DOI: 10.5061/dryad.8gtht76pw
title: EEG p-adic quantum potential accurately identifies depression, schizophrenia and cognitive decline

description: 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&lt;0.0001), depression
 (p&lt;0.0001), Alzheimer’s disease (AD; p&lt;0.0001), and mild
 cognitive impairment (MCI; p&lt;0.0001) as well as in differentiating
 participants with schizophrenia from those with depression
 (p&lt;0.0001), AD (p&lt;0.0001) or MCI (p&lt;0.0001) and in
 differentiating people with depression from those with AD
 (p&lt;0.0001) or MCI (p&lt;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.
description: Online medical health records from two medical centres were used
 to identify all participants that underwent at least one routine EEG
 examination between the years 2011 and 2019. The participants were then
 divided into the following groups:   
 Depression: Participants with a diagnosis of major depressive
 disorder (MDD), hospitalized during the index time. This diagnosis had
 been established by two senior psychiatrists according to DSM-IV and DSM-V
 criteria, following a psychiatric interview where the severity of
 depression was found to be at least moderate. In addition, the
 participants (range: 33–91 years; average age: 69.7 ±14.8 years; 20
 females) had to have had at least one previous major depressive episode,
 prior to age 30, namely, the index episode was a recurrent one.
    Schizophrenia: Diagnosis of schizophrenia had been
 established by two senior psychiatrists according to the ICD-10 criteria.
 In addition, the participant had to be hospitalized during the index time.
   Cognitive impairment: Participants in the study had
 been diagnosed by two senior neurologists, with either MCI or AD according
 to the criteria of the National Institute on Aging and the Alzheimer’s
 Association. Controls: Participants undergoing
 routine EEG due to indications unrelated to neuropsychiatric diseases.
 None of the participants in this group had been diagnosed with any
 condition defining any of the other groups. In this group, exclusion
 criteria also included diagnosis of bipolar disorder; substance abuse,
 psychiatric or general medical conditions requiring hospitalization,
 history of epilepsy or conditions requiring anticonvulsants, ECT, vagal
 nerve stimulation, or transcranial magnetic stimulation (TMS), history of
 traumatic brain injury and history or imaging findings of cerebrovascular
 diseases including ischaemic and haemorrhagic stroke.
 EEG data acquisition and analysis
 Routine EEG recordings were retrospectively obtained from the
 medical records of all patients. EEGs had been performed in a routine
 clinical setting by an experienced technician. All included participants
 had undergone EEG between 8 am and 1 pm using a Nihon Koden surface EEG
 (19-electrode standard according to the international 10-20 electrode
 placement system) with a sampling frequency of 500 Hz (Nihon Kohden,
 Japan). Participants had been resting with open and closed eyes. Those who
 underwent sleep-EEGs were excluded.
description: Matlab is needed to read the files