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Alzheimer’s disease (AD) is the most common cause of dementia that involves a progressive and irrevocable decline in cognitive abilities and social behavior, thus annihilating the patient’s autonomy. The theoretical assumption that disease-modifying drugs are most effective in the early stages hopefully in the prodromal stage called mild cognitive impairment (MCI) urgently pushes toward the identification of robust and individualized markers of cognitive decline to establish an early pharmacological intervention. This requires the combination of well-established neural mechanisms and the development of increasingly sensitive methodologies. Among the neurophysiological markers of attention and cognition, one of the sub-components of the ‘cognitive brain wave’ P300 recordable in an odd-ball paradigm -namely the P3b- is extensively regarded as a sensitive indicator of cognitive performance. Several studies have reliably shown that changes in the amplitude and latency of the P3b are strongly related to cognitive decline and aging both healthy and pathological. Here, we used a P3b spatial filter to enhance the electroencephalographic (EEG) characteristics underlying 175 subjects divided into 135 MCI subjects, 20 elderly controls (EC), and 20 young volunteers (Y). The Y group served to extract the P3b spatial filter from EEG data, which was later applied to the other groups during resting conditions with eyes open and without being asked to perform any task. The group of 135 MCI subjects could be divided into two subgroups at the end of a month follow-up: 75 with stable MCI (MCI-S, not converted to AD), 60 converted to AD (MCI-C). The P3b spatial filter was built by means of a signal processing method called Functional Source Separation (FSS), which increases signal-to-noise ratio by using a weighted sum of all EEG recording channels rather than relying on a single, or a small sub-set, of channels. A clear difference was observed for the P3b dynamics at rest between groups. Moreover, a machine learning approach showed that P3b at rest could correctly distinguish MCI from EC (80.6% accuracy) and MCI-S from MCI-C (74.1% accuracy), with an accuracy as high as 93.8% in discriminating between MCI-C and EC. Finally, a comparison of the Bayes factor revealed that the group differences among MCI-S and MCI-C were 138 times more likely to be detected using the P3b dynamics compared with the best performing single electrode (Pz) approach. In conclusion, we propose that P3b as measured through spatial filters can be safely regarded as a simple and sensitive marker to predict the conversion from an MCI to AD status eventually combined with other non-neurophysiological biomarkers for a more precise definition of dementia having neuropathological Alzheimer characteristics.

Summarization: The primary somatosensory cortex remains one of the most investigated brain areas. However, there is still an absence of an integrated methodology to describe the early temporal alterations in the primary somatosensory network. Source analysis based on combined Electro-(EEG) and Magneto-(MEG) Encephalography (EMEG) has been recently shown to outperform the one's based on single modality EEG or MEG. The study and potential of combined EMEG form the goal of the current study, which investigates the time-variant connectivity of the primary somatosensory network. A subject-individualized pipeline combines a functional source separation approach with the effective connectivity analysis of different spatiotemporal source patterns using a realistic and skull-conductivity calibrated head model. Three-time windows are chosen for each modality EEG, MEG, and EMEG to highlight the thalamocortical and corticocortical interactions. The results show that EMEG is promising in suppressing a so-called connectivity 'leakage' effect when later components seem to influence earlier components, just due to too similar leadfields. Our current results support the notion that EMEG is superior in suppressing the spurious flows within a network of very rapid alterations. Παρουσιάστηκε στο: 19th International Conference on Bioinformatics and Bioengineering

Background: Recent morpho-functional evidence pointed out that abnormalities in the thalamus could play a major role in the expression of migraine neurophysiological and clinical correlates. Whether this phenomenon is primary or secondary to its functional disconnection from the brainstem remains to be determined. We used a Functional Source Separation algorithm of EEG signal to extract the activity of the different neuronal pools recruited at different latencies along the somatosensory pathway in interictal migraine without aura (MO) patients. Methods: Twenty MO patients and 20 healthy volunteers (HV) underwent EEG recording. Four ad-hoc functional constraints, two sub-cortical (FS14 at brainstem and FS16 at thalamic level) and two cortical (FS20 radial and FS22 tangential parietal sources), were used to extract the activity of successive stages of somatosensory information processing in response to the separate left and right median nerve electric stimulation. A band-pass digital filter (450-750 Hz) was applied offline in order to extract high-frequency oscillatory (HFO) activity from the broadband EEG signal. Results: In both stimulated sides, significant reduced sub-cortical brainstem (FS14) and thalamic (FS16) HFO activations characterized MO patients when compared with HV. No difference emerged in the two cortical HFO activations between the two groups. Conclusions: Present results are the first neurophysiological evidence supporting the hypothesis that a functional disconnection of the thalamus from the subcortical monoaminergic system may underline the interictal cortical abnormal information processing in migraine. Further studies are needed to investigate the precise directional connectivity across the entire primary subcortical and cortical somatosensory pathway in interictal MO. Written informed consent to publication was obtained from the patient(s).

Objective: To investigate the dynamics of communication within the primary somatosensory neuronal network. Methods: Multichannel EEG responses evoked by median nerve stimulation were recorded from six healthy participants. We investigated the directional connectivity of the evoked responses by assessing the Partial Directed Coherence (PDC) among five neuronal nodes (brainstem, thalamus and three in the primary sensorimotor cortex), which had been identified by using the Functional Source Separation (FSS) algorithm. We analyzed directional connectivity separately in the low (1-200. Hz, LF) and high (450-750. Hz, HF) frequency ranges. Results: LF forward connectivity showed peaks at 16, 20, 30 and 50. ms post-stimulus. An estimate of the strength of connectivity was modulated by feedback involving cortical and subcortical nodes. In HF, forward connectivity showed peaks at 20, 30 and 50. ms, with no apparent feedback-related strength changes. Conclusions: In this first non-invasive study in humans, we documented directional connectivity across subcortical and cortical somatosensory pathway, discriminating transmission properties within LF and HF ranges. Significance: The combined use of FSS and PDC in a simple protocol such as median nerve stimulation sheds light on how high and low frequency components of the somatosensory evoked response are functionally interrelated in sustaining somatosensory perception in healthy individuals. Thus, these components may potentially be explored as biomarkers of pathological conditions. © 2012 International Federation of Clinical Neurophysiology.

Summarization: Motivation: We attempt to cover the lack of an integrated way to describe the early interactions within the primary somatosensory network. A combination of EEG and MEG (EMEG) has been shown to outperform single EEG or MEG in source analysis. EMEG may be a promising integrated way for the goal of the current study. Objective: The current study investigates the time-variant connectivity network of the primary somatosensory cortex by means of a functional source separation approach and source analysis of different measurement modalities. Methods: The brain signals are recorded by the non-invasive modalities of electro- and magneto- encephalography (EEG and MEG) on a healthy subject, who participated in an experiment for measuring somatosensory evoked responses by median nerve stimulations on the right wrist. After the prepossessing, time-locked analysis (TLA) is applied for the reduction of the non-cerebral activity in both, EEG and MEG. After the estimation of the somatosensory evoked potentials (SEP) and fields (SEF), the goal was the extraction of the time-functional (or functional) components. The separation of the somatosensory functional components is accomplished by a semi-blind algorithm, the functional source separation (FSS), which uses a priori information of each functional component to extract the functional sources (FSs) for each modality. The back-projected SEP and SEF responses are calculated for each functional source. The EMEG measurement modality is estimated by these EEG and MEG back-projected data of the same components. Then, for every back-projected data of each modality (EEG, MEG or EMEG) and for each of their time points we find a solution to source localization by using the sLORETA algorithm and we obtain the source waveforms. The used individual and realistic head model includes six tissue compartments (scalp, skull compacta, skull spongiosa, cerebrospinal fluid, gray and white matter), brain anisotropy and calibrated skull conductivities. The source waveforms all of the modalities were set the base for the estimation of the effective and time-varying primary somatosensory connectivity network using time-varying Generalized Orthogonalized Partial Directed Coherence (tv-GOPDC). Περίληψη: Κίνητρο: Θα θέλαμε να καλύψουμε την έλλειψη ενός ολοκληρωμένου τρόπου για την περιγραφή των πρώιμων αλληλεπιδράσεων στο κύριο σωματοαισθητικό δίκτυο. Στόχος: Η τρέχουσα μελέτη διερευνά το δίκτυο συνδεσιμότητας στον χρόνο του πρωτογενούς σωματοαισθητικού φλοιού μέσω μιας προσέγγισης διαχωρισμού των λειτουργικών πηγών και εντοπισμό αυτών για τους διαφορετικούς τρόπους μέτρησης. Μέθοδοι: Τα σήματα του εγκεφάλου καταγράφονται με τις μη επεμβατικές μεθόδους ηλεκτρο- και μαγνητο- εγκεφαλογραφίας (EEG και MEG) για ένα υγιές άτομο, το οποίο υποβλήθηκε σε διέγερση του μέσου νεύρου στον δεξιό καρπό. Μετά την προεπεξεργασία, εφαρμόζουμε χρονική μέση ανάλυση (time-locked analysis -TLA) για τη μείωση της μη εγκεφαλικής δραστηριότητας τόσο στο EEG όσο και στο MEG. Μετά από την εκτίμηση των σωματοαισθητικών προκληθέντων δυναμικών (SEP) και των πεδίων (SEF), ο στόχος είναι η εξαγωγή των χρονικά λειτουργικών (ή λειτουργικών) στοιχείων. Ο διαχωρισμός των σωματοαισθητικών λειτουργικών στοιχείων γίνεται με έναν ημι-αυτοματοποιημένο (semi-blind) αλγόριθμο για διαχωρισμό λειτουργικών πηγών (functional source separation-FSS), ο οποίος χρησιμοποιεί εκ των προτέρων πληροφορία για κάθε λειτουργικό στοιχείο για την εξαγωγή των λειτουργικών πηγών (FSs) για κάθε τύπο. Για κάθε λειτουργική πηγή υπολογίζονται τα οπισθο-προβαλλόμενα (back-projected) SEP και SEF δεδομένα. Το συνδυασμένο σήμα EMEG για κάθε λειτουργική πηγή υπολογίζεται με τη χρήση αυτών των δεδομένων EEG και MEG. Στη συνέχεια, προχωράμε στον εντοπισμό των πηγών σε συγκεκριμένη θέση του μοντέλου του εγκεφάλου με τον αλγόριθμο sLORETA για κάθε για κάθε χρονικό σημείο και σαρώνοντας όλο το χρονικό διάστημα, μέχρι την απόκτηση των κυματομορφών των διπόλων. Ένα ρεαλιστικό μοντέλο κεφαλής παράγεται την μέθοδο πεπερασμένων στοιχείων FEM χρησιμοποιείται στον εντοπισμό των πηγών το οποίο περιλαμβάνεται από έξι τμήματα (τριχωτό μέρος του κεφαλιού, συμπαγές κρανίο, σπογγώδες κρανίο, εγκεφαλονωτιαίο υγρό (ΕΝΥ), γκρι και λευκή ουσία) και συμπεριλαμβάνει την ανισοτροπία της λευκής ύλης και του κρανίου. Τέλος, για κάθε τύπο δεδομένων παρουσιάζουμε τη λειτουργική συνδεσιμότητα που προκύπτει χρησιμοποιώντας τις κατωφλιομένες τιμές από το time-varying Generalized Orthogonalized Partial Directed Coherence (tv-GOPDC) των κυματομορφών διπόλων (sLORETA) για συγκεκριμένες χρονικές στιγμές.