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Method for detecting a conversion from mild cognitive impairment

active electrodes electroencephalographyAlzheimer’s disease (AD)Brain diseases


The purpose of the present invention is to provide a method for detecting the conversion from Mild Cognitive Impairment to Alzheimer’s disease by quantifying the effects that such progression has on the patient’s EEG. In fact, being the clinical evaluation mostly subjective, it is necessary to provide the neurologists with an objective tool that quantifies the effects of the disease progression

Technical features

To study the connectivity of the brain “network”, a “graph” model is adopted where the EEG electrodes represent the nodes of the graph itself. It is then necessary to define the weight of the edge connecting the pair of nodes i and j, which will represent the coupling strength between the corresponding cortical areas associated with the electrodes i and j (estimated through the novel “Permutation Jaccard Distance” parameter). In this way, a “weighted graph” model will be associated with the EEG recorded at time T0 (first patient evaluation) and a model will be associated with time T1 (second patient evaluation), the density of the network will therefore be estimated (i.e. the ratio between the actual number of connections and the total number of possible connections). Brain connectivity changes will be quantified by comparing the connectivity density at time T0 and at time T1

Possible Applications

  • Computation of EEG-based biomarkers of AD progression in an open source software;
  • Objectively evaluate the EEG of a patient;
  • Estimate the probability that the diagnosis is of AD or of MCI and that the subject is going to develop dementia due to AD or not;
  • Used in the hospitals to evaluate patients and carry out cutting-edge research on MCI progression;
  • A non-invasive, well tolerated, quick, predictive tool for AD routine screening.


  • The evaluation of the developed EEG-based brain connectivity models;
  • System for the personalized, predictive, patient follow-up through the release of a software based on the biomarkers extracted from the developed models;
  • Integrated with the most popular EEG review software;
  • Promote a cheap and well tolerated large-scale-screening over the population.