1. 4NeuroInformatics Group, AUTH, Thessaloniki, Greece
Gender and age differences of intra-frequency and phase-to-amplitude coupling (PAC) based on EEG resting-state networks
Dimitriadis SI1,2,3,4, Sallis C5, Tsalikakis D5, Perry G2, Singh K2, Linden D1,2
1Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, UK
2Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
3Artificial Intelligence and Information Analysis Laboratory, Department of Informatics, Aristotle University, Thessaloniki, Greece
4NeuroInformatics Group, AUTH, Thessaloniki, Greece
5 Department of Informatics and Telecommunications Engineering, University of Western Macedonia, Kozani, GR , Greece

2. Aim/Background
Our first goal was to characterize connectivity patterns of age and sex-related alterations in resting-state functional networks across the lifespan.

3. Methods
Here, we estimated resting-state EEG networks based on both intra- frequency and phase-to-amplitude coupling (PAC) from 102 healthy adults
60 females from 20 to 58 years old and
42 males from 19 to 60 years old).
64 sensors IS
Conditions: eyes-open & eyes-closed (1 min)

4. Main Goal
To investigate the simultaneously prediction of age and gender and in both eyes-open (EO) and eyes-closed (EC) conditions, we estimated the prominent type of interaction per pair of sensors.
Brain connectivity in EEG/MEG imaging methods are informative with
features like time, frequency, phase , amplitude etc.
Intra & inter-frequency interactions co-occurred in the brain

5. Semantic Web The ‘myth’ of a neuronal oscillation !!!
Intra & cross-frequency couplings are there !
Separately investigation of each type of interaction diminishes
our understanding of how brain functions and integrate information
from different parts of the brain.
Each part of the brain oscillates on a characteristic frequency and two brain areas communicate via a specific type of interaction.

6. Cross Frequency Coupling (CFC)

7. Different types of cross-frequency interactions

8. Different types of cross-frequency interactions

9. Different types of cross-frequency interactions

10. Defining the prominent type of interaction
Εstimating both intra & inter – frequency coupling using PLV estimator
Surrogate analysis
False-discovery rate
Assign to each pair of sensors ONE type of interaction e.g θ-θ ,θ-γ OR
No-interaction
Advantage : Apart from the weights, we get the type of interaction (SEMANTIC)

11. The Semantic Web
The spatial distribution of intra & inter-frequencies interactions are significant for the understanding of how brain function
In the present study, we estimated the probability distribution (PD) of prominent intra-frequency and cross-frequency interactions namely the phase-to-amplitude (PAC) estimates from the whole brain network of each individual.
Our analysis focused in the frequency range of 1-75 Hz.

12. FEATURES FOR CLASSIFICATION
Young group :

13. FEATURES FOR CLASSIFICATION
Young group :

14. CLASSIFICATION PERFORMANCE
Divide the problem of simultaneously prediction of age & gender into sub-problems
Regression Analysis for the age (LOOCV)
Binary Classification for the age in two subgroups : young : & medium : 40-60
2 k-NN classifiers (LOOCV) per group-age

15. Simultaneously Prediction of Age & Gender
Regression Analysis for the age (PDFs & Strength –F-PO)
Class. Perf :95 %
R2=0.96% , p=2.25*10-15

16. Simultaneously Prediction of Age & Gender
Young Group (PDFs & Strength –F-PO) :
Class. Perf :75 %
b) Medium Group (PDFs & Strength –F-PO):
Class. Perf : 78 %

17. Simultaneously Prediction of Age & Gender
Further improvements:
Dynamic Functional Connectivity Graphs
Transition Rates per pair of sensors
Information Exchange Rates etc.
98 % prediction of both age & gender with large MEG
Database (eyes-closed)