Identifying the Neurocognitive Difference Between Two Groups Using Supervised Learning

  • Ramchandra Rimal Middle Tennessee State University
Keywords: brain imaging, fMRI data, supervised learning, LSTM


Brain Imaging Analysis is a dynamic and exciting field within neuroscience. This study is conducted with two main objectives. First, to develop a classification framework to enhance predictive performance, and second, to conduct a comparative analysis of accuracy versus inference using brain imaging data. The dataset of chess masters and chess novices is utilized to identify neurocognitive differences between the two groups, based on their resting-state functional magnetic resonance imaging data. A network of connections between brain regions is created and analyzed. Standard statistical learning techniques and machine learning models are then applied to distinguish connectivity patterns between the groups. The trade-off between model precision and interpretability is also assessed. Finally, model performance measures, including accuracy, sensitivity, specificity, and AUC, are reported to demonstrate the effectiveness of the model framework.


Meenakshi Khosla et al. “Machine learning in resting-state fMRI analysis”. In: Magnetic resonance imaging 64 (2019), pp. 101–121.

F. Bowman. “Brain Imaging Analysis.” In: Annual review of statistics and its application 1 (2014), pp. 61–8.

R Cameron Craddock et al. “Disease state prediction from resting state functional connectivity”. In: Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 62.6 (2009), pp. 1619–1628.

MBCFDS De Luca et al. “fMRI resting state networks define distinct modes of long-distance interactions in the human brain”. In: Neuroimage 29.4 (2006), pp. 1359–1367.

Christian F Beckmann et al. “Investigations into resting-state connectivity using independent component analysis”. In: Philosophical Transactions of the Royal Society B: Biological Sciences 360.1457 (2005), pp. 1001–1013.

Jared A Nielsen et al. “Multisite functional connectivity MRI classification of autism: ABIDE results”. In: Frontiers in human neuroscience 7 (2013), p. 599.

Nathalie Tzourio-Mazoyer et al. “Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain”. In: Neuroimage 15.1 (2002), pp. 273–289.

Kamalaker Dadi et al. “Benchmarking functional connectome-based predictive models for resting-state fMRI”. In: NeuroImage 192 (2019), pp. 115–134.

Margot D Sullivan et al. “Intrinsic neurocognitive network connectivity differences between normal aging and mild cognitive impairment are associated with cognitive status and age”. In: Neurobiology of aging 73 (2019), pp. 219–228.

Epifanio Bagarinao et al. “Reorganization of brain networks and its association with general cognitive performance over the adult lifespan”. In: Scientific reports 9.1 (2019), pp. 1–15.

Naseer Ahmed Khan et al. “A Three-Stage Teacher, Student Neural Networks and Sequential Feed Forward Selection-Based Feature Selection Approach for the Classification of Autism Spectrum Disorder”. In: Brain sciences 10.10 (2020), p. 754.

Michal Assaf et al. “Abnormal functional connectivity of default mode sub-networks in autism spectrum disorder patients”. In: Neuroimage 53.1 (2010), pp. 247–256.

Tyler B Jones et al. “Sources of group differences in functional connectivity: an investigation applied to autism spectrum disorder”. In: Neuroimage 49.1 (2010), pp. 401–414.

Milan N. Parikh, Hailong Li, and Lili He. “Enhancing Diagnosis of Autism With Optimized Machine Learning Models and Personal Characteristic Data”. In: Frontiers in Computational Neuroscience 13 (2019), p. 9. ISSN: 1662-5188. DOI: 10.3389/fncom.2019.00009. URL:

Harish RaviPrakash et al. “Morphometric and functional brain connectivity differentiates chess masters from amateur players”. In: Frontiers in Neuroscience 15 (2021), p. 629478.

Hariharan Ravishankar et al. “Recursive feature elimination for biomarker discovery in resting-state functional connectivity”. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE. 2016, pp. 4071–4074.

Md Rahman et al. “A Review of machine learning methods of feature selection and classification for autism spectrum disorder”. In: Brain sciences 10.12 (2020), p. 949.

Jerome Friedman, Trevor Hastie, Robert Tibshirani, et al. The elements of statistical learning. Vol. 1. 10. Springer series in statistics New York, 2001.

Gareth James et al. An introduction to statistical learning. Vol. 112. Springer, 2013.

Frank de Vos et al. “A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer’s disease”. In: Neuroimage 167 (2018), pp. 62–72.

Logan Grosenick, Stephanie Greer, and Brian Knutson. “Interpretable classifiers for FMRI improve prediction of purchases”. In: IEEE transactions on neural systems and rehabilitation engineering 16.6 (2008), pp. 539–548.

Destie Provenzano et al. “Logistic Regression Algorithm Differentiates Gulf War Illness (GWI) Functional Magnetic Resonance Imaging (fMRI) Data from a Sedentary Control”. In: Brain sciences 10.5 (2020), p. 319.

Max Kuhn, Kjell Johnson, et al. Applied predictive modeling. Vol. 26. Springer, 2013.

Mengyue Wang et al. “Support vector machine for analyzing contributions of brain regions during task-state fMRI”. In: Frontiers in neuroinformatics 13 (2019), p. 10.

Jiangfen Wu et al. “Resting state fMRI feature-based cerebral glioma grading by support vector machine”. In: International journal of computer assisted radiology and surgery 10.7 (2015), pp. 1167–1174.

Xia-an Bi et al. “Random support vector machine cluster analysis of resting-state fMRI in Alzheimer’s disease”. In: PloS one 13.3 (2018), e0194479.

Xin Yang, Ramchandra Rimal, and Tiffany Rogers. “Functional Connectivity Based Classification for Autism Spectrum Disorder Using Spearman’s Rank Correlation”. In: 2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES). IEEE. 2022, pp. 46–51.

Xi Zhu et al. “Random forest based classification of alcohol dependence patients and healthy controls using resting state MRI”. In: Neuroscience letters 676 (2018), pp. 27–33.

Maya A Reiter et al. “Performance of machine learning classification models of autism using restingstate fMRI is contingent on sample heterogeneity”. In: Neural Computing and Applications 33.8 (2021), pp. 3299–3310.

Jac Fredo Agastinose Ronicko et al. “Diagnostic classification of autism using resting-state fMRI data improves with full correlation functional brain connectivity compared to partial correlation”. In: Journal of Neuroscience Methods 345 (2020), p. 108884.

Alexey Natekin and Alois Knoll. “Gradient boosting machines, a tutorial”. In: Frontiers in neurorobotics 7 (2013), p. 21.

Abhishek Das, Saumendra Kumar Mohapatra, and Mihir Narayan Mohanty. “Brain Image Classification Using Optimized Extreme Gradient Boosting Ensemble Classifier”. In: Biologically Inspired Techniques in Many Criteria Decision Making: Proceedings of BITMDM 2021. Springer, 2022, pp. 221–229.

Lawrence V Fulton et al. “Classification of Alzheimer’s disease with and without imagery using gradient boosted machines and ResNet-50”. In: Brain sciences 9.9 (2019), p. 212.

PM Siva Raja and K Ramanan. “Lesion localization and extreme gradient boosting characterization with brain tumor MRI images”. In: Advances in Data Science and Management: Proceedings of ICDSM 2019. Springer. 2020, pp. 395–409.

Farzana Z Ali et al. “Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression”. In: Neuroscience informatics (2022), p. 100110.

Sepp Hochreiter. “The vanishing gradient problem during learning recurrent neural nets and problem solutions”. In: International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 6.02 (1998), pp. 107–116.

Sepp Hochreiter and J¨urgen Schmidhuber. “Long short-term memory”. In: Neural computation 9.8 (1997), pp. 1735–1780.

Felix A Gers, J¨urgen Schmidhuber, and Fred Cummins. “Learning to forget: Continual prediction with LSTM”. In: 1999 Ninth International Conference on Artificial Neural Networks ICANN 99 (1999).

Felix A. Gers, Nicol N. Schraudolph, and J¨urgen Schmidhuber. “Learning Precise Timing with Lstm Recurrent Networks”. In: J. Mach. Learn. Res. 3.null (Mar. 2003), 115–143. ISSN: 1532-4435. DOI: 10. 1162/153244303768966139. URL:

Hum Nath Bhandari et al. “Predicting stock market index using LSTM”. In: Machine Learning with Applications 9 (2022), p. 100320.

K. Greff et al. “LSTM: A Search Space Odyssey”. In: IEEE Transactions on Neural Networks and Learning Systems 28.10 (2017), pp. 2222–2232. DOI: 10.1109/TNNLS.2016.2582924.

Nicha C Dvornek et al. “Identifying autism from resting-state fMRI using long short-term memory networks”. In: International Workshop on Machine Learning in Medical Imaging. Springer. 2017, pp. 362–370.

Benjamin Lindemann et al. “A survey on long short-term memory networks for time series prediction”. In: Procedia CIRP 99 (2021), pp. 650–655.

Vikas Khullar et al. “Deep Learning-Based Binary Classification of ADHD Using Resting State MR Images”. In: Augmented Human Research 6.1 (2021), pp. 1–9.

Rui Liu et al. “Multi-LSTM Networks for Accurate Classification of Attention Deficit Hyperactivity Disorder from Resting-State fMRI Data”. In: 2020 2nd International Conference on Industrial Artificial Intelligence (IAI). IEEE. 2020, pp. 1–6.

Nicha C Dvornek et al. “Jointly discriminative and generative recurrent neural networks for learning from fMRI”. In: International Workshop on Machine Learning in Medical Imaging. Springer. 2019, pp. 382–390.

Ramchandra Rimal et al. “Comparative study of various machine learning methods on ASD classification”. In: International Journal of Data Science and Analytics (2023), pp. 1–15.

Ahmed El-Gazzar et al. “A hybrid 3dcnn and 3dc-lstm based model for 4d spatio-temporal fMRI data: an abide autism classification study”. In: OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging. Springer, 2019, pp. 95–102.

Kyunghyun Cho et al. “Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation”. In: EMNLP. 2014.

Nawa Raj Pokhrel et al. “Predicting nepse index price using deep learning models”. In: Machine Learning with Applications 9 (2022), p. 100385.

Rahul Dey and Fathi M Salem. “Gate-variants of gated recurrent unit (GRU) neural networks”. In: 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS). IEEE. 2017, pp. 1597–1600.

Kaiming Li et al. “A multimodal MRI dataset of professional chess players”. In: Scientific data 2.1 (2015), pp. 1–9.

Yan Chao-Gan and Zang Yu-Feng. “DPARSF: a MATLAB toolbox for “pipeline” data analysis of restingstate fMRI”. In: Frontiers in systems neuroscience 4 (2010).

Hugh A Chipman, Eric D Kolaczyk, and Robert E McCulloch. “Adaptive Bayesian wavelet shrinkage”. In: Journal of the American Statistical Association 92.440 (1997), pp. 1413–1421.

St´efan van der Walt et al. “scikit-image: image processing in Python”. In: PeerJ 2 (June 2014), e453. ISSN: 2167-8359. DOI: 10.7717/peerj.453. URL:

Zini Jian et al. “Research on BOLD-fMRI Data Denoising Based on Bayesian Estimation and Adaptive Wavelet Threshold”. In: Oxidative Medicine and Cellular Longevity 2021 (2021).

Nilotpal Sanyal and Marco AR Ferreira. “Bayesian wavelet analysis using nonlocal priors with an application to FMRI analysis”. In: Sankhya B 79.2 (2017), pp. 361–388.

Alle Meije Wink and Jos BTM Roerdink. “Denoising functional MR images: a comparison of wavelet denoising and Gaussian smoothing”. In: IEEE transactions on medical imaging 23.3 (2004), pp. 374–387.

Karl Pearson. “Correlation coefficient”. In: Royal Society Proceedings. Vol. 58. 1895, p. 214.

Stephen M Smith. “The future of FMRI connectivity”. In: Neuroimage 62.2 (2012), pp. 1257–1266.

Joanne C. Beer et al. “Incorporating prior information with fused sparse group lasso: Application to prediction of clinical measures from neuroimages”. In: Biometrics 75.4 (2019), pp. 1299–1309. DOI: eprint: URL:

Junghi Kim et al. “Testing group differences in brain functional connectivity: using correlations or partial correlations?” In: Brain connectivity 5.4 (2015), pp. 214–231.

Jerome Friedman, Trevor Hastie, and Robert Tibshirani. “Sparse inverse covariance estimation with the graphical lasso”. In: Biostatistics 9.3 (2008), pp. 432–441.

R Artusi, P Verderio, and EJTIjobm Marubini. “Bravais-Pearson and Spearman correlation coefficients: meaning, test of hypothesis and confidence interval”. In: The International journal of biological markers 17.2 (2002), pp. 148–151.

Jake Lever, Martin Krzywinski, and Naomi Altman. “Points of significance: Principal component analysis”. In: Nature methods 14.7 (2017), pp. 641–643.

Naomi Altman and Martin Krzywinski. “The curse (s) of dimensionality”. In: Nat Methods 15.6 (2018), pp. 399–400.

I Daubechies et al. “Independent component analysis for brain fMRI does not select for independence”. In: Proceedings of the National Academy of Sciences 106.26 (2009), pp. 10415–10422.

F. Pedregosa et al. “Scikit-learn: Machine Learning in Python”. In: Journal of Machine Learning Research 12 (2011), pp. 2825–2830.

Stefan Van der Walt et al. “scikit-image: image processing in Python”. In: PeerJ 2 (2014), e453.

Mart´ın Abadi et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from 2015. URL:

Hum Nath Bhandari et al. “LSTM-SDM: An integrated framework of LSTM implementation for sequential data modeling”. In: Software Impacts 14 (2022), p. 100396.

How to Cite
Rimal, R. (2023). Identifying the Neurocognitive Difference Between Two Groups Using Supervised Learning. Statistics, Optimization & Information Computing, 12(1), 15-33.
Research Articles