1. Heart Rate Variability and Self-Regulation Report Assignment
Heart Rate Variability and Self-Regulation Report AssignmentORDER NOW FOR
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AssignmentThis needs to be 100% plagiarism free and fully APA 6th formatted. Please
reference every statement that you make (ideally 3-4 references per paragraph). Please use
correct English grammar when completing the assignment. I will ask to rewrite if there are
too many grammatical mistakes. Only top quality work is required – will be met with
a generous tip if the standard is met. Heart Rate Variability and Self-Regulation Report
Assignment.PLEASE: use the average values as participant’s data (my data), as I my lecturer
did not find my actual results (hence said, that using the mean data values would be
acceptable).You will find the excel spreadsheet with all data attached, as well as the full
written overview of the lab report that you require to complete the assignment.Keep things
relatively simple, and show you understand the key stuff. Self-regulation, autonomic
balance, heart-rate variability and the psychometric measures are key. Also, the hypotheses
now are around negative correlations between HRV and the psychometric measures listed –
all of these are significant.Given you have 2000-2500 words, a GUIDE might be (this is just a
guide, actual numbers could vary)Abstract – 100 words maximum (not a guide here, that’s
relatively standard).Introduction – approximately 1000 wordsMethod – approximately 100
words (this section has to an extent been provided in the handout, just re-word and add
details around participant numbers etc)Results – approximately 300 words (plus tables,
figures etc)Discusion – approximately 1000 wordsNote – reference lists do not count in
word count.In terms of marking, the asignment will be marked by section as
follows:Abstract worth 10 marksIntroduction worth 25 marksMethod worth 5 marks
(reflecting above)Results worth 25 marksDiscussion worth 25 marksReferencing/APA etc
worth 10 marksHope that all makes sense, the key is to use the information you’ve been
provided with, read a few key papers and keep it simple showing a good understanding of
the key issues and rationale for the study, presenting and interpreting the results clearly,
then discussing them and their implications clearly and
thoughtfully.brain_behaviour_2017_worksheet.xlsxbrain_behaviour_labor Brain &
Behaviour 2017 Laboratory Project Heart Rate Variability and Self-RegulationDaniel
Shepherd and Jason Landon In this assessment you are expected to: Seek out relevant
literature from the library and online databasesAttend a laboratory session and provide
electrophysiological measuresObserve the operation of state-of-the-equipment to measure
biopotentialsInterpret the results of statistical analysesProduce a high quality laboratory
2. report Introduction Self-regulated responding within a hosting environment is an adaptive
trait affording the realisation and evaluation of an individual’s goals. Self-regulation is
considered a stable personality trait by which an individual attempts to control their
thoughts, feelings, and impulses whilst enacting goal-directed behaviour. More formally,
self-regulation involves the suppression of problematic mental tendencies, such as those
that occur when we get unnecessarily anxious. Individuals with high levels of self-regulation
are described as having enhanced cognitive control, particularly the control of task-
irrelevant thoughts and impulsive behaviours. Low self-regulation is associated with higher
or lower scores on the Big Five personality dimensions. Neuroticism is characterised by
unstable personality types, described as more anxious, negative, bitter, depressed and
lonely than others, and who are more likely to use avoidant coping strategies and emotion-
based coping styles. It would be expected, and indeed has been shown, that a negative
association between self-regulation and neuroticism exists. Furthermore, individual
differences in self-regulation have been linked to higher levels of Heart Rate Variability
(HRV). If high levels of HRV are associated with less problematic cognitions and higher self-
regulation, then it is expected that a negative relationship between HRV and psychological
constructs such as neuroticism should exist. Heart Rate Variability and Self-Regulation
Report AssignmentORDER NOW FOR COMPREHENSIVE SOLUTION PAPERSFor this
laboratory we will obtain electrophysiological recordings reflecting cardiac activity
measured during relaxation. When relaxed we would expect a low heart rate (60-70 beats-
per-minute: BPM) and high HRV. Additionally, a battery of psychometric inventories
measuring constructs associated with self-regulation will be completed. Our main
hypotheses will be that HRV will be correlated with psychometric measures purporting to
represent maladaptive thought processes. Heart Rate Variability Linking self-regulation to
physiological processes goes back to the ancient Greeks, and a multitude of plausible
biological mechanisms have been proposed (John et al., 2008). A promising avenue of
exploration has been to associate personality and self-regulatory processes with the
Autonomic Nervous System (ANS), given that maladaptive autonomic responses have been
linked to inflexible executive function, counterproductive coping strategies (e.g., avoidance),
and high levels of arousal (Saus, Johnsen, Eid, & Thayer, 2012). Such an approach is not new,
the role of the ANS is central to the argument posed by Hans Eysenck in his seminal book
‘The Biological Basis of Personality’ (Eysenck, 1967). In more recent times, Thayer and
colleagues (e.g., Thayer & Brosschot, 2005) have proposed a neurovisceral integrative
model of dynamic autonomic regulation that provides a way of understanding personality
and self-regulation in terms of autonomic function. A functional ANS matches energy
mobilisation and consumption to environmental demands (i.e., allostasis), with the
Parasympathetic Nervous System (PNS) acting quickly to decrease heart rate, and thus
facilitate restoration and energy retention, while the Sympathetic Nervous System (SNS)
acts more slowly, accelerating the heart rate and the metabolism of energy stores. This
difference in speed is due to the SNS being primarily dependent on endocrine means of
communication, while the PNS utilises both myelinated and unmyelinated nerves,
principally the vagal nerve (Porges, 1995). In any one instant, physiological arousal is
determined by the balance between the SNS and the PNS, with this proportional activation
3. being referred to as ‘autonomic balance’ (Abboud, 2010). Autonomic balance has been
described as a ‘seesaw’, as when one arm of the ANS increases the other arm decreases
(Montano et al., 2009). Resting heart rate in the absence of autonomic influences (e.g.,
through pharmaceutical blockade), is approximately 100 BPMs or more, significantly faster
than the 60-70 BPMs expected in a healthy individual (Jose & Collison, 1970). This suggests
that during normal, baseline, resting conditions the PNS is the dominant arm in this seesaw.
This balance can, however, become dysregulated with environmental events, resulting in
tonic sympathetic activation and inflexibility. Although the SNS and the ‘fight or flight’ type
response have been integral to the evolutionary success of the human species, SNS over-
arousal has been connected with various somatic conditions, especially hypertension and
cardiovascular disease (Thayer, Yamamoto & Brosschot, 2010), as well as psychological
disorders such as anxiety (Melzig, Weike, Hamm & Thayer, 2009), depression (Rottenberg,
2007), attention deficit hyperactivity disorder (Lackschewitz, Hüther & Kröner-Herwig,
2008), posttraumatic stress disorder (Hauschildt, Peters, Moritz & Jelinek, 2011), and
eating disorders (Mazurak, Enck, Muth, Teufel & Zipfel, 2011). Interestingly, many of these
studies imply a link between autonomic balance and self-regulation, as classification
systems categorizing psychological disorders typically describe symptoms occupying the
extreme poles of personality dimensions representative of self-regulation. Parasympathetic
dominance is associated with greater cardiac flexibility due to its faster signalling speed
(Thayer & Lane, 2000) and slower cardiac rhythm. This is important, as to achieve
allostasis, cardiovascular function must be variable, as environmental demands are in a
constant flux. Heart Rate Variability (HRV), the temporal variation of the latency between
consecutive heartbeats, is an indirect measure of the autonomic influences on the heart. A
plethora of HRV indices exist, all derived from the analysis of interbeat intervals extracted
from the cardiac time series. As the PNS is responsible for most of the variation in the
interbeat interval (Porges, 1997), greater HRV is thought to reflect greater parasympathetic
dominance, and so HRV is considered a non-invasive index of vagal tone, autonomic
balance, and autonomic flexibility (Rajendra et al., 2006; Task Force of the European Society
of Cardiology and the North American Society of Pacing Electrophysiology [Task Force],
1996). Some argue that low HRV and impeded autonomic balance is “a final common
pathway to increased morbidity and mortality from a host of conditions and diseases”
(Thayer & Brosschot, 2005, p.1052). A detailed description of HRV has already been
published elsewhere (Miu, Heilman & Miclea, 2009). Heart Rate Variability and Self-
Regulation Report Assignment The Neurovisceral Integrative System (NIS) of dynamic
autonomic regulation consists of a variety of sparsely distributed cortical and subcortical
structures (the so-called central autonomic network: CAN) that have roles in affective,
social, attentional, executive, and motivational behaviour (Thayer & Lane, 2009). Although a
variety of structures are involved (for a review see Riganello et al., 2012), of relevance are
the prefrontal cortex (both medial- and orbito-frontal), amygdala, insular cortex, and
hypothalamus, as these structures are also implicated in self-regulation (Koelsch, Enge, &
Jentschke, 2012). The NIS proposes that, in healthy individuals, the amygdala is under
constant inhibition via GABAergic pathways from the prefrontal cortex, which results in
dynamic autonomic balance, as well as fast, flexible, and appropriate responding to both
4. novel and familiar stimuli (Thayer & Lane, 2009). During stress the prefrontal cortex
becomes hypoactive (i.e., inhibited) and the amygdala disinhibited, resulting in increased
cardiac arousal by, initially, the release of the heart from PNS inhibition and then by SNS
innervation. These moment-by-moment autonomic influences on the heart are commonly
indexed by Heart Rate Variability (HRV), with higher HRV suggesting higher PNS
dominance. According to the NIS, healthy individuals possess high HRV and respond with
appropriate levels of arousal as the environment dictates, avoiding unnecessary SNS-
mediated cycles of inflexible over-arousal (a facet of Neuroticism). Thus HRV itself is taken
to reflect those inhibitory processes, occurring in the prefrontal regions, which endow upon
an individual the efficacy to realise goal-directed behaviour and self-regulate in order to
adapt to their environment (i.e., flexibility). Further, variability in human behaviour is
described in terms of personality traits, with adaptive (i.e., self-regulated) behaviours
usually framed in terms of maximising rewards and minimising harm in the context of the
hosting environment. Evidently, not only do the CAN and self-regulation systems share
common brain structures, but there are conceptual overlaps between their functions too.
For example, in a study investigating the electrophysiological correlates of Psychopathy
(Hansen, Johnsen, Thornton, Waage, & Thayer, 2007), it was demonstrated that effective
inhibitory processes (as measured by cognitive tasks) were related to both high HRV and
high levels of interpersonal skills such as superficial charm, manipulation, and pathological
lying, all markers of high self-regulation. The authors argued that their findings consistently
linked components of the CAN (HRV and cognitive inhibition) to self-regulation. In relation
to the Big Five personality dimensions, it can be deduced that HRV will be negatively
correlated to the maladaptive trait of Neuroticism. Despite the obvious links between self-
regulation and autonomic balance, few studies to date have directly looked at their
relationship, and findings are inconsistent. Replicating previous findings (i.e., Thayer,
Friedman & Borkovec, 1996) and Miu et al. (2009) both reported significant relationships
between HRV and trait anxiety, the latter considered an important component of
Neuroticism. Ode et al. (2010) reported that HRV was not associated with Neuroticism and,
using a prison population, Hansen et al. (2007) reported moderate associations between
resting HRV and aspects of personality associated with criminality. The aim of this study is
to investigate the relationship between baseline estimates of HRV and psychometric
constructs that tap into self-regulation. Objectives This laboratory project is partly a
replication of a paper published in 2010 by Ode and chums: Ode, S., Hilmert, C. J., Zielke, D.
J., & Robinson, M. D. (2010). Neuroticism’s importance in understanding the daily life
correlates of heart rate variability. Emotion, 10(4), 536-543. Specifically, we will test the
following hypotheses: Negative correlations will be noted between time-domain HRV (as
measured by STD RR) and measures of neuroticism, depression, anxiety, stress, schizotypal
personality and ASD-like traits. Positive correlations will be noted between time-domain
HRV (as measured by STD RR) and measures of resilience. Heart Rate Variability and Self-
Regulation Report Assignment Methods Participants Brain and Behaviour 2017 class
members will be invited to participate in the research. Participants should refrain from
consuming caffeinated beverages two hours prior to the commencement of the
electrophysiological recordings. Design A simple correlational study will be undertaken.
5. This will involve measurements of electrophysiological signals and the completion of
psychometric inventories. These data will then be analysed to obtain HRV metrics and
indirect measures of self-regulation, respectively. Equipment and set-up A DELL computer
running Microsoft Win7 will undertake electrophysiological recordings, using Biotrace
software. A self-calibrating NeXus 10 unit (24 bit) and BioTrace software are used to
produce the electrocardiogram (SF=2048 Hz). The electrocardiogram was measured by
applying three standard Ag-AgCl electrodes on the torso. The skin was first cleansed using
isopropyl alcohol prior to the application of the electrodes. Procedure Following the set-up
of the physiological apparatus, the participant was tethered to the physiological sensors and
then sat upright in a comfortable chair within a sound-attenuating chamber. Recordings
were typically 15 minutes in duration. Later, participants were given an Excel spreadsheet
containing five psychometric inventories: the Ritvo Autism Asperger Diagnostic Scale-
Revised (RAADS-R: Ritvo et al., 2011)the Schizotypal Personality Questionnaire – brief
(SPQ-B: Raine & Benishay, 1995)the 21 item Depression-Anxiety-Stress Scale (DASS-21:
Lovibond & Lovibond, 1995)the Connor Resilience Scalea ten-item neuroticism scale
(Goldberg, 1999) Heart Rate Variability and Self-Regulation Report Assignment. Subscale
scores to be used in the final analyses were automatically calculated by Excel. Instructions
on how to fill out the questionnaires were included in the Excel file. Analysis The primary
aim of this analysis is to calculate heart rate variability indices and correlate them with
measures associated with self-regulation. To this end we will utilise internationally
developed software (Biotrace) to compute HRV measures, based on inter-beat-interval data
extracted from Biotrace, and established psychometric scales to give us indirect measures
of self-regulation. To statistically examine the associations between HRV and self-
regulation, we will use Pearson’s Product Moment Correlation Coefficients (r). All
relationships with p-values less than 0.05 (p
