A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses

Stavros I Dimitriadis, Nikolaos A Laskaris, Malamati P Bitzidou, Ioannis Tarnanas, Magda N Tsolaki, Stavros I Dimitriadis, Nikolaos A Laskaris, Malamati P Bitzidou, Ioannis Tarnanas, Magda N Tsolaki

Abstract

The detection of mild cognitive impairment (MCI), the transitional stage between normal cognitive changes of aging and the cognitive decline caused by AD, is of paramount clinical importance, since MCI patients are at increased risk of progressing into AD. Electroencephalographic (EEG) alterations in the spectral content of brainwaves and connectivity at resting state have been associated with early-stage AD. Recently, cognitive event-related potentials (ERPs) have entered into the picture as an easy to perform screening test. Motivated by the recent findings about the role of cross-frequency coupling (CFC) in cognition, we introduce a relevant methodological approach for detecting MCI based on cognitive responses from a standard auditory oddball paradigm. By using the single trial signals recorded at Pz sensor and comparing the responses to target and non-target stimuli, we first demonstrate that increased CFC is associated with the cognitive task. Then, considering the dynamic character of CFC, we identify instances during which the coupling between particular pairs of brainwave frequencies carries sufficient information for discriminating between normal subjects and patients with MCI. In this way, we form a multiparametric signature of impaired cognition. The new composite biomarker was tested using data from a cohort that consists of 25 amnestic MCI patients and 15 age-matched controls. Standard machine-learning algorithms were employed so as to implement the binary classification task. Based on leave-one-out cross-validation, the measured classification rate was found reaching very high levels (95%). Our approach compares favorably with the traditional alternative of using the morphology of averaged ERP response to make the diagnosis and the usage of features from spectro-temporal analysis of single-trial responses. This further indicates that task-related CFC measurements can provide invaluable analytics in AD diagnosis and prognosis.

Keywords: ERPs; cognitive impairment; connectomic biomarkers; dynamic coordination; dynome; functional connectomics; phase-amplitude coupling.

Figures

Figure 1
Figure 1
The algorithmic steps for PAC estimation. Using the first single-trial signal (A), from the cognitive responses of a control subject, we demonstrate the detection of coupling between θ and β1 rhythm. To estimate θ-β1 PAC, the raw signal was band-pass filtered into both a (B) low-frequency θ (4–8 Hz) component where its envelope is extracted as well as (C) a high-frequency β1 (13–20 Hz) component where its instantaneous phase is extracted. (D) We then extracted the amplitude and the instantaneous phase of the band-passed β1 (13–20 Hz) and filtered this amplitude time series at the same frequency as θ (4–8 Hz), giving us the θ modulation in lower β amplitude. (E) We then extracted the instantaneous phase of both the θ-filtered signal and the θ-filtered lower-β amplitude and computed the phase-locking between these two signals. The latency depended differences (F), will be used in estimating the phase-locking that will reflect the PAC-interaction between the two involved brain rhythms. This phase-locking represents the degree to which the lower β (β1) amplitude is comodulated with the θ phase.
Figure 2
Figure 2
Across-trials PAC-estimation. By repeating the steps shown in the previous figure, the instantaneous phase differences for the whole set of single-trials have been computed (A,B). The TVPAC trace [reflecting PLV(t) measurements for θ → β1 interaction], at full temporal resolution, is shown (C), together with the ensemble average waveform (from the wideband signals). TVPAC traces from a stepping window (of various widths) are shown in D).
Figure 3
Figure 3
An approximation of the temporal patterning of oscillatory cognitive responses by means of Grand Averaging. The GA-traces for Non-impaired controls (A), and aMCI patients (B), have been derived independently for each brain rhythm. Using the corresponding temporal patterns from all the 40 participants, the separability between aMCI patients and NI controls has been measured at every latency and for each brain rhythm. A common scale is used for all the traces within the same stack. (C) To demonstrate the temporal separability of oscillatory responses, Wscore is presented for each individual brain rhythm.
Figure 4
Figure 4
Contrasting the TVPAC profiles from the responses (of an NI subject) to target and non-target stimuli. (A) The averaged evoked/event-related response is shown in black/blue. (B) A rough summary of TVPAC measurements estimated by means of averaging across all frequency-pairs (LF, HF), independently for each latency. (C) A summarizing profile derived by keeping the maximum PLV value at every latency.
Figure 5
Figure 5
Contrasting the CFC during responses to target and non-target stimuli based on group-averaged data from NI subjects. (A) The N100, N200, P300, and SW deflections were first detected in the waveform of Grand-Averaged ERPs response. (B) Using the corresponding temporal segments, PAC-related connectivity snapshots were then derived, for target and non-target stimuli separately, and then used to express the relative increase in coupling. A common color scale was used across for all shown graphs.
Figure 6
Figure 6
Comparing the level of CFC in cognitive responses, between NI and aMCI participants. (A) Grand-Averaged waveforms from the cognitive responses (AERPs) of both groups. (B,C) Group-related (grand-averaged) PAC-connectivity patterns for the temporal-segments corresponding to N100, N200, and P300 deflections. (D) The corresponding patterns of relative differences, derived so as to express deviation from normality; red/blue indicates higher/lower PAC levels in MCI subjects relatively to NI subjects.
Figure 7
Figure 7
Identifying discriminative PAC-interactions (group-level analysis). (A) The temporal profile of the (quasi-instantaneous) maximal separability measure and the identification of the timing of most discriminative PAC couplings. The 9 red discs indicate the local maxima in the timecourse. (B) A graphical representation of the maximal PAC-couplings (stacked across time). (C) Snapshots of differences between grand-averaged PAC-patterns, at instances of high discriminability. The shown graphs correspond to the 9 segments detected in (A). Positive/negative values of Relative-Difference indicate higher/lower PAC for the MCI participants relatively to NI participants. To enhance visibility, edges associated with a Wscore* lower than 2 are not shown.

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