Skip to main content
Springer Nature Link
Log in

Detection and Classification of Alzheimer’s disease from cognitive impairment with resting-state fMRI

  • S.I. : Neural Computing for IOT based Intelligent Healthcare
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Detection and classification of Alzheimer’s disease (AD) are a demanding field of research in medicine throws light on innovative approach in detecting and classifying AD from cognitive impairment with resting-state functional magnetic resonance imaging (rsfMRI). The goal of this research is chiefly aimed to diagnose mild cognitive impairment (MCI) patients who essentially need support for medical intervention. A new concept is presented in classifying AD and MCI from rsfMRI using Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The images are preprocessed using some advanced technique to eliminate noise and parameter variations, and the preprocessed images are used for extracting the raw features. The rsfMRI is applied for feature selection processes in order to reduce feature dimensions using principal component analysis (PCA). The proposed kernel-based PCA-support vector regression (SVR) includes t-distributed stochastic neighbor embedding (tSNE) and polynomial kernel-based tSNE that are separately handled by significantly merging correlated local and class features. The kernel PCA method analysis the new features explicitly based on nonlinear mapping function in the data points of high-dimensional search. The kernel PCA method is suitable to analysis the new feature and feature importance in AD classification. The proposed kernel SVR method has the advantage of effectively analyzing the high-dimensional data to provide linear relationship and suitable to apply in MCI and AD data. The PCA method is applied for feature reduction process due to its capacity to select the relevant features and effectively analyzing the individual features. The proposed kernel-SVR method has the advantage of selecting the relevant features and avoid overfitting problem in the classifier. The SVR uses reduced features that are obtained from different reduction methods for classification of AD and MCI, a polynomial kernel based. The results showed that the proposed kernel-based PCA-SVR showed better average accuracy values 98.53% for kernel PCA when compared the existing models hippocampal visual features of 79.15% and deep neural network of 80.21%

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Abbreviations

A :

Matrix

D :

Dimension of the space

d :

Degree matrix

f w :

Factors

K :

Kernel matrix

k g :

Gaussian kernel

L g :

Gyri surface areas

L s :

Sulci surface areas

P :

Eigen decomposition

p ij :

Resemblance

Q :

Eigen decomposition

u α :

An eigenvector of C

W :

Weighted coefficient

w G, w W, w g, w s :

Weighted vectors

X G :

Gray volumes

X W :

White volumes

x :

Input image

x N :

Normalized image

x max :

Maximum intensity in the image

x min :

Minimum intensity in the image

y 1, y 2, ……y N :

Class of data

\(\alpha_{j}\) :

Least square solution for mapping

\(\beta_{i}\) :

Blending coefficients

\(\phi\) :

Mapping function

\(\sigma_{j}\) :

Variance bandwidth

\(\psi \left( A \right)\) :

Matrix polynomial

\(\emptyset_{G} ,\emptyset_{W} ,\emptyset_{g} ,\,{\text{and}}\,\emptyset_{s}\) :

Kernel-based projection

\(\xi_{n} , \xi_{n}^{^{\prime}}\) :

Slack variables

References

  1. Serrano-Pozo A, Frosch MP, Masliah E, Hyman BT (2011) Neuropathological alterations in Alzheimer disease. Cold Spring HarbPerspect Med 1:a006189

    Google Scholar 

  2. Huang L, Jin Y, Gao Y, Thung K-H, Shen D (2016) Longitudinal clinical score prediction in Alzheimer’s disease with soft-split sparse regression based random forest. Neurobiol Aging 46:180–191

    Article  Google Scholar 

  3. Moradi E, Pepe A, Gaser C, Huttunen H, Tohka J, Alzheimer’s Disease Neuroimaging (2015) Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. Neuroimage 104:398–412

  4. Hojjati SH, Ebrahimzadeh A, Khazaee A, Babajani-Feremi A, Alzheimer's Disease Neuroimaging I (2017) Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM. J Neurosci Methods 282:69–80

  5. Khazaee A, Ebrahimzadeh A, Babajani-Feremi A (2015) Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory. ClinNeurophysiol 126:2132–2141

    Google Scholar 

  6. Khazaee A, Ebrahimzadeh A, Babajani-Feremi A (2016) Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer’s disease. Brain Imaging Behav 10:799–817

    Article  Google Scholar 

  7. Lin Q, Rosenberg MD, Yoo K, Hsu TW, O’Connell TP, Chun MM (2018) Resting-state functional connectivity predicts cognitive impairment Related to Alzheimer’s disease. Front Aging Neurosci 10:94

    Article  Google Scholar 

  8. Ito T, Kulkarni KR, Schultz DH, Mill RD, Chen RH, Solomyak LI et al (2017) Cognitive task information is transferred between brain regions via resting-state network topology. Nat Commun 8:1027

    Article  Google Scholar 

  9. Grieder M, Wang DJJ, Dierks T, Wahlund LO, Jann K (2018) Default mode network complexity and cognitive decline in mild Alzheimer’s disease. Front Neurosci 12:770

    Article  Google Scholar 

  10. Binnewijzend MA, Schoonheim MM, Sanz-Arigita E, Wink AM, van der Flier WM, Tolboom N et al (2012) Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging 33:2018–2028

    Article  Google Scholar 

  11. Lindemer ER, Salat DH, Smith EE, Nguyen K, Fischl B, Greve DN et al (2015) White matter signal abnormality quality differentiates mild cognitive impairment that converts to Alzheimer’s disease from nonconverters. Neurobiol Aging 36:2447–2457

    Article  Google Scholar 

  12. Pagani M, Giuliani A, Oberg J, Chincarini A, Morbelli S, Brugnolo A et al (2016) Predicting the transition from normal aging to Alzheimer’s disease: a statistical mechanistic evaluation of FDG-PET data. Neuroimage 141:282–290

    Article  Google Scholar 

  13. Mateos-Perez JM, Dadar M, Lacalle-Aurioles M, Iturria-Medina Y, Zeighami Y, Evans AC (2018) Structural neuroimaging as clinical predictor: a review of machine learning applications. Neuroimage Clin 20:506–522

    Article  Google Scholar 

  14. Tong T, Gray K, Gao QQ, Chen L, Rueckert D, Initia ADN (2017) Multi-modal classification of Alzheimer’s disease using nonlinear graph fusion. Pattern Recogn 63:171–181

    Article  Google Scholar 

  15. Eskildsen SF, Coupé P, García-Lorenzo D, Fonov V, Pruessner JC, Collins DL et al (2013) Prediction of Alzheimer’s disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning. Neuroimage 65:511–521

    Article  Google Scholar 

  16. Beheshti I, Demirel H, Matsuda H, A. s. D. N. Initiative (2017) Classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm. Comput Biol Med 83:109–119

  17. Peng JL, Zhu XF, Wang Y, An L, Shen DG (2019) Structured sparsity regularized multiple kernel learning for Alzheimer’s disease diagnosis. Pattern Recogn 88:370–382

    Article  Google Scholar 

  18. Ahmed OB et al (2015) Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features. Multimed Tools Appl 74(4):1249–1266

    Article  Google Scholar 

  19. Dyrba M, Grothe M, Kirste T, Teipel SJ (2015) Multimodal analysis of functional and structural disconnection in Alzheimer’s disease using multiple kernel SVM. Hum Brain Mapp 36:2118–2131

    Article  Google Scholar 

  20. Ryali S, Supekar K, Abrams DA, Menon V (2010) Sparse logistic regression for whole-brain classification of fMRI data. Neuroimage 51:752–764

    Article  Google Scholar 

  21. Duc NT, Ryu S, Qureshi MNI, Choi M, Lee KH, Lee B (2020) 3D-deep learning based automatic diagnosis of Alzheimer’s disease with joint MMSE prediction using resting-state fMRI. Neuroinformatics 18(1):71–86

    Article  Google Scholar 

  22. Zareapoor M, Shamsolmoali P, Jain DK, Wang H, Yang J (2018) Kernelized support vector machine with deep learning: an efficient approach for extreme multiclass dataset. Pattern Recogn Lett 115:4–13

    Article  Google Scholar 

  23. Jain DK, Zhang Z, Huang K (2020) Multi angle optimal pattern-based deep learning for automatic facial expression recognition. Pattern Recogn Lett 139:157–165

    Article  Google Scholar 

  24. Segonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK et al (2004) A hybrid approach to the skull stripping problem in MRI. Neuroimage 22:1060–1075

    Article  Google Scholar 

  25. Haghighat M, Abdel-Mottaleb M, Alhalabi W (2016) Discriminant correlation analysis: real-time feature level fusion for multimodal biometric recognition. IEEE Trans Inf Forensics Secur 11:1984–1996

    Article  Google Scholar 

  26. Sehgal S, Singh H, Agarwal M, Bhasker V, Shantanu (2014) Data analysis using principal component analysis. In: International conference on medical imaging, m-Health and emerging communication systems (MedCom). IEEE

  27. Song F, Guo Z, Mei D (2010) Feature selection using principal component analysis. In: 2010 International conference on system science, engineering design and manufacturing informatization. IEEE

  28. Schölkopf B, Smola A, Müller KR (1997) Kernel principal component analysis. In: Gerstner W, Germond A, Hasler M, Nicoud JD (eds) Artificial neural networks—ICANN'97. ICANN 1997. Lecture notes in computer science, vol 1327. Springer, Berlin

  29. Van der Maaten L, Hinton G (2008) Visualizing high-dimensional data using t-SNE. J Mach Learn Res 9:2579–2605

    MATH  Google Scholar 

  30. Sidhu G, Asgarian N, Greiner R, Brown M (2012) Kernel principal component analysis for dimensionality reduction in fMRI-based diagnosis of ADHD. Front Syst Neurosci 12(74):1–16

    Google Scholar 

  31. Oliveira FHM, Machado ARP, Andrade AO (2018) On the use of t-distributed stochastic neighbor embedding for data visualization and classification of individuals with Parkinson’s disease. Comput Math Methods Med 8019232:17

  32. Gisbrecht A, Schulz A, Hammer B (2015) Parametric nonlinear dimensionality reduction using kernel t-SNE. Neurocomputing 147:71–82

    Article  Google Scholar 

  33. Gisbrecht A, Lueks W, Mokbel B, Hammer B (2012) Out-of-sample kernel extensions for nonparametric dimensionality reduction. In: European symposium on artificial neural networks, computational intelligence and machine learning. Bruges (Belgium), pp 25–27

  34. Lin S, Zeng J (2019) Fast learning with polynomial kernels. IEEE Trans Cybern 9(10)

  35. Samosir RS, Gaol FL, Abbas BS, Sabarguna BS, Heryadi Y (2019) Comparation between linear and polynomial kernel function for ovarian cancer classification. In: The 3rd international conference on computing and applied informatics 2018, Journal of Physics: Conf. Series, vol 1235

  36. Qu H, Zhang Y (2016) A new kernel of support vector regression for forecasting high-frequency stock returns. Math Probl Eng 9

  37. Jain DK, Dubey SB, Choubey RK, Sinhal A, Arjaria SK, Jain A, Wang H (2018) An approach for hyperspectral image classification by optimizing SVM using self organizing map. J Comput Sci 25:252–259

    Article  Google Scholar 

Download references

Funding

This study was not funded by any organization.

Author information

Authors and Affiliations

  1. Department of ECE, Saveetha Engineering College, Chennai, TamilNadu, India

    PR. Buvaneswari

  2. Deptartment of ECE, Sri Venkateswara College of Engineering, Chennai, TamilNadu, India

    R. Gayathri

Authors
  1. PR. Buvaneswari
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. R. Gayathri
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to PR. Buvaneswari.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Buvaneswari, P., Gayathri, R. Detection and Classification of Alzheimer’s disease from cognitive impairment with resting-state fMRI. Neural Comput & Applic 35, 22797–22812 (2023). https://doi.org/10.1007/s00521-021-06436-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06436-2

Keywords