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Insights into affective word processing and geriatric depression through the combination of Event-Related Potentials with Cortical Brain Network Analysis

Maria Karagianni1, Ioanna Tepelena1, Christos Frantzidis1, Evangelia Romanopoulou1, Sotiria D. Gilou1, Ana B. Vivas2 and Panagiotis D. Bamidis1*
  • 1 Aristotle University of Thessaloniki, Medical School, Greece
  • 2 City College, The University of Sheffield International Faculty, Psychology, Greece

Research in the field of affective word processing and the neural correlates of language is a rapidly growing field [1]. Emotions are involved in many cognitive and behavioral states such as attention, memory and social interaction [2]. They are usually described by two primary dimensions which are the arousal and pleasantness degree or valence [3]. Lang et al. performed pioneering research on these two emotional dimensions employing affective visual stimuli [4]. Ongoing research shows that there is an impact of pleasure and arousal in central nervous and peripheral physiological indicators of emotional processing [5]. There is neuroscientific evidence that arousal and pleasure activate distinct cortical networks [6]. Affective visual word stimuli have also employed in young populations to investigate whether they induce distinct brain patterns according to their emotional dimensions. Aiming to extend the emotional model of how the brain processes word stimuli during mood disorders and aging we employed a group of healthy senior citizens and elderly patients suffering from geriatric depression. This study presents early results derived from both the temporal responses on the sensor level and cortical brain network analysis induced due to the stimulus presentation. There were 120 stimuli equally divided to three emotional categories: pleasant, unpleasant and neutral. The stimulus duration was 500 ms. Among the stimuli a fixation cross appeared to the screen for 1.5 second. The electroencephalographic data were recorded through 57 active electrodes. The sampling rate was set at 500 Hz. Participants were instructed to passively watch visual word stimuli presented on a screen. The brain electrodes were re-referenced through the common average model. Then digital filtering was applied as follows: (a) High-pass filter (1 Hz), (b) Band-stop filter (47-53 Hz) (c) Low-pass filter (100 Hz) and (d) Band-stop (97-103 Hz). Independent Component Analysis was then applied to identify and reject potentially contaminated source components. Finally, the data were epoched. An epoch consisted of 500 ms pre-stimulus period, 500 ms stimulus duration and 800 ms post-stimulus duration. Then visual inspection took place for rejecting epochs contaminated with artifacts. Finally, the resulting epochs were averaged. The cortical activity was reconstructed by employing the default anatomy model in terms of Magnetic Resonance Imaging (MRI) volume, envelope of the cortex and the head surface. The head model was computed through the Open MEEG Boundary Elements Method (BEM). The solution space was constrained to the cerebral cortex which was modeled as a 3-dimensional grid of 10000 fixed dipoles oriented normally to the cortical surface. Then the sLORETA methodology was employed to estimate the resting-state sources. Then 512 cortical Regions Of Interest (ROIs) were defined. Then synchronization analysis was performed for each participant regarding her/his average signal per emotional category through the Orthogonal Discrete Wavelet Transform (ODWT). Its scope is to extract the co-operation degree among pairs of cortical ROIs through comparison of the relative energy distribution of EEG frequency rhythm (delta, theta, alpha, beta and gamma). The wavelet coefficients of each rhythm were squared and summed so as to compute the energy of each frequency band. Then, relative (probabilistic) energy distributions were estimated for each cortical region. The synchronization degree among two ROIs was quantified as the similarity of their energy distribution (pj and qj, j=1…5). The results for the ERP temporal analysis are presented in the following Table: Table 1 Description of the ERP amplitudes during the various experimental conditions for both groups Group HV LV NV P100 P200 P300 P100 P200 P300 P100 P200 P300 Nold 1,7626 1,5372 1,7120 1,5755 1,6294 1,6817 1,6436 1,5644 1,7103 Depr. 1,3408 1,1747 1,1996 1,3823 1,1576 1,2902 1,2641 1,1488 1,2686 The results of the brain network cortical analysis for the three temporal windows corresponding to P100, P200 and P300 are reported in Tables 2-4: Table 2 Brain-networks characteristics during early (P1) ERP interval Group HV LV NV SW Cluster CharPath SW Cluster CharPath SW Cluster CharPath Nold 6,405 0,503 5,035 5,999 0,487 5,177 5,958 0,500 5,377 Depr. 6,212 0,498 5,091 6,298 0,507 5,158 6,520 0,507 4,954 Table 3 Brain network characteristics during mid (P2) ERP interval Group HV LV NV SW Cluster CharPath SW Cluster CharPath SW Cluster CharPath Nold 6,045 0,503 5,336 6,156 0,495 5,141 6,078 0,502 5,2888 Depr. 6,084 0,512 5,386 6,234 0,498 5,168 6,188 0,494 5,105 Table 4 Brain network characteristics during late (P3) ERP interval Group HV LV NV SW Cluster CharPath SW Cluster CharPath SW Cluster CharPath Nold 6,320 0,506 5,093 5,889 0,497 5,419 6,019 0,502 5,345 Depr. 6,227 0,507 5,191 6,156 0,501 5,217 6,245 0,494 5,040 The results from the ERP analysis demonstrate that geriatric depression results in a generally diminished activation of neuronal circuits (lower amplitude on all experimental conditions). Processing of negative stimuli seems to be facilitated during the early recognition phase (P100) and the later stages that involve also memory processes (P300). In these two phases depressive patients elicit greater amplitude for the unpleasant stimuli than the pleasant ones. Healthy senior citizens seem to focus on pleasant and neutral stimuli during the late P3 component which may validate the positivity effect [7]. However, the ERP analysis created some additional research questions. More specifically, it is unclear whether emotional processing is generally disorganized due to the depression or the specific emotional disorder alters the functional wiring of the patients’ brain in order to pay more attention and retain emotional stimuli. The aforementioned brain network results indicate that geriatric depression may alter the organization of the functional connectome so as to facilitate processing of unpleasant stimuli. So, depressive patients exhibit greater small-world property (optimal organization) values than the healthy controls for the unpleasant stimuli. This is attributed to the better local information processing (denser clustering) and to the faster (except from the mid interval) global information flow during the passive viewing of unpleasant stimuli. To sum up, the present study demonstrates that the combination of the traditional ERP analysis with contemporary tools derived from graph theory may provide a more holistic understanding of the brain neuropathology due to the geriatric depression.

References

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2. Rowe G, Hirsh JB, Anderson AK. Positive affect increases the breadth of attentional selection. Proceedings of the National Academy of Sciences. 2007; 104:383–388.
3. Russell J.A. Core affect and the psychological construction of emotion. Psychological Review. 2003;110(1):145–172. [PubMed: 12529060]
4. Lang, P.J. (1979) Presidential address, 1978A bio-informational theory of emotional imagery. Psychophysiology, 16: 495–512
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Keywords: Affective Neuroscience, Brain network analysis, event-related potential (ERP), word stimuli, geriatric depression

Conference: SAN2016 Meeting, Corfu, Greece, 6 Oct - 9 Oct, 2016.

Presentation Type: Oral Presentation in SAN 2016 Conference

Topic: Oral Presentations

Citation: Karagianni M, Tepelena I, Frantzidis C, Romanopoulou E, Gilou SD, Vivas AB and Bamidis PD (2016). Insights into affective word processing and geriatric depression through the combination of Event-Related Potentials with Cortical Brain Network Analysis. Conference Abstract: SAN2016 Meeting. doi: 10.3389/conf.fnhum.2016.220.00003

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Received: 29 Jul 2016; Published Online: 30 Jul 2016.

* Correspondence: Prof. Panagiotis D Bamidis, Aristotle University of Thessaloniki, Medical School, Thessaloniki, -- SELECT --, 54124, Greece, pdbamidis@gmail.com