1. Neuroscience of Developmental Disorders. Introduction, Approaches and
DLD
1. What are Developmental Disorders?
o Specific learning disabilities (e.g., dyslexia, mathematical disorder)
o Intellectual Disabilities (e.g., Down syndrome)
o Mental health / Psychiatric disorders (e.g., ADHD, conduct disorder)
o Autism spectrum disorders
o Speech language and communication difficulties (e.g., developmental language disorder)
2. Special Educational Needs
o As of October 2018 (Department for Education statistics):
million children, or about 15% of all students in England, have some kind of SEN
About 253,000 have severe problems that require SEN statements or education health and care plans
Individuals with SEN are 4x more likely to have an anxiety disorder
o This shows you data published by the DFE from 2017. You can see that
of the 1.2 million who have SEN support of some level, most have
specific and moderate learning difficulties (like dyslexia), or
social/emotional/mental health and speech language and communication
needs. Of the 250,000 who have statements or EHC plans, most have
autism spectrum disorder.
3. Approaches to Studying Developmental Disorders
o Typical Development as Context
“The only way to understand developmental disorders is to relate them to
studies of typical development” (Hulme & Snowling, 2009, p 19)
And vice versa e.g.
o Development is a process
Need to study the development as a process, not just as an end product.
Wolff et al (2012)
- measured white matter tract integrity by capturing how water molecules move through fibres in
the brain.
- 92 infants tested at 6, 12, 24 months.
- At 6 months, infants with autism had higher white matter integrity
than infants without autism.
- At 12 months, no group differences.
- By 24 months, the 6-month-old pattern had reversed.
- Results: demonstrates value of studying developmental trajectories + process of change
only focused on data from 12-month-olds, they would have concluded that there were no
brain differences in infants who go on to receive a diagnosis of ASD versus those who do not
cross sectional data at different time points (e.g., 6 or 24 months) may have shown
differences between groups, but longitudinal data were necessary for understanding
individual trajectories.
=> captured the richness of developmental change.
Karmiloff-Smith et al. - Focussing on processes can show us how and when certain individuals veer
away from the expected path.
o Development Timing
Developmental Timing may be key.
Atypical populations often experience unusual timings, which affect them in at least two ways:
1) Altering the environmental input, they get in comparison to a child who is developing typically
(e.g., a child who is late to walk will receive a different view of the world than a child who is
more mobile)
2) Misaligns growth across domains (e.g., a child who is late to walk might still be developing the
desire to interact with others at the typical rate, meaning a discrepancy/conflict between these
two domains)
, E.g., Estes et al; (2015) found that infants later diagnosed with ASD had motor impairments at 6
months, which preceded deficits in communicative skills at 12 months - argued that motor delays
might reduce gesture use, which hampers language learning opportunities.
- Results: a misalignment between motor - lang development may cause lang learning difficulties.
o Multiple Methods are important (Paterson, Parish-Morris, Hirsh-Pasek & Golinkoff, 2016)
Sometimes behavioural performance can be equivalent between groups, but the brain processes
supporting performance might be different.
E.g., Massand et al (2013) – both adults with ASD and control adults could remember new words to
the same degree, but EEG responses to old versus new words occurred in different areas with
different intensities (i.e., anterior regions for TD, but parietal and posterior regions for ASD).
=> This is important as it impacts what we need to do to help and support those with developmental
disorders. One method won’t work for all if root causes are different
o Common Developmental Designs - most often used:
I. Cross-sectional Studies
Examining at a single point in time
Do not allow study of developmental change
(Karmiloff-Smith, 1981) - need to examine
growth not age-differences in development
They rely on the use of a control group
- An age matched group tells you whether a group is not where they should be relative to their age.
But one obvious problem with such a finding is whether the skill under question is a product of
the disorder rather than a cause. A solution to this is to use an ability matched control group. In
other words, if children with dyslexia are worse on a measure of speech perception than younger
children matched on reading age, this difference cannot simply be a produce of differences in
reading skill. Disadvantage is that this approach is quite conservative and group differences
might be less likely to emerge – but if they do, we know that the deficit is a serious one. It is
common-place and useful to have both CA matched and ability-matched control groups to
compare to a clinical group as each provides different information about the extent of difficulties
shown by the clinical group.
Examining Group Difference
- Characterising particular groups
- E.g., This study compares infectious yawning in children with or without autism, both in
response to seeing somebody else yawning and in a control condition (where they saw people
who were not yawning). Results: children with autism showed a lower number of yawns when
viewing others yawning.
=> useful at characterizing the profiles of different groups + at identifying areas for focus in
intervention (e.g., studies finding a phonological deficit in groups of children with dyslexia led to
intervention research that aimed to improve phonological skills)
=> help us form hypotheses for causal theories - we might predict that children with autism might
have a causal deficit in imitation. Humphreys et al (2013) data on sleep duration from
Examining changes with age the Avon Longitudinal Study of Parents and Children
- Quick
- Associational
- E.g. This study it was shown that in the typical population, but not in
ADHD, visuospatial working memory capacity increases with age.
These studies are obviously quicker to run than longitudinal studies,
they are cheaper. But they don’t lead to associational data.
Control Groups
1. Age Matched
- Matched by chronological age
Shows whether a group is where they should be on a skill relative to their age
But, is this because the skill under question is product of the disorder rather than a cause?
2. Skills Matched
- Matched by ability, so controls could be chronologically younger or older.
DLD
1. What are Developmental Disorders?
o Specific learning disabilities (e.g., dyslexia, mathematical disorder)
o Intellectual Disabilities (e.g., Down syndrome)
o Mental health / Psychiatric disorders (e.g., ADHD, conduct disorder)
o Autism spectrum disorders
o Speech language and communication difficulties (e.g., developmental language disorder)
2. Special Educational Needs
o As of October 2018 (Department for Education statistics):
million children, or about 15% of all students in England, have some kind of SEN
About 253,000 have severe problems that require SEN statements or education health and care plans
Individuals with SEN are 4x more likely to have an anxiety disorder
o This shows you data published by the DFE from 2017. You can see that
of the 1.2 million who have SEN support of some level, most have
specific and moderate learning difficulties (like dyslexia), or
social/emotional/mental health and speech language and communication
needs. Of the 250,000 who have statements or EHC plans, most have
autism spectrum disorder.
3. Approaches to Studying Developmental Disorders
o Typical Development as Context
“The only way to understand developmental disorders is to relate them to
studies of typical development” (Hulme & Snowling, 2009, p 19)
And vice versa e.g.
o Development is a process
Need to study the development as a process, not just as an end product.
Wolff et al (2012)
- measured white matter tract integrity by capturing how water molecules move through fibres in
the brain.
- 92 infants tested at 6, 12, 24 months.
- At 6 months, infants with autism had higher white matter integrity
than infants without autism.
- At 12 months, no group differences.
- By 24 months, the 6-month-old pattern had reversed.
- Results: demonstrates value of studying developmental trajectories + process of change
only focused on data from 12-month-olds, they would have concluded that there were no
brain differences in infants who go on to receive a diagnosis of ASD versus those who do not
cross sectional data at different time points (e.g., 6 or 24 months) may have shown
differences between groups, but longitudinal data were necessary for understanding
individual trajectories.
=> captured the richness of developmental change.
Karmiloff-Smith et al. - Focussing on processes can show us how and when certain individuals veer
away from the expected path.
o Development Timing
Developmental Timing may be key.
Atypical populations often experience unusual timings, which affect them in at least two ways:
1) Altering the environmental input, they get in comparison to a child who is developing typically
(e.g., a child who is late to walk will receive a different view of the world than a child who is
more mobile)
2) Misaligns growth across domains (e.g., a child who is late to walk might still be developing the
desire to interact with others at the typical rate, meaning a discrepancy/conflict between these
two domains)
, E.g., Estes et al; (2015) found that infants later diagnosed with ASD had motor impairments at 6
months, which preceded deficits in communicative skills at 12 months - argued that motor delays
might reduce gesture use, which hampers language learning opportunities.
- Results: a misalignment between motor - lang development may cause lang learning difficulties.
o Multiple Methods are important (Paterson, Parish-Morris, Hirsh-Pasek & Golinkoff, 2016)
Sometimes behavioural performance can be equivalent between groups, but the brain processes
supporting performance might be different.
E.g., Massand et al (2013) – both adults with ASD and control adults could remember new words to
the same degree, but EEG responses to old versus new words occurred in different areas with
different intensities (i.e., anterior regions for TD, but parietal and posterior regions for ASD).
=> This is important as it impacts what we need to do to help and support those with developmental
disorders. One method won’t work for all if root causes are different
o Common Developmental Designs - most often used:
I. Cross-sectional Studies
Examining at a single point in time
Do not allow study of developmental change
(Karmiloff-Smith, 1981) - need to examine
growth not age-differences in development
They rely on the use of a control group
- An age matched group tells you whether a group is not where they should be relative to their age.
But one obvious problem with such a finding is whether the skill under question is a product of
the disorder rather than a cause. A solution to this is to use an ability matched control group. In
other words, if children with dyslexia are worse on a measure of speech perception than younger
children matched on reading age, this difference cannot simply be a produce of differences in
reading skill. Disadvantage is that this approach is quite conservative and group differences
might be less likely to emerge – but if they do, we know that the deficit is a serious one. It is
common-place and useful to have both CA matched and ability-matched control groups to
compare to a clinical group as each provides different information about the extent of difficulties
shown by the clinical group.
Examining Group Difference
- Characterising particular groups
- E.g., This study compares infectious yawning in children with or without autism, both in
response to seeing somebody else yawning and in a control condition (where they saw people
who were not yawning). Results: children with autism showed a lower number of yawns when
viewing others yawning.
=> useful at characterizing the profiles of different groups + at identifying areas for focus in
intervention (e.g., studies finding a phonological deficit in groups of children with dyslexia led to
intervention research that aimed to improve phonological skills)
=> help us form hypotheses for causal theories - we might predict that children with autism might
have a causal deficit in imitation. Humphreys et al (2013) data on sleep duration from
Examining changes with age the Avon Longitudinal Study of Parents and Children
- Quick
- Associational
- E.g. This study it was shown that in the typical population, but not in
ADHD, visuospatial working memory capacity increases with age.
These studies are obviously quicker to run than longitudinal studies,
they are cheaper. But they don’t lead to associational data.
Control Groups
1. Age Matched
- Matched by chronological age
Shows whether a group is where they should be on a skill relative to their age
But, is this because the skill under question is product of the disorder rather than a cause?
2. Skills Matched
- Matched by ability, so controls could be chronologically younger or older.
