Algorithmic Complexity of EEG for Prognosis of Neurodegeneration in Idiopathic Rapid Eye Movement Behavior Disorder (RBD)

Abstract

Idiopathic rapid eye movement sleep behavior disorder (RBD) is a serious risk factor for neurodegenerative processes such as Parkinson's disease (PD). We investigate the use of EEG algorithmic complexity derived metrics for its prognosis. We analyzed resting state EEG data collected from 114 idiopathic RBD patients and 83 healthy controls in a longitudinal study forming a cohort in which several RBD patients developed PD or dementia with Lewy bodies. Multichannel data from ~ 3 min recordings was converted to spectrograms and their algorithmic complexity estimated using Lempel-Ziv-Welch compression. Complexity measures and entropy rate displayed statistically significant differences between groups. Results are compared to those using the ratio of slow to fast frequency power, which they are seen to complement by displaying increased sensitivity even when using a few EEG channels. Poor prognosis in RBD appears to be associated with decreased complexity of EEG spectrograms stemming in part from frequency power imbalances and cross-frequency amplitude algorithmic coupling. Algorithmic complexity metrics provide a robust, powerful and complementary way to quantify the dynamics of EEG signals in RBD with links to emerging theories of brain function stemming from algorithmic information theory.

Hallucinations, somatic‐functional disorders of PD‐DLB as expressions of thalamic dysfunction

“…In this study, the presence of EEG theta activity was considered an indicator of REM sleep intrusions, rather than the sudden onset of desynchronization and muscle atonia (eg, as it happens in narcolepsy); it was the presence of theta activity that drove the categorization of possible REM intrusions . However, the presence of continuous theta activity in PDD and DLB argues against any possible categorization of REM sleep phases, different from a healthy population in which transient theta is observed in coincidence with REM sleep onset and desynchronization …”

“…Instead, continuous theta activity is observed during REM sleep in PD patients and REM sleep behavior disorder of PD‐RBD . Detection of the theta‐pre‐alpha rhythm in PD is now a core feature of machine‐learning projects developed for assessment of PD and RBD …”

“…110 Detection of the theta-pre-alpha rhythm in PD is now a core feature of machine-learning projects developed for assessment of PD 103,111 and RBD. 112 The TCD theory 30 originally postulated that portions of the thalamocortical system become locked in spindle-like, waking to sleep status theta activity, 31,113 whereas other parts of the brain remain in a waking state, a condition similar to a parasomnia. The original TCD 113 study suggested that the theta burst mode shown by thalamic neurons, replacing alpha and gamma frequencies, is because of activation of low-threshold calcium spike bursts that are induced by long-lasting hyperpolarization generated on altered projections from the globus pallidus or the pedunculopontine nuclei (PPN) to the thalamoreticular nuclei (TRN).…”

“…32 However, the presence of continuous theta activity in PDD and DLB argues against any possible categorization of REM sleep phases, different from a healthy population in which transient theta is observed in coincidence with REM sleep onset and desynchronization. 110,112 The TCD-driven DMN decoupling model suggests that occurrence of psychosis is because of entry into a condition in which DMN is decoupled such as during dreaming, yet that is not a transient, triggering event, like in narcolepsy. Instead it is a status that could be initially overruled by reentering frontal control networks to later evolve with disease progression into the dominant status.…”

Deep learning with EEG spectrograms in rapid eye movement behavior disorder

“…To a large extent, progress in their analysis has been driven by the study of spectral features in electrode space, which has indeed proven useful to study the human brain in both health and disease. For example, the "slowing down" of EEG is known to characterize neurodegenerative diseases [5,17,20]. It is worth mentioning that the selection of disease characterizing features from spectral analysis is mostly done manually after an extensive search in the frequency-channel domain.…”