Barriers to learning - a science of learning is incomplete without understanding variability
Duncan Astle
Key messages for this talk
Genes
Cells
Networks
Systems
Behaviour
Developmental Neuroinformatics
Cognitive development varies widely across children. Worldwide ~15% would meet criteria for a recognised neurodevelopmental condition.
These differences are often life-long and can act as barriers to education, wellbeing, healthcare and employment.
How much variability should I expect?
Where does this variability come from?
What does this mean for education?
How much variability should I expect?
A large cohort (N=1000) enriched with children at ‘neurodevelopmental risk’
Most children come from 15 different referral routes within Children’s Services, including CAMHS, Paediatrics, Educational Psychology, Specialist Teachers
Children can come with single, multiple or no formal diagnostic label
Genes
Brain Structure
Brain Function
Cognition
Learning
Behaviour
Mental Health
All in mainstream education, mostly aged 7-11, 67% boys
A rich multilevel dataset, fuelling >75 projects in Cambridge so far
5 year longitudinal data collection almost complete
Now a managed access database
Unsupervised machine learning: Simple artificial neural networks
v1
v2
v3
w1
w2
w3
Kohonen, T. (1982). Biological cybernetics
Dittenbach, M., Merkl, D., & Rauber, A. (2000). IEEE
Size
Acidity
Seeds?
Size
Acidity
Seeds?
SIZE
ACIDITY
SEEDS
Kohonen, T. (1982). Biological cybernetics
Dittenbach, M., Merkl, D., & Rauber, A. (2000). IEEE
Unsupervised machine learning: Simple artificial neural networks
Why use this machine learning approach?
‘Berries’
Why use this machine learning approach?
Unsupervised machine learning: Simple artificial neural networks
Kohonen, T. (1982). Biological cybernetics
Dittenbach, M., Merkl, D., & Rauber, A. (2000). IEEE
Memory
Vocabulary
Listening skills
Attention
CALM Referrals:
Cognitive Battery
ADHD?
Dyslexia?
Autism?
Other stuff?
Siugzdaite et al. (2020). Current Biology
Unsupervised machine learning: Simple artificial neural networks
Cognition
Around 2-3 years ahead of what you would expect for their age
Around 2-3 years behind what you would expect for their age
What predicts your location in cognitive space?
Seeing the Speech & Language Therapist
Dyslexia diagnoses
ADHD diagnoses
Diagnosis is not a good predictor of a child’s cognitive profile
Siugzdaite et al. (2020). Current Biology
Autism diagnoses
What predicts your location in cognitive space?
Siugzdaite et al. (2020). Current Biology
Quality of Prediction
Learning
What predicts your location in cognitive space?
Siugzdaite et al. (2020). Current Biology
Quality of Prediction
What predicts your location in cognitive space?
Siugzdaite et al. (2020). Current Biology
‘Modularity’
‘Integration’
‘Centrality’
Cognitive performance
More ‘small world’
More random
Quality of Prediction
What have we learnt so far?
Cognitive profiles of children in mainstream school differ widely
These differences do not align well with formal diagnostic labels
But they are related to individual differences in attainment
How children’s brains are organised is predictive of these differences in cognition
ADHD diagnoses
Quality of Prediction
How much variability should I expect?
Developmental Neuroinformatics
Where does this variability come from?
Using generative networks to simulate brain development
Akarca et al. (2021). Nature Communications
“What I cannot build, I do not understand” Richard Feynman
Complexity emerges from simplicity
Kaiser & Hilgetag (2004) Neurocomputing
Vértes et al. (2012). PNAS
Betzel et al. (2016) Neuroimage
Complexity emerges from simplicity
Akarca et al. (2021). Nature Communications
Rapid updating
of wiring probabilities
Simulating network formation
Akarca et al. (2021). Nature Communications
Evaluate with an
energy equation
Evaluate energy
for different parameter
combinations
Evaluate energy for different K terms
Different K terms
Dan Akarca
Tiny amounts of variability generates diverse trajectories
Akarca et al. (2021). Nature Communications
Simulating network formation
The low energy simulations come from very specific parameter combinations
So where does network diversity come from?
Akarca et al. (2021). Nature Communications
It’s the trade-off that produces diversity
Why?
Akarca et al. (2021). Nature Communications
What shapes the trade-off?
Monaghan et al. (2023). In review
Alicja
Monaghan
Three sets of genes
PGS genes
‘Costs’ genes
‘Values’ genes
Genes associated with cognitive ability also associated with the values part of the equation
Sofia Carozza
Carozza et al. (2023). Developmental Psychobiology
Early environments causally shape the parameters
What have we learnt so far?
We can simulate the formation of children’s brain networks by trading off the ‘costs’ of forming a connection with the ‘value’ that connection brings
For each child, this trade-off is slightly different
These small differences generate diverse trajectories of brain development
In turn these differences are likely shaped by both early environmental experiences and genetics
PGS genes
‘Costs’ genes
‘Values’ genes
Developmental Neuroinformatics
How much variability should I expect?
Where does this variability come from?
What does this mean for education?
So we boost cognition… right?
A long history of training interventions designed to boost cognition directly
These interventions can alter neural networks in children’s brains
But they have no wider benefits
The Diverse Trajectories to Good Developmental Outcomes Workshop (December 2022)
If we cannot change cognition, then how do we make education inclusive?
Four targeted focus groups
100 expert delegates from a diverse range of fields and lived experiences
Four evidence-based symposia followed by open discussions
Co-create resources to guide schools about inclusive practices
Background: inclusion in the UK
“The system for supporting pupils with SEND is not, on current trends, financially sustainable” National Audit Office (2019, p.11)
“even significant funding increases might make little difference to children and young people with SEND” House of Commons, Education Committee (2019, p.3)
Key Message #1: Children with SEND are disproportionately likely to face bullying and exclusion
To target this, we can:
What did we learn at the workshop?
Learning About Neurodiversity at School (LEANS)
LEANS is a new resource pack to introduce neurodiversity concepts to mainstream primary classes in an accessible and engaging way.
Download LEANS for free and find out more!
Overview talk this Thursday June 22nd!
UCD Neurodiversity Masterclass Seminar
Online, free (17:00-18:00)
https://www.eventbrite.ie/e/neurodiversity-masterclass-series-tickets-140051587067
Or visit the LEANS event page for booking link
Key Message #2: Universal approaches to teaching can facilitate learning for all students
To enable all learners to flourish, the UK’s educational system needs to move from inclusion on demand to inclusion by design.
Universal Design for Learning (UDL) systems don’t cater to a minority of students - they consider all students’ needs and create open and accessible systems for everyone, neurotypical or neurodivergent, diagnosed or undiagnosed.
Provide multiple means of Engaging with learning
Provide information in multiple Representational Formats
Provide multiple means of Demonstrating Knowledge
What did we learn at the workshop?
Key Message #2: Universal approaches to teaching can facilitate learning for all students
What did we learn at the workshop?
Key Message #2: Universal approaches to teaching can facilitate learning for all students
What did we learn at the workshop?
Key Message #3: Schools should aim to co-produce inclusive policies with parents, staff and students with lived experiences of neurodivergence
What did we learn at the workshop?
Key Message #4: A neurodiversity-oriented approach can promote a sense of belongingness at school
What did we learn at the workshop?
Belonging at School
Coming in September 2023!
New FREE resources for educators about neurodevelopmental differences, school inclusion, and co-designing inclusive policies.
Join our mailing list so you won’t miss them!
Scan QR or mail inclusive-education-subscribe@lists.mrc-cbu.cam.ac.uk
What did we learn so far?
Attempts to “fix” cognitive differences have so far had poor results
With around 1.4m children in England having additional needs, this requires that we make schools more inclusive places
We can do this by shifting attitudes about differences
Moving to inclusion by design, rather than inclusion on demand
Plans need to be local and co-produced with children and parents within schools
The end goal is to make schools a place where children feel they belong, regardless of their differences
We should expect a lot of variability in cognition
This is a natural consequence of the mechanics of brain development
So we need to make sure that schools are inclusive places that can accommodate these differences
Find out more at: www.astlelab.com
Twitter: @duncansastle
Current Funders:
4D Research Group
Susan Gathercole
Joni Holmes
Tom Manly
Kate Baker
Rogier Kievit
Joe Bathelt
Giacomo Bignardi
Sarah Bishop
Erica Bottacin
Lara Bridge
Annie Bryant
Sally Butterfield
Elizabeth Byrne
Gemma Crickmore
Fánchea Daly
Edwin Dalmaijer
Tina Emery
Laura Forde
Delia Fuhrmann
Andrew Gadie
Sara Gharooni
Jacalyn Guy
Erin Hawkins
Agniezska Jaroslawska
Amy Johnson
Jonathan Jones
Silvana Mareva
Elise Ng-Cordell
Sinead O’Brien
Cliodhna O’Leary
Joseph Rennie
Ivan Simpson-Kent
Roma Siugzdaite
Tess Smith
Stepheni Uh
Francesca Woolgar
Mengya Zhang
Find out more at: www.astlelab.com
Twitter: @duncansastle
The CALM Team:
GNMs
Petra Vértes
Ed Bullmore
Collaborators:
MEAs
Stephen Eglen
Ole Paulsen
Manuel Schroeter
Alex Dunn
Biophysical RNNs
Jascha Achterberg
John Duncan
Matt Botvinick
Child Development
Gaia Scerif
Kia Nobre
Sarah-Jayne Blakemore
Tamsin Ford
Derek Jones
Mark Johnson
Sue Gathercole
Joni Holmes
Maros Rovny
PhD student
Chris Iordanov
Placement Student
Belonging at School
Scan QR or mail inclusive-education-subscribe@lists.mrc-cbu.cam.ac.uk
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