  Increased variability in performance has been associated with the emergence
of several neurological and psychiatric pathologies. However, whether and how
consistency of neuronal activity may also be indicative of an underlying
pathology is still poorly understood. Here we propose a novel method for
evaluating consistency from non-invasive brain recordings. We evaluate the
consistency of the cortical activity recorded with magnetoencephalography in a
group of subjects diagnosed with Mild Cognitive Impairment (MCI), a condition
sometimes prodromal of dementia, during the execution of a memory task. We use
metrics coming from nonlinear dynamics to evaluate the consistency of cortical
regions. A representation known as (parenclitic networks) is constructed, where
atypical features are endowed with a network structure, the topological
properties of which can be studied at various scales. Pathological conditions
correspond to strongly heterogeneous networks, whereas typical or normative
conditions are characterized by sparsely connected networks with homogeneous
nodes. The analysis of this kind of networks allows identifying the extent to
which consistency is affecting the MCI group and the focal points where MCI is
specially severe. To the best of our knowledge, these results represent the
first attempt at evaluating the consistency of brain functional activity using
complex networks theory.
