Recurrence analysis of the EEG during sleep accurately identifies subjects with mental health symptoms

An original method for staging sleep based on dynamical analysis of a single EEG signal

BACKGROUND

The dynamical complexity of brain electrical activity manifested in the EEG is quantifiable using recurrence analysis (RA). Employing RA, we described and validated an originative method for automatically classifying epochs of sleep that is conceptually and instrumentally distinct from the existing method.

NEW METHOD

Complexity in single overnight EEGs was characterized second-by-second using four RA variables that were each averaged over consecutive 30-sec epochs to form four-component vectors. The vectors were staged using four-component cluster analysis. Method validity and utility were established by showing: (1) inter- and intra-subject consistency of staging results (method insusceptible to nonstationarity of the EEG); (2) use of method to eliminate costly and arduous visual staging in a binary classifications task for detecting a neurogenic disorder; (3) ability of method to provide new physiological insights into brain activity during sleep.

RESULTS

RA of sleep-acquired EEGs yielded four continuous measures of complexity and its change-rate that allowed automatic classification of epochs into four statistically distinct clusters ("stages"). Matched subjects with and without mental distress were accurately classified using biomarkers based on stage designations.

COMPARISON WITH EXISTING METHODS

For binary-classification purposes, the method was cheaper, faster, and at least as accurate as the existing staging method. Epoch-by-epoch comparison of new versus existing methods revealed that the latter assigned epochs having widely different dynamical complexities into the same stage (dynamical incoherence).

CONCLUSIONS

Sleep can be automatically staged using an originative method that is fundamentally different from the existing method.

Transthyretin familial amyloid polyneuropathy (TTR-FAP): Parameters for early diagnosis

BACKGROUND

Familial transthyretin amyloidosis is a life-threatening disease presenting with sensorimotor and autonomic polyneuropathy. Delayed diagnosis has a detrimental effect on treatment and prognosis. To facilitate diagnosis, we analyzed data patterns of patients with transthyretin familial amyloid polyneuropathy (TTR-FAP) and compared them to polyneuropathies of different etiology for clinical and electrophysiological discriminators.

METHODS

Twenty-four patients with TTR-FAP and 48 patients with diabetic polyneuropathy (dPNP) were investigated (neurological impairment score NIS; neurological disability score NDS) in a cross-sectional design. Both groups were matched for gender and presence of pain. Quantitative sensory testing (QST), sympathetic skin response (SSR), heart rate variability (HRV), and nerve conduction studies (NCV) were performed. Both groups were compared using univariate analysis. In a stepwise discriminant analysis, discriminators between both neuropathies were identified. These discriminators were validated comparing TTR-FAP patients with a cohort of patients with chemotherapy-induced polyneuropathy (CIN) and chronic inflammatory demyelinating neuropathy (CIDP).

RESULTS

TTR-FAP patients scored higher in NDS and NIS and had impaired cold detection (CDT, p = .024), cold-warm discrimination (TSL, p = .019) and mechanical hyperalgesia (MPT, p = .029) at the hands, SSR (upper limb, p = .022) HRV and ulnar and sural NCS (all p < .05) were more affected in TTR-FAP. Ulnar nerve sensory NCV, CDT, and the MPT but not the other parameters discriminated TTR-FAP from dPNP (82% of cases), from CIN (86.7%) and from CIDP (68%; only ulnar sNCV).

CONCLUSION

Low ulnar SNCV, impaired cold perception, and mechanical hyperalgesia at the hands seem to characterize TTR-FAP and might help to differentiate from other polyneuropathies.

Statistical Frequency-Dependent Analysis of Trial-to-Trial Variability in Single Time Series by Recurrence Plots

For decades, research in neuroscience has supported the hypothesis that brain dynamics exhibits recurrent metastable states connected by transients, which together encode fundamental neural information processing. To understand the system's dynamics it is important to detect such recurrence domains, but it is challenging to extract them from experimental neuroscience datasets due to the large trial-to-trial variability. The proposed methodology extracts recurrent metastable states in univariate time series by transforming datasets into their time-frequency representations and computing recurrence plots based on instantaneous spectral power values in various frequency bands. Additionally, a new statistical inference analysis compares different trial recurrence plots with corresponding surrogates to obtain statistically significant recurrent structures. This combination of methods is validated by applying it to two artificial datasets. In a final study of visually-evoked Local Field Potentials in partially anesthetized ferrets, the methodology is able to reveal recurrence structures of neural responses with trial-to-trial variability. Focusing on different frequency bands, the δ-band activity is much less recurrent than α-band activity. Moreover, α-activity is susceptible to pre-stimuli, while δ-activity is much less sensitive to pre-stimuli. This difference in recurrence structures in different frequency bands indicates diverse underlying information processing steps in the brain.