Ethics Statement

Ethics approval was obtained from the School of Medicine, São Paulo University. All recruited participants provided written consent.

Participants

Seventy six participants were recruited through the Reference Center of Behavioral Disturbances and Dementia at the Clinical Hospital, School of Medicine, São Paulo University. AD diagnosis was made according to NINCDS-ADRDA [25] and DSM-IV-TR [26] criteria and disease progression classification was based on the Brazilian version of the Mini-Mental State Examination (MMSE) [27] and the Clinical Dementia Rating (CDR) scale. These 76 participants were separated into three age-matched groups, namely NS, AD1 and AD2. Group NS comprised 27 healthy older adults (age: , 15 female), group AD1 was composed by 27 mild-AD patients (, 16 female) and the third group (AD2) included 22 patients with moderate AD (, 15 female). All subjects from the NS group had , for the AD1 group the inclusion criterion was , and the AD2 group only included patients with . The mean MMSE scores of three cohorts were significantly different at and , respectively. The two AD cohorts were education matched (AD1: years; AD2: years) but the healthy controls had a significantly higher level of education (NS: years). Participants had no history of diabetes mellitus, kidney diseases, thyroid diseases, alcoholism, liver disease, lung disease, or vitamin B12 deficiency, factors which could also lead to cognitive impairment.

Data Collection and Pre-processing

Twenty-channel EEG signals were collected using the Braintech 3.0 instrumentation (EMSA Equipamentos Médicos Inc., Brazil), digitized with a 12-bit analog-to-digital converter and sampled at a rate of 200 Hz; impedance was maintained below 10 k. Placement of scalp electrodes (referential montage) followed the international 10–20 system. Linked-ear referential electrodes (A1 and A2) were used, as recommended by the Brazilian Society of Clinical Neurophysiology and the American EEG Society. During examination, EEG was recorded with the participants awake and resting with their eyes closed. An infinite impulse response low-pass elliptic filter with a zero at 60 Hz was applied to eliminate any power grid interference. For each participant, between 28 and 40 eight-second epochs (mean 37.97, sd 3.86) were selected per EEG channel by experienced physicians. The selected epochs were free of eye movement, electromyographic activity, and head motion artifacts. Furthermore, to ameliorate the effects of different amplifier/impedance settings between channels and participants, EEG epoch data for each channel was normalized by the total EEG power present in the given channel.

EEG Spectro-temporal Amplitude Modulation Analysis

Figure 1 depicts the signal processing steps involved in the spectro-temporal EEG amplitude modulation analysis technique. First, the full-band EEG signal is decomposed into five sub-bands, well-known in the literature as the delta ( Hz), theta ( Hz), alpha ( Hz), beta ( Hz), and gamma ( Hz) bands [28]. The temporal envelope of each of the five sub-band EEG signals is then computed by means of a Hilbert transform [29]. The subplots on the right of Fig. 1 illustrate representative EEG sub-band signals (gray) and their respective Hilbert amplitude envelopes (black). In order to quantify the temporal dynamics of the sub-band envelopes, we perform a second frequency decomposition into five so-called modulation bands using second-order bandpass modulation filters (with quality factor ). The resulting frequency-frequency signal representation conveys rate-of-change information of each of the five sub-band envelopes.

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Figure 1. Signal processing steps used to compute resting EEG spectro-temporal modulation energy.

Subplots on the right illustrate the five sub-band EEG signals (gray) and their respective Hilbert temporal envelopes (black).

https://doi.org/10.1371/journal.pone.0072240.g001

In order to explore possible cross-frequency modulation interaction, the ranges of the modulation frequency bands were empirically designed to coincide with the ranges of the conventional frequency bands. Cross-frequency amplitude modulation interaction is an under explored area that may lead to insights into complex temporal pattern changes with different stages of AD [30]. To distinguish between modulation and frequency bands, we will refer to the former as m-delta ( Hz), m-theta ( Hz), m-alpha ( Hz), m-beta ( Hz), and m-gamma ( Hz) throughout the remainder of this paper. From properties of the Hilbert transform and following Bedrosian’s theorem, the envelope signal can only contain frequencies (i.e., modulation frequencies) up to the bandwidth of its originating signal [31], [32]. Having this said, it only makes sense to compute cross-frequency amplitude modulation interaction (here represented by “E(frequency band; modulation band),” the percentage of modulation energy present in a given frequency and modulation band relative to the energy across all bands) for the following scenarios: E(delta; m-delta), E(theta; m-delta, m-theta), E(alpha; m-delta, m-theta), E(beta; m-delta, m-theta, m-alpha, m-beta), and E(gamma; m-delta, m-theta, m-alpha, m-beta, m-gamma). In our experiments, these 14 percentage cross-frequency modulation parameters are computed for each of the 19 EEG channels. The interested reader is referred to [23] for a more detailed description of the signal processing steps involved.

EEG Power Spectrum Analysis: Benchmark

A prominent change reported in the literature between AD and healthy controls is that of ‘EEG slowing’. This slowing is commonly measured as an increase in EEG power in the delta and theta frequency bands and a decrease in spectral power in the alpha and beta bands, particularly in the occipital and temporo-parietal regions [13], [33]–[37]. In order to benchmark, as well as test the complementarity of the proposed spectro-temporal features, a conventional EEG power spectrum analysis was also performed across the five well-known frequency bands.

Statistical and Correlation Analyses

Statistical significance was established at 1% level for all tests. Normality was verified for all parameters using a Jarque-Bera test [38] with critical values computed by Monte-Carlo simulation [39]. For normal distributed features, one-way ANOVA with parameters across the three groups at different locations on the scalp was used. A non-parametric Kruskal-Wallis test was applied when the assumption of normal distribution was not confirmed for a given feature. In either case, a Dunn-Sidak post-hoc test was used for multiple comparisons correction. Additionally, estimates of reliability were obtained using split-half Pearson correlations. In this reliability test, epochs from each participant were separated into two disjoint subsets assuring that no temporal overlap existed between the epochs from each subset, thus consequently assuring statistical independence between the observations. Pearson correlations were then computed between the two subsets; this was done separately for the healthy participants, and mild- and moderate-AD patients.

Characterizing Mild-Stage AD

Previous EEG studies have shown significant increases and decreases in theta and alpha spectral powers, respectively, in mild-stage AD relative to healthy controls (e.g., [13], [36]). Here, we have shown that not only are the aforementioned frequency band powers affected (particularly in occipital and parietal regions), but also how their envelopes are modulated over time, thus potentially providing complementary information over traditional spectral insights, as shown by [24]. In relationship to the DFA-based amplitude analysis proposed by [21], we have also found decreased alpha modulation in parietal regions but did not find significant differences in prefrontal theta modulations. As such, further studies are needed to investigate the complementarity of the DFA and spectro-temporal based amplitude analysis techniques. Moreover, we have found significant increases in delta modulations of the delta frequency band across several temporal, parietal, and occipital regions. Previous literature, however, has shown an increase in delta band power [40] and a decrease in beta band power [13] only in late-stage AD, with only subtle a increase in delta band power seen with mild-AD in the occipital regions [36]. These findings suggest that changes in slow-wave envelope dynamics may be detectable at earlier stages of the disease, thus potentially assisting clinicians with earlier diagnostics.

Characterizing Disease Progression

From Tables 1–2 and Figures 2–3, it can be seen that as disease severity progressed (AD1 to AD2), further increases in theta band amplitude modulations occurred across a wider network of brain regions. Moreover, a decrease in beta band amplitude modulation across temporal, central, parietal, and occipital regions was observed, thus corroborating previous findings on the shift of beta band power to more anterior positions as disease progresses [41]. Notwithstanding, previous literature has observed increases in theta band and decreases in beta band powers across all brain regions only in patients with severe AD (CDR = 3) [13]. Being able to characterize amplitude modulation changes at earlier stages of the disease may allow for quantitative treatment outcome measures to be developed, thus improving disease progression monitoring. Moreover, using conventional power spectrum analysis, it was found that most significant differences in beta power occurred in the left hemisphere, thus corroborating previous findings [37]. With the proposed spectro-temporal features, on the other hand, significant differences were found across the two hemispheres, with beta m-alpha differences, for example, occurring between inter-hemispheric mirror-image locations P3–P4 and C3–C4. Such findings may be linked to the inter-hemispheric disconnection previously-reported for AD [16].

Characterizing Cross-Frequency Interactions: Some Hypotheses

An advantage of the used signal processing tool is its capability to characterize amplitude-amplitude cross-frequency interaction, thus potentially leading to greater insight into the changes in functional dynamics of brain systems with AD progression. For example, it was observed that beta rhythms were mostly modulated at a rate that coincides with the theta band frequency (see Table 2), with decreased interaction (lower modulation energy) as disease progressed (AD2 vs AD1). Interestingly, reduced beta-theta interaction has been attributed to lower reward-gain motivation [42], a behavioural and psychological symptom observed with disease progression [43].

Moreover, from Tables 1–2 and Fig. 3, it can be seen that a significant beta-delta interaction disappeared when comparing the AD2 vs AD1 groups relative to AD1 vs NS. Decoupling of beta-delta interaction has been associated with behavioural activation and fearlessness [44], [45], a symptom associated with frontal lobe impairments commonly observed with late-stage AD [46]. On the other hand, existence of beta-delta interaction has been associated with anxiety [47], [48], a prevalent neuropsychiatric symptom observed in patients with mild cognitive impairment and mild-AD [49], [50], thus corroborating the findings presented in Table 1 for mild-AD patients. It can also be observed from the Tables that there was a significant drop in beta-alpha interaction (lower modulation energy) as the disease severity increased across multiple temporal, parietal, central, and occipital regions. Decreased beta-alpha phase coupling has been previously linked with lower cognitive ability [51], a symptom commonly observed and expected from AD and its progression. Further studies are needed to investigate if the obtained amplitude modulation interaction findings are also related to cognitive load.

Lastly, the most significant change observed from the AD1 vs NS and AD2 vs AD1 comparisons was with the theta-delta cross-frequency interactions (see Tables 1–2 and Fig. 2). In the former scenario, significant differences only occurred in the occipital regions, whereas in the latter, they occurred across almost all tested sites, spanning pre-frontal, frontal, temporal, central, parietal and occipital regions. In transgenic mouse models of Alzheimers disease, increased theta-delta interaction has been observed in non-rapid eye movement sleep EEG relative to control wild type mice, with more prominent increases occurring with age, i.e., directly related to increased amyloid beta deposition [52], [53]. While a direct comparison with humans is not possible, this is an interesting finding as amyloid beta deposits can be found in abundance in the occipital cortex of demented individuals [54]. Further studies are needed to validate the relationship between theta-delta interaction and amyloid deposits, as such relationship may be useful in very early diagnosis of AD.

Study Limitations

Findings reported here are based on a sample size of 76 participants, divided into three groups. Future studies should focus on a larger, more gender-balanced participant pool, as gender differences may also play a factor, as reported by [55]. Notwithstanding, a prevalence of AD within women has been previously reported [55], [56]. Moreover, an effort was made to achieve education-matched groups; however, this could only be achieved with the AD1 and AD2 patient groups, thus corroborating the previously-reported link between AD and years of schooling [56]–[58]. Lastly, while this study looked at differences between healthy older adults and early-stage AD, future studies should focus on EEG amplitude modulation differences between healthy controls and patients with mild cognitive impairment.