Functional Connectivity Assessment in Alzheimer's Disease: A Comparative Study of Linear and Non-linear fMRI Analysis Approaches
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Abstract
Purpose: Brain connectivity studies unveil the intricate interactions within neural networks. Various approaches exist to explore brain connectivity, yet the debate between the efficacy of linear versus non-linear methods remains unresolved due to the advantages and limitations of each.
This study aims to provide a comprehensive evaluation of neuroimaging data analysis to gain insights into the functional aspects of the brain, particularly in the context of Alzheimer's Disease (AD). The objective is to identify potential pathways for early intervention and prevention, despite the controversies arising from diverse neuroimaging modalities and analytical techniques.
Materials and Methods: Using fMRI data, both linear and non-linear approaches are investigated. The linear approach employs the Pearson Correlation Coefficient (PCC) to create whole-brain graphs. For non-linear approaches, Distance Correlation (DC) and the kernel trick are utilized. Functional brain networks are constructed and sparsified for each AD stage, followed by calculating global graph measures.
Results: The findings indicate that non-linear approaches are more effective in distinguishing between different stages of AD. Among these, the kernel trick method performs better than the DC technique. Polynomial kernel (degree 3) showed better group separability, with significantly different graph measures such as clustering, transitivity, modularity, and small-worldness. Kernel analysis revealed that within-region connectivity was more disrupted in AD. Notably, the functional graphs of the brain are more significantly degraded in the early stages of AD.
Conclusion: In the initial phases of AD, both functional integration and segregation of the brain are compromised, with a more pronounced decline in functional segregation as the disease progresses. The clustering coefficient, indicative of brain functional segregation, emerges as the most distinguishing feature across all stages of AD, highlighting its potential as a biomarker for early diagnosis.
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24- Jacob Benesty, Jingdong Chen, Yiteng Huang, and Israel Cohen, "Pearson correlation coefficient." in Noise reduction in speech processing: Springer, (2009), pp. 1-4.
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33- John Michael Harris, Jeffry L Hirst, and Michael J Mossinghoff, Combinatorics and graph theory. Springer, (2008).
34- Mikail Rubinov and Olaf Sporns, "Complex network measures of brain connectivity: uses and interpretations." Neuroimage, Vol. 52 (No. 3), pp. 1059-69, (2010).
35- Jeremy Kepner and John Gilbert, Graph algorithms in the language of linear algebra. SIAM, (2011).
36- Mark D Humphries and Kevin Gurney, "Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence." PloS one, Vol. 3 (No. 4), p. e0002051, (2008).
37- Thomas E Nichols and Andrew P Holmes, "Nonparametric permutation tests for functional neuroimaging: a primer with examples." Human brain mapping, Vol. 15 (No. 1), pp. 1-25, (2002).
38- Mikail Rubinov and Olaf %J Neuroimage Sporns, "Complex network measures of brain connectivity: uses and interpretations." Vol. 52 (No. 3), pp. 1059-69, (2010).
39- Matthew R Brier et al., "Tau and Aβ imaging, CSF measures, and cognition in Alzheimer’s disease." Vol. 8 (No. 338), pp. 338ra66-38ra66, (2016).
40- Cornelis J %J Nature Reviews Neuroscience Stam, "Modern network science of neurological disorders." Vol. 15 (No. 10), pp. 683-95, (2014).
41- Hesam Ahmadi, Emad Fatemizadeh, and Ali Motie %J Frontiers in Biomedical Technologies Nasrabadi, "A comparative study of the effect of weighted or binary functional brain networks in fMRI data analysis." (2020).
2- M Ten Kate, F Barkhof, and Adam J Schwarz, "Consistency between Treatment Effects on Clinical and Brain Atrophy Outcomes in Alzheimer’s Disease Trials." The Journal of Prevention of Alzheimer's Disease, Vol. 11 (No. 1), pp. 38-47, (2024).
3- Seong-Gi Kim and Peter A Bandettini, "Principles of BOLD functional MRI." in Functional Neuroradiology: Principles and Clinical Applications: Springer, (2023), pp. 461-72.
4- Mikaela Koutrouli, Evangelos Karatzas, David Paez-Espino, and Georgios A Pavlopoulos, "A guide to conquer the biological network era using graph theory." Frontiers in bioengineering and biotechnology, Vol. 8p. 34, (2020).
5- Jun Cao et al., "Brain functional and effective connectivity based on electroencephalography recordings: A review." Human brain mapping, Vol. 43 (No. 2), pp. 860-79, (2022).
6- Hessam Ahmadi, Emad Fatemizadeh, and Ali Motie-Nasrabadi, "fMRI functional connectivity analysis via kernel graph in Alzheimer’s disease." Signal, Image and Video Processing, pp. 1-9, (2020).
7- Biao Yang, Jinmeng Cao, Tiantong Zhou, Li Dong, Ling Zou, and Jianbo Xiang, "Exploration of neural activity under cognitive reappraisal using simultaneous eeg-fmri data and kernel canonical correlation analysis." Computational and mathematical methods in medicine, Vol. 2018(2018).
8- Gábor J Székely, Maria L Rizzo, and Nail K Bakirov, "Measuring and testing dependence by correlation of distances." The annals of statistics, Vol. 35 (No. 6), pp. 2769-94, (2007).
9- Hessam Ahmadi, Emad Fatemizadeh, and Ali Motie-Nasrabadi, "Deep sparse graph functional connectivity analysis in AD patients using fMRI data." Computer Methods and Programs in Biomedicine, Vol. 201p. 105954, (2021).
10- Hessam Ahmadi, Emad Fatemizadeh, and Ali Motie-Nasrabadi, "Multiclass classification of patients during different stages of Alzheimer’s disease using fMRI time-series." Biomedical Physics & Engineering Express, Vol. 6 (No. 5), p. 055022, (2020).
11- Russell A Poldrack, Jeanette A Mumford, and Thomas E Nichols, Handbook of functional MRI data analysis. Cambridge University Press, (2024).
12- Neha Garg, Mahipal Singh Choudhry, and Rajesh M Bodade, "A review on Alzheimer’s disease classification from normal controls and mild cognitive impairment using structural MR images." Journal of neuroscience methods, Vol. 384p. 109745, (2023).
13- Harald Hampel et al., "Blood-based biomarkers for Alzheimer’s disease: Current state and future use in a transformed global healthcare landscape." Neuron, Vol. 111 (No. 18), pp. 2781-99, (2023).
14- Shaymaa E Sorour, Amr A Abd El-Mageed, Khalied M Albarrak, Abdulrahman K Alnaim, Abeer A Wafa, and Engy El-Shafeiy, "Classification of Alzheimer’s disease using MRI data based on Deep Learning Techniques." Journal of King Saud University-Computer and Information Sciences, Vol. 36 (No. 2), p. 101940, (2024).
15- Seyed Hani Hojjati, Ata Ebrahimzadeh, Ali Khazaee, Abbas Babajani-Feremi, and Alzheimer's Disease Neuroimaging Initiative, "Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI." Computers in biology and medicine, Vol. 102pp. 30-39, (2018).
16- PR Buvaneswari and R Gayathri, "Detection and Classification of Alzheimer’s disease from cognitive impairment with resting-state fMRI." Neural Computing and Applications, Vol. 35 (No. 31), pp. 22797-812, (2023).
17- Ju-Hyeon Noh, Jun-Hyeok Kim, and Hee-Deok Yang, "Classification of alzheimer’s progression using fMRI data." Sensors, Vol. 23 (No. 14), p. 6330, (2023).
18- Seyed Hani Hojjati, Ata Ebrahimzadeh, and Abbas Babajani-Feremi, "Identification of the early stage of Alzheimer’s disease using structural MRI and resting-state fMRI." Frontiers in neurology, Vol. 10p. 904, (2019).
19- Emad Fatemizadeh Hessam Ahmadi, Ali Motie-Nasrabadi "A Comparative Study of Correlation Methods in Functional Connectivity Analysis using fMRI Data of Alzheimer’s Patients." Journal of Biomedical Physics and Engineering, (2021).
20- Hessam Ahmadi, Emad Fatemizadeh, and Ali Motie-Nasrabadi, "Identifying brain functional connectivity alterations during different stages of Alzheimer’s disease." International Journal of Neuroscience, pp. 1-13, (2020).
21- Ronald Carl Petersen et al., "Alzheimer's disease neuroimaging initiative (ADNI): clinical characterization." Neurology, Vol. 74 (No. 3), pp. 201-09, (2010).
22- Yan Chao-Gan and Zang Yu-Feng, "DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI." Frontiers in systems neuroscience, Vol. 4(2010).
23- Nathalie Tzourio-Mazoyer et al., "Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain." Neuroimage, Vol. 15 (No. 1), pp. 273-89, (2002).
24- Jacob Benesty, Jingdong Chen, Yiteng Huang, and Israel Cohen, "Pearson correlation coefficient." in Noise reduction in speech processing: Springer, (2009), pp. 1-4.
25- Md Ashad Alam, Vince D Calhoun, and Yu-Ping Wang, "Identifying outliers using multiple kernel canonical correlation analysis with application to imaging genetics." Computational Statistics & Data Analysis, Vol. 125pp. 70-85, (2018).
26- Thomas Hofmann, Bernhard Schölkopf, and Alexander J Smola, "Kernel methods in machine learning." The annals of statistics, pp. 1171-220, (2008).
27- Martin Hofmann, "Support vector machines-kernels and the kernel trick." Notes, Vol. 26 (No. 3), (2006).
28- Sun Yuan Kung, Kernel methods and machine learning. Cambridge University Press, (2014).
29- Adam Towsley, Jonathan Pakianathan, and David H Douglass, "Correlation angles and inner products: Application to a problem from physics." ISRN Applied Mathematics, Vol. 2011(2011).
30- Bernhard Scholkopf and Alexander J Smola, Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press, (2001).
31- Olaf Sporns, "Graph theory methods: applications in brain networks." Dialogues in clinical neuroscience, Vol. 20 (No. 2), p. 111, (2018).
32- Brent R Logan and Daniel B Rowe, "An evaluation of thresholding techniques in fMRI analysis." Neuroimage, Vol. 22 (No. 1), pp. 95-108, (2004).
33- John Michael Harris, Jeffry L Hirst, and Michael J Mossinghoff, Combinatorics and graph theory. Springer, (2008).
34- Mikail Rubinov and Olaf Sporns, "Complex network measures of brain connectivity: uses and interpretations." Neuroimage, Vol. 52 (No. 3), pp. 1059-69, (2010).
35- Jeremy Kepner and John Gilbert, Graph algorithms in the language of linear algebra. SIAM, (2011).
36- Mark D Humphries and Kevin Gurney, "Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence." PloS one, Vol. 3 (No. 4), p. e0002051, (2008).
37- Thomas E Nichols and Andrew P Holmes, "Nonparametric permutation tests for functional neuroimaging: a primer with examples." Human brain mapping, Vol. 15 (No. 1), pp. 1-25, (2002).
38- Mikail Rubinov and Olaf %J Neuroimage Sporns, "Complex network measures of brain connectivity: uses and interpretations." Vol. 52 (No. 3), pp. 1059-69, (2010).
39- Matthew R Brier et al., "Tau and Aβ imaging, CSF measures, and cognition in Alzheimer’s disease." Vol. 8 (No. 338), pp. 338ra66-38ra66, (2016).
40- Cornelis J %J Nature Reviews Neuroscience Stam, "Modern network science of neurological disorders." Vol. 15 (No. 10), pp. 683-95, (2014).
41- Hesam Ahmadi, Emad Fatemizadeh, and Ali Motie %J Frontiers in Biomedical Technologies Nasrabadi, "A comparative study of the effect of weighted or binary functional brain networks in fMRI data analysis." (2020).
| Files | ||
| Issue | Vol 12 No 3 (2025) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v12i3.19171 | |
| Keywords | ||
| functional Magnetic Resonance Imaging Functional Connectivity Linear Analysis Graph Theory Alzheimer's Disease Non-Linear Dynamics | ||
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How to Cite
1.
Ahmadi H, Fatemizadeh E, Motie Nasrabadi A. Functional Connectivity Assessment in Alzheimer’s Disease: A Comparative Study of Linear and Non-linear fMRI Analysis Approaches. Frontiers Biomed Technol. 2025;12(3):489-501.
