Österreichischen Gesellschaft für Neurorehabilitation , Graz, 7th November 2014 Structural and functional neuroimaging in neurorehabilitation NICK WARD, UCL INSTITUTE OF NEUROLOGY, QUEEN SQUARE n.ward@ucl.ac.uk Slides at www.ucl.ac.uk/ion/departments/sobell/Research/NWard or look on @WardLab @WardLab Imaging based neurorehabilitation Overview 1. Brain imaging in neurorehabilitation – what for? 2. Predicting outcomes and treatment effects after stroke? 3. Biomarkers of plasticity? Imaging based neurorehabilitation - I Which bit of neurorehabilitation? Rehabilitation is a process of active change by which a person who has become disabled acquires the knowledge and skills needed for optimum physical, psychological and social function Treatments aimed at reducing impairments (Task-specific) training cortical stimulation other drugs Imaging based neurorehabilitation - I What type of imaging? T1 structural DTI fMRI M/EEG Imaging based neurorehabilitation - I Brain imaging in neurorehabilitation – what for? 1. Predicting long term outcome e.g. EXPLICIT, PREP, PLORAS 2. Predicting response to treatment behavioural training plasticity enhancement Imaging based neurorehabilitation - II Predicting long term outcome after stroke Imaging based neurorehabilitation - II Predicting long term outcome after stroke - anatomy stroke damage damaged pathways cortical reorganisation? Imaging based neurorehabilitation - II Predicting long term outcome – structural imaging Track from fMRI-defined hand areas in 4 different cortical motor areas Shultz et al, Stroke 2012 Corrrelation with poststroke hand grip strength Imaging based neurorehabilitation - II Predicting long term outcome – structural imaging Imaging based neurorehabilitation - II Predicting long term outcome 1. SAFE = Shoulder Abduction + Finger Extension (MRC scale) 72 h after stroke (range 0–10) 2. TMS at 2 weeks Stinear, C. M. et al. Brain 2012 Aug;135:2527-35 Copyright restrictions may apply. 3. MRI/DTI at 2 weeks Imaging based neurorehabilitation - II Predicting treatment effect – structural imaging Damage to M1 pathway limits response to robot assisted therapy Imaging based neurorehabilitation - II Predicting long term outcome – structural imaging 1. Database of (i) hi-res structural MRI, (ii) language scores and (iii) time since stroke 2. MRI converted to 3D image with index of degree of damage at each 2mm3 voxel 3. A machine learning approach is used to compare lesion images to others in database and similar patients identified 4. Different ‘recovery’ curves can then be estimated for different behavioural measures Months since stroke Imaging based neurorehabilitation - II Speech production score Speech production score Predicting long term outcome – structural imaging Months since stroke Hope et al, NeuroImage Clinical 2013 Imaging based neurorehabilitation - II Differences in residual functional architecture affected side A 10 days post stroke infarct B 17 days post stroke 24 days post stroke 31 days post stroke 3 months post stroke affected side OUTCOMES Barthel ARAT GRIP NHPT Patient A 20/20 57/57 98.7% 78.9% Patient B 20/20 57/57 64.2% 14.9% Imaging based neurorehabilitation - II Differences in residual functional architecture unaffected + affected - unaffected + Predicting the effects of NIBS? affected - Imaging based neurorehabilitation - II Predicting treatment effect – functional imaging Cramer et al., Stroke 2007; 38: 2108-14 Less activity in M1 limits response to robot assisted therapy Imaging based neurorehabilitation - II Predicting treatment effect – multimodal? • 17 chronic stroke patients (FM 4-25) 30 days of UL training – 30 mins +/- APBT • Change in FM relatively small 0 – 6 points • Overall FA (DTI) symmetry predicted ΔFM (r2 = 0.38) • But better model by considering those with and without MEPs Copyright restrictions may apply. Stinear, C. M. et al. Brain 2007 130:170-180 Imaging based neurorehabilitation - II Predicting treatment effect Needs to inform…… ‘what kind of treatment?’ not ‘who should we treat?’ Imaging based neurorehabilitation - III Enhancing Neuroplasticity – the key to recovery? Rehabilitation is a process of active change by which a person who has become disabled acquires the knowledge and skills needed for optimum physical, psychological and social function Treatments aimed at reducing impairments (Task-specific) training cortical stimulation other drugs Imaging based neurorehabilitation - III Enhancing Neuroplasticity – the key to recovery? Drugs NIBS Other Imaging based neurorehabilitation - III Enhancing Neuroplasticity – the key to recovery? less disability more disability amphetamine Several agents considered: • Acetylcholinesterase inhibitors • Amphetamine • SSRIs (e.g. FLAME, FOCUS in UK) • DA agonists (e.g. DARS in UK) Reduced GABAergic inhibition? Increased glutamatergic/BDNF mediated LTP? Imaging based neurorehabilitation - III Enhancing Neuroplasticity – the key to recovery? Imaging based neurorehabilitation - III Enhancing Neuroplasticity – the key to recovery? Why not perform large RCTs? Inhibitory TBS? Excitatory TBS? Hamada M et al. Cereb. Cortex 2013;23:1593-1605 TBS (and TDCS) is very variable! Imaging based neurorehabilitation - III Enhancing Neuroplasticity – the key to recovery? Imaging based neurorehabilitation - III Enhancing Neuroplasticity – the key to recovery? Getting plasticity enhancement into clinical practice in stroke Imaging based neurorehabilitation - III Enhancing Neuroplasticity – the key to recovery? ctDCS to contralesional M1 reduced SICI (less inhibition) in ipsilesional M1 tDCS-induced enhancement of skill acquisition Reduced intracortical inhibition re-opens periods of plasticity in chronic stroke? Imaging based neurorehabilitation - III Enhancing Neuroplasticity – a range of scales Intracortical networks MESOSCOPIC Task related networks Large scale networks MACROSCOPIC Imaging based neurorehabilitation - III Enhancing Neuroplasticity – macroscopic scale? Network connectivity with Graph Theory for fMRI/MEG graph metrics - efficiency Imaging based neurorehabilitation - III Enhancing Neuroplasticity – macroscopic scale? Dynamic Causal Modelling of motor network after stroke Grefkes, Neuroimage (2009) Imaging based neurorehabilitation - III Enhancing Neuroplasticity – mesoscopic scale? “…the spectral characteristics of MEG recordings provide a marker of cortical GABAergic activity” BASELINE BETA-BAND POWER POST-MOVEMENT REBOUND • Greater baseline beta-power = more inhibition? • Greater rebound in beta power after grip = more inhibition? • Increased by diazepam (GABAA effect?) • Increased by tiagabine, but not diazepam (GABAB effect?) • Increased by cTBS (decreases excitability) • Increased with ageing MOVEMENT RELATED BETA-DECREASE • Greater decrease in beta-power with grip = more inhibition? • Greater MRBD with diazepam and tiagabine (GABAA effect?) Rossiter et al., Neuroimage 2014 • Less MRBD in chronic stroke patients (particularly those with more impairment) Rossiter et al., J Neurophysiol 2014 Imaging based neurorehabilitation - III Enhancing Neuroplasticity – mesoscopic scale? inhibitory cTBS reduces MEPs Biomarker increases Beta power Behaviour increases RTs Imaging based neurorehabilitation - III Enhancing Neuroplasticity – a range of scales Platform for stratification ... Mechanistic framework ... Predictions macroscopic behaviour motor biomarkers language cognitive interventions mesoscopic biomarkers patients Bridge the gap ... stratification + Imaging based neurorehabilitation Summary Early prediction of outcome Prediction of therapy effects Patient pathway Prediction of plasticity modification Imaging based neurorehabilitation Summary • Advances in neurorehabilitation are coming about through advances in neuroscience • The dose of treatment is critical - more is generally better • Enhancement of plasticity is possible • Neuroimaging should help in stratification • Understanding the mechanisms of recovery and treatment might allow targeted or individualised therapy in future Rehabilitation & Neuroimaging Acknowledgements FIL: ABIU/NRU: SOBELL DEPARTMENT : Karl Friston Diane Playford Holly Rossiter Rosalyn Moran Fran Brander Stephanie Bowen Richard Frackowiak Kate Kelly Muddy Bhatt Will Penny Alan Thompson Ella Clark Jennie Newton Marie-Helene Boudrias Chang-hyun Park Sven Bestman John Rothwell Penny Talelli Slides at www.ucl.ac.uk/ion/departments/sobell/Research/NWard FUNDING: