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Short Summary - Neurobehavioural Functioning
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This summary contains a clear overview of all the tasks of Neurobehavioural Functioning. It includes everything you need to know for your exam!

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  • 24 september 2024
  • 24 september 2024
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Voorbeeld van de inhoud

Neurobehavioural
Functioning
Short Summary


26-10-2023
Maastricht University
Emma Leibbrand




Table of Contents
Task 1 – A Matter of Definition… And of Assessment (page 1)
Task 2 – Typical Neurobehavioural Assessment (page 8)
Task 3 – Neurobehavioural Functioning in Adulthood (page 20)
Task 4 – Attention! Everything is Under Control! (page 31)
Task 5 – Atypical Neurobehavioural Development (page 42)
Task 6 – Fear or Anxiety: Two Sides of the Same Coin? (page 55)
Task 7 – Differentiation of Neurobehavioural Functioning in Older Age (page 68)

, Task 1 – A Matter of Definition… And of Assessment


Categorical approach => The current system for diagnosing mental disorders (DSM-5). Relies on
diagnostic criteria to determine the presence or absence of disruptive or other abnormal behaviours.
Dimensional approach => Places such behaviours on a continuum of frequency and/or severity.

Symptomatology in the broader population exists as a continuum of severity, but in clinical practice,
disease is often seen as an all-or-nothing phenomenon.

Pros of Categorical Approach Pros of Dimensional Approach

 Facilitates the decision making process. The  Higher validity  It reflects the true
presence of a diagnosis necessitates continuum of psychosis in the population +
treatment, while the lack of a diagnosis captures its high degree of heterogeneity +
negates the need of treatment. The variation.
diagnostic system provides a frame of  It removes the loss of information that
reference for treating patients. occurs when continuous-level data are
 Pro for research  there is consistency organised + greater statistical power.
across studies (e.g. cut-off criteria), cost-  It explains comorbidity + symptom overlap
effectiveness (costly and high-risk  likely due to shared susceptibilities.
treatments are kept for those who cross  Higher predictive power for clinical
thresholds), + it allows comparison of symptoms, treatment response and
studies resulting in more information. outcomes.
 Improves diagnostic reliability  There is
more agreement, consistency, and stability.
 Improves communication among clinicians,
researchers and the lay community. It is a
good method for understanding a
particular syndrome.


Network approach => Explains why certain disorders may co-occur more than others. A mental
disorder can be viewed as a system of interacting symptoms.
 Example = MDD symptoms are fatigue  insomnia  concentration problems  sadness,
anhedonia  and suicidal ideation.

Comorbidity = The presence of multiple disorders at the same time.
Patients diagnosed with multiple disorders have a poorer prognosis,
worse treatment outcomes and higher suicide rates.
 Traditional view = Comorbid disorders are seen as different
disorders.
 Network view = Disorders co-occur due to mutual interactions
among symptoms.

In other words, comorbidity arises when there are symptoms that
bridge two disorders, so-called bridge symptoms. These can spread
activation from one disorder to the other (figure 2).
 Example = Bridge symptoms between GAD + MDD include
sleep problems, fatigue, concentration problems or psychomotor agitation.

1

,The prediction of psychopathology onset is one of the most important because many people
experience single symptoms, but only part of them develop a mental disorder.
 Early warning signals indicate the upcoming onset of psychopathology for a specific patient.
 Characteristics of group-level networks predict the future course of psychopathology.

Complex system => the conceptualisation of mental disorders as network of interacting symptoms.
 One of the most important features of this is that they can display phase transitions.
 Healthy => the individual shows no problems.
 Early warning signs => displayed before a system reaches its tipping point.
o Example = people with high inertia levels in their emotion dynamics are more likely
to develop depression in 2.5 years.
 Critical slowing down => before a transition occurs in the next step, the system slows down,
meaning it takes longer to recover from perturbations.
 Phase transition => involves the transition from healthy to disordered state (IMPORTANT).
o For weakly connected symptom networks, negative external events (e.g. stressful
events) lead to a gradual increase in symptoms.
o For strongly connected networks, negative external events lead to a sudden shift
from a healthy to depressed state.
o Sheds light on the dimensional vs. categorical debate: weak networks may behave as
a continuum in response to stress, while strong networks may behave as categorical.
o This connectivity implies that people may have the same diagnosis, but that the
connectivity of the network structure would determine whether the disorder is a
continuum or a dimension for them.
Early Critical
Phase Disordered
Healthy warning slowing
transition state
signs down


Strong temporal emotion means => the state of an emotion at a certain time-point depends strongly
on the state of emotions at the previous time-point.
 Temporal emotion networks of patients with MDD and psychosis are more strongly
connected than temporal emotion networks of healthy controls.
 Research => The more densely connected temporal network structures, the more vulnerable
someone is to psychopathology.
o Example = higher levels of connectivity in patients with depression are associated
with worse outcomes at 2 year follow up.
 The nature of symptoms also plays a role => the most central symptoms of a disorder were
more predictive of the onset of the disorder.

Centrality => how connected + clinically relevant a symptom is in
a network; it quantifies the importance of a node in a network.

Degree of centrality => the number of connections of a symptom.
 High degree centrality (red node)  the more of a risk
factor it is  the more likely it will lead to the
development of other symptoms.

Difficult to find overview of which symptoms are more central
than others because studies differ in...
 Variables used (e.g. MDD questionnaires).
 Design (i.e. are cross sectional or time series).

2

,  Samples (e.g. healthy, moderately depressed, severely depressed).
 Network estimation methods

Taking these variables into account, the central symptoms of disorders include…
 MDD = depressed mood, loss of interest / pleasure + fatigue.
 PTSD = hypervigilance, impaired concentration, physiological reactivity to reminders of
trauma + sleep difficulty.

Standard  The construct, e.g. schizophrenia = latent variable = common cause of its
symptoms.
 Symptoms = effects of this latent variable / passive psychometric
indicators.
 Interactions between symptoms  gets no attention in research.
 Assumption = environmental factors affect symptoms via the latent
disorder; the disorder mediates relation environment and symptoms.
o Problematic since individual symptoms are influenced by different
risk factors.
o E.g. childhood trauma is associated with hallucinations and
delusions but not with negative symptoms.

Network  Solves this problematic assumption by representing individual differences
in vulnerability as differences in the connectivity of the network model.
 In network models, mental disorders are not conceptualised as common
causes of symptoms, but as conditions that arise from the interaction
between symptoms.
 More strongly connected networks  higher level of interaction between
symptoms  more activation of one another  render system less
resilient.
 Nodes = variables (risk factors).
 Edge = seen between 2 nodes demonstrating their association
o Presence of an edge is suggestive of a causal relation BUT note:
nature + direction of this relation is not specified.



 Green edges  positive
connections
 Red edges  negative
connections




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