The democratic aspect of machine learning: Limitations and opportunities for Parkinson's disease

Currently, great efforts are being put toward the early identification of preclinical, biological, clinical, and laboratory markers that are able to predict the conversion of “cognitively intact” patients to overt dementia. An example of a successful biomarker for neurodegenerative disorders, and specifically of conversion from early stage of dementia of Alzheimer’s disease or Lewy body dementia (encompassing dementia with Lewy bodies and Parkinson’s disease (PD) dementia) and of progression of the diseases from mild to more pronounced stages of dementia, is represented by resting state, eyes-closed electroencephalographic (EEG) rhythms. Patients with dementia exhibit a general reduction of power in the alpha and beta bands and high power of widespread delta and theta rhythms when compared with nondemented patients. Specifically, dementia is associated with dominant frequency variability with the appearance of a prealpha (a fast theta rhythm of 5.5-7.5 Hz) band, now listed as a supportive biomarker in the latest consensus criteria for the diagnosis of dementia with Lewy bodies, and a progressively increased relative power of theta band is described in PD dementia patients as cognition declines. The renaissance of applying clinical EEG to dementia in neurodegenerative diseases has been associated with the development of new analytical methods and breakthrough discoveries pertaining to the neuronal mechanisms underlying EEG features. Quantitative EEG (QEEG) is a derivative of regular EEG in which an offline analysis of frequency and amplitude allows the identification of specific, discrete patterns of brain wave activity. It is important to note that the source analogical data must first be visually inspected and evaluated by an expert neurophysiologist before mathematical translation of the data occurs. A thorough understanding and firm knowledge of clinical EEG features, and of mathematics and computing science, is required to prevent erroneous interpretations of digitally displayed mathematical constructs (eg, amplitude, frequency, coherence maps). The application of artificial intelligence, and more specifically machine-learning techniques, to EEG signal analysis is one possible solution that will allow wider application of EEG analysis in clinical practice as it does not require a deep knowledge of the internal workings of the machine when processing input data to give output results, assuming that machine learning could be envisioned, in clinical practice, as a “black box” (although it is not, strictly). The black box metaphor, which dates back to the early days of cybernetics and behaviorism, typically refers to a system in which we can observe only the inputs and outputs, but not the internal workings. To entirely understand the internal workings would require a meta system, that is, a system with a higher degree than the system itself that allows one to examine the internal working of the system from the outside. This essentially represents a variation of the Gödel incompleteness theorems, whose explanation is well beyond the scope of this editorial. Machine-learning algorithms use mathematical, computational methods to derive information directly from data without applying predetermined mathematical models or equations. These algorithms adaptively improve their own performances as the analyzed examples increase in number. Machine learning might thus appear as the keystone of replacing human intelligence when interpreting complex signals (including EEG signals) and applying the results to routine clinical practice. It is important to distinguish between learning tasks that human examiners can already do well and learning those tasks where physicians have only limited success. Interesting examples of machine learning in medical research are algorithms that allow computers to make increasingly accurate predictions to prevent infectious epidemics, improve global health, or accurately and timely diagnose tumors or rare diseases. These are examples of a supervised learning method where the computer is provided both the input dataset and *Corresponding to: Dr. Laura Bonanni, Department of Neuroscience and Imaging, University G. d’Annunzio of Chieti-Pescara, Via dei Vestini 66100 Chieti, Italy; E-mail: l.bonanni@unich.it