1
Q
Describe complexity in healthy individuals?
A
- HEALTHY physiologic function is a complex interaction of multiple control mechanisms that enable an individual to adapt to the unpredictable changes of everyday life
2
Q
How does ageing relate to complexity?
A
- aging is associated with loss complexity
- reducing organ system function and hypothesize, this loss of complexity leads to an impaired ability to adapt to physiologic stress.
e.g.: cardiovascular control and hormone release
3
Q
What is spatial self-similarity (Goldberg et al., 2002)?
A
- Same structures and shapes seen when you zoom in/ increased magnitude, at different scales e.g.: lungs - go along the tube and divide, repeat
- Found in healthy people (complexity)
- ageing affects lung structure(reduced)
- Increases lung surface area in healthy people compared to others
4
Q
What is temporal self-similarity (Goldberg, 2002)?
A
- in muscle force/torque and sEMG = complex temporal structure & Higher adaptability.
- Indicates presence of similar sequences/patterns in a data series over time.
e.g.: seconds, minutes, hours fluctuations. - Idea that when you shorten the time length the magnitude increases
5
Q
What are the inter-individual factors that ageing depends on?
A
- genetics, diet and PA –> these limit their markers of ageing.
- A loss/impairment of functional components
6
Q
What are the different types of noise (Gisinger, 2001)?
A
- White noise: randomly fluctuates in the system, not perfectly controlled, can be at any value around mean
- Pink noise(1/f): Some relevance between values, and increase connectivity. Amplitude of spectrum varies with the freq, more connected to past values of noise (e.g.: shells & sea)
- Brown noise: idea of brownian motion(pollen grains in water that bounce around). Can have complete variability, but follows a sort of path(temporally constrained by what previous value was)
–> ordered by least to most constraints
7
Q
Describe the Chaos Theory:
simple systems producing a complex output
A
- All have really small dev in start position = very different behaviour along the line
- Cannot predict which path the parts of the pendulum will follow
- Referred to as a butterfly effect
8
Q
Define:
- Fractals
- Chaos
A
- Fractals: infinitely complex patterns that repeat themselves on different scales e.g.: lungs
—> Quantified by computing a fractal dimension = how much space a particular object fills. e.g.: More bushy branches = higher complexity - Chaos: describes an unpredictable behaviour that may arise from the internal feedback loops of certain nonlinear systems. —> Generates complex fluctuations that do not have a single/characteristic scale of time.(erratic & unpredictable)
9
Q
Explain and draw the complexity-energy consumption graph (Macklem, 2008).
A
- Far away from equilibrium is chaotic systems e.g.; the weather
- Small energy consumption are = fixed systems
- somewhere in the middle has enough complexity (not too much chaos & stability) –> creates a body that can adapt to a new environment, allows for development of a motor skill = response to perturbations
10
Q
How is complexity defined?
How do fractals and fatigue relate?
A
- the extent that a process generates aperiodic fluctuations that resemble nonlinear chaos
- studies demonstrated decreases in the fractal dimension of surface EMG with neuromuscular fatigue (increasing in motor unit synchronisation)
11
Q
What are the statistics for complexity analysis?
A
- Magnitude – Standard deviation/coefficient of variation, Takes no account for order
- Temporal pattern: looks at order and stability
- Entropy measures (ApEn and SampEn): quantify signal regularity
–> 0 (completely predictable), 2 (white noise) - Fractal scaling: Detrended fluctuation analysis, separate data into boxes of different scales, and best fit contents
- α exponent: 0.5 – white noise, 1.0 pink (1/f) noise, 1.5 Brownian (red, 1/f2) noise
- detects self-similarity
12
Q
What is entropy?
What is the simple entropy calculation?
A
- originates from thermodynamic principles
- entropy increases as freedom of choice increases and decreases as uncertainty decreases.
- (qlog (q) + plog (p))
- Higher p=0.75, is more predictable. Therefore, more likely to get p than q, so can predict what will happen
–> used to calculate complexity
13
Q
What is the difference between ApEn & SampEn?
A
- ApEn is a measure of unpredictability of fluctuations, quantifying the likelihood of similar data points repeating, using a point-to-point analysis
- SampEn is a modification of ApEn assessing complexity but adjusts for self-similarity = more reliable
- Low En values(0) = periodic (e.g. a sine wave)
- Higher En values (2) = greater irregularity and complexity.
- ApEn was derived from Kolmogrov-Sinai entropy –> measures the difference between the logarithmic frequencies
14
Q
What is DFA (Peng et al., 1995)?
A
- done using α scaling exponent => calculated by root mean square error for the detrended time series over various box sizes
- shows long term correlations & power
- plotted as log root mean square error against log of corresponding boxes
–> α 0.5 = white noise, α 0.5-1 = pink noise, α>1 = brown noise
15
Q
Describe the findings of the 2 studies on the historical use of complexity analysis in biology:
A
(Lipsitz & Goldberger, 1992) - Measured HR in old & yound people
- Mean & SD are the same for both subjects, but they look very different
- ApEn value in old people is lower = increased predictability, less complexity
(Pincus et al., 1993) - Measured HR in aborted SIDS & normal infants
- Aborted SIDS - are babies that had an episode that needed intervention but survived
- This allows for predictions to prevent hospitalisation, recognising symptoms early
16
Q
What are some reasons why we fluctuate?
A
- Because of non-linear systems (e.g.: brain, neuromuscular junction, muscle tendon interactions)
- Motor Unit Recruitment
- Motor Unit Firing
- Muscle – Tendon Interactions
17
Q
Describe the findings of the study on SampEn & ApEn on younger vs. older females at (%) MVC:
A
- SampEn and ApEn decreased with increase in age = reduced complexity –> becoming closer to brown noise
- no changes in SampEn after strength training in either age group
- Alpha values increased with increasing effort level reflecting a transition to more slowly repeating processes, greater in the older age group
18
Q
Define fatigue:
What is exercise-induced fatigue?
A
- exhaustion is an event
- fatigue is the process that can lead to exhaustion
- Exercised-induced fatigue is the decline in ability to exert force
- Fatigue is reflected in EMG as an increase in amplitude and reduction in frequency
- conscious awareness of changes in subconscious homeostatic control
- decreased maximal voluntary force/power produced by a muscle/muscle group
19
Q
What is the operational definition of neuromuscluar fatigue (NHLBI, 1990)?
What can continuous contraction lead to?
A
- A loss in the capacity for developing force and/or velocity of a muscle, resulting from muscle activity under load and which is reversible by rest
- Fatigue is related to ischemia (CV system unable to supply enough O2 to muscles = task failure, as MVC cannot be reached)
- MVC comes down over time of submaximal contractions = fatigue
20
Q
Describe how different levels of exercise affect fatige:
A
- High-intensity exercise: maximum falls until it equals required force/power
- Sub Max exercise: is possible to compensate for fatigue by increasing muscle recruitment
- Low intensity exercise: force/power reserve present at task failure - EMG is constant
21
Q
Describe the findings from (Pethick et al., 2015) on fatigue reducing torque complexity.
A
- On raw knee extensor torque with 6:4s duty cycle
- ApEn and SampEn higher in first min compared to last min = fatigue
- DFA α lower(closer to 0) in first min than last
- Fatigue causes loss of complexity (gone from white noise to more brown noise last min)
- Fatigue –> associated with decreased adaptability to perturbations
- Increases injury
22
Q
What concept was found by (A.V.Hill, 1925)?
A
- speed-duration relationship
- As you increase the speed of movement it decreases the time you can maintain the movement
23
Q
What is critical power (Monod & Scherrer, 1965)?
How does this relate to fatigue?
A
- the asymptote on a graph where the MAX rate a muscle can keep up for a very long time period without fatiguing
- Above CP = fatigue and fixed energetic reserve is used & determines exercise duration (curve constant, W’)
- below CP = task should be fatigueless & energetic
24
Q
Does the fatigue-induced loss of complexity occur below the critical torque (Seely & Macklem, 2012)?
What was the trial design?
A
- inverse relationship between metabolic rate and complexity
- metabolic rate can be stabilized below but not above CT = progressive reductions in torque complexity above CT
- Fatigue-induced loss of complexity evident >40% MVC, was less so at 20% MVC
- 6 trials: 4>CP and 2<CP (at 50% and 90% of CT) performed for 30 min or to task failure
- complexity assessed using ApEn, SampEn and DFA
25
What is the relationship during contractions below and above the critical torque (Pethick et al, 2016)?
- Above a certain point there is a limit on the time we can maintain that task for
- Above CP the ability to maintain the steady state breaks down (W') --> creates asymptote relationship = more regular signals
- Lost mobility to adapt internally e.g.: running harder, adapt metabolically
- More unpredictable below critical torque, above critical torque = more regular signals
- Lost mobility to adapt internally e.g.: running harder, adapt metabolically
26
Describe the MVC with stimuli: twitch interpolation technique: superimposed twitch
- (1- a/b) x 100 where a = superimposed twitch and b = resting twitch
- this is then followed by a potentiated resting twitch, then peripheral fatigue.
- inverted-U shaped relationship between contraction intensity and complexity.
- ApEn increases, to max at ∼40% MVC, then decreases with increased intensity. In knee extension and elbow flexion
- extra torque when superimposed twitch shows that MVC is not fully activating all muscle fibre = central fatigue
- decreased resting twitch = peripheral fatigue
27
Describe potential fatigue mechanisms:
central: muscle activation
- motor command --> descending drive --> spinal activation --> afferent feedback to muscles
peripheral: contractile function
- neuromuscular propagation --> excitation-contraction coupling
28
What are the findings of the study on caffeine fatigue: caffeine administration (Pethick et al, 2018)
* Caffeine before test decreases central fatigue
* Voluntary activation is preserved better with caffeine than placebo
* ApEn is also better with caffeine, associated with the reduction in the loss of complexity
* consequent to a slowed rate of decrease
in torque generating capacity and a slowed development of central
fatigue
29
What is the relationship between loss of complexity and peripheral fatigue?
Describe the study findings of (Pethick et al., 2018) loss of complexity: peripheral fatigue
- Muscle occlusion, with preventing blood flow into muscle
- 6s on 4s off contraction cycles
- Quick recovery from peripheral fatigue during small periods of lower intensity/rest
- Occlusion = loss of complexity is reduced, loss of recovery
30
What was the example shown of peripheral fatigue by (Pethick, 2019)?
- 40% MVC had lower ApEn (decreased complexity) than in 20% MVC trial (less predictable), as there was less deoxygenated blood produced at 20% MVC = more fatigue at 40% MVC (more predictable)
31
Explain how both central and peripheral fatigue relate to complexity loss (Hunter et al., 2022)?
- showed central & peripheral fatigue, decreasing complexity in repeated 8 s isometric force production
- decreased complexity in last bout = more simple fluctuation patterns and decreased motor unit recruitment & respond to perturbations
- increased variability in last bout = magnitude of fluctuations increased
32
Explain what was seen by the first study on complexity (Vaillancourt & Newell, 2003):
- decreased complexity of index finger abduction force (ApEn and DFA α)
- during low-intensity isometric contractions of young adults-old adults.
- Also seen in e.g.: isometric knee extension
contractions.
- Shows an age-induced loss of muscle force complexity in small and large limb muscles
- reducing fine motor skills and locomotion.
33
Define:
Complexity
Variability
and describe factors which reduce physiological complexity
* Complexity is a key feature of homeostatic control that has patterns of fluctuations
* Variability – magnitude of fluctuations
* Ageing and disease reduce physiological complexity & So does neuromuscular fatigue – above the critical torque
- ultimately the complexity of the isometric torque at task failure seems to mimic the cumulative spike train complexity -- capacity of processes reduced so adaptability is reduced.
34
Describe the neuroendocrine function and age relationship (Lewis et al., 1992):
* EEG of aging subjects showed a loss fast waves & increasing periodic slow waves
* Decreased reaction size and increased reaction time of EEG in response to sensory stimuli with ageing
* Impaired cerebral energy metabolism, with reduced spatial complexity in the frontal cortex of brain and spinal cord with ageing
35
Describe the cardiovascular function and age relationship(Lewis et al., 1992):
* Decreased HR variability with age as heart adrenoceptors become less responsive
* Structural and functional changes reduced the complexity of heart rate control in older individuals = less ability to adapt to stresses e.g.: hypertension.
36
Define emergence:
- An emergent phenomena is system behaviours that cannot be predicted or explained by examining the system’s components in isolation
- e.g.: spinal motorneruons, muscle fibres, muscle afferents
37
Why is it important to measure complexity in humans (Pethick et al., 2021)?
- to see changes in HR with ageing. e.g.: low complexity HR dynamics can be a predictor of death after an acute myocardial infarction
- to see postural tremor in Parkinson's disease
- to see torque output during neuromuscular fatigue in healthy adults.
