redundancy Maximum Relevance (MRMR) and Neighbourhood Component Analysis (NCA) as the feature selection techniques. Based on the evaluation, SVM based models with Gaussian kernel proved better compared to other classification models and Wilcoxon feature selection emerged as the best feature selection approach. Moreover, in terms of data modality the performance on integration of the grey matter and white matter proved better compared to the performance on the grey and white matter individually. Our evaluation showed that classification algorithms along with the feature selection approaches impact the diagnosis of Schizophrenia disease. This indicates that proper selection of the features and the classification models can improve the diagnosis of Schizophrenia.

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JBHI Journal 2022 Journal Article

Predicting Brain Age Using Machine Learning Algorithms: A Comprehensive Evaluation

Iman Beheshti
M. A. Ganaie
Vardhan Paliwal
Aryan Rastogi
Imran Razzak
M. Tanveer

Machine learning (ML) algorithms play a vital role in the brain age estimation frameworks. The impact of regression algorithms on prediction accuracy in the brain age estimation frameworks have not been comprehensively evaluated. Here, we sought to assess the efficiency of different regression algorithms on brain age estimation. To this end, we built a brain age estimation framework based on a large set of cognitively healthy (CH) individuals ( $N = 788$ ) as a training set followed by different regression algorithms (22 different algorithms in total). We then quantified each regression-algorithm on independent test sets composed of 88 CH individuals, 70 mild cognitive impairment patients as well as 30 Alzheimer’s disease patients. The prediction accuracy in the independent test set (i. e. , CH set) varied in regression algorithms mean absolute error (MAE) from 4. 63 to 7. 14 yrs, $R^2$ from 0. 76 to 0. 88. The highest and lowest prediction accuracies were achieved by Quadratic Support Vector Regression algorithm (MAE $= 4. 63$ yrs, $R^2 = 0. 88, 95\%$ CI $= [-1. 26, 1. 42]$ ) and Binary Decision Tree algorithm (MAE $= 7. 14$ yrs, $R^2 = 0. 76, 95\%$ CI $= [-1. 50, 2. 62]$ ), respectively. Our experimental results demonstrate that the prediction accuracy in brain age frameworks is affected by regression algorithms, indicating that advanced machine learning algorithms can lead to more accurate brain age predictions in clinical settings.

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YNIMG Journal 2019 Journal Article

A novel patch-based procedure for estimating brain age across adulthood

Iman Beheshti
Pierre Gravel
Olivier Potvin
Louis Dieumegarde
Simon Duchesne

Aging is associated with structural alterations in many regions of the brain. Monitoring these changes contributes to increasing our understanding of the brain's morphological alterations across its lifespan, and could allow the identification of departures from canonical trajectories. Here, we introduce a novel and unique patch-based grading procedure for estimating a synthetic estimate of cortical aging in cognitively intact individuals. The cortical age metric is computed based on image similarity between an unknown (test) cortical label and known (training) cortical labels using machine learning algorithms. The proposed method was trained on a dataset of 100 cognitively intact individuals aged 19–61 years, within the 31 bilateral cortical labels of the Desikan-Killiany-Tourville parcellation, then tested on an independent test set of 78 cognitively intact individuals spanning a similar age range. The proposed patch-based framework yielded a R 2 = 0. 94, as well as a mean absolute error of 1. 66 years, which compared favorably to the literature. These experimental results demonstrate that the proposed patch-based grading framework is a reliable and robust method to estimate brain age from image data, even with a limited training size.

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YNICL Journal 2019 Journal Article

Bias-adjustment in neuroimaging-based brain age frameworks: A robust scheme

Iman Beheshti
Scott Nugent
Olivier Potvin
Simon Duchesne

The level of prediction error in the brain age estimation frameworks is associated with the authenticity of statistical inference on the basis of regression models. In this paper, we present an efficacious and plain bias-adjustment scheme using chronological age as a covariate through the training set for downgrading the prediction bias in a Brain-age estimation framework. We applied proposed bias-adjustment scheme coupled by a machine learning-based brain age framework on a large set of metabolic brain features acquired from 675 cognitively unimpaired adults through fluorodeoxyglucose positron emission tomography data as the training set to build a robust Brain-age estimation framework. Then, we tested the reliability of proposed bias-adjustment scheme on 75 cognitively unimpaired adults, 561 mild cognitive impairment patients as well as 362 Alzheimer's disease patients as independent test sets. Using the proposed method, we gained a strong R2 of 0. 81 between the chronological age and brain estimated age, as well as an excellent mean absolute error of 2. 66 years on 75 cognitively unimpaired adults as an independent set; whereas an R2 of 0. 24 and a mean absolute error of 4. 71 years was achieved without bias-adjustment. The simulation results demonstrated that the proposed bias-adjustment scheme has a strong capability to diminish prediction error in brain age estimation frameworks for clinical settings.

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