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Dosenbach et al. (2010) recently reported that individual functional brain maturity can be estimated from only 5 min of functional magnetic resonance imaging (fMRI) data at rest, explains 55% of the sample variance, and “could one day … aid in the screening, diagnosis, and prognosis of individuals with disordered brain function”. Surprisingly, their report makes no reference to strikingly similar electroencephalogram (EEG) research published more than a decade ago.
In fact, speculations on ‘physiological time’, conceived as a proper time-scale of organismic development and related non-linearly to physical (chronological) time, have been around since early 20th century (Carrel 1931). More specifically, indices of functional brain maturation estimated from resting-state brain activity were proposed in the 1970s (Matoušek and Petersén 1973a, b). Statistical approaches at differential diagnosis of brain (dys)functions dubbed ‘neurometrics’ (John et al. 1977, 1988) included ‘developmental equations’ of frequency domain EEG-parameters validated across countries and ethnicities (John et al. 1980; Ahn et al. 1980). Similar developmental changes were subsequently reported for non-linear dynamics (Meyer-Lindenberg 1996), functional microstates (Koenig et al. 2002), or global descriptors (Wackermann and Allefeld 2009) of the EEG. Importantly, Wackermann and Matoušek (1998) proposed ‘EEG age’ as a reliable measure of individual brain maturation, which was based on a nonlinear relation between log-transformed frequency profiles of the resting EEG and log-transformed chronological age, and accounted for nearly 80% of the sample variance.
These important parallels support a neural origin of the hemodynamic findings published by Dosenbach et al. (2010), and raise intriguing questions regarding the physiological link between fMRI connectivity and spectral EEG composition. Discussing such questions would be in line with the fruitful tradition of brain maturation studies paying attention to historical continuity (Toga et al. 2006). Furthermore this past experience also suggests some caution regarding the claimed potential of Dosenbach et al.’s study for screening, diagnosis and prognosis of neuropsychiatric disorders. These remarks are not to relativize the innovative character of their fMRI application and the importance of their findings, but to frame them into the context of research of the last few decades.
References
Ahn H, Prichep L, John ER, Baird H, Trepetin M, Kaye H (1980) Developmental equations reflect brain dysfunctions. Science 210:1259–1262
Carrel A (1931) Physiological time. Science 74:618–621
Dosenbach NUF, Nardos B, Cohen AL, Fair DA, Power JD, Church JA, Nelson SM, Wig GS, Vogel AC, Lessov-Schlaggar CN, Barnes KA, Dubis JW, Feczko E, Coalson RS, Pruett JR, Barch DM, Petersen SE, Schlaggar BL (2010) Prediction of individual brain maturity using fMRI. Science 329:1358–1361
John ER, Karmel BZ, Corning WC, Easton P, Brown D, Ahn H, John M, Harmony T, Prichep L, Toro A, Gerson I, Bartlett F, Thatcher F, Kaye H, Valdes P, Schwartz E (1977) Neurometrics. Science 196:1393–1410
John ER, Ahn H, Prichep L, Trepetin M, Brown D, Kaye H (1980) Developmental equations for the electroencephalogram. Science 210:1255–1258
John ER, Prichep LS, Fridman J, Easton P (1988) Neurometrics: computer-assisted differential diagnosis of brain dysfunctions. Science 239:162–169
Koenig T, Prichep L, Valdes-Sosa P, Braeker E, Kleinlogel H, Isenhart R, John ER (2002) Millisecond by millisecond, year by year: normative EEG microstates and developmental stages. NeuroImage 16:41–48
Matoušek M, Petersén I (1973a) Automatic evaluation of background activity by means of age-dependent EEG quotients. Electroencephal Clin Neurophysiol 35:603–612
Matoušek M, Petersén I (1973b) Frequency analysis of the EEG background activity by means of age dependent EEG quotients. In: Kellaway P, Petersén I (eds) Automation of clinical electroencephalography. Raven Press, New York, pp 75–102
Meyer-Lindenberg A (1996) The evolution of complexity in human brain development: an EEG study. Electroencephal Clin Neurophysiol 99:405–411
Toga AW, Thompson PM, Sowell ER (2006) Mapping brain maturation. Trends Neurosci 29:148–159
Wackermann J, Allefeld C (2009) State space representation and global descriptors of brain electrical activity. In: Michel CM, Koenig T, Brandeis D, Gianotti LRR, Wackermann J (eds) Electrical neuroimaging. Cambridge University Press, Cambridge, pp 191–214
Wackermann J, Matoušek M (1998) From the “EEG age” to a rational scale of brain electric maturation. Electroencephal Clin Neurophysiol 107:415–421
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This is one of several papers published together in Brain Topography on the “Special Issue: Brain Imaging across the Lifespan”.
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Brandeis, D., Koenig, T. & Wackermann, J. Individual Brain Maturity: From Electrophysiology to fMRI. Brain Topogr 24, 187–188 (2011). https://doi.org/10.1007/s10548-011-0184-z
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DOI: https://doi.org/10.1007/s10548-011-0184-z