Investigating Cortical Complexity in Mixed Dementia through Nonlinear Dynamic Analyses: A Resting-State EEG Study
-
Harikumar Pallathadka
,
Zhanna Gardanova
,
Ahmed Read Al-Tameemi
,
Aiman Mohammed Baqir Al-Dhalimy
,
Eftikhaar Hasan Kadhum
,
Ahmed huseen Redhee
Abstract
Objective: Dementia is a broad term referring to a decline in problem-solving abilities, language skills, memory, and other cognitive functions to a degree that it significantly disrupts everyday activities. The underlying cause of dementia is the impairment or loss of nerve cells and their connections within the brain. The particular symptoms experienced are contingent upon specific regions of the brain affected by this damage. In this research, we aimed to investigate the nonlinear dynamics of the mixed demented brain compared to healthy subjects using electroencephalogram (EEG) analysis.
Method: For this purpose, EEG was recorded from 66 patients with mixed dementia and 65 healthy subjects during rest. After signal preprocessing, sample entropy and Katz fractal dimension analyses were applied to the preprocessed EEG data. Analysis of variance with repeated measures was utilized to compare the nonlinear dynamics of brain activity between dementia and healthy states and partial correlation analysis was employed to explore the relationship between EEG complexity measures and cognitive and neuropsychiatric symptoms of patients.
Results: Based on repeated measures ANOVA, there was a significant main effect between groups for both Katz fractal dimension (F = 4.10, P = 0.01) and sample entropy (F = 4.81, P = 0.009) measures. Post hoc comparisons revealed that EEG complexity was significantly reduced in dementia mainly in the occipitoparietal and temporal areas (P < 0.05). MMSE scores were positively correlated with EEG complexity measures, while NPI scores were negatively correlated with EEG complexity measures, mainly in the occipitoparietal and temporal areas (P < 0.05). Moreover, using a KNN classifier, all significant complexity measures yielded the best classification performance with an accuracy of 98.05%, sensitivity of 97.03% and specificity of 99.16% in detecting dementia.
Conclusion: This study demonstrated a unique dynamic system within the brain impacted by dementia that results in more predictable patterns of cortical activity mainly in the occipitoparietal and temporal areas. These abnormal patterns were associated with patients' cognitive capacity and neuropsychiatric symptoms.
1. Arvanitakis Z, Shah RC, Bennett DA. Diagnosis and Management of Dementia: Review. Jama. 2019;322(16):1589-99.
2. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health. 2022;7(2):e105-e25.
3. Kalaria RN. The pathology and pathophysiology of vascular dementia. Neuropharmacology. 2018;134(Pt B):226-39.
4. Akhtar A, Singh S, Kaushik R, Awasthi R, Behl T. Types of memory, dementia, Alzheimer's disease, and their various pathological cascades as targets for potential pharmacological drugs. Ageing Res Rev. 2024;96:102289.
5. Kaivola K, Chia R, Ding J, Rasheed M, Fujita M, Menon V, et al. Structural variants in Lewy body dementia and frontotemporal dementia spectrum. Alzheimer's & Dementia. 2023;19:e078497.
6. Hendriks S, Peetoom K, Bakker C, van der Flier WM, Papma JM, Koopmans R, et al. Global Prevalence of Young-Onset Dementia: A Systematic Review and Meta-analysis. JAMA Neurol. 2021;78(9):1080-90.
7. Tahami Monfared AA, Byrnes MJ, White LA, Zhang Q. Alzheimer's Disease: Epidemiology and Clinical Progression. Neurol Ther. 2022;11(2):553-69.
8. Cao Q, Tan CC, Xu W, Hu H, Cao XP, Dong Q, et al. The Prevalence of Dementia: A Systematic Review and Meta-Analysis. J Alzheimers Dis. 2020;73(3):1157-66.
9. Kapasi A, Schneider JA. Neuropathology of Alzheimer’s Disease and Vascular Dementia. Vascular Disease, Alzheimer's Disease, and Mild Cognitive Impairment: Advancing an Integrated Approach. 2020;333.
10. Rahmani F, Fathi M, Kazemi M, Bahadori E. Recognition of Famous and Unfamiliar Faces among Patients Suffering from Amnesia Mild Cognitive Impairment (AMCI) and Alzheimer's Disease. Iran J Psychiatry. 2019;14(3):227-35.
11. Castellazzi G, Cuzzoni MG, Cotta Ramusino M, Martinelli D, Denaro F, Ricciardi A, et al. A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features. Front Neuroinform. 2020;14:25.
12. Knopman DS, Amieva H, Petersen RC, Chételat G, Holtzman DM, Hyman BT, et al. Alzheimer disease. Nat Rev Dis Primers. 2021;7(1):33.
13. Hernández RM, Ponce-Meza JC, Saavedra-López M, Campos Ugaz WA, Chanduvi RM, Monteza WC. Brain Complexity and Psychiatric Disorders. Iran J Psychiatry. 2023;18(4):493-502.
14. Khaleghi A, Zarafshan H, Mohammadi MR. Visual and auditory steady-state responses in attention-deficit/hyperactivity disorder. Eur Arch Psychiatry Clin Neurosci. 2019;269(6):645-55.
15. Xiao W, Manyi G, Khaleghi A. Deficits in auditory and visual steady-state responses in adolescents with bipolar disorder. J Psychiatr Res. 2022;151:368-76.
16. Syaifulloh MK, Hanafi M, Ardyanto TD, Wiyono N, Budianto P, Jumiatmoko J. Electroencephalography (EEG) Frontal Alpha Asymmetry Index as an Indicator of Children's Emotions in the Three Quran Learning Methods: Visual, Auditory, and Memory. Iran J Psychiatry. 2023;18(1):93-6.
17. Khaleghi A, Mohammadi MR, Shahi K, Nasrabadi AM. Computational Neuroscience Approach to Psychiatry: A Review on Theory-driven Approaches. Clin Psychopharmacol Neurosci. 2022;20(1):26-36.
18. Ekhlasi A, Motie Nasrabadi A, Mohammadi MR. Analysis of Effective Connectivity Strength in Children with Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy. Iran J Psychiatry. 2021;16(4):374-82.
19. Şeker M, Özbek Y, Yener G, Özerdem MS. Complexity of EEG Dynamics for Early Diagnosis of Alzheimer's Disease Using Permutation Entropy Neuromarker. Comput Methods Programs Biomed. 2021;206:106116.
20. Giustiniani A, Danesin L, Bozzetto B, Macina A, Benavides-Varela S, Burgio F. Functional changes in brain oscillations in dementia: a review. Rev Neurosci. 2023;34(1):25-47.
21. Law ZK, Todd C, Mehraram R, Schumacher J, Baker MR, LeBeau FEN, et al. The Role of EEG in the Diagnosis, Prognosis and Clinical Correlations of Dementia with Lewy Bodies-A Systematic Review. Diagnostics (Basel). 2020;10(9).
22. Khaleghi A, Mohammadi MR, Moeini M, Zarafshan H, Fadaei Fooladi M. Abnormalities of Alpha Activity in Frontocentral Region of the Brain as a Biomarker to Diagnose Adolescents With Bipolar Disorder. Clin EEG Neurosci. 2019;50(5):311-8.
23. Khaleghi A, Sheikhani A, Mohammadi MR, Moti Nasrabadi A. Evaluation of Cerebral Cortex Function in Clients with Bipolar Mood Disorder I (BMD I) Compared With BMD II Using QEEG Analysis. Iran J Psychiatry. 2015;10(2):93-9.
24. Moeini M, Khaleghi A, Mohammadi MR. Characteristics of Alpha Band Frequency in Adolescents with Bipolar II Disorder: A Resting-State QEEG Study. Iran J Psychiatry. 2015;10(1):8-12.
25. Mohammadi MR, Khaleghi A, Nasrabadi AM, Rafieivand S, Begol M, Zarafshan H. EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Eng Lett. 2016;6:66-73.
26. Rossini PM, Miraglia F, Vecchio F. Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis. Alzheimers Dement. 2022;18(12):2699-706.
27. Zarafshan H, Khaleghi A, Mohammadi MR, Moeini M, Malmir N. Electroencephalogram complexity analysis in children with attention-deficit/hyperactivity disorder during a visual cognitive task. J Clin Exp Neuropsychol. 2016;38(3):361-9.
28. Khaleghi A, Mohammadi MR, Shahi K, Motie Nasrabadi A. Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study. Iran J Psychiatry. 2023;18(2):127-33.
29. Al-Nuaimi AHH, Jammeh E, Sun L, Ifeachor E. Complexity measures for quantifying changes in electroencephalogram in Alzheimer’s disease. Complexity. 2018;2018.
30. Hogan MJ, Kilmartin L, Keane M, Collins P, Staff RT, Kaiser J, et al. Electrophysiological entropy in younger adults, older controls and older cognitively declined adults. Brain Res. 2012;1445:1-10.
31. Houmani N, Vialatte F, Gallego-Jutglà E, Dreyfus G, Nguyen-Michel VH, Mariani J, et al. Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework. PLoS One. 2018;13(3):e0193607.
32. Ouchani M, Gharibzadeh S, Jamshidi M, Amini M. A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals. Biomed Res Int. 2021;2021:5425569.
33. Sun J, Wang B, Niu Y, Tan Y, Fan C, Zhang N, et al. Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer's Disease: A Review. Entropy (Basel). 2020;22(2).
34. Khaleghi A, Sheikhani A, Mohammadi MR, Nasrabadi AM, Vand SR, Zarafshan H, et al. EEG classification of adolescents with type I and type II of bipolar disorder. Australas Phys Eng Sci Med. 2015;38(4):551-9.
35. Khaleghi A, Birgani PM, Fooladi MF, Mohammadi MR. Applicable features of electroencephalogram for ADHD diagnosis. Research on Biomedical Engineering. 2020;36:1-11.
36. Patel P, R R, Annavarapu RN. EEG-based human emotion recognition using entropy as a feature extraction measure. Brain Inform. 2021;8(1):20.
37. Jianbiao M, Xinzui W, Zhaobo L, Juan L, Zhongwei Z, Hui F. EEG signal classification of tinnitus based on SVM and sample entropy. Comput Methods Biomech Biomed Engin. 2023;26(5):580-94.
38. Gallegos M, Morgan ML, Cervigni M, Martino P, Murray J, Calandra M, et al. 45 Years of the mini-mental state examination (MMSE): A perspective from ibero-america. Dement Neuropsychol. 2022;16(4):384-7.
39. Tsoi KKF, Chan JYC, Hirai HW, Wong A, Mok VCT, Lam LCW, et al. Recall Tests Are Effective to Detect Mild Cognitive Impairment: A Systematic Review and Meta-analysis of 108 Diagnostic Studies. J Am Med Dir Assoc. 2017;18(9):807.e17-.e29.
40. Cummings J. The Neuropsychiatric Inventory: Development and Applications. J Geriatr Psychiatry Neurol. 2020;33(2):73-84.
41. Zhang S. Challenges in KNN classification. IEEE Trans Knowl Data Eng. 2021;34(10):4663-75.
42. Chatzikonstantinou S, McKenna J, Karantali E, Petridis F, Kazis D, Mavroudis I. Electroencephalogram in dementia with Lewy bodies: a systematic review. Aging Clin Exp Res. 2021;33(5):1197-208.
43. Torres-Simón L, Doval S, Nebreda A, Llinas SJ, Marsh EB, Maestú F. Understanding brain function in vascular cognitive impairment and dementia with EEG and MEG: A systematic review. Neuroimage Clin. 2022;35:103040.
44. Sánchez-Reyes L-M, Rodríguez-Reséndiz J, Avecilla-Ramírez GN, García-Gomar M-L, Robles-Ocampo J-B. Impact of eeg parameters detecting dementia diseases: A systematic review. IEEE Access. 2021;9:78060-74.
45. Yang AC, Wang SJ, Lai KL, Tsai CF, Yang CH, Hwang JP, et al. Cognitive and neuropsychiatric correlates of EEG dynamic complexity in patients with Alzheimer's disease. Prog Neuropsychopharmacol Biol Psychiatry. 2013;47:52-61.
46. Labate D, La Foresta F, Morabito G, Palamara I, Morabito FC. Entropic measures of EEG complexity in Alzheimer's disease through a multivariate multiscale approach. IEEE Sensors Journal. 2013;13(9):3284-92.
47. McBride JC, Zhao X, Munro NB, Smith CD, Jicha GA, Hively L, et al. Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease. Comput Methods Programs Biomed. 2014;114(2):153-63.
48. Vecchio F, Miraglia F, Curcio G, Altavilla R, Scrascia F, Giambattistelli F, et al. Cortical brain connectivity evaluated by graph theory in dementia: a correlation study between functional and structural data. J Alzheimers Dis. 2015;45(3):745-56.
49. Serra L, Bruschini M, Di Domenico C, Mancini M, Bechi Gabrielli G, Bonarota S, et al. Behavioral psychological symptoms of dementia and functional connectivity changes: a network-based study. Neurobiol Aging. 2020;94:196-206.
50. Meghdadi AH, Stevanović Karić M, McConnell M, Rupp G, Richard C, Hamilton J, et al. Resting state EEG biomarkers of cognitive decline associated with Alzheimer's disease and mild cognitive impairment. PLoS One. 2021;16(2):e0244180.
51. Jiao B, Li R, Zhou H, Qing K, Liu H, Pan H, et al. Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer's disease using EEG technology. Alzheimers Res Ther. 2023;15(1):32.
52. Nicastro N, Malpetti M, Cope TE, Bevan-Jones WR, Mak E, Passamonti L, et al. Cortical Complexity Analyses and Their Cognitive Correlate in Alzheimer's Disease and Frontotemporal Dementia. J Alzheimers Dis. 2020;76(1):331-40.
53. Lee H, Brekelmans GJ, Roks G. The EEG as a diagnostic tool in distinguishing between dementia with Lewy bodies and Alzheimer's disease. Clin Neurophysiol. 2015;126(9):1735-9.
54. Engedal K, Snaedal J, Hoegh P, Jelic V, Bo Andersen B, Naik M, et al. Quantitative EEG Applying the Statistical Recognition Pattern Method: A Useful Tool in Dementia Diagnostic Workup. Dement Geriatr Cogn Disord. 2015;40(1-2):1-12.
55. Campos-Ugaz WA, Palacios Garay JP, Rivera-Lozada O, Alarcón Diaz MA, Fuster-Guillén D, Tejada Arana AA. An Overview of Bipolar Disorder Diagnosis Using Machine Learning Approaches: Clinical Opportunities and Challenges. Iran J Psychiatry. 2023;18(2):237-47.
56. Pappalettera C, Miraglia F, Cotelli M, Rossini PM, Vecchio F. Analysis of complexity in the EEG activity of Parkinson's disease patients by means of approximate entropy. Geroscience. 2022;44(3):1599-607.
57. Alvi AM, Siuly S, Wang H. Neurological abnormality detection from electroencephalography data: a review. Artificial Intelligence Review. 2022;55(3):2275-312.
2. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health. 2022;7(2):e105-e25.
3. Kalaria RN. The pathology and pathophysiology of vascular dementia. Neuropharmacology. 2018;134(Pt B):226-39.
4. Akhtar A, Singh S, Kaushik R, Awasthi R, Behl T. Types of memory, dementia, Alzheimer's disease, and their various pathological cascades as targets for potential pharmacological drugs. Ageing Res Rev. 2024;96:102289.
5. Kaivola K, Chia R, Ding J, Rasheed M, Fujita M, Menon V, et al. Structural variants in Lewy body dementia and frontotemporal dementia spectrum. Alzheimer's & Dementia. 2023;19:e078497.
6. Hendriks S, Peetoom K, Bakker C, van der Flier WM, Papma JM, Koopmans R, et al. Global Prevalence of Young-Onset Dementia: A Systematic Review and Meta-analysis. JAMA Neurol. 2021;78(9):1080-90.
7. Tahami Monfared AA, Byrnes MJ, White LA, Zhang Q. Alzheimer's Disease: Epidemiology and Clinical Progression. Neurol Ther. 2022;11(2):553-69.
8. Cao Q, Tan CC, Xu W, Hu H, Cao XP, Dong Q, et al. The Prevalence of Dementia: A Systematic Review and Meta-Analysis. J Alzheimers Dis. 2020;73(3):1157-66.
9. Kapasi A, Schneider JA. Neuropathology of Alzheimer’s Disease and Vascular Dementia. Vascular Disease, Alzheimer's Disease, and Mild Cognitive Impairment: Advancing an Integrated Approach. 2020;333.
10. Rahmani F, Fathi M, Kazemi M, Bahadori E. Recognition of Famous and Unfamiliar Faces among Patients Suffering from Amnesia Mild Cognitive Impairment (AMCI) and Alzheimer's Disease. Iran J Psychiatry. 2019;14(3):227-35.
11. Castellazzi G, Cuzzoni MG, Cotta Ramusino M, Martinelli D, Denaro F, Ricciardi A, et al. A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features. Front Neuroinform. 2020;14:25.
12. Knopman DS, Amieva H, Petersen RC, Chételat G, Holtzman DM, Hyman BT, et al. Alzheimer disease. Nat Rev Dis Primers. 2021;7(1):33.
13. Hernández RM, Ponce-Meza JC, Saavedra-López M, Campos Ugaz WA, Chanduvi RM, Monteza WC. Brain Complexity and Psychiatric Disorders. Iran J Psychiatry. 2023;18(4):493-502.
14. Khaleghi A, Zarafshan H, Mohammadi MR. Visual and auditory steady-state responses in attention-deficit/hyperactivity disorder. Eur Arch Psychiatry Clin Neurosci. 2019;269(6):645-55.
15. Xiao W, Manyi G, Khaleghi A. Deficits in auditory and visual steady-state responses in adolescents with bipolar disorder. J Psychiatr Res. 2022;151:368-76.
16. Syaifulloh MK, Hanafi M, Ardyanto TD, Wiyono N, Budianto P, Jumiatmoko J. Electroencephalography (EEG) Frontal Alpha Asymmetry Index as an Indicator of Children's Emotions in the Three Quran Learning Methods: Visual, Auditory, and Memory. Iran J Psychiatry. 2023;18(1):93-6.
17. Khaleghi A, Mohammadi MR, Shahi K, Nasrabadi AM. Computational Neuroscience Approach to Psychiatry: A Review on Theory-driven Approaches. Clin Psychopharmacol Neurosci. 2022;20(1):26-36.
18. Ekhlasi A, Motie Nasrabadi A, Mohammadi MR. Analysis of Effective Connectivity Strength in Children with Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy. Iran J Psychiatry. 2021;16(4):374-82.
19. Şeker M, Özbek Y, Yener G, Özerdem MS. Complexity of EEG Dynamics for Early Diagnosis of Alzheimer's Disease Using Permutation Entropy Neuromarker. Comput Methods Programs Biomed. 2021;206:106116.
20. Giustiniani A, Danesin L, Bozzetto B, Macina A, Benavides-Varela S, Burgio F. Functional changes in brain oscillations in dementia: a review. Rev Neurosci. 2023;34(1):25-47.
21. Law ZK, Todd C, Mehraram R, Schumacher J, Baker MR, LeBeau FEN, et al. The Role of EEG in the Diagnosis, Prognosis and Clinical Correlations of Dementia with Lewy Bodies-A Systematic Review. Diagnostics (Basel). 2020;10(9).
22. Khaleghi A, Mohammadi MR, Moeini M, Zarafshan H, Fadaei Fooladi M. Abnormalities of Alpha Activity in Frontocentral Region of the Brain as a Biomarker to Diagnose Adolescents With Bipolar Disorder. Clin EEG Neurosci. 2019;50(5):311-8.
23. Khaleghi A, Sheikhani A, Mohammadi MR, Moti Nasrabadi A. Evaluation of Cerebral Cortex Function in Clients with Bipolar Mood Disorder I (BMD I) Compared With BMD II Using QEEG Analysis. Iran J Psychiatry. 2015;10(2):93-9.
24. Moeini M, Khaleghi A, Mohammadi MR. Characteristics of Alpha Band Frequency in Adolescents with Bipolar II Disorder: A Resting-State QEEG Study. Iran J Psychiatry. 2015;10(1):8-12.
25. Mohammadi MR, Khaleghi A, Nasrabadi AM, Rafieivand S, Begol M, Zarafshan H. EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Eng Lett. 2016;6:66-73.
26. Rossini PM, Miraglia F, Vecchio F. Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis. Alzheimers Dement. 2022;18(12):2699-706.
27. Zarafshan H, Khaleghi A, Mohammadi MR, Moeini M, Malmir N. Electroencephalogram complexity analysis in children with attention-deficit/hyperactivity disorder during a visual cognitive task. J Clin Exp Neuropsychol. 2016;38(3):361-9.
28. Khaleghi A, Mohammadi MR, Shahi K, Motie Nasrabadi A. Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study. Iran J Psychiatry. 2023;18(2):127-33.
29. Al-Nuaimi AHH, Jammeh E, Sun L, Ifeachor E. Complexity measures for quantifying changes in electroencephalogram in Alzheimer’s disease. Complexity. 2018;2018.
30. Hogan MJ, Kilmartin L, Keane M, Collins P, Staff RT, Kaiser J, et al. Electrophysiological entropy in younger adults, older controls and older cognitively declined adults. Brain Res. 2012;1445:1-10.
31. Houmani N, Vialatte F, Gallego-Jutglà E, Dreyfus G, Nguyen-Michel VH, Mariani J, et al. Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework. PLoS One. 2018;13(3):e0193607.
32. Ouchani M, Gharibzadeh S, Jamshidi M, Amini M. A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals. Biomed Res Int. 2021;2021:5425569.
33. Sun J, Wang B, Niu Y, Tan Y, Fan C, Zhang N, et al. Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer's Disease: A Review. Entropy (Basel). 2020;22(2).
34. Khaleghi A, Sheikhani A, Mohammadi MR, Nasrabadi AM, Vand SR, Zarafshan H, et al. EEG classification of adolescents with type I and type II of bipolar disorder. Australas Phys Eng Sci Med. 2015;38(4):551-9.
35. Khaleghi A, Birgani PM, Fooladi MF, Mohammadi MR. Applicable features of electroencephalogram for ADHD diagnosis. Research on Biomedical Engineering. 2020;36:1-11.
36. Patel P, R R, Annavarapu RN. EEG-based human emotion recognition using entropy as a feature extraction measure. Brain Inform. 2021;8(1):20.
37. Jianbiao M, Xinzui W, Zhaobo L, Juan L, Zhongwei Z, Hui F. EEG signal classification of tinnitus based on SVM and sample entropy. Comput Methods Biomech Biomed Engin. 2023;26(5):580-94.
38. Gallegos M, Morgan ML, Cervigni M, Martino P, Murray J, Calandra M, et al. 45 Years of the mini-mental state examination (MMSE): A perspective from ibero-america. Dement Neuropsychol. 2022;16(4):384-7.
39. Tsoi KKF, Chan JYC, Hirai HW, Wong A, Mok VCT, Lam LCW, et al. Recall Tests Are Effective to Detect Mild Cognitive Impairment: A Systematic Review and Meta-analysis of 108 Diagnostic Studies. J Am Med Dir Assoc. 2017;18(9):807.e17-.e29.
40. Cummings J. The Neuropsychiatric Inventory: Development and Applications. J Geriatr Psychiatry Neurol. 2020;33(2):73-84.
41. Zhang S. Challenges in KNN classification. IEEE Trans Knowl Data Eng. 2021;34(10):4663-75.
42. Chatzikonstantinou S, McKenna J, Karantali E, Petridis F, Kazis D, Mavroudis I. Electroencephalogram in dementia with Lewy bodies: a systematic review. Aging Clin Exp Res. 2021;33(5):1197-208.
43. Torres-Simón L, Doval S, Nebreda A, Llinas SJ, Marsh EB, Maestú F. Understanding brain function in vascular cognitive impairment and dementia with EEG and MEG: A systematic review. Neuroimage Clin. 2022;35:103040.
44. Sánchez-Reyes L-M, Rodríguez-Reséndiz J, Avecilla-Ramírez GN, García-Gomar M-L, Robles-Ocampo J-B. Impact of eeg parameters detecting dementia diseases: A systematic review. IEEE Access. 2021;9:78060-74.
45. Yang AC, Wang SJ, Lai KL, Tsai CF, Yang CH, Hwang JP, et al. Cognitive and neuropsychiatric correlates of EEG dynamic complexity in patients with Alzheimer's disease. Prog Neuropsychopharmacol Biol Psychiatry. 2013;47:52-61.
46. Labate D, La Foresta F, Morabito G, Palamara I, Morabito FC. Entropic measures of EEG complexity in Alzheimer's disease through a multivariate multiscale approach. IEEE Sensors Journal. 2013;13(9):3284-92.
47. McBride JC, Zhao X, Munro NB, Smith CD, Jicha GA, Hively L, et al. Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease. Comput Methods Programs Biomed. 2014;114(2):153-63.
48. Vecchio F, Miraglia F, Curcio G, Altavilla R, Scrascia F, Giambattistelli F, et al. Cortical brain connectivity evaluated by graph theory in dementia: a correlation study between functional and structural data. J Alzheimers Dis. 2015;45(3):745-56.
49. Serra L, Bruschini M, Di Domenico C, Mancini M, Bechi Gabrielli G, Bonarota S, et al. Behavioral psychological symptoms of dementia and functional connectivity changes: a network-based study. Neurobiol Aging. 2020;94:196-206.
50. Meghdadi AH, Stevanović Karić M, McConnell M, Rupp G, Richard C, Hamilton J, et al. Resting state EEG biomarkers of cognitive decline associated with Alzheimer's disease and mild cognitive impairment. PLoS One. 2021;16(2):e0244180.
51. Jiao B, Li R, Zhou H, Qing K, Liu H, Pan H, et al. Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer's disease using EEG technology. Alzheimers Res Ther. 2023;15(1):32.
52. Nicastro N, Malpetti M, Cope TE, Bevan-Jones WR, Mak E, Passamonti L, et al. Cortical Complexity Analyses and Their Cognitive Correlate in Alzheimer's Disease and Frontotemporal Dementia. J Alzheimers Dis. 2020;76(1):331-40.
53. Lee H, Brekelmans GJ, Roks G. The EEG as a diagnostic tool in distinguishing between dementia with Lewy bodies and Alzheimer's disease. Clin Neurophysiol. 2015;126(9):1735-9.
54. Engedal K, Snaedal J, Hoegh P, Jelic V, Bo Andersen B, Naik M, et al. Quantitative EEG Applying the Statistical Recognition Pattern Method: A Useful Tool in Dementia Diagnostic Workup. Dement Geriatr Cogn Disord. 2015;40(1-2):1-12.
55. Campos-Ugaz WA, Palacios Garay JP, Rivera-Lozada O, Alarcón Diaz MA, Fuster-Guillén D, Tejada Arana AA. An Overview of Bipolar Disorder Diagnosis Using Machine Learning Approaches: Clinical Opportunities and Challenges. Iran J Psychiatry. 2023;18(2):237-47.
56. Pappalettera C, Miraglia F, Cotelli M, Rossini PM, Vecchio F. Analysis of complexity in the EEG activity of Parkinson's disease patients by means of approximate entropy. Geroscience. 2022;44(3):1599-607.
57. Alvi AM, Siuly S, Wang H. Neurological abnormality detection from electroencephalography data: a review. Artificial Intelligence Review. 2022;55(3):2275-312.
| Files | ||
| Issue | Vol 19 No 3 (2024) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/ijps.v19i3.15808 | |
| Keywords | ||
| Cognition Complexity Analysis Dementi Diagnosis Electroencephalography | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Pallathadka H, Gardanova Z, Al-Tameemi AR, Al-Dhalimy AMB, Kadhum EH, Redhee A huseen. Investigating Cortical Complexity in Mixed Dementia through Nonlinear Dynamic Analyses: A Resting-State EEG Study. Iran J Psychiatry. 2024;19(3):327-336.
