Parkinson bradykinesia correlates with EEG background frequency and perceptual forward projection

Classification of Parkinson's disease motor phenotype: a machine learning approach

To assess the cortical activity in people with Parkinson's disease (PwP) with different motor phenotype (tremor-dominant-TD and postural instability and gait difficulty-PIGD) and to compare with controls. Twenty-four PwP (during OFF and ON medication) and twelve age-/sex-/handedness-matched healthy controls underwent electrophysiological assessment of spectral ratio analysis through electroencephalography (EEG) at resting state and during the hand movement. We performed a machine learning method with 35 attributes extracted from EEG. To verify the efficiency of the proposed phenotype-based EEG classification the random forest and random tree were tested (performed 30 times, using a tenfolds cross validation in Weka environment). The analyses based on phenotypes indicated a slowing down of cortical activity during OFF medication state in PwP. PD with TD phenotype presented this characteristic at resting and the individuals with PIGD presented during the hand movement. During the ON state, there is no difference between phenotypes at resting nor during the hand movement. PD phenotypes may influence spectral activity measured by EEG. Random forest machine learning provides a slightly more accurate, sensible and specific approach to distinguish different PD phenotypes. The phenotype of PD might be a clinical characteristic that could influence cortical activity.

Quantitative Electroencephalography Characteristics for Parkinson's Disease: A Systematic Review

BACKGROUND

Individualized treatment guided by biomarkers certainly will play a crucial role in the more effective treatment of various neurological diseases in the near future. Identifying the electroencephalographic biomarkers in the brain of patients with Parkinson's disease (PD) may help in the decision-making process of health professionals regarding the non-invasive brain stimulation (NIBS) protocols.

OBJECTIVE

To summarize quantitative electroencephalographic (qEEG) characteristics of patients with PD with motor symptoms at rest or during movement to identify potential biomarker associated with motor impairment in PD.

METHODS

A systematic search was conducted in the databases MEDLINE/PubMed, LILACS/BIREME, CINAHL/EBSCO, Web of Science, and CENTRAL, performed according to PRISMA-statement guidelines. Two independent authors searched for studies that reported qEEG data related to motor outcomes at rest or during movements in patients with PD and compared the data with control healthy group. The studies' methodological quality was examined using the Cochrane Handbook. Studies/sample characteristics, qEEG parameters/analyses, and the studies' results were summarized. Prospero-register: CRD42018085660.

RESULTS

Nineteen studies (18 cross-sectional/one cross-over) with 312 PD patients and 277 controls, published between 1994-2018, were included for the qualitative analysis. In comparison to healthy controls, our findings suggest a slowing down of the cortical activity in patients with PD due to an increase of slower band waves activity and a decrease of fast band waves at resting and during complex movement execution mainly in the central and frontal cortex.

CONCLUSION

Slowing down of cortical waves suggest excitatory NIBS for motor impairment in PD. However, qEEG biomarker for motor symptoms of PD cannot be established yet because the studies that related qEEG with motor outcomes presented methodological poor quality.

Linking Pathological Oscillations With Altered Temporal Processing in Parkinsons Disease: Neurophysiological Mechanisms and Implications for Neuromodulation

Emerging evidence suggests that Parkinson's disease (PD) results from disrupted oscillatory activity in cortico-basal ganglia-thalamo-cortical (CBGTC) and cerebellar networks which can be partially corrected by applying deep brain stimulation (DBS). The inherent dynamic nature of such oscillatory activity might implicate that is represents temporal aspects of motor control. While the timing of muscle activities in CBGTC networks constitute the temporal dimensions of distinct motor acts, these very networks are also involved in somatosensory processing. In this respect, a temporal aspect of somatosensory processing in motor control concerns matching predicted (feedforward) and actual (feedback) sensory consequences of movement which implies a distinct contribution to demarcating the temporal order of events. Emerging evidence shows that such somatosensory processing is altered in movement disorders. This raises the question how disrupted oscillatory activity is related to impaired temporal processing and how/whether DBS can functionally restore this. In this perspective article, the neural underpinnings of temporal processing will be reviewed and translated to the specific alternated oscillatory neural activity specifically found in Parkinson's disease. These findings will be integrated in a neurophysiological framework linking somatosensory and motor processing. Finally, future implications for neuromodulation will be discussed with potential implications for strategy across a range of movement disorders.