Electrophysiological changes during adolescence: A review

Segalowitz, Sidney J.; Santesso, Diane L.; Jetha, Michelle K.

doi:10.1016/j.bandc.2009.10.003

Cited by 235 publications

(238 citation statements)

References 188 publications

Supporting

Mentioning

200

Contrasting

Unclassified

Order By: Relevance

“…Although this in vivo study proved a positive correlation between WM integrity (i.e., FA values) and complexity, it might be still questioned if such association meets the rationale of our previous hypothesis, that is, if WM is related to the generation of oscillatory activity in the brain. EEG/MEG signals are composed of the summation of multiple electromagnetic oscillations at different frequencies which derive from the collective and synchronous behaviour of neural populations located in different brain regions (Cantero et al, 2009;Segalowitz et al, 2010). Therefore, synchronisation among brain regions appears as a basic mechanism to explain the frequencies' variability observed in EEG/MEG signals, which is in turn intimately related to oscillatory complexity estimates (Lutzenberger et al, 1995).…”

Section: Discussionmentioning

confidence: 99%

Brain oscillatory complexity across the life span

Fernández

Zuluaga

Abásolo

et al. 2012

Clinical Neurophysiology

View full text Add to dashboard Cite

Objective: Considering the increasing use of complexity estimates in neuropsychiatric populations, a normative study is critical to define the 'normal' behaviour of brain oscillatory complexity across the life span. Method:This study examines changes in resting-state magnetoencephalogram (MEG) complexity -quantified with the Lempel-Ziv complexity (LZC) algorithm -due to age and gender in a large sample of 222 (100 males/122 females) healthy participants with ages ranging from 7 to 84 years.Results: A significant quadratic (curvilinear) relationship (p < 0.05) between age and complexity was found, with LZC maxima being reached by the sixth decade of life.Once that peak was crossed, complexity values slowly decreased until late senescence.Females exhibited higher LZC values than males, with significant differences in the anterior, central and posterior regions (p < 0.05). Conclusions:These results suggest that the evolution of brain oscillatory complexity across the life span might be considered a new illustration of a 'normal' physiological rhythm.Significance: Previous and forthcoming clinical studies using complexity estimates might be interpreted from a more complete and dynamical perspective. Pathologies not 3 only cause an 'abnormal' increase or decrease of complexity values but they actually 'break' the 'normal' pattern of oscillatory complexity evolution as a function of age.Keywords: Life Span, Ageing, Complexity, Brain Development, White Matter Development Highlights 1. A significant quadratic (curvilinear) relationship between age and oscillatory complexity exists, with complexity maxima reached by the sixth decade of life.2. As in previous studies, females exhibit higher complexity values than males, at least in some brain regions.3. The evolution of oscillatory complexity across the life span is interpreted as a physiological rhythm which is altered by several brain pathologies.

show abstract

Section: Discussionmentioning

confidence: 99%

Brain oscillatory complexity across the life span

Fernández

Zuluaga

Abásolo

et al. 2012

Clinical Neurophysiology

View full text Add to dashboard Cite

show abstract

“…A slow decline in frequency was observed from 20 years onwards. This confirms earlier reports on the age dependency of the alpha rhythm as discussed in (van der Stelt, 2008;Marcuse et al, 2008;Segalowitz et al, 2010;Aurlien et al, 2004). The mean PDR frequency over age was also reported by (Aurlien et al, 2004).…”

Section: Discussionsupporting

confidence: 91%

“…Instead of having a single generator, the current view is that it is generated by nonlinear interactions of pyramidal cells and modulated by thalamic input and other complex cortico-cortical processes (Steriade et al, 1990;Silva, 1991;Stam et al, 1999;Naruse et al, 2010;Hughes and Crunelli, 2005;Nunez and Ramesh, 2005). The peak frequency for young children typically resides around 3-4 Hz and gradually increases with age until reaching a maturation point at around 8-13 Hz when adolescence or young adulthood is reached (van der Stelt, 2008;Marcuse et al, 2008;Segalowitz et al, 2010;Chiang et al, 2011). In adults, a small decrease in frequency with ageing can be observed at a considerably slower rate.…”

Section: Introductionmentioning

confidence: 99%

“…The alpha rhythm in general has mostly been studied in subject groups of 15 to 100 individuals and only a small number of studies have used datasets containing one thousand individuals or more (Segalowitz et al, 2010;van der Stelt, 2008;Chiang et al, 2011). In some cases, obtaining characteristics would require the researcher to either read each individual patient report, or to visually inspect every EEG.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computer assisted interpretation of the human EEG

Lodder¹

View full text Add to dashboard Cite

“…As described by Segalowitz and co-workers (Segalowitz, et al 2010), EEG/MEG signals are composed of the summation of multiple electrical oscillations at different frequencies. Therefore, the most common analysis procedure is spectral analysis, used to obtain the power of each frequency band.…”

Section: Introductionmentioning

confidence: 99%

The correlation between white-matter microstructure and the complexity of spontaneous brain activity: A difussion tensor imaging-MEG study

Fernández¹,

Ríos-Lago²,

Abásolo³

et al. 2011

NeuroImage

View full text Add to dashboard Cite

The advent of new signal processing methods, such as non-linear analysis techniques, represents a new perspective which adds further value to brain signals' analysis. Particularly, Lempel-Ziv's Complexity (LZC) has proven to be useful in exploring the complexity of the brain electromagnetic activity. However, an important problem is the lack of knowledge about the physiological determinants of these measures. Although a correlation between complexity and connectivity has been proposed, this hypothesis was never tested in vivo.Thus, the correlation between the microstructure of the anatomic connectivity and the functional complexity of the brain needs to be inspected. In this study we analysed the correlation between LZC and fractional anisotropy (FA), a scalar quantity derived from diffusion tensors that is particularly useful as an estimate of the functional integrity of myelinated axonal fibers, in a group of sixteen healthy adults (all female, mean age 65.56 ± 6.06 years, interval 58-82). Our results showed a positive correlation between FA and LZC scores in regions including clusters in the splenium of the corpus callosum, cingulum, parahipocampal regions and the sagittal stratum. This study supports the notion of a positive correlation between the functional complexity of the brain and the microstructure of its anatomical connectivity. Our investigation proved that a combination of neuroanatomical and neurophysiological techniques may shed some light on the underlying physiological determinants of brain's oscillations.

show abstract

Electrophysiological changes during adolescence: A review

Cited by 235 publications

References 188 publications

Brain oscillatory complexity across the life span

Brain oscillatory complexity across the life span

Computer assisted interpretation of the human EEG

The correlation between white-matter microstructure and the complexity of spontaneous brain activity: A difussion tensor imaging-MEG study

Contact Info

Product

Resources

About