Age-Related Alterations in EEG Network Connectivity in Healthy Aging.

paper Cited Public

Authors: Javaid, Hamad; Kumarnsit, Ekkasit; Chatpun, Surapong
Year: 2022
Journal: Brain sciences
PMID: 35203981
DOI: 10.3390/brainsci12020218
PMCID: PMC8870284

Figure 1

Illustration of experimental protocol.

Figure 2

Eyes-open in middle-aged vs. elderly subjects’ network features analysis (Wilcoxon’s test; * p < 0.05): (a) global efficiency, (b) local efficiency, (c) clustering coefficient, (d) node betweenness centrality, (e) assortativity, (f) characteristic path length, and (g) node strength.

Figure 3

Eyes-closed in middle-aged vs. elderly individuals’ network features analysis (Wilcoxon’s test; * p < 0.05): (a) global efficiency, (b) local efficiency, (c) characteristic path length, (d) clustering coefficient, (e) assortativity, (f) node strength, and (g) node betweenness centrality.

Figure 4

WM task in middle-aged vs. elderly individuals’ network features analysis (Wilcoxon’s test; * p < 0.05): (a) global efficiency, (b) local efficiency, (c) characteristic path length, (d) clustering coefficient, (e) assortativity, (f) node strength, and (g) node betweenness centrality.

Figure 5

Topographic map of EEG network features for middle-aged vs. elderly in eyes-open state: (a) mean global efficiency, (b) mean local efficiency, and (c) mean clustering coefficient.

Figure 6

Topographic map of EEG network features for middle-aged vs. elderly in eyes-closed state: (a) mean global efficiency, (b) mean local efficiency, and (c) mean clustering coefficient.

Figure 7

Topographic map of EEG network features for middle-aged vs. elderly in the visual WM task state: (a) mean global efficiency, (b) mean local efficiency, and (c) mean clustering coefficient.

Figure 8

Confusion matrices of all classifiers in eyes-open state.

Figure 9

Confusion matrices of all classifiers in eyes-closed state.

Figure 10

Confusion matrices of all classifiers in WM task state.

Figure 11

ROC plot of all classifiers in eyes-open state.

Figure 12

ROC plot of all classifiers in eyes-closed state.

Figure 13

ROC plot of all classifiers in WM task state.

#	Section	Preview
20	2. Materials and Methods — 2.4. Network Construction	The characteristic path length (CP) measures the mean path length in the network, and it is computed…
21	2. Materials and Methods — 2.4. Network Construction	A network may experience random failure in its components. Resiliency against failures is vital for…
22	2. Materials and Methods — 2.4. Network Construction	We obtained the epochs with the length of 20 s and extracted 504 features for each subject by…
23	2. Materials and Methods — 2.5. Statistical Analysis	In our current work, we used a non-parametric Wilcoxon’s rank sum test to assess the statistically…
24	2. Materials and Methods — 2.6. Classification Algorithms and Performance Measures	In order to perform the classification, we used three classifiers: KNN, RF, and SVM. Three different…
25	2. Materials and Methods — 2.6. Classification Algorithms and Performance Measures	of its abilities to select a suitable kernel function. We used the Pearson VII function-based…
26	2. Materials and Methods — 2.6. Classification Algorithms and Performance Measures	We used 10-fold cross validation to evaluate the classification models. The performance of…
27	3. Results	We analyzed the network properties of the middle-aged and elderly groups during a resting state and…
28	3. Results	Significant changes in global efficiency, local efficiency, clustering coefficient, and node…
29	3. Results	We used three classification algorithms to classify the middle-aged and elderly EEG, and the…
30	3. Results	Figure 5 shows the topographical map of mean global efficiency, local efficiency, and mean…
31	3. Results	Table 1 shows the good sensitivity and specificity values of KNN, and the highest Kappa value of…
32	3. Results	Table 4 shows the weighted average precision and the area under the curve (AUC) for ROC curve of all…
33	3. Results	The confusion matrices of all classifiers are presented for eyes-open, eyes-closed and visual WM…
34	3. Results	The Figure 11, Figure 12 and Figure 13 show the ROC plot of all three classifiers in eyes-open,…
35	4. Discussion	In this work, a middle-aged group was compared with an elderly group in eyes-open and eyes-closed…
36	4. Discussion	Our results indicate reduced network characteristics in terms of local efficiency, global…
37	4. Discussion	Our results in Figure 5 and Figure 6 show the transition from middle-aged to older age in the…
38	4. Discussion	task induced changes, including a reduced clustering coefficient and network efficiency, in the…
39	4. Discussion	Neuroanatomical changes are considered to cause cognitive decline during the process of aging. We…

Citation	PMID	DOI	Status
Achard, S. et al., PLoS Comput. Biol., 2007, Efficiency and cost of economical brain functional networks	17274684	10.1371/journal.pcbi.0030017	Cited
Autthasan, P. et al., IEEE Sens. J., 2019, A single-channel consumer-grade EEG device for brain–computer interface: Enhancing detection of SSVEP and its amplitude modulation	—	10.1109/JSEN.2019.2958210	Cited
Bansal, D. et al., Procedia Comput. Sci., 2018, Comparative analysis of various machine learning algorithms for detecting dementia	—	10.1016/j.procs.2018.05.102	Cited
Bassett, D.S. et al., Proc. Natl. Acad. Sci. USA, 2011, Dynamic reconfiguration of human brain networks during learning	21502525	10.1073/pnas.1018985108	Cited
Becker, H. et al., IEEE Trans. Affect. Comput., 2017, Emotion recognition based on high-resolution EEG recordings and reconstructed brain sources	—	10.1109/TAFFC.2017.2768030	Cited
Betzel, R.F. et al., Neuroimage, 2016, Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks	26687667	10.1016/j.neuroimage.2015.12.001	Cited
Bopp, K.L. et al., Psychol. Aging, 2009, Working memory and aging: Separating the effects of content and context	20025410	10.1037/a0017731	Cited
Breiman, L., Mach. Learn., 1996, Bagging predictors	—	10.1007/BF00058655	Cited
Bullmore, E. et al., Nat. Rev. Neurosci., 2009, Complex brain networks: Graph theoretical analysis of structural and functional systems	19190637	10.1038/nrn2575	Cited
Cai, L. et al., Neurocomputing, 2018, Reconstruction of functional brain network in Alzheimer’s disease via cross-frequency phase synchronization	—	10.1016/j.neucom.2018.07.019	Cited
Calhoun, V.D. et al., Neuron, 2014, The chronnectome: Time-varying connectivity networks as the next frontier in fMRI data discovery	25374354	10.1016/j.neuron.2014.10.015	Cited
Cassani, R. et al., Biomed. Signal Process. Control, 2017, Towards automated electroencephalography-based Alzheimer’s disease diagnosis using portable low-density devices	—	10.1016/j.bspc.2016.12.009	Cited
Chen, Y. et al., Neuroradiology, 2018, Age-related early/late variations of functional connectivity across the human lifespan	29383434	10.1007/s00234-017-1973-1	Cited
Chong, J.S.X. et al., J. Neurosci., 2019, Longitudinal changes in the cerebral cortex functional organization of healthy elderly	31109962	10.1523/JNEUROSCI.1451-18.2019	Cited
Costumero, V. et al., Sci. Rep., 2020, Opening or closing eyes at rest modulates the functional connectivity of V1 with default and salience networks	32499585	10.1038/s41598-020-66100-y	Cited
Davison, E.N. et al., PLoS Comput. Biol., 2015, Brain network adaptability across task states	25569227	10.1371/journal.pcbi.1004029	Cited
Delorme, A. et al., J. Neurosci. Methods, 2004, EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis	15102499	10.1016/j.jneumeth.2003.10.009	Cited
Dong, S. et al., Brain Res., 2015, Individual differences in working memory capacity are reflected in different ERP and EEG patterns to task difficulty	25976774	10.1016/j.brainres.2015.05.003	Cited
Duffy, F.H. et al., Ann. Neurol., 1984, Age-related differences in brain electrical activity of healthy subjects	6497352	10.1002/ana.410160403	Cited
Fjell, A.M. et al., Rev. Neurosci., 2010, Structural brain changes in aging: Courses, causes and cognitive consequences	20879692	10.1515/REVNEURO.2010.21.3.187	Cited
Gaál, Z.A. et al., Neurosci. Lett., 2010, Age-dependent features of EEG-reactivity—Spectral, complexity, and network characteristics	20560166	10.1016/j.neulet.2010.05.037	Cited
Gola, M. et al., Int. J. Psychophysiol., 2013, EEG beta band activity is related to attention and attentional deficits in the visual performance of elderly subjects	23688673	10.1016/j.ijpsycho.2013.05.007	Cited
Guevara, M.A. et al., Brain Res., 2018, EEG activity during the spatial span task in young men: Differences between short-term and working memory	29425909	10.1016/j.brainres.2018.02.004	Cited
Hinrichs, H. et al., Sci. Rep., 2020, Comparison between a wireless dry electrode EEG system with a conventional wired wet electrode EEG system for clinical applications	32251333	10.1038/s41598-020-62154-0	Cited
Hou, F. et al., Front. Hum. Neurosci., 2018, Age-related alterations in electroencephalography connectivity and network topology during n-back working memory task	30574079	10.3389/fnhum.2018.00484	Cited
Hussain, I. et al., Brain Sci., 2021, Quantitative evaluation of task-induced neurological outcome after stroke	34356134	10.3390/brainsci11070900	Cited
Hussain, I. et al., IEEE Access, 2020, HealthSOS: Real-time health monitoring system for stroke prognostics	—	10.1109/ACCESS.2020.3040437	Cited
Hussain, I. et al., Sensors, 2021, Driving-induced neurological biomarkers in an advanced driver-assistance system	34770304	10.3390/s21216985	Cited
Iordan, A.D. et al., Hum. Brain Mapp., 2021, Age differences in functional network reconfiguration with working memory training	33534925	10.1002/hbm.25337	Cited
Jacobs, E.G. et al., Cereb. Cortex, 2017, Reorganization of functional networks in verbal working memory circuitry in early midlife: The impact of sex and menopausal status	27178194	10.1093/cercor/bhw127	Cited
Jalili, M., Inf. Sci., 2017, Graph theoretical analysis of Alzheimer’s disease: Discrimination of AD patients from healthy subjects	—	10.1016/j.ins.2016.08.047	Cited
Jalili, M., Sci. Rep., 2016, Functional brain networks: Does the choice of dependency estimator and binarization method matter?	27417262	10.1038/srep29780	Cited
Knyazev, G.G. et al., Neurobiol. Aging, 2015, Age-related differences in electroencephalogram connectivity and network topology	25766772	10.1016/j.neurobiolaging.2015.02.007	Cited
La Corte, V. et al., Front. Aging Neurosci., 2016, Cognitive decline and reorganization of functional connectivity in healthy aging: The pivotal role of the salience network in the prediction of age and cognitive performances	27616991	10.3389/fnagi.2016.00204	Cited
Li, H.-J. et al., Neurosci. Biobehav. Rev., 2015, Putting age-related task activation into large-scale brain networks: A meta-analysis of 114 fMRI studies on healthy aging	26318367	10.1016/j.neubiorev.2015.08.013	Cited
Lotfan, S. et al., Biocybern. Biomed. Eng., 2019, Support vector machine classification of brain states exposed to social stress test using EEG-based brain network measures	—	10.1016/j.bbe.2018.10.008	Cited
Martínez-Cagigal, V., Topographic EEG/MEG Plot	—	—	—
Marvel, C.L. et al., Neuropsychol. Rev., 2010, Functional topography of the cerebellum in verbal working memory	20563894	10.1007/s11065-010-9137-7	Cited
Micheloyannis, S. et al., Hum. Brain Mapp., 2009, The influence of ageing on complex brain networks: A graph theoretical analysis	17990300	10.1002/hbm.20492	Cited
Miltiadous, A. et al., Diagnostics, 2021, Alzheimer’s disease and frontotemporal dementia: A robust classification method of EEG signals and a comparison of validation methods	34441371	10.3390/diagnostics11081437	Cited
Moezzi, B. et al., Neuroscience, 2019, Characterization of young and old adult brains: An EEG functional connectivity analysis	31806080	10.1016/j.neuroscience.2019.08.038	Cited
Newman, M.E.J., Phys. Rev. Lett., 2002, Assortative mixing in networks	12443515	10.1103/PhysRevLett.89.208701	Cited
Opsahl, T. et al., Soc. Netw., 2010, Node centrality in weighted networks: Generalizing degree and shortest paths	—	10.1016/j.socnet.2010.03.006	Cited
Ota, M. et al., Dement. Geriatr. Cogn. Disord., 2020, Structural brain network correlated with reading impairment in Alzheimer’s disease	32810848	10.1159/000508406	Cited
Padgaonkar, N.A. et al., Neurobiol. Aging, 2017, Predictive cues and age-related declines in working memory performance	27736673	10.1016/j.neurobiolaging.2016.09.002	Cited
Pellegrini, E. et al., Alzheimer’s Dement. Diagn. Assess. Dis. Monit., 2018, Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review	30364671	10.1016/j.dadm.2018.07.004	Cited
Peláez Suárez, A.A. et al., Behav. Sci., 2021, EEG-derived functional connectivity patterns associated with mild cognitive impairment in Parkinson’s disease	33806841	10.3390/bs11030040	Cited
Petti, M. et al., Comput. Intell. Neurosci., 2016, EEG resting-state brain topological reorganization as a function of age	27006652	10.1155/2016/6243694	Cited
Qiu, J.M. et al., Front. Hum. Neurosci., 2019, Assessing feedback response with a wearable electroencephalography system	31402858	10.3389/fnhum.2019.00258	Cited
Qureshi, S. et al., Turk. J. Electr. Eng. Comput. Sci., 2018, Human sleep scoring based on K-nearest neighbors	—	10.3906/elk-1805-12	Cited
Roux, F. et al., Trends Cogn. Sci., 2014, Working memory and neural oscillations: Alpha-gamma versus theta-gamma codes for distinct WM information?	24268290	10.1016/j.tics.2013.10.010	Cited
Rubinov, M. et al., Neuroimage, 2010, Complex network measures of brain connectivity: Uses and interpretations	19819337	10.1016/j.neuroimage.2009.10.003	Cited
Rypma, B. et al., Proc. Natl. Acad. Sci. USA, 1999, The roles of prefrontal brain regions in components of working memory: Effects of memory load and individual differences	10339627	10.1073/pnas.96.11.6558	Cited
Samson, V.R.R. et al., Proceedings of 2nd International Conference on Micro-Electronics, Electromagnetics and Telecommunications, 2018, Electroencephalogram-based OpenBCI devices for disabled people	—	—	—
Smit, D.J.A. et al., PLoS ONE, 2012, The brain matures with stronger functional connectivity and decreased randomness of its network	22615837	10.1371/journal.pone.0036896	Cited
Stam, C.J. et al., Hum. Brain Mapp., 2004, Scale-free dynamics of global functional connectivity in the human brain	15108297	10.1002/hbm.20016	Cited
Stam, C.J., Neurosci. Lett., 2000, Brain dynamics in theta and alpha frequency bands and working memory performance in humans	10825650	10.1016/S0304-3940(00)01109-5	Cited
Tan, B. et al., Comput. Math. Methods Med., 2013, The difference of brain functional connectivity between eyes-closed and eyes-open using graph theoretical analysis	23690886	10.1155/2013/976365	Cited
Teng, C. et al., Cogn. Neurodyn., 2018, Aging-related changes of EEG synchronization during a visual working memory task	30483364	10.1007/s11571-018-9500-6	Cited
Tian, L. et al., Neuroimage, 2018, Changes in dynamic functional connections with aging	29414496	10.1016/j.neuroimage.2018.01.040	Cited
Toppi, J. et al., Front. Hum. Neurosci., 2018, Different topological properties of EEG-derived networks describe working memory phases as revealed by graph theoretical analysis	29379425	10.3389/fnhum.2017.00637	Cited
Van den Heuvel, M.I. et al., Dev. Cogn. Neurosci., 2018, Hubs in the human fetal brain network	29448128	10.1016/j.dcn.2018.02.001	Cited
Van der Hiele, K. et al., J. Clin. Neurophysiol., 2008, EEG markers of future cognitive performance in the elderly	18340274	10.1097/WNP.0b013e31816a5b25	Cited
Vecchio, F. et al., Clin. Neurophysiol. Pract., 2017, Connectome: Graph theory application in functional brain network architecture	30214997	10.1016/j.cnp.2017.09.003	Cited
Vecchio, F. et al., J. Alzheimer’s Dis., 2014, Human brain networks in physiological aging: A graph theoretical analysis of cortical connectivity from EEG data	24820018	10.3233/JAD-140090	Cited
Vecchio, F. et al., Neuroscience, 2016, Cortical connectivity and memory performance in cognitive decline: A study via graph theory from EEG data	26724581	10.1016/j.neuroscience.2015.12.036	Cited
Vergauwe, E. et al., Neuroimage, 2015, Domain-general involvement of the posterior frontolateral cortex in time-based resource-sharing in working memory: An fMRI study	25944611	10.1016/j.neuroimage.2015.04.059	Cited
Von Stein, A. et al., Int. J. Psychophysiol., 2000, Different frequencies for different scales of cortical integration: From local gamma to long range alpha/theta synchronization	11102669	10.1016/S0167-8760(00)00172-0	Cited
Wang, J. et al., Phys. A Stat. Mech. Its Appl., 2016, Functional brain networks in Alzheimer’s disease: EEG analysis based on limited penetrable visibility graph and phase space method	—	10.1016/j.physa.2016.05.012	Cited
Wang, L. et al., Front. Aging Neurosci., 2017, Beta-band functional connectivity influences audiovisual integration in older age: An EEG study	28824411	10.3389/fnagi.2017.00239	Cited
Wang, X. et al., Front. Hum. Neurosci., 2019, Detection analysis of epileptic EEG using a novel random forest model combined with grid search optimization	30846934	10.3389/fnhum.2019.00052	Cited
Wu, K. et al., Front. Hum. Neurosci., 2013, A longitudinal study of structural brain network changes with normal aging	23565087	10.3389/fnhum.2013.00113	Cited
Yu, H. et al., Front. Neurosci., 2020, Identification of Alzheimer’s EEG With a WVG network-based fuzzy learning approach	32848530	10.3389/fnins.2020.00641	Cited
Zalesky, A. et al., Proc. Natl. Acad. Sci. USA, 2014, Time-resolved resting-state brain networks	24982140	10.1073/pnas.1400181111	Cited
Zhang, J. et al., Brain, 2016, Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders	27421791	10.1093/brain/aww143	Cited
Zhu, W. et al., Neurobiol. Aging, 2012, Changing topological patterns in normal aging using large-scale structural networks	20724031	10.1016/j.neurobiolaging.2010.06.022	Cited

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