Edited by: Nicola Toschi, University of Rome Tor Vergata, Italy
Reviewed by: Krasimira Tsaneva-Atanasova, University of Exeter, United Kingdom; Luis Diambra, National University of La Plata, Argentina; Maria Grazia Puxeddu, Sapienza University of Rome, Italy
This article was submitted to Biophysics, a section of the journal Frontiers in Physics
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Given the majority of age-related diseases have been described as disconnection syndromes, understanding the functional connections of normal aging is of considerable importance. Here, an EEG-based scalp level analysis has been performed to identify the alterations in the synchronized brain regions in aged, compared to young persons. Two groups, aged and young subjects were studied, each consisting of 18 participants. First, conventionally extracted broadband topographic maps, also called microstate maps, were examined. The results showed an overall dominant alteration: a uniform decrease in synchronization of brain regions related to cognitive processing resources that was observed only when the maps C and D were characterized in temporal parameters. However, no remarkable change in the spatial distribution was found between the groups. This failure in identifying differences in the spatial distribution was hypothesized to be due to the presence of superimposed signals of several frequencies in the broadband signal that is used for the extraction of microstate maps. Second, spectrally resolved band-wise topographic maps, which we have shown, in a previous study, are able to detect spectral details associated with broadband microstates maps, were used to address this failure. The use of the instantaneous frequency concept is essential in the extraction of band-wise topographic maps, and represents a novelty compared to current studies. The method consists of three steps: (a) from EEG signal, the Empirical Mode Decomposition method is used to extract underlying oscillatory components; (b) these intrinsic oscillatory components are then amplitude demodulated and subjected to numerical equations for the calculation of instantaneous features, such as amplitude, and frequency; finally, (c) based on these instantaneous features, band-wise topographic maps are extracted. Here, as a first application to aging data, these band-wise topographic maps have shown the capability of capturing the age-related changes in both spatial distributions, and in temporal characterization. Spatially, the potential distribution in the aged and the young subject groups, respectively, showed differences, while, in temporal characterization, both increases and decreases were observed, suggesting the lengths of synchronized activities vary differentially, and in accordance with results from fMRI studies. These observed differences also support the dedifferentiation and compensation mechanisms.
Numerous studies have shown age-related alterations in functional connectivity of brain regions while evaluating task-based performance as well as during rest, likely ensuing from a decline in cognitive performance. Intra and inter-networks changes in functional connectivity of resting-state networks have been recurrently reported [
In the literature, the complex nature of aging-related changes is based on two main hypotheses i.e., dedifferentiation and compensation. First, dedifferentiation is the term used to explain the loss of underlying functional resources required to perform the given task [
In recent years, research on brain changes related to aging increasingly relied on functional magnetic resonance imaging (fMRI). Numerous insights were provided e.g., key brain areas like the anterior cingulate cortex involved in emotional and cognitive processing has been found to be significantly affected by aging, even when its functional connections were investigated during rest [
In EEG data analysis, several methods have been used to assess coupling and synchronizations among EEG signals [
These considerations encouraged the present analysis to explicitly investigate aging-related resting-state alterations using microstate analysis. A related work in which microstates analysis was used to study developmental stages of brain was published in 2002 by Koenig et al. [
Eyes closed resting-state EEG data were recorded in 36 healthy subjects equally divided into aged and young adults. The aged subjects group ranged between the ages of 62–85 years (mean age: 71.8 ± 5.6, 12 males), whereas, young subjects had age ranging from 19 to 31 years (mean age: 23.2 ± 4.1, 12 males). Scalp potentials were measured using Electrical Geodesics sensor net. No subject had a history of neurological disorders, head injuries causing loss of consciousness or mental illness. All subjects were right-handed, tested and confirmed by Edinburgh Manuality test. The acquisitions were performed at, and under the ethical guidelines of “Gabriele d'Annunzio” University of Chieti, after signed written informed consent. The subjects were instructed to close their eyes while staying conscious.
The analysis has been performed for spatiotemporal assessment of the EEG data in two ways. First, the conventional microstate analysis was implemented using the well-established standardized procedure [
where
For an optimal selection of a number of microstates, the cluster size (number of microstates in the cluster) was varied from 2 to 7. The optimality criteria consisted of Cross Validation (CV)—a modified version of the predictive residual variance [
Calculate GFP waveform by computing standard deviation across electrodes for each time point.
Find time points where GFP waveform has local peaks.
Input topographic maps of EEG potential at time points found in step 2 to a clustering algorithm.
Pre-assign cluster size or set criteria for optimal selection of microstate maps.
Repeat clustering algorithm for multiple time (300 iterations performed commonly) to identify microstate maps explaining maximum variance present in the data.
The EEG data were segmented into a topographic sequence of extracted group averaged four microstates as shown in
Example of a representative young subject,
Second, the conventional microstate analysis was extended to spectrally resolved topographic analysis using band-wise topographic maps [
where,
with
where
where,
Extract IMFs for a pre-processed signal as in equation 2 by employing CEEMD algorithm.
Calculate instantaneous frequencies (IFs) and instantaneous amplitudes (IAs) for each IMF and for all time-samples using equation 3 and 5, respectively.
Define frequency bands (e.g. δ, θ, α, β, and γ) and construct their amplitude-time-series based on above calculated IAs and IFs i.e., by assigning IA of given sample to the frequency band determined by IF of that sample. This is repeated for all IMFs and resultants are summed up in respective frequency bands to get single amplitude-time-series.
Above steps are repeated for all electrodes in a data individually.
After construction of band's amplitude time series, conventional microstate procedure as explained above is applied to get topographic maps for each band.
Optimality criterion is applied for each band's topographic maps to get final set of band-wise topographic maps.
The EEG data were spectrally transformed into five fundamental EEG bands based on the estimated IFs at each time point, providing the same temporal resolution as in the time domain EEG data. As will be shown below, the preserved timescale allowed us to analyze spatial patterns at each frequency band and to identify the differences between young and aged subjects that could not be captured by conventional microstate analysis due to the use of full band data. The procedure [
Moreover, the differences between the aged and young subjects in temporal dynamics of the topographic sequence are quantitatively analyzed for both conventional and band-wise topographic analysis using the following parameters:
- Mean-duration (MD): average stability time of each microstate.
- Frequency-of-occurrence (FO): average number of appearances of each microstate within a window size of 1 min.
- Coverage (Cov): the ratio of time covered by each microstate per total time.
- Transition-probability-matrix: the probability of each microstate transiting into other microstates e.g., transition probability of microstate A to microstate B symbolized by A → B. For example, in resting-state literature, it has been found that, on average, twelve transitions between microstates can occur in a second if the number of microstates is four.
In addition to these parameters, EV is also calculated to demonstrate the fit percentage of extracted microstate maps to the EEG data for both groups. Whereas, for spatial changes, the dissimilarity index has been calculated. The dissimilarity index represents the strength of spatial similarity, the value of which ranges from 0 to 2 with 0 representing the same spatial configuration with similar polarity and 2 for the same spatial configuration with inverted polarity. It should be noted that instead of strictly restricting the definition of similarity to these extremes, we used the range of 0–0.2 and 1.8–2 for similar and inverted polarity configuration, respectively, in our study to account for the variance induced due to averaging of maps across subjects (i.e., group averaged topographic maps).
As mentioned in above section, the analysis is performed in two ways and their results highlighting the differences between two groups in respective analysis are presented in separate subsections below.
Based on optimality criteria, for the conventional microstate analysis, four microstate maps are found to be optimal for both young and aged subject group. Four microstate maps are also found to be consistent with the normative and existing literature of microstate analysis. Based on resemblance in the topographic configurations of extracted microstate maps from both groups with the existing literature, standard labels of A, B, C, and D are assigned as shown in
Four group-microstate maps extracted from young and aged datasets separately. The maps are labeled conventionally based on maximum resemblance.
The repeated measures ANOVA (rmANOVA) has been separately (2 × 4) conducted for the three metrics that include duration, frequency of occurrence, and coverage. Each rmANOVA had one factor for groups (Aged, or Young) and one factor for microstate maps (A, B, C, or D). The difference in mean values for metrics presented in
Bars representing average values of microstate metrics calculated for both aged (red) and young (green) subject groups to visualize within group differences for each group-microstate maps.
Statistical comparison of microstate temporal dynamics in aged and young subjects.
Group | 1;36 | 5.538 | |||||
Map | 3;72 | 16.143 | |||||
Group* Map | 3;72 | 10.919 | 0.902 | 0.819 | 0.056 | ||
Group | |||||||
Map | 3;72 | 10.403 | |||||
Group* Map | 3;72 | 1.684 | 0.182 | 0.232 | 0.339 | 0.023 | 0.913 |
Group | |||||||
Map | 3;72 | 15.270 | |||||
Group* Map | 3;72 | 7.732 | 0.025 | 0.058 | 0.636 |
Additionally, the syntax analysis, i.e., analyzing the non-randomness or directional dominance in the microstate transitioning, probabilities for each transition pair (in total: twelve pairs, e.g., X↔Y represents two pairs that are X → Y and X←Y) of four microstates are calculated. Our analysis reports discernable patterns for aged and young subjects group i.e., directional dominance is always found opposite (i.e., for example, if aged subjects group has dominant transition from A to B, then young subjects group found having dominant transitions from B to A) for each pair as shown in
Directional predominance: difference between transition probabilities of each pair i.e., X↔Y = (X → Y)-(X←Y). The sign indicating dominant direction [+ve = (X → Y) and –ve = (X←Y)]. The values are averaged across subjects in respective groups. Asterisk is for significant differences (
Apart from evaluation of age-related changes in the temporal parameters of conventional microstates, spatial changes across groups are also quantified using the dissimilarity index. The results are presented in
Dissimilarity index among the group averaged microstates of young and aged subjects group.
A | 1.89 | 1.39 | 1.84 | 1.33 |
B | 1.92 | 1.66 | 1.31 | |
C | 1.92 | 1.47 | ||
D | 0.69 |
In this analysis, three topographic maps are found optimal for each band in both groups using the same optimal map selection procedure explained in conventional microstate analysis. The topographic maps of each band are presented in
Band-wise group averaged topographic maps of young subject data in
Like conventional microstate analysis, the temporal dynamics of band-wise topographic segmentation are also analyzed. Same metrics: mean duration, frequency of occurrence, and coverage are calculated for all band maps i.e., D1, D2, D3 of the delta, T1, T2, T3 of theta, A1, A2, A3 of alpha, B1, B2, B3 of beta and G1, G2, G3 of gamma band. The results are presented in
Average values along with standard deviations of microstate metrics:
Statistical analysis of the temporal dynamics of band-wise topographic maps in aged and young subjects.
Group | 1;36 | 10.2 | 0.005 | 6.1 | 0.024 | 10.3 | 0.005 | 10.3 | 0.005 | 7.4 | 0.014 |
Map | 2;54 | 41.7 | 0.000 | 2.7 | 0.078* | 30.9 | 0.000 | 30.9 | 0.000 | 17.3 | 0.000 |
Group* Map | 2;54 | 10.8 | 0.000 | 2.9 | 0.064* | 30.5 | 0.000 | 30.5 | 0.000 | 10.5 | 0.000 |
Group | 1;36 | 7.4 | 0.014 | 7.4 | 0.014 | 15.3 | 0.001 | 8.0 | 0.12 | 10.9 | 0.004 |
Map | 2;54 | 17.3 | 0.000 | 17.3 | 0.000 | 30.9 | 0.000 | 9.6 | 0.000 | 49.6 | 0.000 |
Group* Map | 2;54 | 10.5 | 0.000 | 10.5 | 0.000 | 15.5 | 0.000 | 2.6 | 0.089* | 3.4 | 0.044 |
Group | |||||||||||
Map | 2;54 | 49.5 | 0.000 | 48.5 | 0.000 | 38.8 | 0.00 | 18.0 | 0.00 | 32.3 | 0.00 |
Group* Map | 2;54 | 3.4 | 0.044 | 9.5 | 0.001 | 35.5 | 0.00 | 7.2 | 0.03 | 5.6 | 0.08* |
A
Delta | D1 | 0.00 | 0.000 | |
D2 | 0.001 | 0.005 | 0.005 | |
D3 | 0.005 | |||
Theta | T1 | |||
T2 | 0.003 | |||
T3 | 0.007 | |||
Alpha | A1 | 0.000 | 0.000 | |
A2 | 0.000 | 0.000 | ||
A3 | 0.000 | 0.000 | ||
Beta | B1 | 0.001 | 0.003 | |
B2 | 0.001 | 0.002 | ||
B3 | 0.004 | |||
Gamma | G1 | 0.013 | ||
G2 | 0.009 | |||
G3 | 0.003 | 0.004 |
In addition to the analysis of temporal dynamics, the dissimilarity index has been used to quantify the spatial changes between groups. The dissimilarity index has been calculated across the band-wise topographic maps to give us intra and inter-band similarities if there exist any between two groups. The results averaged across subjects are presented in
Intra and inter band dissimilarity Indices between topographic maps of young (x-axis) and aged (y-axis) subject groups.
In this study, by means of band-wise microstate analysis, we have for the first time, to the best of our knowledge, observed age-related EEG differences in spectrally resolved, spatial domain, scalp EEG data. Conventional microstate analysis which constructs spatially synchronized topographies using the whole bandwidth of EEG data was also used. This conventional analysis served few purposes in the study. First, the extent to which age-related changes are identifiable using broad-band EEG data was still to be analyzed in detail. Second, this provided a reference for comparison of the band-wise topographic method which can be considered as a spectral extension of the former. Third, due to our recent study [
A relevant work of Koenig et al. [
Temporal parameters of microstate analysis have their own neurophysiologic significance. The Mean Duration (MD) is representative of stability in underlying neuronal patterns, the Frequency of Occurrence (FO) is representative of propensity of specific neuronal generators to be activated in a given time-period, and coverage is interpreted as the amount of time neuronal generators remain dominant [
Furthermore, we have also computed the syntax of microstate-based segmentation of EEG. The results in
Besides inferring that the change in microstate D is due to compensatory activity, to further support the link of observed changes in our conventional microstate analysis with the dedifferentiation and compensatory mechanisms, we highlight that, in
One possible reason which we thought of to help us solve this issue of observing age-related changes spatially at scalp-level analysis, was to spectrally decompose the data. The intuition behind is that we did not observe the age-related changes occurring in local brain areas could be due to the amalgamation of signals of different frequencies into one signal which will consequently describe only the prominent change even if multiple changes have occurred at different frequencies. In such a case, it would be reasonable to assume that the failure in capturing such changes could be due to the use of broad-bandwidth of the signal for the extraction of the conventional microstate maps. Therefore, we hypothesized that decomposing spectrally the EEG signals, and then evaluating the spatial patterns could capture the complex changes which are already known from fMRI studies. This brings forth the need to apply the band-wise topographic analysis to investigate differences between young and aged subjects.
To strengthen our point of using separate microstate maps for young and aged subjects, EV has been calculated for band-wise topographic maps at each frequency band using EEG data. The statistically significant (
On the other hand, in the spatial domain, from
One of the most frequently reported age-related factors is the change in cognitive and perceptual systems, which may consequently affect behavior. In turn, the majority of age-related diseases, including Alzheimer, which are related to these systems, are reported as disconnection syndromes. Therefore, the need to carry out this work lies in the importance of identifying the scalp-level electrophysiological correlates of fMRI findings. As it is believed that the results found via different modalities, more so with the one that directly measures neuronal potentials, and recent analysis tools, will be helpful in developing consensus over aging-related alterations; inching closer to the underlying mechanism which is still elusive, and consequently helping in limiting the differences between young and elder brain. In this work, we first showed that conventional microstate analysis can only identify the prominent changes in normal aging and is unable to detect complex changes. However, to conclude on results of conventional microstate analysis if one wants to use it for, let say, identification of any potential electrophysiological biomarkers of a given disease, we suggest using separate microstate maps for young and aged subject groups. Second, to get further insights, we applied our recently proposed band-wise topographic analysis which has shown more sensitivity in detecting the changes between the young and aged groups. However, we are constrained in drawing conclusions on their relevance since, to the best of our knowledge, this is the first study evaluating spectrally resolved spatial changes of aging. And unlike conventional microstate analysis where the corresponding resting state networks are known for each microstate map, a simultaneous study of EEG and fMRI is an imminent future prospect for band-wise topographic analysis to unfold its functional significance. Having said that, it is also important to mention that the band-wise topographic method has shown the glimpse of advancements that could converge the efforts of linking the results from different modalities to one another.
The datasets generated for this study are available on request to the corresponding author.
The studies involving human participants were reviewed and approved by Comitato di Bioetica Università Gabriele D'Annunzio, Chieti, Italy. The patients/participants provided their written informed consent to participate in this study.
EJ with the supervision of CD contributed to design, implementation, and writing of the manuscript. PC and FZ collected and pre-processed data. PC provided technical assistance during implementation phase. All authors contributed to the analysis and discussed results.
The authors declare that the research has been conducted independent of any financial or commercial interests.