¹ Felsenstein Medical Research Center, Petach Tikva, Israel,

² Sackler Faculty of Medicine, Tel AvivUniversity, Tel Aviv, Israel,

³ Department of Neurology, Rabin Medical Center, Petach Tikva, Israel,

⁴ Cognitive Neurology Clinic, Rabin Medical Center, Petach Tikva, Israel,

⁵ Geha Mental Health Center, Petach Tikva, Israel,

⁶ Faculty of Technology, Department of Mathematics Linnaeus University, Växjö, Sweden

No diagnostic or predictive instruments to help with early diagnosis and timely therapeutic intervention are available as yet for most neuro-psychiatric disorders. A quantum potential mean and variability score (qpmvs), to identify neuropsychiatric and neurocognitive disorders with high accuracy, based on routine EEG recordings, was developed. Information processing in the brain is assumed to involve integration of neuronal activity in various areas of the brain. Thus, the presumed quantum-like structure allows quantification of connectivity as a function of space and time (locality) as well as of instantaneous quantum-like effects in information space (non-locality). EEG signals reflect the holistic (nonseparable) function of the brain, including the highly ordered hierarchy of the brain, expressed by the quantum potential according to Bohmian mechanics, combined with dendrogram representation of data and p-adic numbers. Participants consisted of 230 participants including 28 with major depression, 42 with schizophrenia, 65 with cognitive impairment, and 95 controls. Routine EEG recordings were used for the calculation of qpmvs based on ultrametric analyses, closely coupled with p-adic numbers and quantum theory. Based on area under the curve, high accuracy was obtained in separating healthy controls from those diagnosed with schizophrenia (p<0.0001), depression (p<0.0001), Alzheimer’s disease (AD; p<0.0001), and mild cognitive impairment (MCI; p<0.0001) as well as in differentiating participants with schizophrenia from those with depression (p<0.0001), AD (p<0.0001) or MCI (p<0.0001) and in differentiating people with depression from those with AD (p<0.0001) or MCI (p<0.0001). The novel EEG analytic algorithm (qpmvs) seems to be a useful and sufficiently accurate tool for diagnosis of neuropsychiatric and neurocognitive diseases and may be able to predict disease course and response to treatment.

Disorders of the brain, such as schizophrenia, epilepsy, depression and dementia, constitute approximately 27% of the global disease burden in terms of disability-adjusted life-years (DALYs) and that surpasses cardiovascular diseases and cancer combined [1]. For most brain disorders no single accurate, diagnostic tool is available as yet [2–4]. Several biomarkers exist to substantiate the diagnosis, including biological markers from serum or cerebrospinal fluid (CSF), neuroimaging techniques, including magnetic resonance imaging (MRI), functional MRI (fMRI), and positron emission tomography (PET). Those, however, are often expensive possibly quite invasive and none of these techniques has yielded a biomarker sufficient for accurate diagnosis of disorders such as Alzheimer’s disease (AD) [5], major depression or schizophrenia [6]. Electroencephalography (EEG) is an inexpensive and well-established tool [7, 8] used for resting-state power, spectral and functional connectivity analyses as well as microstate analysis, which may assist in diagnosing these disorders [9–14] with variable success and little use in clinical practice.

The recent years were characterized by tremendous development of quantum information theory [15–17]. Nowadays quantum-like modelling is widely used in microbiology, genetics, cognition, psychology, decision making and social science [18–22]. Furthermore, it is widely accepted that the brain that is considered a “black box” in this model, is a highly hierarchic organ in terms of communication and subsystem function [23, 24]. Baring this in mind, we decided to compare the EEG-pattern of healthy controls and those of peoples with neuropsychiatric diseases. The way to represent hierarchy in mathematical terms is by dendrogram trees that can be expressed as p-adic numbers, [21, 22, 25] representing an emergent property of the holistic brain (where throughout this study the 2-adic numbers are in the use thus p = 2). The quantum (Bohmian mechanics) formalism was used operationally to describe holistic information processing by the brain in accordance with Bohr and Hilley who treated it as a field of “active information” [26].

The current study’s main objective was to develop a novel and relatively simple tool to diagnose and predict multiple neuropsychiatric diseases. This tool combines routine EEG and mathematical structures of quantum Bohmian theory, to extract characteristic information patterns presented in dendrograms, expressing the hierarchical treelike structure of information processing in the brain [27]. This novel method accurately identified participants with mild cognitive impairment (MCI), AD, schizophrenia, or depression, by routine EEG records analysed by this novel approach.

The study adhered to rules and regulations of the Helsinki Declaration and was approved by the Institutional Review Board (IRB) of the Rabin Medical Center, Petach Tikva, Israel (0275-20-RMC). The study was approved as retrospective clinical and need for consent was waived by the ethics committee. All patient data were fully anonymized before review.

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https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255529

https://doi.org/10.1371/journal.pone.0255529