RESUMEN
Aims: Our aim was to differentiate patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from cognitively normal (CN) individuals and predict the progression from MCI to AD within a 3-year longitudinal follow-up. A newly developed Holo-Hilbert Spectral Analysis (HHSA) was applied to resting state EEG (rsEEG), and features were extracted and subjected to machine learning algorithms. Methods: A total of 205 participants were recruited from three hospitals, with CN (n = 51, MMSE > 26), MCI (n = 42, CDR = 0.5, MMSE ≥ 25), AD1 (n = 61, CDR = 1, MMSE < 25), AD2 (n = 35, CDR = 2, MMSE < 16), and AD3 (n = 16, CDR = 3, MMSE < 16). rsEEG was also acquired from all subjects. Seventy-two MCI patients (CDR = 0.5) were longitudinally followed up with two rsEEG recordings within 3 years and further subdivided into an MCI-stable group (MCI-S, n = 36) and an MCI-converted group (MCI-C, n = 36). The HHSA was then applied to the rsEEG data, and features were extracted and subjected to machine-learning algorithms. Results: (a) At the group level analysis, the HHSA contrast of MCI and different stages of AD showed augmented amplitude modulation (AM) power of lower-frequency oscillations (LFO; delta and theta bands) with attenuated AM power of higher-frequency oscillations (HFO; beta and gamma bands) compared with cognitively normal elderly controls. The alpha frequency oscillation showed augmented AM power across MCI to AD1 with a reverse trend at AD2. (b) At the individual level of cross-sectional analysis, implementation of machine learning algorithms discriminated between groups with good sensitivity (Sen) and specificity (Spec) as follows: CN elderly vs. MCI: 0.82 (Sen)/0.80 (Spec), CN vs. AD1: 0.94 (Sen)/0.80 (Spec), CN vs. AD2: 0.93 (Sen)/0.90 (Spec), and CN vs. AD3: 0.75 (Sen)/1.00 (Spec). (c) In the longitudinal MCI follow-up, the initial contrasted HHSA between MCI-S and MCI-C groups showed significantly attenuated AM power of alpha and beta band oscillations. (d) At the individual level analysis of longitudinal MCI groups, deploying machine learning algorithms with the best seven features resulted in a sensitivity of 0.9 by the support vector machine (SVM) classifier, with a specificity of 0.8 yielded by the decision tree classifier. Conclusion: Integrating HHSA into EEG signals and machine learning algorithms can differentiate between CN and MCI as well as also predict AD progression at the MCI stage.
RESUMEN
The pedunculopontine nucleus (PPN) is situated in the upper pons in the dorsolateral portion of the ponto-mesencephalic tegmentum. Its main mass is positioned at the trochlear nucleus level, and is part of the mesenphalic locomotor region (MLR) in the upper brainstem. The human PPN is divided into two subnuclei, the pars compacta (PPNc) and pars dissipatus (PPNd), and constitutes both cholinergic and non-cholinergic neurons with afferent and efferent projections to the cerebral cortex, thalamus, basal ganglia (BG), cerebellum, and spinal cord. The BG controls locomotion and posture via GABAergic output of the substantia nigra pars reticulate (SNr). In PD patients, GABAergic BG output levels are abnormally increased, and gait disturbances are produced via abnormal increases in SNr-induced inhibition of the MLR. Since the PPN is vastly connected with the BG and the brainstem, dysfunction within these systems lead to advanced symptomatic progression in Parkinson's disease (PD), including sleep and cognitive issues. To date, the best treatment is to perform deep brain stimulation (DBS) on PD patients as outcomes have shown positive effects in ameliorating the debilitating symptoms of this disease by treating pathological circuitries within the parkinsonian brain. It is therefore important to address the challenges and develop this procedure to improve the quality of life of PD patients.
RESUMEN
Sleep is an indispensable normal physiology of the human body fundamental for healthy functioning. It has been observed that Parkinson's disease (PD) not only exhibits motor symptoms, but also non-motor symptoms such as metabolic irregularities, altered olfaction, cardiovascular dysfunction, gastrointestinal complications and especially sleep disorders which is the focus of this review. A good understanding and knowledge of the different brain structures involved and how they function in the development of sleep disorders should be well comprehended in order to treat and alleviate these symptoms and enhance quality of life for PD patients. Therefore it is vital that the normal functioning of the body in relation to sleep is well understood before proceeding on to the pathophysiology of PD correlating to its symptoms. Suitable treatment can then be administered toward enhancing the quality of life of these patients, perhaps even discovering the cause for this disease.