Other biomarker modalities
Transcranial sonography shows that enlarged areas of the SN echogenicity
is a characteristic and stabile feature in idiopathic PD [46]. Thus,
increased echogenicity of the SN seems to serve as a convenient and
inexpensive biomarker candidate for the early diagnosis of idiopathic PD
and differentiating it from atypical parkinsonian syndromes.
Electroencephalography (EEG) can be used to identify biomarker
candidates for PD. Elevated spectral beta power within basal ganglia is
implicated in PD, and recent advances in EEG-based analytic approaches
to quantify oscillatory beta band synchrony seem promising [59].
Lower background rhythm frequency and increased relative power in delta
and theta bands in resting-state EEG hold potential as predictive
biomarkers for cognitive deterioration in PD [60]. In a prospective
study, marked EEG slowing during rapid eye movement (REM) sleep together
with increased relative powers in delta and theta frequencies were
predictive biomarkers for patients who later develop dementia [60,
61]. EEG readouts are very well suited for machine and deep learning
based techniques that may assist in objective screening and staging of
PD as discussed in a recent review [62]. Despite being an
inexpensive and easily accessible approach, the predictive and
translational value of EEG biomarkers still needs to be confirmed.
Digital biomarkers (or technology-based objective measures) form a
rapidly emerging field of research to improve the longitudinal tracking
of neurodegenerative diseases in clinical care and research [63].
Digital biomarkers refer to the use of inbuilt sensors in portable (e.g.
smartphone), wearable (e.g. smartwatch or ring) or implantable devices
allowing for active or passive data collection on biological (e.g. blood
glucose), physiological (e.g. heart rate or body temperature) or
functional (e.g. motor activities, speech or facial expressions)
parameters [64]. By their nature, digital biomarkers are unbiased
and offer an opportunity to collect dense datasets during everyday life.
Real-world data together with sophisticated artificial intelligence (AI)
-based algorithms can detect even slight changes in daily life which
could hardly be detected in a clinical setting. This could help
predicting PD before the onset of classical symptoms or estimating the
course of the disease, thus allowing for more personalized care. The
diagnostic utility of digital biomarkers, however, needs further
validation.