Phases of the connectivity maps methodology.

<p>Phases of the connectivity maps methodology.</p

In addition to the direct connectivity approach (left panel), the connectivity maps approach brings features involving a reference electrode when addressing bi-electrode connectivity.

<p>Adding such an electrode expands the connectivity approach, as the relative changes in connectivity are uncovered.</p

Discrimination accuracy for each tested methodology.

<p>Discrimination accuracy for each tested methodology.</p

Accuracy of methodology prediction as a function of time window within each stimulus recording.

<p>The most variance (or local peaks of methodology accuracy) is best achieved with a ~300 ms window placed in the recording window post-stimulus.</p

Connectivity maps showing electrode connectivity from posterior sources to frontal destinations with Fisher scores of at least >0.6, zoomed in to the relevant interval.

<p>Connectivity maps showing electrode connectivity from posterior sources to frontal destinations with Fisher scores of at least >0.6, zoomed in to the relevant interval.</p

Phases of the TFFO methodology.

<p>Phases of the TFFO methodology.</p

Accuracy of prediction for each electrode.

<p>The frontal-central scalp area showed the most significant variance between healthy subjects and patients diagnosed with schizophrenia.</p

Accuracy of prediction as a function of events obtained from each subject’s initial recording of stimulation events.

<p>By using only the first 7 or 8 events from each subject, the prediction accuracy of the methodology is close to optimum.</p

Discrimination accuracy per tested methodology.

<p>The accuracy achieved by the TFFO methodology is significantly better than other reaction latency related implementations, proving the added value of the analysis beyond latency differences between healthy subjects and schizophrenia patients (P-Value<1%). Moreover, the results obtained did not include any false positives, i.e., no healthy subject was classified as a schizophrenia patient. Therefore, it is desirable in such problems to have high specificity rather than high sensitivity. Generally, frontal area EEG input correlation to cognitive-related features and mental disorders is evident. Recent work in patients with schizophrenia [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123033#pone.0123033.ref013" target="_blank">13</a>;<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123033#pone.0123033.ref022" target="_blank">22</a>;<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123033#pone.0123033.ref024" target="_blank">24</a>] and Parkinson’s disease [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123033#pone.0123033.ref025" target="_blank">25</a>] has shown high input variance in that segment. Such evident correlations can explain the obtained results.</p><p>Discrimination accuracy per tested methodology.</p

The Fisher score for each electrode based on the average score of the top five maximal connections in which it is involved.

<p>There is a significant fall in the aggregated Fisher score in the prefrontal area from 0.4 to 2.</p