The vast majority of research reporting age-related changes to neural (e.g. BOLD activity, spike frequencies), behavioural (e.g. reaction times, error rates), or other biological variables is concerned with changes in measures of central tendency (e.g. means), that may change monotonically or nonlinearly with age. Changes to other statistical properties of these variables may also be relevant, however. Variability and/or (when definable) noise are two such properties, and whilst the study of these in the context of ageing is not new, recent findings have some interesting, slightly counterintuitive theoretical implications for how we understand the ageing process.
There are two main strands to this work that comes together nicely - one from cognitive psychology and cognitive neuroimaging (e.g. Garrett, McDonald, Grady), and the other from theoretical and computational neuroscience (e.g. Jirsa, Deco, Ghosh). Randy McIntosh seems to be the link between the two and his 201X review article ties up the two nicely.
The argument goes something like this:
Intra-individual neural variability increases with maturation and declines with senescence, and from a cognitive health point of view the decline is a 'bad' thing...
(Interjection: this is the counterintuitive part, since from both a cognitive and an engineering perspective we normally associate variability with imprecision, inaccuracy, and general lack of system 'integrity'. This leads to an important point:
exactly
what variables and what kind of variability we're talking about here is an extremely important detail. More on this below.)
...Both the maturation and senescence patterns are interpreted in terms of there being an "optimal state characterized by a greater ability to transition between brain states and by increased dynamic range", whereas "older, slower, more behaviourally inconsistent participants lack a critical amount of brain variability for optimal neural function" (Garrett et al. 2011). The idea of there being an optimal level of variability/noise (possibly a stochastic resonance effect) has been developed by some of the contributing authors (McIntosh, Jirsa) in a number of simulation studies, where the brain is modelled as a network of coupled oscillators/dynamical systems that display neurophysiologically plausible behaviours such as intermittent synchrony. Their key soundbyte message is that possessing a small amount of endogeneously generated noise facilitates 'exploration of the brain's dynamic repertoire' - i.e. the space of possible brain states (see McIntosh et al. (2010) for a review that brings together the ageing data and the dynamical systems ideas most comprehensively). Other explanations offered by Garrett et al. (2011) for the reduced variability in old age are weakened functional connectivity (c.f. Fox et al. 2006; Nir et al. 2008), poorer neuronal signal detection (Li et al. 2006), or reduced dynamic range (Shew et al. 2009), and some vague comments linking it to 'Bayesian Brain' theories.
I think this is a fascinating and exciting hypothesis and am keen to see how it develops over the next few years. Here's my thoughts:
Comments + criticisms:
1. The list of empirical studies showing decreased variability/noise with (old) age is still quite low:
- Garrett et al. (2010a): examined effects of age on both mean- and standard deviation- (SD) based patterns of Blood Oxygenation Level Dependent (BOLD) signal in a simple visual fixation task. Although both the mean- and SD-based patterns were significantly predictive of age, the significance of the SD-based pattern was more than five times that of the mean-based pattern. Moreover, the brain regions comprising the multivariate spatial patterns identified by the SD-based and mean-based versions of their technique were non-overlapping - indicating that age-related changes in BOLD signal variability (measured here through standard deviation) constitute a distinct neural signature from changes in mean BOLD signal.
- Garrett et al. (2011): Similar to Garrett et al. (2010a), but different task and subjects (?)
- Park et al. (2010): Different group, different analysis methods: "Neural specificity was estimated based on how accurately multivariate pattern analysis identified neural activation patterns associated with specific experimental conditions.". Conclusion = loss of neural specificity (which they interpret as de-differentiation) contributes to reduced fluid processing ability in old age.
- (for maturation: McIntosh et al (2008): found that intra-trial EEG signal variability decreased between the ages of 8 and 25 years. The level of variability was highly correlated with performance on a face memory task, and so the authors conclude that the move towards greater variability in brain activity during development improves cognitive flexibility and capacity)
- (in cardiac electrophysiology: Costa et al. (2005) develop a multi-scale entropy measure (which I believe is also used by Vakorin & McIntosh on EEG data) and apply it to cardiogram data, concluding that "The method consistently indicates a loss of complexity with aging, with an erratic cardiac arrhythmia satrial fibrillationd, and with a life-threatening syndrome scongestive heart failured....The results support a general “complexity-loss” theory of aging and disease")
- (in human motor control, Costa et al. (2007) apply the same measure to demonstrate that application of subsensory noise to the feet improves postural stability and multiscale complexity of sway fluctuations in healthy elderly subjects)
2. Noise
One of the seminal recent works discussing variability and noise in the nervous system is Faisal et al. (2008), who define 'variability' as simply
“…changes in some measurable quantity, such as spike timing, or movement duration”.
They also offer the following definition of noise (the subject of their review), following the Oxford English Dictionary:
“…random or irregular fluctuations or disturbances which are not part of a signal […] or which interfere with or obscure a signal or more generally any distortions or additions which interfere with the transfer of information”.
Faisal et al. note that the term variability, unlike the term noise, is
a) agnostic to the source of the variability (random / deterministic), and
b) carries no implications of being either beneficial or detrimental.
We can add to these a further point, namely that 'variability', unlike 'noise'
c) does not necessitate a signal processing or information-theoretic interpretation of the phenomenon under investigation,
which is clearly not the case for the OED definition given above.
This final point reflects limitations of both the experimentalist and the theoretician, as it is simply not possible at our present level of understanding and technical finesse to define concretely the signals that a given pattern of neural activity might constitute or represent, beyond certain early sensory systems. And since you can only define 'noise' when you can define a 'signal', the fact that some of the papers discussed above use 'variability' and 'noise' are used interchangeably, is a bit sloppy.
The authors are of course aware of this, e.g. McIntosh et al. (2010) say
"An important clarification here is that while these three measures are sensitive to different aspects of the brain signal, at present we take the liberty of considering them all as measures of brain noise. We acknowledge that this is not ideal, as noise has different implications at different levels of the system."
I think they sometimes they go for 'noise' rather than 'variability' because it sounds jazzier.
Another reason not to use the term noise is because of potential confusion with 'noisy brain' theories of sensory ageing, which go back several decades (e.g. Slathouse & Lichty 1985; Gregory?). Moreover, in electrophysiology studies of early sensory systems where signals can be straightforwardly defined, neurons older animals (monkeys) show increased spontaneous action potential firing rates, decreased stimulus selectivity, and lower signal-to-noise ratios (Schmolensky et al. 2000, Hua et al. 2006,Yu et al. 2006, Yang et al. 2009, Liang et al. 2010). Dopamine may also play a signal-enhancement role in cortical function, its steady decrease with age thus potentially also contributing to increased neural noise (e.g. Li et al.2001).
3. Relation to de-differentiation?
Park et al. (2010) consider age-related increases in variability of multivariate spatial BOLD activity patterns as an index of 'decreased neural specificity', i.e. de-differentiation. Is this inconsistent with Garrett et al. 's ideas? This would seem to be the case for any spatial (rather than temporal) measure of variability.
References:
Costa et al. (2002a). Multiscale entropy analysis of complex physiologic time series. Phys. Rev. Lett., 89: 068102
Costa et al. (2005). Multiscale entropy analysis of biological signals. Phys. Rev. E. Stat. Nonlin. Soft Matter Phys., 71: 021906, 2005.
Deco et al. (2009). Key role of coupling, delay, and noise in resting brain fluctuations. PNAS 106, 10302-10307
Faisal et al. (2008). Noise in the nervous system. Nat. Rev. Neurosci 9, 292-303
Garrett et al. (2010). Blood Oxygen Level-Dependent Signal Variability Is More than Just Noise. J. Neurosci. 30, 4914-4921.
Ghosh et al. (2008). Noise during Rest Enables the Exploration of the Brainʼs Dynamic Repertoire. PLoS Comp Biol 4(10), 12.
Grady et al. (2010). A Multivariate Analysis of Age-Related Differences in Default Mode and Task-Positive Networks across Multiple Cognitive Domains. Cereb. Cortex 20, 1432-144.
Li et al. (2006). A neurocomputational model of stochastic resonance and aging. Neurocomputing, 69(13-15), 1553-1560.
Liang et al. (2010). Aging affects the direction selectivity of MT cells in rhesus monkeys. Neurobiology of Aging 31, 863-873
MacDonald et al. (2003). Performance variability is related to change in cognition: Evidence from the Victoria Longitudinal Study. Psychology and Aging 18, 510-523.
McIntosh et al. (2008). Increased brain signal variability accompanies lower behavioral variability in development. PLoS Comput. Biol., 4: e1000106.
McIntosh et al. (2010). The development of a noisy brain. Archives Italiennes De Biologie,148(3), 323-337.
Park et al. (2010). Neural specificity predicts fluid processing ability in older adults. Journal of Neuroscience, 30(27), 9253-9259.
Salthouse & & Lichty (1985). Tests of the neural noise hypothesis of age-related cognitive change. J Gerontol 40, 443-450.
Schmolesky et al. (2000). Degradation of stimulus selectivity of visual cortical cells in senescent rhesus monkeys. Nature Neuroscience 3, 384-390.
Yang et al. (2009). Aging Affects the Neural Representation of Speed in Macaque Area MT. Cereb. Cortex 19, 1957-1967
Yu et al. (2006). Functional degradation of extrastriate visual cortex in senescent rhesus monkeys. Neuroscience 140, 1023-1029.
See also:
Anstey et al. (1999). Sensorimotor Variables and Forced Expiratory Volume as Correlates of Speed, Accuracy, and Variability in Reaction Time Performance in Late Adulthood. Aging, Neuropsychology, and Cognition 6, 84.
Hultsch & MacDonald (2004). Intraindividual variability in performance as a theoretical window onto cognitive aging. New Frontiers in Cognitive Aging 65–88
Nesselroade, & Salthouse (2004). Methodological and Theoretical Implications of Intraindividual Variability in Perceptual-Motor Performance. Journals of Gerontology - Series B Psychological Sciences and Social Sciences 59, (2004).
Rabbitt et al. (2001). There are stable individual differences in performance variability, both from moment to moment and from day to day. The Quarterly Journal of Experimental Psychology A 54, 981-1003 (2001).
Suckling et al. (2008). Endogenous multifractal brain dynamics are modulated by age, cholinergic blockade and cognitive performance. Journal of Neuroscience Methods 174, 292-300.
Vakorin et al. (2011). Variability of brain signals processed locally transforms into higher connectivity with brain development. J Neurosci. 31(17): 6405-13.
Welford, A.T (1984). Between bodily changes and performance: some possible reasons for slowing with age. Exp Aging Res 10, 73-88.
Welford, A.T. (1981). Signal, Noise, Performance, and Age. Human Factors: The Journal of the Human Factors and Ergonomics Society 23, 97-109
JG
