Cathy Price
Wellcome Trust Centre for Neuroimaging
Predicting Language Outcome and
Recovery After Stroke (PLORAS)
Toward a Clinical Application
Importance
~ 150,000 new stroke patients per year in UK alone
~ 50,000 survive with APHASIA (1 every 11 minutes)
impairs:
Speech,
Reading,
Writing,
Comprehension
Communication
Dependence,
Depression,
Social isolation
Unemployment
PLORAS: Predicting Language Outcome and Recovery after Stroke
~ Patients are not currently given individualised
predictions for how or when they will recover
Common Myths
Outcome approximates to:
Extent of
left hemisphere damage
Predicted outcome
for individual patients:
Not possible
(too much inconsistency)
Profile of Recovery:
Within first year
or not at all.
PLORAS: Predicting Language Outcome and Recovery after Stroke
~ Patients are not currently given individualised
predictions for how they will recover
Common Myths
Our Research
Outcome approximates to:
Extent of
left hemisphere damage
Precise
Lesion Location
Predicted outcome
for individual patients:
Not possible
(too much inconsistency)
Possible !
(with >90% accuracy)
Within first year
or not at all.
Continues for decades
rate varies with lesion site.
Profile of Recovery:
PLORAS: Predicting
PLORAS:
Predicting Language
Language Outcome
Outcome and
and Recovery
Recovery after
after Stroke
Stroke
THEORETICAL FRAMEWORK
~Multiple pathways can support each cognitive task
~Effect of damage to one pathway depends on integrity of other pathways
Pathways can be segregated
using fMRI and
dynamic causal modelling (DCM)
PLORAS:
Predicting Language
Language Outcome
Outcome and
and Recovery
Recovery after
after Stroke
Stroke
PLORAS: Predicting
APPLYING THE THEORY
~Lesions characterised precisely in terms of 3D volumes with mm resolution.
More pathways damaged
(Less available to support recovery)
Fewer pathways damaged
(more available to support recovery)
PLORAS:
Predicting Language
Language Outcome
Outcome and
and Recovery
Recovery after
after Stroke
Stroke
PLORAS: Predicting
RESULTS
~When lesions are precisely matched, consistent outcomes are observed,
interacting with robust effects of time post stroke
Speech production scores in 53 patients with “matched” lesions
A
Speech
score
P
Non-aphasic
range
Aphasic
range
A&
P
Aphasic
range
Non-aphasic
range
PLORAS:
Predicting Language
Language Outcome
Outcome and
and Recovery
Recovery after
after Stroke
Stroke
PLORAS: Predicting
PREDICTED VS MEASURED RESPONSES
In NEW patients
RECOVERY within patient
PLORAS:
Predicting Language
Language Outcome
Outcome and
and Recovery
Recovery after
after Stroke
Stroke
PLORAS: Predicting
INDIVIDUAL PATIENT PROGNOSIS
~
~
Predicted in black (with confidence interval)
measured in red
PLORAS:
Predicting Language
Language Outcome
Outcome and
and Recovery
Recovery after
after Stroke
Stroke
PLORAS: Predicting
RELEVANCE FOR INTERVENTION STUDIES
Does intervention accelerate the expected time course of recovery?
I
I
I
I
I
I
PLORAS:
Predicting Language
Language Outcome
Outcome and
and Recovery
Recovery after
after Stroke
Stroke
PLORAS: Predicting
LESSONS LEARNED

Lesion site information predicts behavioural score

Lesion site information predicts recovery over years

The effect of Intervention will depend on lesion site.

Predictions provide a baseline for measuring intervention
PLORAS:
Predicting Language
Language Outcome
Outcome and
and Recovery
Recovery after
after Stroke
Stroke
PLORAS: Predicting
PREDICTION ACCURACY
Requires a database
of 100s of patients, with precisely defined lesions,
& Behavioural measures at different times post stroke
PLORAS:
Predicting Language
Language Outcome
Outcome and
and Recovery
Recovery after
after Stroke
Stroke
PLORAS: Predicting
PREDICTION ACCURACY
depends on technical/computational advances
that match lesion features in the brain scans of different patients
New Patient
Data
Inference
Database
Predictions
PLORAS:
Predicting Language
Language Outcome
Outcome and
and Recovery
Recovery after
after Stroke
Stroke
PLORAS: Predicting
LESION – DEFICIT ANALYSES
Find Better Lesion Features for better predictions:

3 critical lesion sites, identified 5-10yrs post stroke:


Any >90% damage  impaired (0-10yrs)
All <10% damage  unimpaired (5-10yrs)

200+ patients

>90% accuracy

Other lesion sites cause
temporary aphasia (<5yrs)
PLORAS:
Predicting Language
Language Outcome
Outcome and
and Recovery
Recovery after
after Stroke
Stroke
PLORAS: Predicting
RESULTS OF LESION ANALYSES
Fed back into language model
PLORAS:
Predicting Language
Language Outcome
Outcome and
and Recovery
Recovery after
after Stroke
Stroke
PLORAS: Predicting
Patient recruitment
Community
stroke Clubs
Clinical contacts
e.g. NHNN, UCLH,
Homerton, St Georges
Patient facing
events
Clinical events
e.g. UK Stroke Assembly
e.g. Stroke Forum,
RCSLT conference
Advertising and PR
Research Networks
e.g. Stroke News, Saga,
local press
International collaborations
China, Kuwait, Chile, Greece
Stroke Research Network,
Primary Care Research
Network
PLORAS: Predicting Language Outcome and Recovery after Stroke
STEPS TOWARDS CLINICAL TRANSLATION:
ARNI Institute
Engaging Patients
(charity for Stroke survivors in the rehabilitation phase of their stroke).
- Provide information on what patients want to know about recovery.
- Communication is a top priority
Stroke Support charities (e.g. “Different Strokes”, “Speakability” “Connect”)
- Publish articles via websites and newsletters.
- Provide forum for oral presentations and meetings with patients
Engaging Clinicians
Stroke Forum Conference-
Speech and language therapists; General Practitioners, Stroke consultants , Nurses
- We inform them of our findings; they recruit patients and participate in the research study.
Royal College of Physicians (RCP)
Royal College of Speech & Language Therapists (RCSLT)
stroke guideline representatives: - Outline targets (e.g. information for patients re their recovery).
Focus group for health care professionals:
- Discussions on when and how patients should be given information about recovery.
Influencing NHS Strategy
National Health Service (NHS) improvement team:
Update Department of Health (DoH)
- Publish information about the study and results on their website; pull through into practice
PLORAS
: Predicting
Language
and Recovery after Stroke
PLORAS
Team
& Outcome
Collaborators
Recruitment
Louise Lim
Zula Haigh
Rachel Browne
Deborah Ezekiel
Johanna Rae
Lucy Clayton
Other UCL Collaborators
Dr Alexander P. Leff,
Dr Jenny Crinion
Dr Nick Ward
Professor Tarek Yousry
Dr Kenji Yamamoto
Professor David W. Green,
Dr Marinella Cappelletti
Dr Mairead MacSweeney
Dr Jennifer Aydelott
Dr Joseph Devlin
Professor Michael Thomas
Professor Roger Lemon
Application
Data Management
Professor Cathy J. Price
Dr Mohamed L. Seghier
Dr Thomas Hope,
Dr 'Ōiwi Parker Jones
Dr Zoe Woodhead
Dr Ana Sanjuan
Sue Ramsden
Suz Prejawa
Marion Oberhuber
International Collaborators
Dr Randy McIntosh
Dr Viktor Jirsa
Dr Hartwig Siebner
Dr Gesa Hartwigsen
Professor Eraldo Paulasu
Professor Atsushi Iriki
Professor Li-Hai Tan
(Toronto, Canada)
(Marseille, France)
(Copenhagen, Denmark)
(Leipzig, Germany)
(Milan, Italy
(Riken, Japan)
(Beijing, China)
Funded by:
Wellcome Trust
James S McDonnell Foundation
(part of Brain Network Recovery Group initiative)