Fingelkurts An.A., Fingelkurts Al.A. and Kähkönen S.A.- New Perspectives in Pharmaco-Electroencephalography

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  1 Below is the unedited draft of the article that has been accepted for publication (©Progress in Neuro-Psychopharmacology and Biological Psychiatry, 2005, V. 29. No 2. P. 193-199) New Perspectives in Pharmaco-Electroencephalography Andrew A. Fingelkurtsa,b*, Alexander A. Fingelkurtsa,b, Seppo Kähkönenb,c BM-Science Brain and Mind Technologies Research Centre, P.O. Box 77, FI-02601, Espoo, Finland b BioMag Laboratory, Engineering Centre, Helsinki University Central Hospital, P.O. Box 442 FIN-002
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   1  Below is the unedited draft of the article that has been accepted for publication( ©  Progress in Neuro-Psychopharmacology and Biological Psychiatry, 2005, V. 29. No 2. P. 193-199) New Perspectives in Pharmaco-Electroencephalography Andrew A. Fingelkurts a,b* , Alexander A. Fingelkurts a,b , Seppo Kähkönen b,c a  BM-Science Brain and Mind Technologies Research Centre, P.O. Box 77, FI-02601, Espoo, Finland  b  BioMag Laboratory, Engineering Centre, Helsinki University Central Hospital, P.O. Box 442FIN-00290 Helsinki, Finland  c Cognitive Brain Research Unit, Department of Psychology, University of Helsinki, Finland  Abstract: Recent research emphasizes that majority of brain disorders andpsychiatric problems are accompanied by disruption in the temporal structure of brain activity. From this perspective, disruption is viewed as a disorder of themetastable balance between large-scale integration and independent processing inthe brain, in favor of either independent or hyper-ordered processing. This paperproposes that the future of psychopharmacology lies in its ability to design thepsychotropic drugs which can restore the normal temporal structure andmetastable structure of brain activity. Quantitative electroencephalography(QEEG) is one of the key complex technologies utilized in psychopharmacologyfor this purpose. However, conventional approaches for EEG analysis used inclinical practice are   not suitable for studying temporal structure of brain activity.To overcome this limitation, and in order to reveal dynamic and temporalcharacteristics of brain activity, the advanced analysis of EEG micro-structureshould be used. Key words: brain dynamics, EEG micro-structure, functional connectivity,lorazepam, mental disease, metastability, psychiatry, psychopharmacology,psychotropic drugs, temporal activity. Abbreviations: ERD/ERS – event related desynchronization / event related synchronizationERP – event related potentialGABA – gamma amino butyric acidGFP – global field powerMEG – magnetoencephalographyQEEG – quantitative electroencephalography   2 1. Introduction Recent advances in basic brain and neuro-medical sciences have inevitably ledresearchers to a new understanding of brain and mental health – at all levels of brainorganization it is the balance of autonomy and connectedness that sustains health (Buchman,2002; the Complexity and Dynamics of Human Health Conference supported by the EuropeanCommission, 2001). Consequently, disease is a process with a change in the dynamics fromwhat is normal, rather than regularity or irregularity of those dynamics (Glass, 2001). Indeed,recent research emphasizes that the majority of brain disorders and psychiatric/mental problemsare accompanied by disruption in the temporal structure of brain activity (Dawson, 2004),where this temporal structure could be either more irregular (uncorrelated randomness) or moreregular (excessive order) than normal (Glass, 2001; Buchman, 2002). However, in general, therole of temporal and dynamic aspects in brain disorders and psychiatry is often ignored. Mostcurrent studies are designed in a way that they avoid the temporal structure of the phenomenonunder investigation (VanRullen and Koch, 2003). On the contrary, other than temporal aspectsof brain functioning have been intensively studied and discussed in an appropriate literature.Thus, the purpose of the present review was to emphasize the role and importance of metastable and temporal organization of brain activity for psychopharmacology. 2. Metastable regime and temporal organization of the brain One group of evidence for this came from studying healthy subjects. It has beensuggested that the operational elements of behavioral and mental activity in norm are srcinatedin the periods of  short-term spatio-temporal patterns (metastable states) in the activity of thewhole brain and its individual subsystems (see reviews Kaplan, 1998; Fingelkurts andFingelkurts, 2001; 2003). In this metastable regime (Fig. 1), the brain operates in a state thatallows both integration and segregation of function (Friston, 1997; Bressler and Kelso, 2001):individual neuronal networks are dynamically balanced in their tendency to functionautonomously and their tendency for coordinated activity (Bressler, 2003). Together, theseprocesses reflect the temporal and spatial organization of the brain. Thus, the disruption inbrain metastability and temporal dynamic is suggested as a contributing factor to thedisorganization syndrome (which has long been deemed to be a condition of impaired cognitiveassociation) in many psychiatric and brain diseases (Haig et al, 2000; Dawson, 2004). Fromthis perspective, then, disorganization is viewed as a disorder of the metastable balance  between large-scale integration and independent processing in the cortex, in favor of eitherindependent or hyper-ordered processing (Bressler, 2003). As an example, one predicted   3consequence would be that large-scale patterns of neural population coordination – to bemeasured in patients – would be diminished and disjointed, when compared to normals. Thissupposition was confirmed in the pilot studies with schizophrenic patients (Kaplan andBorisov, 2002). Generally, the wide range of mental illnesses is associated with temporalbreakdown in the brain activity (Dawson, 2004). This strengthens the point that time is animportant etiological factor underlying mental illness. Figure 1.   Schematic illustration of metastability principle of brain functioning .  A ,Individual neurons can quickly become associated (or dis-associated) by synchronizingtheir activity and giving rise to transient assembles. Each of these functional assemblesrepresent discrete elemental brain operations or local microstates.  B , The temporalsynchronization of the activity of several such neuronal assemblies gives rise tooperational modules, which characterized by a new level of brain abstractness – metastablebrain states. C  , Operational modules may be further synchronized (on the other temporalscale) to form new operational modules of even larger abstractness from the initial brainstate. Also the reverse process is possible – when complex operational modules isdecomposed to several simpler ones. For a complete argumentation, see Fingelkurts andFingelkurts, 2003. A   CB   4Taken together, these findings and analytical studies suggest that the future of psychopharmacology lies in its ability to design such psychotropic drugs, which can restore   thenormal temporal structure and metastable organization of brain activity. This approach seemsmore physiologically adequate to integrative, nonstationary and self-organized nature of brainprocesses and fits with a new understanding of the dynamical nature of brain diseases, where “lesions in time” become more evident especially at earlier stages of disease, than “lesions instructure” (Tirsch et al., 2004). In this context, it is important to study how different brain andmind pathologies alter the temporal structure and metastable regimen of brain activity and how  different psychotropic drugs can modify temporal structure of brain activity in healthy subjectsand patients. 3. Pharmaco-electroencephalography Quantitative electroencephalography (QEEG) is one of the key complex technologiesutilized for this purpose. QEEG scanning is a computerized statistical technique used tomeasure objectively precise electrophysiological activity in particular regions of the brain andrelations between them (Duffy et al., 1994). Unfortunately, conventional approaches for EEGanalysis usually used in the clinical practice are   not suitable for studying temporal structure of brain activity. The reason for that is multifold: (i) Spectral EEG parameters are usually derivedfrom averaged EEG power spectra, based on extended periods of time and/or broad fixedfrequency bands for a specific lead. However, the averaging of the EEG signal masks the dynamic and temporal structure of EEG (Fig. 2), and often may lead to ambiguous datainterpretation (Fingelkurts et al., 2003a). (ii) It is well known that an EEG signal is extremely  NONstationary (Fell et al., 2000). However, invariants usually used in EEG studies, such as themean spectrum, average ERP and ERD/ERS, coherency, fractal dimensions, Lyapunovexponents and others have a sense only for stationary dynamics (Landa et al., 2000). Thus,regardless of how powerful or statistically significant the different estimations of thesemeasures may be, there might be difficulties in the meaningful interpretation of these if they arenot matched to their piecewise stationary structure (Fingelkurts et al., 2002). (iii) Practically allexisted measures of brain functional connectivity do not directly estimate metastability andhave several limitations in that they do not take into account the nonstationary nature of theEEG data, require long periods of analysis, and use linear mathematical models of the signalwhich, for the brain, is not typically the case (Landa et al., 2000).
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