EEG may serve as a biomarker in Huntington’s disease using machine learning automatic classification

Abstract

Reliable markers measuring disease progression in Huntington’s disease (HD), before and after disease manifestation, may guide a therapy aimed at slowing or halting disease progression. Quantitative electroencephalography (qEEG) may provide a quantification method for possible (sub)cortical dysfunction occurring prior to or concomitant with motor or cognitive disturbances observed in HD. In this pilot study we construct an automatic classifier distinguishing healthy controls from HD gene carriers using qEEG and derive qEEG features that correlate with clinical markers known to change with disease progression in HD, with the aim of exploring biomarker potential. We included twenty-six HD gene carriers (49.7 ± 8.5 years) and 25 healthy controls (52.7 ± 8.7 years). EEG was recorded for three minutes with subjects at rest. An EEG index was created by applying statistical pattern recognition to a large set of EEG features, which was subsequently tested using 10-fold cross-validation. The index resulted in a continuous variable ranging from 0 to 1: a low value indicating a state close to normal and a high value pointing to HD. qEEG features that correlate specifically with commonly used clinical markers in HD research were derived. The classification index had a specificity of 83%, a sensitivity of 83% and an accuracy of 83%. The area under the curve of the receiver operator characteristic curve was 0.9. qEEG analysis on subsets of electrophysiological features resulted in two highly significant correlations with clinical scores. The results of this pilot study suggest that qEEG may serve as a biomarker in HD. The indices correlating with modalities changing with the progression of the disease may lead to tools based on qEEG that help monitor efficacy in intervention studies.

Examining neurophysiological markers of apathy and processing speed in late premanifest and early-stage manifest Huntington’s disease

“…Previous studies have not collected resting state data with participants’ eyes open, limiting the comparability of the current findings with previous research (Barry & De Blasio, 2017; Boytsova & Danko, 2010). Higher delta power within the complete EEG signal during the resting eyes closed state, however, has been a consistent finding in previous HD research (Delussi et al, 2020; Leuchter et al, 2017; Nguyen, Bradshaw, Stout, Croft, & Georgiou-Karistianis, 2010; Odish et al, 2018).…”

“…Our sample size was comparable to other studies of resting state EEG in HD (De Tommaso et al, 2003;Odish et al, 2018;Piano, Imperatori, et al, 2017;Piano, Mazzucchi, et al, 2017;Van Der Hiele et al, 2007). Nevertheless, a larger sample size of people with HD would have been preferable to increase statistical power and generalizability of the findings.…”

“…There is extensive evidence of the association between these neurophysiological indices and various aspects of cognition, emotion and behaviour in diverse populations (Başar & Güntekin, 2013; Bertrand et al, 2016; Choi et al, 2021; Herrmann, Strüber, Helfrich, & Engel, 2016; Iyer et al, 2015; Klimesch, 1999; Knyazev, 2012; Nobukawa et al, 2020; Sanchez-Dinorin et al, 2021). In HD, altered spectral power in the delta and alpha frequency bands, and increased functional connectivity in the delta band have been consistently identified (Delussi, Nazzaro, Ricci, & De Tommaso, 2020; Hunter, Bordelon, Cook, & Leuchter, 2010; Odish, Johnsen, van Someren, Roos, & van Dijk, 2018; Painold et al, 2010; Piano, Imperatori, et al, 2017; Piano, Mazzucchi, et al, 2017; Ponomareva et al, 2014). Altered spectral power and connectivity has also been found to correlate with performance on clinical indices and cognitive tests (including measures of processing speed) in people with HD; including the UHDRS® total motor scale, number of CAG repeats on the affected allele, and performance on the Stroop Word Reading Test, Mini Mental Status Examination, verbal fluency (FAS) and the Symbol Digit Modalities Test (Delussi et al, 2020; Hunter et al, 2010; Odish et al, 2018; Painold et al, 2010; Piano, Imperatori, et al, 2017; Piano, Mazzucchi, et al, 2017; Ponomareva et al, 2014).…”

Circadian dysfunction in the Q175 model of Huntington's disease: Network analysis

“…In response to a homeostatic challenge, the R6/2 line did show deficits in recovery sleep (Fisher et al, ). The increase in sleep fragmentation is commonly associated with the sleep disturbances observed in HD patients (e.g., Goodman et al, ) and there is growing appreciation that quantitative EEG may serve as a biomarker for HD (Leuchter et al, ; Odish, Johnsen, van Someren, Roos, & van Dijk, ).…”

Clinical, Imaging, and Laboratory Markers of Premanifest Spinocerebellar Ataxia 1, 2, 3, and 6: A Systematic Review

“…More researches are investigating the clinical, laboratory, and neuroimaging markers of the prodromal condition in other neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, and Huntington's disease. 33 34 35 36 37 38 39 40 41 However, only a few studies have investigated polyglutamine SCAs.…”