Summary:
DYNAMIC STUDY THROUGH THE USE OF MAGNETOENCEPHALOGRAPHY –
ELECTROENCEPHALOGRAPHY OF PATIENTS WITH COGNITIVE DEFICITS.
Elissaios Karageorgiou.
PhD Dissertation. Division of Social Medicine, Psychiatry and Neurology, Medical
School, National and Kapodistrian University of Athens, Greece. January 12,
2015.
Thesis Supervisors: Nikolaos Smyrnis, Apostolos P. Georgopoulos.
Key Words: Magnetoencephalography, Cognitive Deficits, Electroencephalography.
The main goal of this doctoral thesis was to elucidate the characteristics of
synchronous
neural interactions (SNI), as represented in the encephalographic signal, in
patients with
cognitive disorders.
In the first part of the present thesis, a diagnostic test to assess the
dynamic brain
function at high temporal resolution using magnetoencephalography (MEG) is
described.
The essence of the test is the measurement of the dynamic SNI, an essential
aspect of
brain function. MEG signals were recorded from 248 axial gradiometers while 142
human subjects fixated a spot of light for 45–60 s. After fitting an
autoregressive
integrative moving average (ARIMA) model and taking the stationary residuals,
all
pairwise, zero-lag, partial cross-correlations ( 0
ij PCC ) and their z-transforms ( )
between individual sensors were calculated, providing estimates of the strength
and sign
(positive, negative) of direct synchronous coupling at 1 ms temporal
resolution. We
found that subsets of SNI successfully classified individual subjects to their
respective
groups (multiple sclerosis, Alzheimer’s disease, schizophrenia, Sjogren’s
syndrome,
0
ij z
xvi
chronic alcoholism, facial pain, healthy controls) and gave excellent external
cross-validation results.
In the second part of the thesis, a direct comparison is made between SNI and
cognitive domains, as represented through neuropsychological scores (NP), in
healthy controls and patients suffering from Alzheimer’s disease or mild
cognitive impairment. First, we performed individual correlations between each
SNI and each NP, which provided an initial link between SNI and specific
cognitive tests. Second, we performed canonical correlation analysis between
the two sets of variables (SNI and NP), after factor analyzing each set, which
optimally associated the entire MEG signal to cognitive function. The results
revealed that SNI as a whole best related to memory and language and less to
executive function. On an individual variable level there were many more SNI
relating to memory, which carried considerable common information between them.
These findings provide a direct interpretation to the information carried by
the SNI and set the basis for identifying specific disease phenotypes according
to cognitive deficits.
Keywords:
Μagnetoencephalography, Electroencephalography, Cognitive disorders, Diagnosis, Linear discriminant analysis, Genetic algorithms
