Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

AD Motor Control S2 > W1&2 - Complexity > Flashcards

W1&2 - Complexity Flashcards

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
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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
Q

What is the relationship during contractions below and above the critical torque (Pethick et al, 2016)?

A
  • 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
Q

Describe the MVC with stimuli: twitch interpolation technique: superimposed twitch

A
  • (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
Q

Describe potential fatigue mechanisms:

A

central: muscle activation
- motor command –> descending drive –> spinal activation –> afferent feedback to muscles
peripheral: contractile function
- neuromuscular propagation –> excitation-contraction coupling

28
Q

What are the findings of the study on caffeine fatigue: caffeine administration (Pethick et al, 2018)

A
  • 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
Q

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

A
  • 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
Q

What was the example shown of peripheral fatigue by (Pethick, 2019)?

A
  • 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
Q

Explain how both central and peripheral fatigue relate to complexity loss (Hunter et al., 2022)?

A
  • 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
Q

Explain what was seen by the first study on complexity (Vaillancourt & Newell, 2003):

A
  • 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
Q

Define:
Complexity
Variability
and describe factors which reduce physiological complexity

A
  • 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
Q

Describe the neuroendocrine function and age relationship (Lewis et al., 1992):

A
  • 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
Q

Describe the cardiovascular function and age relationship(Lewis et al., 1992):

A
  • 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
Q

Define emergence:

A
  • 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
Q

Why is it important to measure complexity in humans (Pethick et al., 2021)?

A
  • 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.

AD Motor Control S2 (10 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions