A multicenter study of the early detection of synaptic dysfunction in Mild Cognitive Impairment using Magnetoencephalography-derived functional connectivity

Abstract

Synaptic disruption is an early pathological sign of the neurodegeneration of Dementia of the Alzheimer's type (DAT). The changes in network synchronization are evident in patients with Mild Cognitive Impairment (MCI) at the group level, but there are very few Magnetoencephalography (MEG) studies regarding discrimination at the individual level. In an international multicenter study, we used MEG and functional connectivity metrics to discriminate MCI from normal aging at the individual person level. A labeled sample of features (links) that distinguished MCI patients from controls in a training dataset was used to classify MCI subjects in two testing datasets from four other MEG centers. We identified a pattern of neuronal hypersynchronization in MCI, in which the features that best discriminated MCI were fronto-parietal and interhemispheric links. The hypersynchronization pattern found in the MCI patients was stable across the five different centers, and may be considered an early sign of synaptic disruption and a possible preclinical biomarker for MCI/DAT.

Keywords: Data mining; Functional connectivity; Machine learning; Magnetoencephalography; Mild Cognitive Impairment; Multicenter study; Synaptic dysfunction.

Fig. 1

General structure of the CliDaPa algorithm: (1) the iterative greedy partitioning loop, (2) where the R partition criteria are generated, and (3) each partition criterion includes N partitions of the massive data records which is classified and (4) validated by means of a bootstrap method.

Fig. 2

The external validation is performed for each of the partitions defined by the obtained tree (using demographic attributes), from the validation data it is divided into different groups according to the application of the same partitioning criteria, and for each of the partitions the classification model is applied. The results are the average of the classification results.

Fig. 3

Graphical representation of the synchronization links selected as classifier features in model 1. Interhemispheric and antero-posterior links are shown in green and yellow, respectively.

Fig. 4

HeatMap representation showing the z-score value of the average synchronization links, divided according to True Positive, True Negative, False Positive and False Negative cases from the classification using the subjects from both Datasets 1 and 2.